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2,005 results on '"Institut Bergonié [Bordeaux]"'

1. Molecular Profiling of Advanced Soft-tissue Sarcomas (MULTISARC)

Author: Commissariat A L'energie Atomique, Institut Bergonié, Plateforme labellisée Inca - Institut Bergonié, Bordeaux, Plateforme labellisée Inca - Hôpital Européen Georges Pompidou, Paris, and EUCLID Clinical Trial Platform
Published: 2024

2. EPIgenetics and in Vivo Resistance of Chronic Myeloid Leukemia Stem Cells to Tyrosine Kinase Inhibitors (EPIK)

Author: Institut Paoli-Calmettes, CHRU Lille, hematology department, CH Annecy Genevois, Institut Bergonié Bordeaux, hematology department, CHU Nancy, hematology department, CHU Saint-Etienne, hematology department, Institut Universitaire du Cancer de Toulouse - Oncopole, hematology department, CHU Caen, hematology department, CHU Nice, hematology department, CHU Lyon, hematology department, AP-HP, hematology department, AP-HP Hôpital Henri-Mondor, hematology department, Versailles Hospital, Centre Leon Berard, and University Hospital, Grenoble
Published: 2024

3. Molecular Profiling to Improve Outcome of Patients in Cancer. A Pilot Study (MULTIPLI-0)

Author: Institut Bergonié, Plateforme labellisée Inca - Institut Bergonié, Bordeaux, Plateforme labellisée Inca - Hôpital Européen Georges Pompidou, Paris, CNRGH, Evry, and EUCLID Clinical Trial Platform
Published: 2018

4. Mutational spectrum in a worldwide study of 29,700 families with BRCA1 or BRCA2 mutations

Author: [ 1 ] Harvard TH Chan Sch Publ Hlth, Boston, MA USA Show more [ 2 ] Chaim Sheba Med Ctr, Inst Human Genet, Susanne Levy Gertner Oncogenet Unit, IL-52621 Ramat Gan, Israel Show more [ 3 ] Tel Aviv Univ, Sackler Sch Med, Tel Aviv, Israel Show more [ 4 ] German Canc Res Ctr, Mol Genet Breast Canc, Heidelberg, Germany Show more [ 5 ] Univ Chicago, Ctr Clin Canc Genet & Global Hlth, Chicago, IL 60637 USA [ 6 ] Hong Kong Sanat & Hosp, Canc Genet Ctr, Hong Kong Hereditary Breast Canc Family Registry, Hong Kong, Hong Kong, Peoples R China Show more [ 7 ] Natl Inst Oncol, Dept Mol Genet, Budapest, Hungary Show more [ 8 ] Univ Buenos Aires, CONICET, Fac Med, INBIOMED, Buenos Aires, DF, Argentina Show more [ 9 ] CEMIC, Dept Clin Chem, Med Direct, Buenos Aires, DF, Argentina [ 10 ] Sime Darby Med Ctr, Canc Res Initiat Fdn, Subang Jaya, Malaysia Show more [ 11 ] Odense Univ Hosp, Dept Clin Genet, Odense, Denmark Show more [ 12 ] City Hope Canc Ctr, Div Clin Canc Genom, Duarte, CA USA [ 13 ] Hong Kong Sanat & Hosp, Dept Pathol, Div Mol Pathol, Happy Valley, Hong Kong, Peoples R China [ 14 ] Dept Lab Med & Pathol, Rochester, MN USA Show more [ 15 ] Univ Utah, Sch Med, Dept Dermatol, Salt Lake City, UT USA Show more [ 16 ] Barretos Canc Hosp, Mol Oncol Res Ctr, Sao Paulo, Brazil Show more [ 17 ] Seoul Natl Univ, Coll Med, Dept Prevent Med, Seoul, South Korea Show more [ 18 ] Seoul Natl Univ, Grad Sch, Dept Biomed Sci, Seoul, South Korea Show more [ 19 ] Seoul Natl Univ, Canc Res Ctr, Seoul, South Korea Show more [ 20 ] Pontificia Univ Javeriana, Inst Human Genet, Bogota, Colombia Show more [ 21 ] Univ Pretoria, Dept Genet, Canc Genet Lab, Pretoria, South Africa Show more [ 22 ] Univ Cambridge, Dept Publ Hlth & Primary Care, Ctr Canc Genet Epidemiol, Cambridge, England Show more [ 23 ] QIMR Berghofer Med Res Inst, Genet & Computat Biol Dept, Brisbane, Qld, Australia [ 24 ] Acad Med Ctr, Dept Clin Genet, Amsterdam, Netherlands [ 25 ] City Hope Clin Canc Genom Community Res Network, D, Harvard TH Chan School of Public Health and Dana Farber Cancer Institute; Boston USA, The Susanne Levy Gertner Oncogenetics Unit; Institute of Human Genetics; Chaim Sheba Medical Center, Ramat Gan 52621, and the Sackler School of Medicine; Tel-Aviv University; Tel-Aviv Israel, Molecular Genetics of Breast Cancer; German Cancer Research Center (DKFZ); Heidelberg Germany, Center for Clinical Cancer Genetics and Global Health; University of Chicago; Chicago USA, The Hong Kong Hereditary Breast Cancer Family Registry; Cancer Genetics Center; Hong Kong Sanatorium and Hospital; Hong Kong China, Department of Molecular Genetics; National Institute of Oncology; Budapest Hungary, INBIOMED; Faculty of Medicine, University of Buenos Aires/CONICET and CEMIC, Department of Clinical Chemistry; Medical Direction; Buenos Aires Argentina, Cancer Research Initiatives Foundation; Sime Darby Medical Centre; Subang Jaya Malaysia, Department of Clinical Genetics; Odense University Hospital; Odense Denmark, Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty; University Hospital Cologne; Cologne Germany, Clinical Genetics Services; Dept. of Medicine; Memorial Sloan-Kettering Cancer Center; New York USA, Division of Gynecologic Oncology; North Shore University Health System; University of Chicago; Evanston USA, All Wales Medical Genetics Services; University Hospital of Wales; Cardiff UK, Department of Gynecology; Vilnius University Hospital Santariskiu Clinics; Centre of Woman's Health and pathology; Vilnius Lithuania, Center for Genomic Medicine; Rigshospitalet; University of Copenhagen; Copenhagen Denmark, Clinical Cancer Genetics Program; Division of Human Genetics; Department of Internal Medicine; The Comprehensive Cancer Center; The Ohio State University; Columbus USA, Cancer Genetics Laboratory, Department of Genetics; University of Pretoria; South Africa, Department of Genetics and Pathology; Pomeranian Medical University; Szczecin Poland, Department of Medicine, Abramson Cancer Center; Perelman School of Medicine at the University of Pennsylvania; Philadelphia USA, Department of Internal Medicine; Division of Oncology; University of Kansas Medical Center; Westwood USA, North East Thames Regional Genetics Service; Great Ormond Street Hospital for Children NHS Trust; London UK, Genomics Center; Centre Hospitalier Universitaire de Québec Research Center and Laval University; Quebec City Canada, Dept of OB/GYN and Comprehensive Cancer Center; Medical University of Vienna; Vienna Austria, Department of Clinical Genetics; Aarhus University Hospital; Aarhus N Denmark, Division of Clinical Cancer Genomics; City of Hope Cancer Center; California USA, Medical Genetics Unit; University of London; St George's UK, Département Oncologie Génétique; Prévention et Dépistage; Institut Paoli-Calmettes; Marseille Medical School-AM University; Marseille France, Department of Breast Medical Oncology and Clinical Cancer Genetics Program; University Of Texas MD Anderson Cancer Center; Houston USA, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care; University of Cambridge; Cambridge UK, Department of Population Sciences; Beckman Research Institute of City of Hope; Duarte USA, Institute of Cell and Molecular Pathology; Hannover Medical School; Hannover Germany, Institute of Human Genetics; University Hospital Heidelberg; Heidelberg Germany, National Human Genome Research Institute; National Institutes of Health; Bethesda USA, Dept of OB/GYN, Comprehensive Cancer Center; Medical University of Vienna; Vienna Austria, Department of Genetics; Portuguese Oncology Institute of Porto (IPO Porto); Porto Portugal, Department of Epidemiology; Columbia University; New York USA, Genetic Counseling Unit; Hereditary Cancer Program; IDIBELL (Bellvitge Biomedical Research Institute); Catalan Institute of Oncology, CIBERONC; Gran Via de l'Hospitalet; Barcelona Spain, Department of Health Sciences Research; Mayo Clinic; Rochester USA, Genetics and Computational Biology Department; QIMR Berghofer Medical Research Institute; Brisbane Australia, Department of Medicine; Magee-Womens Hospital; University of Pittsburgh School of Medicine; Pittsburgh USA, Program in Cancer Genetics; Departments of Human Genetics and Oncology; McGill University; Montreal Canada, Immunology and Molecular Oncology Unit; Veneto Institute of Oncology IOV - IRCCS; Padua Italy, Division of Human Genetics; Departments of Internal Medicine and Cancer Biology and Genetics; Comprehensive Cancer Center; The Ohio State University; Columbus USA, Clinical Genetics Research Laboratory, Dept. of Medicine; Memorial Sloan-Kettering Cancer Center; New York USA, Parkville Familial Cancer Centre; Royal Melbourne Hospital; Melbourne Australia, Department of Medical Oncology; Beth Israel Deaconess Medical Center; Massachusetts USA, Department of Clinical Genetics; Leiden University Medical Center; Leiden The Netherlands, Department of Genetics; University Medical Center; Groningen University; Groningen The Netherlands, Family Cancer Clinic; Netherlands Cancer Institute; Amsterdam The Netherlands, Department of Medical Genetics; University Medical Center; Utrecht The Netherlands, Center for Medical Genetics; Ghent University; Gent Belgium, Unit of Hereditary Cancer; Department of Epidemiology, Prevention and Special Functions; IRCCS (Istituto Di Ricovero e Cura a Carattere Scientifico) AOU San Martino - IST Istituto Nazionale per la Ricerca sul Cancro; Genoa Italy, Institute of Human Genetics; Campus Virchov Klinikum; Berlin Germany, Fundación Pública Galega de Medicina Xenómica-SERGAS, Grupo de Medicina Xenómica-USC, CIBERER, IDIS, Santiago de Compostela; Spain, Departamento de Investigacion y de Tumores Mamarios del; Instituto Nacional de Cancerologia; Mexico City Mexico, Department of Oncology; Karolinska University Hospital; Stockholm Sweden, Institute of Genetic Medicine; Centre for Life; Newcastle Upon Tyne Hospitals NHS Trust; Newcastle upon Tyne UK, Oxford Regional Genetics Service; Churchill Hospital; Oxford UK, Department of Gynaecology and Obstetrics; University Hospital; Ulm Germany, Department of Clinical Genetics; Academic Medical Center; Amsterdam The Netherlands, Institute of Human Genetics; Regensburg University; Regensburg Germany, Molecular Diagnostics Laboratory, INRASTES (Institute of Nuclear and Radiological Sciences and Technology); National Centre for Scientific Research “Demokritos”; Athens Greece, Unit of Medical Genetics, Department of Medical Oncology and Hematology; Fondazione IRCCS (Istituto Di Ricovero e Cura a Carattere Scientifico) Instituto Nazionale Tumori (INT); Milan Italy, Institute of Oncology; Rivka Ziv Medical Center; Zefat Israel, Magee-Womens Hospital; University of Pittsburgh School of Medicine; Pittsburgh USA, Institute of Human Genetics; University Leipzig; Leipzig Germany, Center for Medical Genetics; North Shore University Health System; Evanston USA, Medical Director, Center for Medical Genetics, NorthShore University HealthSystem, Clinical Assistant Professor of Medicine; University of Chicago Pritzker School of Medicine; Evanston USA, City of Hope Clinical Cancer Genomics Community Research Network; Duarte USA, Yorkshire Regional Genetics Service; Chapel Allerton Hospital; Leeds UK, Department of Clinical Genetics; Helsinki University Hospital; Helsinki Finland, Hereditary Cancer Clinic; Prince of Wales Hospital; Randwick Australia, Lunenfeld-Tanenbaum Research Institute; Toronto Canada, Laboratory of Cell Biology, Department of Pathology, hus 9, Landspitali-LSH v/Hringbraut, 101 Reykjavik, Iceland and BMC (Biomedical Centre), Faculty of Medicine; University of Iceland; Reykjavik Iceland, Department of Gynaecology & Oncology; Medical University of Vienna; Austria, Department of Medical Oncology; Vall d'Hebron University Hospital; Barcelona Spain, Division of Cancer Prevention and Genetics; Istituto Europeo di Oncologia (IEO); Milan Italy, Department of Gynaecology and Obstetrics; University Hospital Düsseldorf, Heinrich-Heine University; Düsseldorf Germany, Human Genetics Group and Genotyping Unit (CEGEN), Human Cancer Genetics Programme; Spanish National Cancer Research Centre (CNIO); Madrid Spain, The Institute of Oncology; Chaim Sheba Medical Center; Ramat Gan Israel, UCSF Cancer Genetics and Prevention Program; San Francisco USA, Department of Clinical Genetics; Maastricht University Medical Center; Maastricht The Netherlands, Unité de Prévention et d'Epidémiologie Génétique; Centre Léon Bérard, 28 rue Laënnec; Lyon France, N.N. Petrov Institute of Oncology; St. Petersburg Russia, Department of Clinical Genetics; Royal Devon & Exeter Hospital; Exeter UK, Service de Génétique; Institut Curie, 26 rue d'Ulm; Paris France, Department of Medicine; Huntsman Cancer Institute; Salt Lake City USA, Molecular Oncology Laboratory; Hospital Clinico San Carlos; Instituto de Investigación Sanitaria San Carlos (IdISSC); Centro Investigación Biomédica en Red de Cáncer (CIBERONC); Madrid Spain, Institute of Human Genetics; University Hospital of Schleswig-Holstein; Kiel Germany, Section of Molecular Genetics, Dept. of Laboratory Medicine; University Hospital of Pisa; Pisa Italy, Research Division; Peter MacCallum Cancer Centre; Melbourne Australia, CRCHU de Quebec-oncologie, Centre des maladies du sein Deschênes-Fabia; Hôpital du Saint-Sacrement; Sainte-Foy Canada, Lombardi Comprehensive Cancer Center; Georgetown University; Washington USA, Departments of Pediatrics and Medicine; Columbia University; New York USA, Department of Clinical Genetics, Family Cancer Clinic; Erasmus University Medical Center; Rotterdam The Netherlands, Sheffield Clinical Genetics Service; Sheffield Children's Hospital; Sheffield UK, Department of Clinical Genetics; South Glasgow University Hospitals; Glasgow UK, Unité d'oncogénétique; ICO-Centre René Gauducheau; Saint Herblain France, Oncogenetics Group, Vall d'Hebron Institute of Oncology (VHIO), Clinical and Molecular Genetics Area; Vall d'Hebron University Hospital; Barcelona Spain, Department of Gynaecology and Obstetrics; Ludwig-Maximilian University; Munich Germany, Cáncer Hereditario, Instituto de Biología y Genética Molecular, IBGM; Universidad de Valladolid; Valladolid Spain, Institute of Human Genetics; University of Münster; Münster Germany, Nottingham Clinical Genetics Service; Nottingham University Hospitals NHS Trust; Nottingham UK, Oncogenetics Team; The Institute of Cancer Research and Royal Marsden NHS Foundation Trust; London UK, Department of Clinical Genetics; Lund University Hospital; Lund Sweden, Clinical Genetics; Guy's and St. Thomas’ NHS Foundation Trust; London UK, Department of Oncology, Rigshospitalet; Copenhagen University Hospital; Copenhagen Denmark, Institute for Medical Informatics, Statistics and Epidemiology; University of Leipzig; Leipzig Germany, Department of Gynaecology and Obstetrics, Division of Tumor Genetics, Klinikum rechts der Isar; Technical University; Munich Germany, Genomic Medicine, Manchester Academic Health Sciences Centre, Division of Evolution and Genomic Sciences; University of Manchester, Central Manchester University Hospitals NHS Foundation Trust; Manchester UK, Centre de Lutte Contre le Cancer Georges François Leclerc, France and Genomic and Immunotherapy Medical Institute; Dijon University Hospital; Dijon France, Molecular Diagnostic Unit, Hereditary Cancer Program, ICO-IDIBELL (Catalan Institute of Oncology-Bellvitge Biomedical Research Institute); Barcelona Spain, Laboratoire de Génétique Chromosomique; Hôtel Dieu Centre Hospitalier; Chambéry France, Department of Cancer Epidemiology and Genetics; Masaryk Memorial Cancer Institute; Brno Czech Republic, Columbus Cancer Council, Ohio State University; Columbus USA, Genetic Counseling Unit, Hereditary Cancer Program, IDIBGI (Institut d'Investigació Biomèdica de Girona); Catalan Institute of Oncology; Girona Spain, Oncogenetics Department; Barretos Cancer Hospital; Barretos Brazil, UCLA Schools of Medicine and Public Health, Division of Cancer Prevention & Control Research; Jonsson Comprehensive Cancer Center; Los Angeles USA, Cancer Risk and Prevention Clinic; Dana-Farber Cancer Institute; Boston USA, Centre of Familial Breast and Ovarian Cancer, Department of Medical Genetics, Institute of Human Genetics; University of Würzburg, Germany; Würzburg, Department of Clinical Genetics; Copenhagen Denmark, Service Régional Oncogénétique Poitou-Charentes; Centre Hospitalier; Niort France, Department of Molecular Medicine; University La Sapienza, and Istituto Pasteur - Fondazione Cenci-Bolognetti; Rome Italy, Bâtiment Cheney D; Centre Léon Bérard; Lyon France, Ontario Cancer Genetics Network: Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; Toronto Canada, Department of Pathology and Laboratory Medicine; University of Kansas Medical Center; Kansas City USA, Clinical Genetics Branch, DCEG, NCI; NIH; Bethesda USA, Parkville Familial Cancer Centre; Peter MacCallum Cancer Centre; Melbourne Australia, Hematology, oncology and transfusion medicine center, Dept. of Molecular and Regenerative Medicine; Vilnius University Hospital Santariskiu Clinics; Vilnius Lithuania, Department of Epidemiology, Cancer Prevention Institute of California; Fremont USA, Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute; Cedars-Sinai Medical Center; Los Angeles USA, Division of Molecular Pathology; Department of Pathology; Hong Kong Sanatorium & Hospital; Happy Valley Hong Kong, Department of Gynecology and Obstetrics; Medical Faculty and University Hospital Carl Gustav Carus; Dresden Germany, Research Department, Peter MacCallum Cancer Centre, Melbourne, Victoria; Australia and The Sir Peter MacCallum Department of Oncology University of Melbourne; Parkville Australia, Department of Surgery; Daerim St. Mary's Hospital; Seoul Korea, The Gyneco-Oncology Department; Chaim Sheba Medical Center; Ramat Gan Israel, Servicio de Genética-CIBERER U705; Hospital de la Santa Creu i Sant Pau; Barcelona Spain, The Feinstein Institute for Medical Research; Manhasset USA, Department of Laboratory Medicine and Pathology; and Health Sciences Research; Rochester USA, Department of Surgery; Soonchunhyang University and Seoul Hospital; Seoul Korea, Inserm U900, Institut Curie; PSL Research University; Paris France, Department of Oncology Radiumhemmet and Institution of Oncology and Patology; Karolinska University Hospital and Karolinska Institutet; Solna Sweden, Department of Health Sciences Research; Mayo Clinic; Scottsdale USA, Oncogénétique; Institut Bergonié; Bordeaux France, Clinical Genetics Branch, DCEG, NCI, NIH; Bethesda USA, Department of Gynecological Oncology and Clinical Cancer Genetics Program; University Of Texas MD Anderson Cancer Center; Houston USA, Department of Dermatology; University of Utah School of Medicine; Salt Lake City USA, Centre Antoine Lacassagne; Nice France, Laboratorio de Genética Molecular, Servicio de Genética; Hospital Universitario Cruces, BioCruces Health Research Institute; Barakaldo Spain, Department of Surgery; National Institute of Oncology; Budapest Hungary, Department of Clinical Genetics; VU University Medical Center; Amsterdam The Netherlands, Department of Human Genetics; Radboud University Medical Center; Nijmegen The Netherlands, Vilnius university Santariskiu hospital; National Center of Pathology; Vilnius Lithuania, NRG Oncology; Statistics and Data Management Center; Roswell Park Cancer Institute; Buffalo USA, Department of Cancer Prevention and Control; Roswell Park Cancer Institute; Buffalo USA, Department of Laboratory Medicine and Pathobiology; University of Toronto; Toronto Canada, Department of Obstetrics and Gynecology; University of Helsinki and Helsinki University Hospital; HUS Finland, Cancer Genetics Service; Division of Medical Oncology; National Cancer Centre Singapore; Bukit Merah Singapore, Institute of Medical Genetics and Applied Genomics; University of Tuebingen; Tuebingen Germany, Molecular Oncology Research Center; Barretos Cancer Hospital; São Paulo Brazil, Cancer Genetics and Prevention Program; University of California San Francisco; San Francisco USA, Clinical Genetics Research Laboratory; Dept. of Medicine; Cancer Biology and Genetics; Memorial Sloan-Kettering Cancer Center; New York USA, Department of Clinical Genetics; Sahlgrenska University Hospital; Gothenburg Sweden, West Midlands Regional Genetics Service; Birmingham Women's Hospital Healthcare NHS Trust; Edgbaston UK, Human Genetics Group; Human Cancer Genetics Programme; Spanish National Cancer Research Centre (CNIO); Biomedical Network on Rare Diseases (CIBERER); Madrid Spain, Unit of Medical Genetics; Department of Biomedical; Experimental and Clinical Sciences; University of Florence; Florence Italy, Department of Medical Sciences; University of Turin; Turin Italy, Section of Molecular Diagnostics; Department of Biochemistry; Aalborg University Hospital; Aalborg Denmark, Department of Preventive Medicine; Seoul National University College of Medicine; Seoul Korea, IFOM; The FIRC (Italian Foundation for Cancer Research) Institute of Molecular Oncology; Milan Italy, Service de Génétique Clinique Chromosomique et Moléculaire; Hôpital Nord; St Etienne France, Unité d'Oncogénétique; CHU Arnaud de Villeneuve; Montpellier France, Unit of Molecular Bases of Genetic Risk and Genetic Testing; Department of Research; Fondazione IRCCS (Istituto Di Ricovero e Cura a Carattere Scientifico), Istituto Nazionale Tumori (INT); Milan Italy, School of Women's and Children's Health; UNSW; Sydney Australia, Department of Clinical Genetics; Karolinska University Hospital; Stockholm Sweden, Rebbeck, Timothy R., Friebel, Tara M., Friedman, Eitan, Hamann, Ute, Huo, Dezheng, Kwong, Ava, Olah, Edith, Olopade, Olufunmilayo I., Solano, Angela R., Teo, Soo-Hwang, Thomassen, Mads, Rashid, Muhammad Usman, Rhiem, Kerstin, Robson, Mark, Rodriguez, Gustavo C., Rogers, Mark T., Rudaitis, Vilius, Schmidt, Ane Y., Schmutzler, Rita Katharina, Senter, Leigha, van Rensburg, Elizabeth J., Gronwald, Jacek, Shah, Payal D., Sharma, Priyanka, Side, Lucy E., Simard, Jacques, Singer, Christian F., Skytte, Anne-Bine, Slavin, Thomas P., Snape, Katie, Sobol, Hagay, Southey, Melissa, Gutierrez-Barrera, Angelica, McGuffog, Lesley, Steele, Linda, Steinemann, Doris, Sukiennicki, Grzegorz, Sutter, Christian, Szabo, Csilla I., Tan, Yen Y., Teixeira, Manuel R., Terry, Mary Beth, Teulé, Alex, Hahnen, Eric, Thomas, Abigail, Parsons, Michael T., Thull, Darcy L., Tischkowitz, Marc, Tognazzo, Silvia, Toland, Amanda Ewart, Topka, Sabine, Trainer, Alison H, Tung, Nadine, van Asperen, Christi J., Hauke, Jan, van der Hout, Annemieke H., van der Kolk, Lizet E., Leslie, Goska, van der Luijt, Rob B., Van Heetvelde, Mattias, Varesco, Liliana, Varon-Mateeva, Raymonda, Vega, Ana, Villarreal-Garza, Cynthia, von Wachenfeldt, Anna, Henderson, Alex, Walker, Lisa, Wang-Gohrke, Shan, Wappenschmidt, Barbara, Aalfs, Cora M., Weber, Bernhard H. F., Yannoukakos, Drakoulis, Yoon, Sook-Yee, Zanzottera, Cristina, Zidan, Jamal, Zorn, Kristin K., Hentschel, Julia, Hutten Selkirk, Christina G., Hulick, Peter J., Chenevix-Trench, Georgia, Spurdle, Amanda B., Abugattas, Julio, Antoniou, Antonis C., Nathanson, Katherine L., Adlard, Julian, Agata, Simona, Aittomäki, Kristiina, Hogervorst, Frans B.L., Andrews, Lesley, Andrulis, Irene L., Arason, Adalgeir, Arnold, Norbert, Arun, Banu K., Asseryanis, Ella, Auerbach, Leo, Azzollini, Jacopo, Balmaña, Judith, Barile, Monica, Honisch, Ellen, Barkardottir, Rosa B., Barrowdale, Daniel, Benitez, Javier, Berger, Andreas, Berger, Raanan, Blanco, Amie M., Blazer, Kathleen R., Blok, Marinus J., Bonadona, Valérie, Bonanni, Bernardo, Imyanitov, Evgeny N., Bradbury, Angela R., Brewer, Carole, Buecher, Bruno, Buys, Saundra S., Caldes, Trinidad, Caliebe, Almuth, Caligo, Maria A., Campbell, Ian, Caputo, Sandrine M., Chiquette, Jocelyne, Isaacs, Claudine, Chung, Wendy K., Claes, Kathleen B.M., Collée, J. Margriet, Cook, Jackie, Davidson, Rosemarie, de la Hoya, Miguel, De Leeneer, Kim, de Pauw, Antoine, Delnatte, Capucine, Diez, Orland, Weitzel, Jeffrey N., Ding, Yuan Chun, Ditsch, Nina, Domchek, Susan M., Dorfling, Cecilia M., Velazquez, Carolina, Dworniczak, Bernd, Eason, Jacqueline, Easton, Douglas F., Eeles, Ros, Ehrencrona, Hans, Izatt, Louise, Ejlertsen, Bent, Engel, Christoph, Engert, Stefanie, Evans, D. Gareth, Faivre, Laurence, Feliubadaló, Lidia, Ferrer, Sandra Fert, Foretova, Lenka, Fowler, Jeffrey, Frost, Debra, Izquierdo, Angel, Galvão, Henrique C. R., Ganz, Patricia A., Garber, Judy, Gauthier-Villars, Marion, Gehrig, Andrea, Gerdes, Anne-Marie, Gesta, Paul, Giannini, Giuseppe, Giraud, Sophie, Glendon, Gord, Jakubowska, Anna, Godwin, Andrew K., Greene, Mark H., James, Paul, Janavicius, Ramunas, Jensen, Uffe Birk, John, Esther M., Vijai, Joseph, Kaczmarek, Katarzyna, Karlan, Beth Y., Chan, TL, Kast, Karin, Investigators, KConFab, Kim, Sung-Won, Konstantopoulou, Irene, Korach, Jacob, Laitman, Yael, Lasa, Adriana, Lasset, Christine, Lázaro, Conxi, Lee, Annette, Couch, Fergus J., Lee, Min Hyuk, Lester, Jenny, Lesueur, Fabienne, Liljegren, Annelie, Lindor, Noralane M., Longy, Michel, Loud, Jennifer T., Lu, Karen H., Lubinski, Jan, Machackova, Eva, Goldgar, David E., Manoukian, Siranoush, Mari, Véronique, Martínez-Bouzas, Cristina, Matrai, Zoltan, Mebirouk, Noura, Meijers-Heijboer, Hanne E.J., Meindl, Alfons, Mensenkamp, Arjen R., Mickys, Ugnius, Miller, Austin, Kruse, Torben A., Montagna, Marco, Moysich, Kirsten B., Mulligan, Anna Marie, Musinsky, Jacob, Neuhausen, Susan L., Nevanlinna, Heli, Ngeow, Joanne, Nguyen, Huu Phuc, Niederacher, Dieter, Nielsen, Henriette Roed, Palmero, Edenir Inêz, Nielsen, Finn Cilius, Nussbaum, Robert L., Offit, Kenneth, Öfverholm, Anna, Ong, Kai-ren, Osorio, Ana, Papi, Laura, Papp, Janos, Pasini, Barbara, Pedersen, Inge Sokilde, Park, Sue Kyung, Peixoto, Ana, Peruga, Nina, Peterlongo, Paolo, Pohl, Esther, Pradhan, Nisha, Prajzendanc, Karolina, Prieur, Fabienne, Pujol, Pascal, Radice, Paolo, Ramus, Susan J., Torres, Diana, Rantala, Johanna, [ 1 ] Harvard TH Chan Sch Publ Hlth, Boston, MA USA Show more [ 2 ] Chaim Sheba Med Ctr, Inst Human Genet, Susanne Levy Gertner Oncogenet Unit, IL-52621 Ramat Gan, Israel Show more [ 3 ] Tel Aviv Univ, Sackler Sch Med, Tel Aviv, Israel Show more [ 4 ] German Canc Res Ctr, Mol Genet Breast Canc, Heidelberg, Germany Show more [ 5 ] Univ Chicago, Ctr Clin Canc Genet & Global Hlth, Chicago, IL 60637 USA [ 6 ] Hong Kong Sanat & Hosp, Canc Genet Ctr, Hong Kong Hereditary Breast Canc Family Registry, Hong Kong, Hong Kong, Peoples R China Show more [ 7 ] Natl Inst Oncol, Dept Mol Genet, Budapest, Hungary Show more [ 8 ] Univ Buenos Aires, CONICET, Fac Med, INBIOMED, Buenos Aires, DF, Argentina Show more [ 9 ] CEMIC, Dept Clin Chem, Med Direct, Buenos Aires, DF, Argentina [ 10 ] Sime Darby Med Ctr, Canc Res Initiat Fdn, Subang Jaya, Malaysia Show more [ 11 ] Odense Univ Hosp, Dept Clin Genet, Odense, Denmark Show more [ 12 ] City Hope Canc Ctr, Div Clin Canc Genom, Duarte, CA USA [ 13 ] Hong Kong Sanat & Hosp, Dept Pathol, Div Mol Pathol, Happy Valley, Hong Kong, Peoples R China [ 14 ] Dept Lab Med & Pathol, Rochester, MN USA Show more [ 15 ] Univ Utah, Sch Med, Dept Dermatol, Salt Lake City, UT USA Show more [ 16 ] Barretos Canc Hosp, Mol Oncol Res Ctr, Sao Paulo, Brazil Show more [ 17 ] Seoul Natl Univ, Coll Med, Dept Prevent Med, Seoul, South Korea Show more [ 18 ] Seoul Natl Univ, Grad Sch, Dept Biomed Sci, Seoul, South Korea Show more [ 19 ] Seoul Natl Univ, Canc Res Ctr, Seoul, South Korea Show more [ 20 ] Pontificia Univ Javeriana, Inst Human Genet, Bogota, Colombia Show more [ 21 ] Univ Pretoria, Dept Genet, Canc Genet Lab, Pretoria, South Africa Show more [ 22 ] Univ Cambridge, Dept Publ Hlth & Primary Care, Ctr Canc Genet Epidemiol, Cambridge, England Show more [ 23 ] QIMR Berghofer Med Res Inst, Genet & Computat Biol Dept, Brisbane, Qld, Australia [ 24 ] Acad Med Ctr, Dept Clin Genet, Amsterdam, Netherlands [ 25 ] City Hope Clin Canc Genom Community Res Network, D, Harvard TH Chan School of Public Health and Dana Farber Cancer Institute; Boston USA, The Susanne Levy Gertner Oncogenetics Unit; Institute of Human Genetics; Chaim Sheba Medical Center, Ramat Gan 52621, and the Sackler School of Medicine; Tel-Aviv University; Tel-Aviv Israel, Molecular Genetics of Breast Cancer; German Cancer Research Center (DKFZ); Heidelberg Germany, Center for Clinical Cancer Genetics and Global Health; University of Chicago; Chicago USA, The Hong Kong Hereditary Breast Cancer Family Registry; Cancer Genetics Center; Hong Kong Sanatorium and Hospital; Hong Kong China, Department of Molecular Genetics; National Institute of Oncology; Budapest Hungary, INBIOMED; Faculty of Medicine, University of Buenos Aires/CONICET and CEMIC, Department of Clinical Chemistry; Medical Direction; Buenos Aires Argentina, Cancer Research Initiatives Foundation; Sime Darby Medical Centre; Subang Jaya Malaysia, Department of Clinical Genetics; Odense University Hospital; Odense Denmark, Center for Familial Breast and Ovarian Cancer, Center for Integrated Oncology (CIO), Medical Faculty; University Hospital Cologne; Cologne Germany, Clinical Genetics Services; Dept. of Medicine; Memorial Sloan-Kettering Cancer Center; New York USA, Division of Gynecologic Oncology; North Shore University Health System; University of Chicago; Evanston USA, All Wales Medical Genetics Services; University Hospital of Wales; Cardiff UK, Department of Gynecology; Vilnius University Hospital Santariskiu Clinics; Centre of Woman's Health and pathology; Vilnius Lithuania, Center for Genomic Medicine; Rigshospitalet; University of Copenhagen; Copenhagen Denmark, Clinical Cancer Genetics Program; Division of Human Genetics; Department of Internal Medicine; The Comprehensive Cancer Center; The Ohio State University; Columbus USA, Cancer Genetics Laboratory, Department of Genetics; University of Pretoria; South Africa, Department of Genetics and Pathology; Pomeranian Medical University; Szczecin Poland, Department of Medicine, Abramson Cancer Center; Perelman School of Medicine at the University of Pennsylvania; Philadelphia USA, Department of Internal Medicine; Division of Oncology; University of Kansas Medical Center; Westwood USA, North East Thames Regional Genetics Service; Great Ormond Street Hospital for Children NHS Trust; London UK, Genomics Center; Centre Hospitalier Universitaire de Québec Research Center and Laval University; Quebec City Canada, Dept of OB/GYN and Comprehensive Cancer Center; Medical University of Vienna; Vienna Austria, Department of Clinical Genetics; Aarhus University Hospital; Aarhus N Denmark, Division of Clinical Cancer Genomics; City of Hope Cancer Center; California USA, Medical Genetics Unit; University of London; St George's UK, Département Oncologie Génétique; Prévention et Dépistage; Institut Paoli-Calmettes; Marseille Medical School-AM University; Marseille France, Department of Breast Medical Oncology and Clinical Cancer Genetics Program; University Of Texas MD Anderson Cancer Center; Houston USA, Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care; University of Cambridge; Cambridge UK, Department of Population Sciences; Beckman Research Institute of City of Hope; Duarte USA, Institute of Cell and Molecular Pathology; Hannover Medical School; Hannover Germany, Institute of Human Genetics; University Hospital Heidelberg; Heidelberg Germany, National Human Genome Research Institute; National Institutes of Health; Bethesda USA, Dept of OB/GYN, Comprehensive Cancer Center; Medical University of Vienna; Vienna Austria, Department of Genetics; Portuguese Oncology Institute of Porto (IPO Porto); Porto Portugal, Department of Epidemiology; Columbia University; New York USA, Genetic Counseling Unit; Hereditary Cancer Program; IDIBELL (Bellvitge Biomedical Research Institute); Catalan Institute of Oncology, CIBERONC; Gran Via de l'Hospitalet; Barcelona Spain, Department of Health Sciences Research; Mayo Clinic; Rochester USA, Genetics and Computational Biology Department; QIMR Berghofer Medical Research Institute; Brisbane Australia, Department of Medicine; Magee-Womens Hospital; University of Pittsburgh School of Medicine; Pittsburgh USA, Program in Cancer Genetics; Departments of Human Genetics and Oncology; McGill University; Montreal Canada, Immunology and Molecular Oncology Unit; Veneto Institute of Oncology IOV - IRCCS; Padua Italy, Division of Human Genetics; Departments of Internal Medicine and Cancer Biology and Genetics; Comprehensive Cancer Center; The Ohio State University; Columbus USA, Clinical Genetics Research Laboratory, Dept. of Medicine; Memorial Sloan-Kettering Cancer Center; New York USA, Parkville Familial Cancer Centre; Royal Melbourne Hospital; Melbourne Australia, Department of Medical Oncology; Beth Israel Deaconess Medical Center; Massachusetts USA, Department of Clinical Genetics; Leiden University Medical Center; Leiden The Netherlands, Department of Genetics; University Medical Center; Groningen University; Groningen The Netherlands, Family Cancer Clinic; Netherlands Cancer Institute; Amsterdam The Netherlands, Department of Medical Genetics; University Medical Center; Utrecht The Netherlands, Center for Medical Genetics; Ghent University; Gent Belgium, Unit of Hereditary Cancer; Department of Epidemiology, Prevention and Special Functions; IRCCS (Istituto Di Ricovero e Cura a Carattere Scientifico) AOU San Martino - IST Istituto Nazionale per la Ricerca sul Cancro; Genoa Italy, Institute of Human Genetics; Campus Virchov Klinikum; Berlin Germany, Fundación Pública Galega de Medicina Xenómica-SERGAS, Grupo de Medicina Xenómica-USC, CIBERER, IDIS, Santiago de Compostela; Spain, Departamento de Investigacion y de Tumores Mamarios del; Instituto Nacional de Cancerologia; Mexico City Mexico, Department of Oncology; Karolinska University Hospital; Stockholm Sweden, Institute of Genetic Medicine; Centre for Life; Newcastle Upon Tyne Hospitals NHS Trust; Newcastle upon Tyne UK, Oxford Regional Genetics Service; Churchill Hospital; Oxford UK, Department of Gynaecology and Obstetrics; University Hospital; Ulm Germany, Department of Clinical Genetics; Academic Medical Center; Amsterdam The Netherlands, Institute of Human Genetics; Regensburg University; Regensburg Germany, Molecular Diagnostics Laboratory, INRASTES (Institute of Nuclear and Radiological Sciences and Technology); National Centre for Scientific Research “Demokritos”; Athens Greece, Unit of Medical Genetics, Department of Medical Oncology and Hematology; Fondazione IRCCS (Istituto Di Ricovero e Cura a Carattere Scientifico) Instituto Nazionale Tumori (INT); Milan Italy, Institute of Oncology; Rivka Ziv Medical Center; Zefat Israel, Magee-Womens Hospital; University of Pittsburgh School of Medicine; Pittsburgh USA, Institute of Human Genetics; University Leipzig; Leipzig Germany, Center for Medical Genetics; North Shore University Health System; Evanston USA, Medical Director, Center for Medical Genetics, NorthShore University HealthSystem, Clinical Assistant Professor of Medicine; University of Chicago Pritzker School of Medicine; Evanston USA, City of Hope Clinical Cancer Genomics Community Research Network; Duarte USA, Yorkshire Regional Genetics Service; Chapel Allerton Hospital; Leeds UK, Department of Clinical Genetics; Helsinki University Hospital; Helsinki Finland, Hereditary Cancer Clinic; Prince of Wales Hospital; Randwick Australia, Lunenfeld-Tanenbaum Research Institute; Toronto Canada, Laboratory of Cell Biology, Department of Pathology, hus 9, Landspitali-LSH v/Hringbraut, 101 Reykjavik, Iceland and BMC (Biomedical Centre), Faculty of Medicine; University of Iceland; Reykjavik Iceland, Department of Gynaecology & Oncology; Medical University of Vienna; Austria, Department of Medical Oncology; Vall d'Hebron University Hospital; Barcelona Spain, Division of Cancer Prevention and Genetics; Istituto Europeo di Oncologia (IEO); Milan Italy, Department of Gynaecology and Obstetrics; University Hospital Düsseldorf, Heinrich-Heine University; Düsseldorf Germany, Human Genetics Group and Genotyping Unit (CEGEN), Human Cancer Genetics Programme; Spanish National Cancer Research Centre (CNIO); Madrid Spain, The Institute of Oncology; Chaim Sheba Medical Center; Ramat Gan Israel, UCSF Cancer Genetics and Prevention Program; San Francisco USA, Department of Clinical Genetics; Maastricht University Medical Center; Maastricht The Netherlands, Unité de Prévention et d'Epidémiologie Génétique; Centre Léon Bérard, 28 rue Laënnec; Lyon France, N.N. Petrov Institute of Oncology; St. Petersburg Russia, Department of Clinical Genetics; Royal Devon & Exeter Hospital; Exeter UK, Service de Génétique; Institut Curie, 26 rue d'Ulm; Paris France, Department of Medicine; Huntsman Cancer Institute; Salt Lake City USA, Molecular Oncology Laboratory; Hospital Clinico San Carlos; Instituto de Investigación Sanitaria San Carlos (IdISSC); Centro Investigación Biomédica en Red de Cáncer (CIBERONC); Madrid Spain, Institute of Human Genetics; University Hospital of Schleswig-Holstein; Kiel Germany, Section of Molecular Genetics, Dept. of Laboratory Medicine; University Hospital of Pisa; Pisa Italy, Research Division; Peter MacCallum Cancer Centre; Melbourne Australia, CRCHU de Quebec-oncologie, Centre des maladies du sein Deschênes-Fabia; Hôpital du Saint-Sacrement; Sainte-Foy Canada, Lombardi Comprehensive Cancer Center; Georgetown University; Washington USA, Departments of Pediatrics and Medicine; Columbia University; New York USA, Department of Clinical Genetics, Family Cancer Clinic; Erasmus University Medical Center; Rotterdam The Netherlands, Sheffield Clinical Genetics Service; Sheffield Children's Hospital; Sheffield UK, Department of Clinical Genetics; South Glasgow University Hospitals; Glasgow UK, Unité d'oncogénétique; ICO-Centre René Gauducheau; Saint Herblain France, Oncogenetics Group, Vall d'Hebron Institute of Oncology (VHIO), Clinical and Molecular Genetics Area; Vall d'Hebron University Hospital; Barcelona Spain, Department of Gynaecology and Obstetrics; Ludwig-Maximilian University; Munich Germany, Cáncer Hereditario, Instituto de Biología y Genética Molecular, IBGM; Universidad de Valladolid; Valladolid Spain, Institute of Human Genetics; University of Münster; Münster Germany, Nottingham Clinical Genetics Service; Nottingham University Hospitals NHS Trust; Nottingham UK, Oncogenetics Team; The Institute of Cancer Research and Royal Marsden NHS Foundation Trust; London UK, Department of Clinical Genetics; Lund University Hospital; Lund Sweden, Clinical Genetics; Guy's and St. Thomas’ NHS Foundation Trust; London UK, Department of Oncology, Rigshospitalet; Copenhagen University Hospital; Copenhagen Denmark, Institute for Medical Informatics, Statistics and Epidemiology; University of Leipzig; Leipzig Germany, Department of Gynaecology and Obstetrics, Division of Tumor Genetics, Klinikum rechts der Isar; Technical University; Munich Germany, Genomic Medicine, Manchester Academic Health Sciences Centre, Division of Evolution and Genomic Sciences; University of Manchester, Central Manchester University Hospitals NHS Foundation Trust; Manchester UK, Centre de Lutte Contre le Cancer Georges François Leclerc, France and Genomic and Immunotherapy Medical Institute; Dijon University Hospital; Dijon France, Molecular Diagnostic Unit, Hereditary Cancer Program, ICO-IDIBELL (Catalan Institute of Oncology-Bellvitge Biomedical Research Institute); Barcelona Spain, Laboratoire de Génétique Chromosomique; Hôtel Dieu Centre Hospitalier; Chambéry France, Department of Cancer Epidemiology and Genetics; Masaryk Memorial Cancer Institute; Brno Czech Republic, Columbus Cancer Council, Ohio State University; Columbus USA, Genetic Counseling Unit, Hereditary Cancer Program, IDIBGI (Institut d'Investigació Biomèdica de Girona); Catalan Institute of Oncology; Girona Spain, Oncogenetics Department; Barretos Cancer Hospital; Barretos Brazil, UCLA Schools of Medicine and Public Health, Division of Cancer Prevention & Control Research; Jonsson Comprehensive Cancer Center; Los Angeles USA, Cancer Risk and Prevention Clinic; Dana-Farber Cancer Institute; Boston USA, Centre of Familial Breast and Ovarian Cancer, Department of Medical Genetics, Institute of Human Genetics; University of Würzburg, Germany; Würzburg, Department of Clinical Genetics; Copenhagen Denmark, Service Régional Oncogénétique Poitou-Charentes; Centre Hospitalier; Niort France, Department of Molecular Medicine; University La Sapienza, and Istituto Pasteur - Fondazione Cenci-Bolognetti; Rome Italy, Bâtiment Cheney D; Centre Léon Bérard; Lyon France, Ontario Cancer Genetics Network: Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital; Toronto Canada, Department of Pathology and Laboratory Medicine; University of Kansas Medical Center; Kansas City USA, Clinical Genetics Branch, DCEG, NCI; NIH; Bethesda USA, Parkville Familial Cancer Centre; Peter MacCallum Cancer Centre; Melbourne Australia, Hematology, oncology and transfusion medicine center, Dept. of Molecular and Regenerative Medicine; Vilnius University Hospital Santariskiu Clinics; Vilnius Lithuania, Department of Epidemiology, Cancer Prevention Institute of California; Fremont USA, Women's Cancer Program at the Samuel Oschin Comprehensive Cancer Institute; Cedars-Sinai Medical Center; Los Angeles USA, Division of Molecular Pathology; Department of Pathology; Hong Kong Sanatorium & Hospital; Happy Valley Hong Kong, Department of Gynecology and Obstetrics; Medical Faculty and University Hospital Carl Gustav Carus; Dresden Germany, Research Department, Peter MacCallum Cancer Centre, Melbourne, Victoria; Australia and The Sir Peter MacCallum Department of Oncology University of Melbourne; Parkville Australia, Department of Surgery; Daerim St. Mary's Hospital; Seoul Korea, The Gyneco-Oncology Department; Chaim Sheba Medical Center; Ramat Gan Israel, Servicio de Genética-CIBERER U705; Hospital de la Santa Creu i Sant Pau; Barcelona Spain, The Feinstein Institute for Medical Research; Manhasset USA, Department of Laboratory Medicine and Pathology; and Health Sciences Research; Rochester USA, Department of Surgery; Soonchunhyang University and Seoul Hospital; Seoul Korea, Inserm U900, Institut Curie; PSL Research University; Paris France, Department of Oncology Radiumhemmet and Institution of Oncology and Patology; Karolinska University Hospital and Karolinska Institutet; Solna Sweden, Department of Health Sciences Research; Mayo Clinic; Scottsdale USA, Oncogénétique; Institut Bergonié; Bordeaux France, Clinical Genetics Branch, DCEG, NCI, NIH; Bethesda USA, Department of Gynecological Oncology and Clinical Cancer Genetics Program; University Of Texas MD Anderson Cancer Center; Houston USA, Department of Dermatology; University of Utah School of Medicine; Salt Lake City USA, Centre Antoine Lacassagne; Nice France, Laboratorio de Genética Molecular, Servicio de Genética; Hospital Universitario Cruces, BioCruces Health Research Institute; Barakaldo Spain, Department of Surgery; National Institute of Oncology; Budapest Hungary, Department of Clinical Genetics; VU University Medical Center; Amsterdam The Netherlands, Department of Human Genetics; Radboud University Medical Center; Nijmegen The Netherlands, Vilnius university Santariskiu hospital; National Center of Pathology; Vilnius Lithuania, NRG Oncology; Statistics and Data Management Center; Roswell Park Cancer Institute; Buffalo USA, Department of Cancer Prevention and Control; Roswell Park Cancer Institute; Buffalo USA, Department of Laboratory Medicine and Pathobiology; University of Toronto; Toronto Canada, Department of Obstetrics and Gynecology; University of Helsinki and Helsinki University Hospital; HUS Finland, Cancer Genetics Service; Division of Medical Oncology; National Cancer Centre Singapore; Bukit Merah Singapore, Institute of Medical Genetics and Applied Genomics; University of Tuebingen; Tuebingen Germany, Molecular Oncology Research Center; Barretos Cancer Hospital; São Paulo Brazil, Cancer Genetics and Prevention Program; University of California San Francisco; San Francisco USA, Clinical Genetics Research Laboratory; Dept. of Medicine; Cancer Biology and Genetics; Memorial Sloan-Kettering Cancer Center; New York USA, Department of Clinical Genetics; Sahlgrenska University Hospital; Gothenburg Sweden, West Midlands Regional Genetics Service; Birmingham Women's Hospital Healthcare NHS Trust; Edgbaston UK, Human Genetics Group; Human Cancer Genetics Programme; Spanish National Cancer Research Centre (CNIO); Biomedical Network on Rare Diseases (CIBERER); Madrid Spain, Unit of Medical Genetics; Department of Biomedical; Experimental and Clinical Sciences; University of Florence; Florence Italy, Department of Medical Sciences; University of Turin; Turin Italy, Section of Molecular Diagnostics; Department of Biochemistry; Aalborg University Hospital; Aalborg Denmark, Department of Preventive Medicine; Seoul National University College of Medicine; Seoul Korea, IFOM; The FIRC (Italian Foundation for Cancer Research) Institute of Molecular Oncology; Milan Italy, Service de Génétique Clinique Chromosomique et Moléculaire; Hôpital Nord; St Etienne France, Unité d'Oncogénétique; CHU Arnaud de Villeneuve; Montpellier France, Unit of Molecular Bases of Genetic Risk and Genetic Testing; Department of Research; Fondazione IRCCS (Istituto Di Ricovero e Cura a Carattere Scientifico), Istituto Nazionale Tumori (INT); Milan Italy, School of Women's and Children's Health; UNSW; Sydney Australia, Department of Clinical Genetics; Karolinska University Hospital; Stockholm Sweden, Rebbeck, Timothy R., Friebel, Tara M., Friedman, Eitan, Hamann, Ute, Huo, Dezheng, Kwong, Ava, Olah, Edith, Olopade, Olufunmilayo I., Solano, Angela R., Teo, Soo-Hwang, Thomassen, Mads, Rashid, Muhammad Usman, Rhiem, Kerstin, Robson, Mark, Rodriguez, Gustavo C., Rogers, Mark T., Rudaitis, Vilius, Schmidt, Ane Y., Schmutzler, Rita Katharina, Senter, Leigha, van Rensburg, Elizabeth J., Gronwald, Jacek, Shah, Payal D., Sharma, Priyanka, Side, Lucy E., Simard, Jacques, Singer, Christian F., Skytte, Anne-Bine, Slavin, Thomas P., Snape, Katie, Sobol, Hagay, Southey, Melissa, Gutierrez-Barrera, Angelica, McGuffog, Lesley, Steele, Linda, Steinemann, Doris, Sukiennicki, Grzegorz, Sutter, Christian, Szabo, Csilla I., Tan, Yen Y., Teixeira, Manuel R., Terry, Mary Beth, Teulé, Alex, Hahnen, Eric, Thomas, Abigail, Parsons, Michael T., Thull, Darcy L., Tischkowitz, Marc, Tognazzo, Silvia, Toland, Amanda Ewart, Topka, Sabine, Trainer, Alison H, Tung, Nadine, van Asperen, Christi J., Hauke, Jan, van der Hout, Annemieke H., van der Kolk, Lizet E., Leslie, Goska, van der Luijt, Rob B., Van Heetvelde, Mattias, Varesco, Liliana, Varon-Mateeva, Raymonda, Vega, Ana, Villarreal-Garza, Cynthia, von Wachenfeldt, Anna, Henderson, Alex, Walker, Lisa, Wang-Gohrke, Shan, Wappenschmidt, Barbara, Aalfs, Cora M., Weber, Bernhard H. F., Yannoukakos, Drakoulis, Yoon, Sook-Yee, Zanzottera, Cristina, Zidan, Jamal, Zorn, Kristin K., Hentschel, Julia, Hutten Selkirk, Christina G., Hulick, Peter J., Chenevix-Trench, Georgia, Spurdle, Amanda B., Abugattas, Julio, Antoniou, Antonis C., Nathanson, Katherine L., Adlard, Julian, Agata, Simona, Aittomäki, Kristiina, Hogervorst, Frans B.L., Andrews, Lesley, Andrulis, Irene L., Arason, Adalgeir, Arnold, Norbert, Arun, Banu K., Asseryanis, Ella, Auerbach, Leo, Azzollini, Jacopo, Balmaña, Judith, Barile, Monica, Honisch, Ellen, Barkardottir, Rosa B., Barrowdale, Daniel, Benitez, Javier, Berger, Andreas, Berger, Raanan, Blanco, Amie M., Blazer, Kathleen R., Blok, Marinus J., Bonadona, Valérie, Bonanni, Bernardo, Imyanitov, Evgeny N., Bradbury, Angela R., Brewer, Carole, Buecher, Bruno, Buys, Saundra S., Caldes, Trinidad, Caliebe, Almuth, Caligo, Maria A., Campbell, Ian, Caputo, Sandrine M., Chiquette, Jocelyne, Isaacs, Claudine, Chung, Wendy K., Claes, Kathleen B.M., Collée, J. Margriet, Cook, Jackie, Davidson, Rosemarie, de la Hoya, Miguel, De Leeneer, Kim, de Pauw, Antoine, Delnatte, Capucine, Diez, Orland, Weitzel, Jeffrey N., Ding, Yuan Chun, Ditsch, Nina, Domchek, Susan M., Dorfling, Cecilia M., Velazquez, Carolina, Dworniczak, Bernd, Eason, Jacqueline, Easton, Douglas F., Eeles, Ros, Ehrencrona, Hans, Izatt, Louise, Ejlertsen, Bent, Engel, Christoph, Engert, Stefanie, Evans, D. Gareth, Faivre, Laurence, Feliubadaló, Lidia, Ferrer, Sandra Fert, Foretova, Lenka, Fowler, Jeffrey, Frost, Debra, Izquierdo, Angel, Galvão, Henrique C. R., Ganz, Patricia A., Garber, Judy, Gauthier-Villars, Marion, Gehrig, Andrea, Gerdes, Anne-Marie, Gesta, Paul, Giannini, Giuseppe, Giraud, Sophie, Glendon, Gord, Jakubowska, Anna, Godwin, Andrew K., Greene, Mark H., James, Paul, Janavicius, Ramunas, Jensen, Uffe Birk, John, Esther M., Vijai, Joseph, Kaczmarek, Katarzyna, Karlan, Beth Y., Chan, TL, Kast, Karin, Investigators, KConFab, Kim, Sung-Won, Konstantopoulou, Irene, Korach, Jacob, Laitman, Yael, Lasa, Adriana, Lasset, Christine, Lázaro, Conxi, Lee, Annette, Couch, Fergus J., Lee, Min Hyuk, Lester, Jenny, Lesueur, Fabienne, Liljegren, Annelie, Lindor, Noralane M., Longy, Michel, Loud, Jennifer T., Lu, Karen H., Lubinski, Jan, Machackova, Eva, Goldgar, David E., Manoukian, Siranoush, Mari, Véronique, Martínez-Bouzas, Cristina, Matrai, Zoltan, Mebirouk, Noura, Meijers-Heijboer, Hanne E.J., Meindl, Alfons, Mensenkamp, Arjen R., Mickys, Ugnius, Miller, Austin, Kruse, Torben A., Montagna, Marco, Moysich, Kirsten B., Mulligan, Anna Marie, Musinsky, Jacob, Neuhausen, Susan L., Nevanlinna, Heli, Ngeow, Joanne, Nguyen, Huu Phuc, Niederacher, Dieter, Nielsen, Henriette Roed, Palmero, Edenir Inêz, Nielsen, Finn Cilius, Nussbaum, Robert L., Offit, Kenneth, Öfverholm, Anna, Ong, Kai-ren, Osorio, Ana, Papi, Laura, Papp, Janos, Pasini, Barbara, Pedersen, Inge Sokilde, Park, Sue Kyung, Peixoto, Ana, Peruga, Nina, Peterlongo, Paolo, Pohl, Esther, Pradhan, Nisha, Prajzendanc, Karolina, Prieur, Fabienne, Pujol, Pascal, Radice, Paolo, Ramus, Susan J., Torres, Diana, and Rantala, Johanna
Abstract: To access publisher's full text version of this article click on the hyperlink below, The prevalence and spectrum of germline mutations in BRCA1 and BRCA2 have been reported in single populations, with the majority of reports focused on White in Europe and North America. The Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA) has assembled data on 18,435 families with BRCA1 mutations and 11,351 families with BRCA2 mutations ascertained from 69 centers in 49 countries on six continents. This study comprehensively describes the characteristics of the 1,650 unique BRCA1 and 1,731 unique BRCA2 deleterious (disease-associated) mutations identified in the CIMBA database. We observed substantial variation in mutation type and frequency by geographical region and race/ethnicity. In addition to known founder mutations, mutations of relatively high frequency were identified in specific racial/ethnic or geographic groups that may reflect founder mutations and which could be used in targeted (panel) first pass genotyping for specific populations. Knowledge of the population-specific mutational spectrum in BRCA1 and BRCA2 could inform efficient strategies for genetic testing and may justify a more broad-based oncogenetic testing in some populations.

5. Strong instrumental variables biased propensity scores in comparative effectiveness research: A case study in oncology

Author: Nicolas H. Thurin, Jérémy Jové, Régis Lassalle, Magali Rouyer, Stéphanie Lamarque, Pauline Bosco-Levy, Corentin Segalas, Sebastian Schneeweiss, Patrick Blin, Cécile Droz-Perroteau, CIC Bordeaux, Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale (INSERM), Plateforme Bordeaux PharmacoEpi [Bordeaux] (BPE), Centre d'Investigation Clinique [Bordeaux], Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Université de Bordeaux (UB)-CHU Bordeaux [Bordeaux]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Université de Bordeaux (UB)-CHU Bordeaux [Bordeaux]-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Harvard Medical School [Boston] (HMS), Financement propre, and Thurin, Nicolas
Subjects: [STAT]Statistics [stat], Prostate cancer, Bias, Propensity score, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Epidemiology, [SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, Matching, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Instrumental variables, SNDS, [STAT] Statistics [stat]
Abstract: International audience; Background and Objectives: Some medications require specific medical procedures in the weeks before their start. Such procedures may meet the definition of instrumental variables (IVs). We examined how they may influence treatment effect estimation in propensity score (PS)-adjusted comparative studies, and how to remedy. Study Design and Setting: Different covariate assessment periods (CAPs) did and did not include the month preceding treatment start were used to compute PS in the French claims database (Syt eme National des Donn ees de Sant e-SNDS), and 1:1 match patients with metastatic castration resistant prostate cancer initiating abiraterone acetate or docetaxel. The 36-month survival was assessed. Results: Among 1, 213 docetaxel and 2, 442 abiraterone initiators, the PS distribution resulting from the CAP [-12; 0 months] distinctly separated populations (c 5 0.93; 273 matched pairs). The CAPs [-12;-1 months] identified 765 pairs (c 5 0.81). Strong docetaxel treatment predictors during the month before treatment start were implantable delivery systems (1% vs. 59%), which fulfilled IV conditions. The 36-month survival was not meaningfully different under the [-12; 0 months] CAP but differed by 10% points (38% vs. 28%) after excluding month À1. Conclusion: In the setting of highly predictive pretreatment procedures, excluding the immediate pre-exposure time from the CAP will reduce the risk of including potential IVs in PS models and may reduce bias.
Published: 2023

6. Toward improved endoscopic examination of urinary stones: a concordance study between endoscopic digital pictures vs microscopy

Author: Olivier Traxer, Vincent Estrade, Grégoire Robert, Christophe Almeras, Jean-Christophe Bernhard, Franck Bladou, Michel Daudon, Baudouin Denis de Senneville, Paul Meria, Hôpital Pellegrin, CHU Bordeaux [Bordeaux]-Groupe hospitalier Pellegrin, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Hopital Saint-Louis [AP-HP] (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Clinique La Croix du Sud, CHU Tenon [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)
Subjects: medicine.medical_specialty, Urology, Urinary system, Concordance, Urinary stone, 030232 urology & nephrology, Flexible ureteroscopy training, engineering.material, #EndoUrology, Kidney Calculi, 03 medical and health sciences, 0302 clinical medicine, morpho‐constitutional analysis of urinary stones, Spectroscopy, Fourier Transform Infrared, Ureteroscopy, medicine, Humans, Digital pictures, LASER fragmentation of stones, Retrospective Studies, Microscopy, business.industry, Whewellite, Morphological type, Significant difference, #UroStone, Original Articles, medicine.disease, Aetiological lithiasis, 030220 oncology & carcinogenesis, engineering, Original Article, Kidney stones, Morpho-constitutional analysis of urinary stones, Radiology, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, #KidneyStones, Endoscopic diagnosis
Abstract: Objective: To improve endoscopic recognition of the most frequently encountered kidney stone morphologies for a better etiological approach in lithiasis by urologists. Materials and methods: An expert urologist intra-operatively and prospectively (between June 2015 and June 2018) examined the surface, the section and the nucleus of all encountered kidney stones. Fragmented stones were subsequently analysed by a biologist based on both microscopic morphological (i.e. binocular magnifying glass) and infrared (i.e. FTIR) examinations (microscopists were blinded to the endoscopic data). Morphological criteria were collected and classified for the endoscopic and microscopic studies. The Wilcoxon–Mann–Whitney test was carried out to detect differences between the endoscopic and microscopic diagnoses. A diagnosis for a given urinary stone was considered "confirmed" for a non-statistically significant difference. Results: A total of 399 urinary stones were included in this study: 51.4% of the stones exhibited only one morphological type while 48.6% were mixed stones (41% had at least two morphologies and 7.6% had three morphologies). The overall matching rate was 81.6%. Diagnostics were confirmed for the following morphologies: whewellite (Ia or Ib), weddellite (IIa or IIb), uric acid (IIIa or IIIb), carbapatite-struvite association (IVb), brushite (IVd). Conclusions: Our preliminary study demonstrates the feasibility of using endoscopic morphology for the most frequently encountered urinary stones and didactic boards of confirmed endoscopic images are provided. The current study constitutes the first step toward endoscopic stone recognition, which is essential in lithiasis. We provide didactic boards of confirmed endoscopic images which paves the way for automatic computer-aided in-situ recognition.
Published: 2020

7. Macro-scale models for fluid flow in tumour tissues: impact of microstructure properties

Author: Cristina Vaghi, Sebastien Benzekry, Clair Poignard, Raphaelle Fanciullino, Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Simulation and Modeling of Adaptive Response for Therapeutics in Cancer (SMARTc), Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Méthodes computationnelles pour la prise en charge thérapeutique en oncologie : Optimisation des stratégies par modélisation mécaniste et statistique (COMPO), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Cancérologie de Marseille (CRCM), Plan Cancer Numep NUMEP, Plan Cancer QUANTIC, Vaghi, Cristina, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Paoli-Calmettes, and Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Aix Marseille Université (AMU)
Subjects: Materials science, [SDV.CAN]Life Sciences [q-bio]/Cancer, Models, Biological, 01 natural sciences, Imaging data, 03 medical and health sciences, Hydraulic conductivity, [SDV.CAN] Life Sciences [q-bio]/Cancer, Neoplasms, Interstitial tissue, Tumor Microenvironment, Fluid dynamics, Fluid flow in tumours, Humans, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], 0101 mathematics, [MATH.MATH-AP] Mathematics [math]/Analysis of PDEs [math.AP], 030304 developmental biology, Interstitial fluid pressure, 0303 health sciences, Tumour microenvironment, Applied Mathematics, Biological Transport, Extracellular Fluid, Mechanics, Fluid transport, Microstructure, Agricultural and Biological Sciences (miscellaneous), Interstitial fluid flow, 010101 applied mathematics, Macroscopic scale, Modeling and Simulation, Two-scale homogenisation
Abstract: Understanding the dynamics underlying fluid transport in tumour tissues is of fundamental importance to assess processes of drug delivery. Here, we analyse the impact of the tumour microscopic properties on the macroscopic dynamics of vascular and interstitial fluid flow by using formal asymptotic techniques.Here, we obtained different macroscopic continuum models that couple vascular and interstitial flows. The homogenization technique allows us to derive two macroscale tissue models of fluid flow that take into account the microscopic structure of the vessels and the interstitial tissue. Different regimes were derived according to the magnitude of the vessel wall permeability and the interstitial hydraulic conductivity. Importantly, we provide an analysis of the properties of the models and show the link between them. Numerical simulations were eventually performed to test the models and to investigate the impact of the microstructure on the fluid transport.Future applications of our models include their calibration with real imaging data to investigate the impact of the tumour microenvironment on drug delivery.
Published: 2022

8. Artificial intelligence in CT for quantifying lung changes in the era of CFTR modulators

Author: Dournes, Gael, Hall, Chase, Willmering, Matthew, Brody, Alan, Macey, Julie, BUI, Stephanie, Denis De Senneville, Baudouin, Berger, Patrick, Laurent, François, Benlala, Ilyes, Woods, Jason, Centre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] (CRCTB), Université Bordeaux Segalen - Bordeaux 2-CHU Bordeaux [Bordeaux]-Institut National de la Santé et de la Recherche Médicale (INSERM), CIC Bordeaux, Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale (INSERM), CHU Bordeaux [Bordeaux], University of Kansas [Kansas City], Cincinnati Children's Hospital Medical Center, Hôpital Pellegrin, CHU Bordeaux [Bordeaux]-Groupe hospitalier Pellegrin, Biothérapies des maladies génétiques et cancers, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), University of Cincinnati (UC), ANR-10-IDEX-03-02, ANR-10-LABX-0057,TRAIL,Translational Research and Advanced Imaging Laboratory(2010), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], and Denis De Senneville, Baudouin
Subjects: [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, [SDV.MHEP.PSR] Life Sciences [q-bio]/Human health and pathology/Pulmonology and respiratory tract, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, [SDV.MHEP.PSR]Life Sciences [q-bio]/Human health and pathology/Pulmonology and respiratory tract
Abstract: International audience; Background: Chest computed tomography (CT) remains the imaging standard for demonstrating cystic fibrosis (CF) airway structural disease in vivo. However, visual scoring systems as an outcome measure are time consuming, require training and lack high reproducibility. Our objective was to validate a fully automated artificial intelligence (AI)-driven scoring system of CF lung disease severity.Methods: Data were retrospectively collected in three CF reference centres, between 2008 and 2020, in 184 patients aged 4-54 years. An algorithm using three 2D convolutional neural networks was trained with 78 patients' CT scans (23 530 CT slices) for the semantic labelling of bronchiectasis, peribronchial thickening, bronchial mucus, bronchiolar mucus and collapse/consolidation. 36 patients' CT scans (11 435 CT slices) were used for testing versus ground-truth labels. The method's clinical validity was assessed in an independent group of 70 patients with or without lumacaftor/ivacaftor treatment (n=10 and n=60, respectively) with repeat examinations. Similarity and reproducibility were assessed using the Dice coefficient, correlations using the Spearman test, and paired comparisons using the Wilcoxon rank test.Results: The overall pixelwise similarity of AI-driven versus ground-truth labels was good (Dice 0.71). All AI-driven volumetric quantifications had moderate to very good correlations to a visual imaging scoring (p0.99).Conclusion: AI allows fully automated volumetric quantification of CF-related modifications over an entire lung. The novel scoring system could provide a robust disease outcome in the era of effective CF transmembrane conductance regulator modulator therapy.Trial registration: ClinicalTrials.gov NCT04760548.
Published: 2022

9. Deciphering Tumour Tissue Organization by 3D Electron Microscopy and machine learning

Author: Etienne Gontier, Jean Ripoche, Kathleen Flosseau, Sophie Branchereau, Stefano Cairo, Alexandre Labedade, Marc Bevilacqua, Baudouin Denis de Senneville, Christophe Grosset, Christophe Chardot, Fatma Zohra Khoubai, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Biothérapies des maladies génétiques et cancers, Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale (INSERM), Bordeaux Imaging Center (BIC), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut François Magendie-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS), Chercheur indépendant, XenTech [Evry], CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), AP-HP Hôpital Bicêtre (Le Kremlin-Bicêtre), Université de Bordeaux (UB), This work was supported by the charity Eva pour la Vie, La Fondation ARC pour la Recherche sur le Cancer (contract N° PJA 20191209631), La Région Nouvelle-Aquitaine, La Fondation Groupama pour la Santé and Groupama Centre-Atlantique. Microscopy Imaging was performed at the Bordeaux Imaging Centre, which is a member of the FranceBioImaging national infrastructure (ANR-10-INBS-04)., ANR-10-INBS-0004,France-BioImaging,Développment d'une infrastructure française distribuée coordonnée(2010), Bioingénierie tissulaire (BIOTIS), Université de Bordeaux (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM), Istituto di Ricerca Pediatrica [Padova, Italy] (IRP), Hôpital Bicêtre, Istituto di Ricerca Pediatrica Città della Speranza, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Université de Bordeaux (UB)-Institut François Magendie-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Paris-Saclay, Institut National de la Santé et de la Recherche Médicale (INSERM), PlaFRIM (https://www.plafrim.fr), and Grosset, Christophe
Subjects: [SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging, Medicine (miscellaneous), Pilot Projects, [MATH] Mathematics [math], Mitochondrion, computer.software_genre, Machine Learning, Tumour tissue, 0302 clinical medicine, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], Image Processing, Computer-Assisted, Biology (General), [MATH]Mathematics [math], Child, Cancer, 0303 health sciences, mathematics, Liver Neoplasms, medicine.anatomical_structure, 030220 oncology & carcinogenesis, Cancer, hepatoblastoma, patient-derived xenograft, 3D imaging, serial blockface scanning electron microscopy, nanotomy, mathematics, General Agricultural and Biological Sciences, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Hepatoblastoma, 3d electron microscopy, patient-derived xenograft, QH301-705.5, [SDV.CAN]Life Sciences [q-bio]/Cancer, Biology, Machine learning, General Biochemistry, Genetics and Molecular Biology, Article, 03 medical and health sciences, nanotomy, [SDV.CAN] Life Sciences [q-bio]/Cancer, 3D imaging, Organelle, Electron microscopy, medicine, Humans, 030304 developmental biology, [SDV.MHEP.PED]Life Sciences [q-bio]/Human health and pathology/Pediatrics, business.industry, [SDV.MHEP.HEG]Life Sciences [q-bio]/Human health and pathology/Hépatology and Gastroenterology, hepatoblastoma, medicine.disease, [STAT.ML] Statistics [stat]/Machine Learning [stat.ML], [SDV.IB.IMA] Life Sciences [q-bio]/Bioengineering/Imaging, Cytoplasm, Microscopy, Electron, Scanning, serial blockface scanning electron microscopy, Ultrastructure, Cancer imaging, Artificial intelligence, serial block-face scanning electron microscopy, business, Nucleus, computer
Abstract: Despite recent progress in the characterization of tumour components, the tri-dimensional (3D) organization of this pathological tissue and the parameters determining its internal architecture remain elusive. Here, we analysed the spatial organization of patient-derived xenograft tissues generated from hepatoblastoma, the most frequent childhood liver tumour, by serial block-face scanning electron microscopy using an integrated workflow combining 3D imaging, manual and machine learning-based semi-automatic segmentations, mathematics and infographics. By digitally reconstituting an entire hepatoblastoma sample with a blood capillary, a bile canaliculus-like structure, hundreds of tumour cells and their main organelles (e.g. cytoplasm, nucleus, mitochondria), we report unique 3D ultrastructural data about the organization of tumour tissue. We found that the size of hepatoblastoma cells correlates with the size of their nucleus, cytoplasm and mitochondrial mass. We also found anatomical connections between the blood capillary and the planar alignment and size of tumour cells in their 3D milieu. Finally, a set of tumour cells polarized in the direction of a hot spot corresponding to a bile canaliculus-like structure. In conclusion, this pilot study allowed the identification of bioarchitectural parameters that shape the internal and spatial organization of tumours, thus paving the way for future investigations in the emerging onconanotomy field., de Senneville et al. demonstrate an integrated workflow combining 3D imaging, manual and machine learning-based semi-automatic segmentation, mathematics and infographics to study the spatial organization of patient-derived hepatoblastoma xenograft tissues. Their approach potentially assists investigations of this childhood liver tumour and other types of tumour tissues.
Published: 2021

10. Estimation for dynamical systems using a population-based Kalman filter – Applications in computational biology

Author: Collin, Annabelle, Prague, Mélanie, Moireau, Philippe, Mathematical and Mechanical Modeling with Data Interaction in Simulations for Medicine (M3DISIM), Laboratoire de mécanique des solides (LMS), École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut Polytechnique de Bordeaux (Bordeaux INP), Centre National de la Recherche Scientifique (CNRS), Vaccine Research Institute (VRI), Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Mines Paris - PSL (École nationale supérieure des mines de Paris), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Centre National de la Recherche Scientifique (CNRS)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS)-MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École polytechnique (X)-Inria Saclay - Ile de France, and Collin, Annabelle
Subjects: Epidemiology, Kalman Filters, COVID-19, [MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC], Mixed-effect estimation, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Non linear mixed-effect models, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], [SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, population-based sequential estimation, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, Pharmacokinetics, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [MATH.MATH-OC]Mathematics [math]/Optimization and Control [math.OC], [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], data assimilation
Abstract: International audience; Estimation of dynamical systems - in particular, identification of their parameters - is fundamental in computational biology, e.g., pharmacology, virology, or epidemiology, to reconcile model runs with available measurements. Unfortunately, the mean and variance priorities of the parameters must be chosen very appropriately to balance our distrust of the measurements when the data are sparse or corrupted by noise. Otherwise, the identification procedure fails. One option is to use repeated measurements collected in configurations with common priorities - for example, with multiple subjects in a clinical trial or clusters in an epidemiological investigation. This shared information is beneficial and is typically modeled in statistics using nonlinear mixed-effects models. In this paper, we present a data assimilation method that is compatible with such a mixed-effects strategy without being compromised by the potential curse of dimensionality. We define population-based estimators through maximum likelihood estimation. We then develop an equivalent robust sequential estimator for large populations based on filtering theory that sequentially integrates data. Finally, we limit the computational complexity by defining a reduced-order version of this population-based Kalman filter that clusters subpopulations with common observational backgrounds. The performance of the resulting algorithm is evaluated against classical pharmacokinetics benchmarks. Finally, the versatility of the proposed method is tested in an epidemiological study using real data on the hospitalisation of COVID-19 patients in the regions and departments of France.
Published: 2021

11. Voluntary Wheel Running Does Not Enhance Radiotherapy Efficiency in a Preclinical Model of Prostate Cancer: The Importance of Physical Activity Modalities?

Author: Suzanne, Dufresne, Cindy, Richard, Arthur, Dieumegard, Luz, Orfila, Gregory, Delpon, Sophie, Chiavassa, Brice, Martin, Laurent, Rouvière, Jean-Michel, Escoffre, Edward, Oujagir, Baudouin, Denis de Senneville, Ayache, Bouakaz, Nathalie, Rioux-Leclercq, Vincent, Potiron, Amélie, Rébillard, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Escoffre, Jean-Michel, Laboratoire Mouvement Sport Santé (M2S), Université de Rennes (UR)-École normale supérieure - Rennes (ENS Rennes)-Université de Brest (UBO)-Université de Rennes 2 (UR2)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Institut de Cancérologie de l'Ouest [Angers/Nantes] (UNICANCER/ICO), UNICANCER, Institut de Recherche Mathématique de Rennes (IRMAR), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-École normale supérieure - Rennes (ENS Rennes)-Université de Rennes 2 (UR2)-Centre National de la Recherche Scientifique (CNRS)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Imagerie et cerveau (iBrain - Inserm U1253 - UNIV Tours ), Université de Tours (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Service d'anatomie et cytologie pathologiques [Rennes] = Anatomy and Cytopathology [Rennes], CHU Pontchaillou [Rennes], Institut de recherche en santé, environnement et travail (Irset), Université d'Angers (UA)-Université de Rennes (UR)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Laboratoire de Biologie des Cancers et de Théranostic (LabCT), UNICANCER-UNICANCER, Apoptosis and Tumor Progression (CRCINA-ÉQUIPE 9), Centre de Recherche en Cancérologie et Immunologie Nantes-Angers (CRCINA), Université d'Angers (UA)-Université de Nantes (UN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre hospitalier universitaire de Nantes (CHU Nantes)-Université d'Angers (UA)-Université de Nantes (UN)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre hospitalier universitaire de Nantes (CHU Nantes), Institut Universitaire de France (IUF), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), This research was funded by la Ligue Contre le Cancer Comité Départemental 35, Comité Départemental 72, Comité Départemental 85, and the Fondation ARC., École normale supérieure - Cachan (ENS Cachan)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Brest (UBO)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), CRLCC René Gauducheau, AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université de Rennes 2 (UR2), Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), Université de Tours-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université de Nantes (UN)-Université de Nantes (UN)-Centre hospitalier universitaire de Nantes (CHU Nantes)-Centre National de la Recherche Scientifique (CNRS)-Université d'Angers (UA), UMR 1253 IBrain Imagerie & Cerveau Equipe 3 'Imagerie, Biomarqueurs & Thérapie' (IBT), Université de Tours (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Tours (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Tours (CHRU Tours), Institut de Chimie de la Matière Condensée de Bordeaux (ICMCB), Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique )-Institut National de la Santé et de la Recherche Médicale (INSERM)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Université d'Angers (UA), Université de Nantes (UN)-Université de Nantes (UN)-Centre hospitalier universitaire de Nantes (CHU Nantes)-Centre National de la Recherche Scientifique (CNRS)-Université d'Angers (UA)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Nantes - UFR de Médecine et des Techniques Médicales (UFR MEDECINE), Université d'Angers (UA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), ANR-11-LABX-0020,LEBESGUE,Centre de Mathématiques Henri Lebesgue : fondements, interactions, applications et Formation(2011), and Université de Tours-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Tours-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Tours (CHRU TOURS)
Subjects: exercise, vascularization, [SDV.CAN] Life Sciences [q-bio]/Cancer, proliferation, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, physical activity, [SDV.CAN]Life Sciences [q-bio]/Cancer, prostate cancer, radiation therapy, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, RC254-282, Article, radiotherapy
Abstract: Simple Summary Physical activity is increasingly incorporated in cancer patient health care as a strategy to improve survival outcomes. However, its effects on treatment efficiency remains unclear. The aim of our preclinical study is to evaluate whether access to a running wheel could enhance the response to radiotherapy in mice with prostate cancer. We observed that voluntary wheel running (VWR) did not slow down tumor growth but appeared to modulate some parameters related to tumor perfusion. However, this did not result in enhanced response to radiotherapy. To investigate whether the lack of benefits on tumor growth observed with VWR could be attributed to the choice of physical activity modality, we conducted additional experiments comparing the effects of treadmill running versus VWR in two different preclinical models of prostate cancer. Only treadmill running was able to slow down tumor growth. Hence, the anti-cancer effects of physical activity seem dependent on its modalities. Abstract Physical activity is increasingly recognized as a strategy able to improve cancer patient outcome, and its potential to enhance treatment response is promising, despite being unclear. In our study we used a preclinical model of prostate cancer to investigate whether voluntary wheel running (VWR) could improve tumor perfusion and enhance radiotherapy (RT) efficiency. Nude athymic mice were injected with PC-3 cancer cells and either remained inactive or were housed with running wheels. Apparent microbubble transport was enhanced with VWR, which we hypothesized could improve the RT response. When repeating the experiments and adding RT, however, we observed that VWR did not influence RT efficiency. These findings contrasted with previous results and prompted us to evaluate if the lack of effects observed on tumor growth could be attributable to the physical activity modality used. Using PC-3 and PPC-1 xenografts, we randomized mice to either inactive controls, VWR, or treadmill running (TR). In both models, TR (but not VWR) slowed down tumor growth, suggesting that the anti-cancer effects of physical activity are dependent on its modalities. Providing a better understanding of which activity type should be recommended to cancer patients thus appears essential to improve treatment outcomes.
Published: 2021

12. Reconstruction of trajectories-Challenge AMIES

Author: Dariva, Kyriaki, Jaramillo, Pedro, Modélisation mathématique, calcul scientifique (MMCS), Institut Camille Jordan [Villeurbanne] (ICJ), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Multi-scale modelling of cell dynamics : application to hematopoiesis (DRACULA), Centre de génétique et de physiologie moléculaire et cellulaire (CGPhiMC), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Camille Jordan [Villeurbanne] (ICJ), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Univ Lyon, Université Claude-Bernard Lyon 1, Université de bordeaux, Dariva, Kyriaki, Institut Camille Jordan (ICJ), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Centre National de la Recherche Scientifique (CNRS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Camille Jordan (ICJ), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux]
Subjects: [MATH] Mathematics [math], [MATH]Mathematics [math]
Abstract: Given a set of successive images which contain the positions of people in a certain space and at a certain time, we propose a method to reconstruct their trajectories. A surveillance 3D-camera takes a photo of the place every a certain fraction of the second and provides us with the data, after processing each picture and retrieving the position of the people in its view. We connect points from successive images by considering that people's position evolves with optimal transport. Our approach is mainly implemented by using a linear programming function, already available in Python. As the camera produces imperfect measurements, we define a framework to classify data and filter out noise. After presenting the problem of optimal transport of finite points as a linear programming problem, we provide some details of our modeling procedure. Finally, we show some of the results obtained with our method while we also suggest ideas to further improve our work.
Published: 2021

13. Global epidemiology of hip fractures: a study protocol using a common analytical platform among multiple countries

Author: Kebede Beyene, Jiannong Liu, Amy Hai Yan Chan, Chor-Wing Sing, Caroline Y. Doyon, Hongxin Zhao, Henrik Toft Sørensen, E. Michael Lewiecki, Anna-Maija Tolppanen, Katia M.C. Verhamme, Kenneth K.C. Man, Ju-Young Shin, Kiyoshi Kubota, Jeff Lange, Ian C. K. Wong, Tzu-Chieh Lin, Grace Hsin-Min Wang, Jenni Ilomäki, Mirhelen Mendes de Abreu, Douglas P. Kiel, Pauline Bosco-Lévy, Sharon Bartholomew, Nicholas Moore, Corina W Bennett, Sawaeng Watcharathanakij, Daniel Prieto-Alhambra, Sirpa Hartikainen, Ganga Ganesan, Nobuhiro Ooba, Edward Chia Cheng Lai, Alma B Pedersen, Kelvin Bryan Tan, James O’Kelly, Manju Chandran, Ching-Lung Cheung, J. Simon Bell, Han Eol Jeong, Cécile Droz-Perroteau, The University of Hong Kong (HKU), Amgen Inc. [Thousand Oaks, CA, USA], Public Health Agency of Canada, Monash University [Melbourne], University of Auckland [Auckland], Plateforme Bordeaux PharmacoEpi [Bordeaux] (BPE), Centre d'Investigation Clinique [Bordeaux], Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Université de Bordeaux (UB)-CHU Bordeaux [Bordeaux]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Université de Bordeaux (UB)-CHU Bordeaux [Bordeaux]-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Bordeaux (UB), National University Hospital [Singapore] (NUH), Ministry of Health [Singapore], University of Eastern Finland, Sungkyunkwan University [Suwon] (SKKU), Harvard Medical School [Boston] (HMS), National Cheng Kung University (NCKU), The University of New Mexico [Albuquerque], Hennepin County Medical Center, Minneapolis, University College of London [London] (UCL), University College London Hospitals (UCLH), Universidade Federal Rural do Rio de Janeiro (UFRRJ), Nihon University, Aarhus University [Aarhus], National University of Singapore (NUS), Erasmus University Medical Center [Rotterdam] (Erasmus MC), Ubon Ratchathani University, and Medical Informatics
Subjects: medicine.medical_specialty, Asia, Population, diabetes & endocrinology, 030209 endocrinology & metabolism, Global Health, 03 medical and health sciences, 0302 clinical medicine, Health care, Epidemiology, medicine, Humans, 030212 general & internal medicine, education, Aged, Retrospective Studies, Hip fracture, education.field_of_study, business.industry, Hip Fractures, Public health, Incidence (epidemiology), Incidence, public health, Retrospective cohort study, General Medicine, Middle Aged, South America, medicine.disease, calcium & bone, 3. Good health, Europe, Systematic review, Medicine, epidemiology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, business, Demography
Abstract: IntroductionHip fractures are associated with a high burden of morbidity and mortality. Globally, there is wide variation in the incidence of hip fracture in people aged 50 years and older. Longitudinal and cross-geographical comparisons of health data can provide insights on aetiology, risk factors, and healthcare practices. However, systematic reviews of studies that use different methods and study periods do not permit direct comparison across geographical regions. Thus, the objective of this study is to investigate global secular trends in hip fracture incidence, mortality and use of postfracture pharmacological treatment across Asia, Oceania, North and South America, and Western and Northern Europe using a unified methodology applied to health records.Methods and analysisThis retrospective cohort study will use a common protocol and an analytical common data model approach to examine incidence of hip fracture across population-based databases in different geographical regions and healthcare settings. The study period will be from 2005 to 2018 subject to data availability in study sites. Patients aged 50 years and older and hospitalised due to hip fracture during the study period will be included. The primary outcome will be expressed as the annual incidence of hip fracture. Secondary outcomes will be the pharmacological treatment rate and mortality within 12 months following initial hip fracture by year. For the primary outcome, crude and standardised incidence of hip fracture will be reported. Linear regression will be used to test for time trends in the annual incidence. For secondary outcomes, the crude mortality and standardised mortality incidence will be reported.Ethics and disseminationEach participating site will follow the relevant local ethics and regulatory frameworks for study approval. The results of the study will be submitted for peer-reviewed scientific publications and presented at scientific conferences.
Published: 2021

14. Image based species identification of Globodera quarantine nematodes using computer vision and deep learning

Author: A. Buisson, Laurent Folcher, Romain Thevenoux, Heloïse Villessèche, Nicolas Parisey, Eric Grenier, Van Linh Le, Marie Beurton-Aimar, Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Unité de Nématologie (LSV Rennes), Laboratoire de la santé des végétaux (LSV), Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES)-Agence nationale de sécurité sanitaire de l'alimentation, de l'environnement et du travail (ANSES), Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), ANSES, Conseil Régional Bretagne, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de Nématologie, Laboratoire de la Santé des Végétaux, and Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)
Subjects: 0106 biological sciences, Species complex, Globodera rostochiensis, Computer science, [SDV]Life Sciences [q-bio], Potato cyst nematode, Horticulture, 01 natural sciences, Convolutional neural network, Automation, Nematode taxonomy, Discriminative model, Machine learning, Globodera pallida, biology, business.industry, Landmarks, Deep learning, Forestry, Pattern recognition, 04 agricultural and veterinary sciences, biology.organism_classification, Computer Science Applications, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Identification (biology), Artificial intelligence, business, Morphometrics, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: International audience; Identification of plant parasitic nematode species is usually achieved following morphobiometric analysis, which requires a certain level of expertise and remains time consuming. Moreover, molecular and morphological discrimination of a number of emergent or cryptic species is sometimes difficult. Finding a way to achieve morphological characterisation quickly and accurately would greatly advance nematology science. Here, we developed a complete method in order to identify the two quarantine nematode species Globodera pallida and Globodera rostochiensis. First, we chose discriminative metrics on the stylet of nematodes that are able to be used by algorithms in order to build an automated process. Second, we used a custom computer vision algorithm (CCVA) and a convolutional neural network (CNN) to measure our metrics of interest. Third, we compared the CCVA and CNN predictions and their discriminative power to distinguish closely related species. Results show accurate identification of G. pallida and G. rostochiensis with the two methods, despite small-scale divergence (one to five µm depending on the metric used). However, the error rate is higher for Globodera mexicana, suggesting that the algorithms are too specific. Nonetheless, these methods represent a promising novel approach to automated morphological identification of nematodes and Globodera species in particular.
Published: 2021

15. Incidence and outcome of BCR‐ABL mutated chronic myeloid leukemia patients who failed to tyrosine kinase inhibitors

Author: Sandrine Hayette, Anna Schmitt, Françoise Huguet, Marie-Pierre Fort, Gabriel Etienne, Francois-Xavier Mahon, Béatrice Turcq, Fanny Robbesyn, Pierre Sujobert, Suzanne Tavitian, Claudine Chollet, Stephane Morisset, Francis Belloc, Axelle Lascaux, Franck E. Nicolini, Stéphanie Dulucq, Françoise Durrieu, Fontanet Bijou, Audrey Bidet, Departement d'hématologie, Institut Bergonié, Bordeaux, Institut Bergonié [Bordeaux], UNICANCER-UNICANCER, Laboratory of Mammary and Leukaemic Oncogenesis, INSERM U1218, Université de Bordeaux, Laboratoire d'Hématologie, Hôpital Haut Lévêque CHU de Bordeaux, Pessac, CHU Bordeaux [Bordeaux], Service d’hématologie Clinique [CHU Toulouse], CHU Toulouse [Toulouse], Service d'Hématologie, Institut Universitaire du Cancer Toulouse‐ Oncopole, Centre Hospitalier Universitaire, Toulouse, UNICANCER, Service des maladies du sang, Hôpital Haut Lévêque CHU de Bordeaux, Pessac, Service d'hématologie [Hôpital Edouard Herriot - HCL], Hôpital Edouard Herriot [CHU - HCL], Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Laboratoire d'Hématologie, Centre Hospitalier Lyon Sud, Pierre Bénite, Hospices Civils de Lyon (HCL), Institut Universitaire du Cancer de Toulouse - Oncopole (IUCT Oncopole - UMR 1037), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale (INSERM), Centre National de la Recherche Scientifique (CNRS), Service d’Hématologie [Centre Hospitalier Lyon Sud - HCL], Centre Hospitalier Lyon Sud [CHU - HCL] (CHLS), Turcq, Beatrice, Service Hématologie - IUCT-Oncopole [CHU Toulouse], Pôle Biologie [CHU Toulouse], Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Pôle IUCT [CHU Toulouse], Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), and Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: 0301 basic medicine, Oncology, Male, Cancer Research, [SDV]Life Sciences [q-bio], DNA Mutational Analysis, Fusion Proteins, bcr-abl, 0302 clinical medicine, Mutation Rate, hemic and lymphatic diseases, tyrosine kinase inhibitors, Cumulative incidence, Original Research, Aged, 80 and over, Incidence (epidemiology), Myeloid leukemia, Middle Aged, Prognosis, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, 3. Good health, [SDV] Life Sciences [q-bio], 030220 oncology & carcinogenesis, Mutation (genetic algorithm), Female, Complete Hematologic Response, Tyrosine kinase, medicine.drug, Adult, medicine.medical_specialty, Antineoplastic Agents, [SDV.CAN]Life Sciences [q-bio]/Cancer, lcsh:RC254-282, 03 medical and health sciences, Young Adult, [SDV.CAN] Life Sciences [q-bio]/Cancer, chronic myeloid leukemia, Internal medicine, Cell Line, Tumor, Leukemia, Myelogenous, Chronic, BCR-ABL Positive, medicine, [SDV.BBM] Life Sciences [q-bio]/Biochemistry, Molecular Biology, Humans, Radiology, Nuclear Medicine and imaging, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, Protein Kinase Inhibitors, Aged, business.industry, Clinical Cancer Research, Imatinib, BCR-ABL kinase domain mutation, Survival Analysis, 030104 developmental biology, Mutation, Mutation testing, business
Abstract: International audience; PurposeTo assess the incidence of BCR‐ABL kinase domain (KD) mutation detection and its prognostic significance in chronic phase chronic myeloid leukemia (CP‐CML) patients treated with tyrosine kinase inhibitors (TKIs).Patients and MethodsWe analyzed characteristics and outcome of 253 CP‐CML patients who had at least one mutation analysis performed using direct sequencing. Of them, 187 patients were early CP (ECP) and 66 were late CP late chronic phase (LCP) and 88% were treated with Imatinib as first‐line TKI.ResultsOverall, 80 (32%) patients harbored BCR‐ABL KD mutations. A BCR‐ABL KD mutation was identified in 57% of patients, who progressed to accelerated or blastic phases (AP‐BP), and 47%, 29%, 35%, 16% and 26% in patients in CP‐CML at the time of mutation analysis who lost a complete hematologic response, failed to achieve or loss of a prior complete cytogenetic and major molecular response, respectively.Overall survival and cumulative incidence of CML‐related death were significantly correlated with the disease phase whatever the absence or presence of a mutation was and for the latter the mutation subgroup (T315I vs P‐loop vs non‐T315I non‐P‐loop) (P
Published: 2019

16. Optimal Scheduling of Bevacizumab and Pemetrexed/Cisplatin Dosing in Non‐Small Cell Lung Cancer

Author: Benjamin K. Schneider, Joseph Ciccolini, Sebastien Benzekry, Arnaud Boyer, Jonathan P. Mochel, Kenneth Wang, Fabrice Barlesi, Iowa State University (ISU), Simulation and Modeling of Adaptive Response for Therapeutics in Cancer (SMARTc), Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Service d'oncologie multidisciplinaire innovations thérapeutiques [Hôpital Nord - APHM], Assistance Publique - Hôpitaux de Marseille (APHM)- Hôpital Nord [CHU - APHM], Mayo Clinic [Rochester], Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Benzekry, Sebastien
Subjects: Lung Neoplasms, Cell, Mice, 0302 clinical medicine, [STAT.AP] Statistics [stat]/Applications [stat.AP], Carcinoma, Non-Small-Cell Lung, Antineoplastic Combined Chemotherapy Protocols, Pharmacology (medical), [STAT.AP]Statistics [stat]/Applications [stat.AP], 0303 health sciences, Articles, 3. Good health, Bevacizumab, Pharmacokinetics-pharmacodynamics, Treatment Outcome, Pemetrexed, medicine.anatomical_structure, Oncology, [SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, 030220 oncology & carcinogenesis, Modeling and Simulation, Female, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, Cancers, [PHYS.PHYS.PHYS-DATA-AN] Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an], [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an], medicine.drug, Optimization, [SDV.CAN]Life Sciences [q-bio]/Cancer, Models, Biological, Article, Drug Administration Schedule, 03 medical and health sciences, [SDV.CAN] Life Sciences [q-bio]/Cancer, Cell Line, Tumor, medicine, Carcinoma, Animals, Humans, Dosing, Lung cancer, 030304 developmental biology, Cisplatin, business.industry, Research, lcsh:RM1-950, medicine.disease, Xenograft Model Antitumor Assays, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, lcsh:Therapeutics. Pharmacology, Pharmacodynamics, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, Cancer research, Mathematical modeling, business
Abstract: International audience; Bevacizumab-pemetrexed/cisplatin (BEV-PEM/CIS) is a first line therapeutic for advanced non-squamous non-small cell lung cancer (NSCLC). Bevacizumab potentiates PEM/CIS cytotoxicity by inducing transient tumor vasculature normalization. BE V- PEM/CIS has a narrow therapeutic window. Therefore, it is an attractive target for administration schedule optimization. The present study leverages our previous work on BEV-PEM/CIS pharmacodynamic modeling in NSCLC-bearing mice to estimate the optimal gap in the scheduling of sequential BEV-PEM/CIS. We predicted the optimal gap in BEV-PEM/CIS dosing to be 2.0 days in mice and 1.2 days in humans. Our simulationssuggest that the efficacy loss in scheduling BEV-PEM/CIS at too great of a gap is much less than the efficacy loss in scheduling BEV-PEM/CIS at too short of a gap.
Published: 2019

17. Molecular apocrine tumours in EORTC 10994/BIG 1-00 phase III study: pathological response after neoadjuvant chemotherapy and clinical outcomes

Author: Richard Iggo, Jean-Michel Picquenot, Denis Larsimont, Véronique Becette, Jonas Bergh, Gaëtan MacGrogan, Jeremy Thomas, Olivier Kerdraon, Leen Slaets, David Cameron, Fanny Pommeret, C. Poncet, Jean-Christophe Tille, Frédéric Bibeau, Hervé Bonnefoi, Alexandre Bodmer, Jean-Pierre Ghnassia, Thomas Grellety, Donnat, Martin, Validation et identification de nouvelles cibles en oncologie (VINCO), Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale (INSERM), CIC Bordeaux, Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale (INSERM), Département de pathologie, UNICANCER-UNICANCER, UNICANCER, European Organisation for Research and Treatment of Cancer [Bruxelles] (EORTC), European Cancer Organisation [Bruxelles] (ECCO), Actions for OnCogenesis understanding and Target Identification in ONcology (ACTION), Institut Jules Bordet [Bruxelles], Faculté de Médecine [Bruxelles] (ULB), Université libre de Bruxelles (ULB)-Université libre de Bruxelles (ULB), Hôpital René HUGUENIN (Saint-Cloud), Centre René Gauducheau, CRLCC René Gauducheau, Institut de Recherche en Cancérologie de Montpellier (IRCM - U1194 Inserm - UM), CRLCC Val d'Aurelle - Paul Lamarque-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Montpellier (UM), Centre Paul Strauss, CRLCC Paul Strauss, Centre de Lutte Contre le Cancer Henri Becquerel Normandie Rouen (CLCC Henri Becquerel), Cancer Research UK Edinburgh Centre [Edinburgh, UK], University of Edinburgh-MRC Institute of Genetics and Molecular Medicine [Edinburgh] (IGMM), University of Edinburgh-Medical Research Council-Medical Research Council, Hôpitaux Universitaires de Genève (HUG), Department of Oncology-Pathology [Karolinska Institutet], Karolinska Institutet [Stockholm], Bordeaux PharmacoEpi, Inserm CIC1401, Université de Bordeaux, Université de Bordeaux (UB)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Université Bordeaux Segalen - Bordeaux 2, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Bordeaux Segalen - Bordeaux 2-Institut Bergonié [Bordeaux], and Swiss Group for Clinical Cancer Research (SAKK)
Subjects: Oncology, Cancer Research, medicine.medical_specialty, Receptor, ErbB-2, Concordance, medicine.medical_treatment, Breast Neoplasms, [SDV.CAN]Life Sciences [q-bio]/Cancer, ddc:616.07, Disease-Free Survival, Article, 03 medical and health sciences, Breast cancer, 0302 clinical medicine, [SDV.CAN] Life Sciences [q-bio]/Cancer, Internal medicine, Antineoplastic Combined Chemotherapy Protocols, medicine, Humans, Survival rate, Chemotherapy, business.industry, Gene Expression Profiling, Apocrine, medicine.disease, Immunohistochemistry, Subtyping, 3. Good health, Cancérologie, ErbB Receptors, Survival Rate, Clinical trial, Biological sciences, Treatment Outcome, Receptors, Estrogen, Chemotherapy, Adjuvant, Receptors, Androgen, [SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, 030220 oncology & carcinogenesis, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, Female, Receptors, Progesterone, business
Abstract: Background: We explored, within the EORTC10994 study, the outcomes for patients with molecular apocrine (MA) breast cancer, and defined immunohistochemistry (IHC) as androgen-receptor (AR) positive, oestrogen (ER) and progesterone (PR) negative. We also assessed the concordance between IHC and gene expression arrays (GEA) in the identification of MA cancers. Methods: Centrally assessed biopsies for AR, ER, PR, HER2 and Ki67 by IHC were classified into six subtypes: MA, triple-negative (TN) basal-like, luminal A, luminal B HER2 negative, luminal B HER2 positive and “other”. The two main objectives were the pCR rates and survival outcomes in the overall MA subtype (and further divided by HER2 status) and the remaining five subtypes. Results: IHC subtyping was obtained in 846 eligible patients. Ninety-three (11%) tumours were classified as the MA subtype. Both IHC and GEA data were available for 64 patients. In this subset, IHC concordance was 88.3% in identifying MA tumours compared with GEA. Within the MA subtype, pCR was observed in 33.3% of the patients (95% CI: 29.4–43.9) and the 5-year recurrence-free interval was 59.2% (95% CI: 48.2–68.6). Patients with MA and TN basal-like tumours have lower survival outcomes. Conclusions: Irrespective of their HER2 status, the prognosis for MA tumours remains poor and adjuvant trials evaluating anti-androgens should be considered., SCOPUS: ar.j, info:eu-repo/semantics/published
Published: 2019

18. Improved 18-FDG PET/CT diagnosis of multiple myeloma diffuse disease by radiomics analysis

Author: Gerald Marit, Jean-Baptiste Pinaquy, Charles Mesguich, Baudouin Denis de Senneville, Elif Hindié, Ghoufrane Tlili, Olivier Saut, Saut, Olivier, CHU Bordeaux [Bordeaux], Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Biothérapies des maladies génétiques et cancers, Université Bordeaux Segalen - Bordeaux 2-Institut National de la Santé et de la Recherche Médicale (INSERM), Équipe Calcul scientifique et Modélisation, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: medicine.medical_specialty, Imaging biomarker, [SDV.CAN]Life Sciences [q-bio]/Cancer, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Text mining, Cohen's kappa, Radiomics, [SDV.CAN] Life Sciences [q-bio]/Cancer, medicine, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], Radiology, Nuclear Medicine and imaging, [MATH.MATH-AP] Mathematics [math]/Analysis of PDEs [math.AP], Multiple myeloma, business.industry, General Medicine, medicine.disease, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Confidence interval, 3. Good health, Random forest, 030220 oncology & carcinogenesis, Diffuse disease, Radiology, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, business, Multiple Myeloma
Abstract: Objectives In multiple myeloma, the diagnosis of diffuse bone marrow infiltration on 18-FDG PET/CT can be challenging. We aimed to develop a PET/CT radiomics-based model that could improve the diagnosis of multiple myeloma diffuse disease on 18-FDG PET/CT. Methods We prospectively performed PET/CT and whole-body diffusion-weighted MRI in 30 newly diagnosed multiple myeloma. MRI was the reference standard for diffuse disease assessment. Twenty patients were randomly assigned to a training set and 10 to an independent test set. Visual analysis of PET/CT was performed by two nuclear medicine physicians. Spine volumes were automatically segmented, and a total of 174 Imaging Biomarker Standardisation Initiative-compliant radiomics features were extracted from PET and CT. Selection of best features was performed with random forest features importance and correlation analysis. Machine-learning algorithms were trained on the selected features with cross-validation and evaluated on the independent test set. Results Out of the 30 patients, 18 had established diffuse disease on MRI. The sensitivity, specificity and accuracy of visual analysis were 67, 75 and 70%, respectively, with a moderate kappa coefficient of agreement of 0.6. Five radiomics features were selected. On the training set, random forest classifier reached a sensitivity, specificity and accuracy of 93, 86 and 91%, respectively, with an area under the curve of 0.90 (95% confidence interval, 0.89-0.91). On the independent test set, the model achieved an accuracy of 80%. Conclusions Radiomics analysis of 18-FDG PET/CT images with machine-learning overcame the limitations of visual analysis, providing a highly accurate and more reliable diagnosis of diffuse bone marrow infiltration in multiple myeloma patients.
Published: 2021

19. Asymptotic analysis of a biphase tumor fluid flow. The weak coupling case

Author: Sebastien Benzekry, Clair Poignard, Cristina Vaghi, Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Méthodes computationnelles pour la prise en charge thérapeutique en oncologie : Optimisation des stratégies par modélisation mécaniste et statistique (COMPO), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Aix Marseille Université (AMU), and Benzekry, Sebastien
Subjects: Asymptotic analysis, Capillary action, Quantitative Biology::Tissues and Organs, Boundary (topology), [SDV.CAN]Life Sciences [q-bio]/Cancer, 01 natural sciences, Spherical geometry, Physics::Fluid Dynamics, 03 medical and health sciences, Mathematics - Analysis of PDEs, [SDV.CAN] Life Sciences [q-bio]/Cancer, [STAT.AP] Statistics [stat]/Applications [stat.AP], FOS: Mathematics, Fluid dynamics, Fluid queue, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], Boundary value problem, 0101 mathematics, 030304 developmental biology, Physics, [STAT.AP]Statistics [stat]/Applications [stat.AP], 0303 health sciences, Applied Mathematics, Mechanics, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 010101 applied mathematics, Computational Mathematics, Boundary layer, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, [SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [PHYS.PHYS.PHYS-DATA-AN] Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an], [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an], Analysis of PDEs (math.AP)
Abstract: International audience; The aim of this paper is to investigate the asymptotic behavior of a biphase tumor fluid flow derived by 2-scale homogenisation techniques in recent works. This biphase fluid flow model accounts for the capillary wall permeability, and the interstitial avascular phase, both being mixed in the limit homogenised problem. When the vessel walls become more permeable, we show that the biphase fluid flow exhibits a boundary layer that makes the computation of the full problem costly and unstable. In the limit, both capillary and interstitial pressures coincide except in the vicinity of the boundary where different boundary conditions are applied. Thanks to a rigorous asymptotic analysis, we prove that the solution to the full problem can be approached at any order of approximation by a monophasic model with appropriate boundary conditions on the tumor boundary and appropriate correcting terms near the boundary are given. Numerical simulations in spherical geometry illustrate the theoretical results.
Published: 2021

20. On a magnetic skin effect in eddy current problems: the magnetic potential in magnetically soft materials

Author: Victor Péron, Clair Poignard, Université de Pau et des Pays de l'Adour (UPPA), Laboratoire de Mathématiques et de leurs Applications [Pau] (LMAP), Université de Pau et des Pays de l'Adour (UPPA)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux]
Subjects: Power series, Ferromagnetic Material, Characteristic length, General Mathematics, General Physics and Astronomy, Magnetic Potential, 01 natural sciences, law.invention, law, Eddy current, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], Impedance Conditions, 0101 mathematics, Electrical impedance, Physics, Asymptotic Expansions, Eddy Current Problems, Applied Mathematics, 010102 general mathematics, Mathematical analysis, 010101 applied mathematics, Skin effect, Magnetic potential, Relative permeability, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA], Scalar curvature
Abstract: This work is concerned with the time-harmonic eddy current problem in a bi-dimensional setting with a high contrast of magnetic permeabilities between a conducting medium and a dielectric medium. We describe a magnetic skin effect by deriving rigorously a multiscale expansion for the magnetic potential in power series of a small parameter $$\varepsilon $$ which represents the inverse of the square root of a relative permeability. We make explicit the first asymptotics up to the order $$\varepsilon ^3$$ . As an application, we obtain impedance conditions up to the fourth order of approximation for the magnetic potential. Finally, we measure this skin effect with a characteristic length that depends on the scalar curvature of the boundary of the conductor.
Published: 2021

21. Pesticides et effets sur la santé : Nouvelles données

Author: Baldi, Isabelle, Jérémie, Botton, Chevrier, Cécile, Coumoul, Xavier, Elbaz, Alexis, Goujon, Stéphanie, Jouzel, Jean-Noël, Monnereau, Alain, Multigner, Luc, Salles, Bernard, Siroux, Valérie, Spinosi, Johan, Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Cancer environnement (EPICENE ), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Pharmaco-épidémiologie des produits de santé et sécurité sanitaire [Site de Saint Denis] (GIS EPIPHARE - ANSM), Agence nationale de sécurité du médicament et des produits de santé [Saint-Denis] (ANSM), Institut de recherche en santé, environnement et travail (Irset), Université d'Angers (UA)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Toxicité environnementale, cibles thérapeutiques, signalisation cellulaire (T3S - UMR_S 1124), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), UFR des Sciences Fondamentales et Biomédicales (Université de Paris), Université Paris Descartes - Paris 5 (UPD5)-Université de Paris (UP), Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Equipe 7 : EPICEA - Epidémiologie des cancers de l'enfant et de l'adolescent (CRESS - U1153), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS (U1153 / UMR_A_1125 / UMR_S_1153)), Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre de sociologie des organisations (CSO), Sciences Po (Sciences Po)-Centre National de la Recherche Scientifique (CNRS), Institut Bergonié [Bordeaux], UNICANCER, Registre des hémopathies malignes de la Gironde [Institut Bergonié, Bordeaux], UNICANCER-UNICANCER, ToxAlim (ToxAlim), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Ecole Nationale Vétérinaire de Toulouse (ENVT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Ecole d'Ingénieurs de Purpan (INPT - EI Purpan), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institute for Advanced Biosciences / Institut pour l'Avancée des Biosciences (Grenoble) (IAB), Centre Hospitalier Universitaire [Grenoble] (CHU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Etablissement français du sang - Auvergne-Rhône-Alpes (EFS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Santé publique France - French National Public Health Agency [Saint-Maurice, France], Institut national de la santé et de la recherche médicale (INSERM), Université d'Angers (UA)-Université de Rennes (UR)-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Structure Fédérative de Recherche en Biologie et Santé de Rennes ( Biosit : Biologie - Santé - Innovation Technologique ), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Université Paris Descartes - Paris 5 (UPD5)-Université Paris Cité (UPCité), Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-Université Sorbonne Paris Cité (USPC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre de sociologie des organisations (Sciences Po, CNRS) (CSO), Dupuis, Christine, Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole Nationale Vétérinaire de Toulouse (ENVT), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN), and Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
Subjects: [SDV] Life Sciences [q-bio], Collection Expertise collective / ISBN 978-2-7598-2629-2, [SDV]Life Sciences [q-bio]
Abstract: Largement utilisés depuis plusieurs décennies, principalement dans le secteur agricole, les pesticides font l’objet de nombreuses études sur les liens entre l’exposition des populations et les effets sur la santé, et ils suscitent toujours autant d’inquiétude, les pathologies suspectées et les populations exposées étant multiples (agriculteurs, consommateurs des produits traités, riverains des parcelles agricoles...).Cette expertise collective Inserm, sollicitée par cinq ministères, a pour objectif d’actualiser les données de l’expertise collective « Pesticides : Effets sur la santé » publiée en 2013. Réuni sous l’égide de l’Inserm, un groupe multidisciplinaire d’experts spécialistes en sociologie, épidémiologie, toxicologie et expologie a analysé la littérature scientifique internationale dans ces domaines afin d’évaluer le lien entre l’exposition aux pesticides et la survenue de certaines pathologies.L’expertise dresse un bilan actualisé des connaissances sur les troubles du développement neuropsychologique et moteur de l’enfant, des pathologies neurologiques de l’adulte, et des pathologies cancéreuses de l’enfant et de l’adulte. La santé respiratoire, les pathologies thyroïdiennes et l’endométriose ont été également abordées et viennent enrichir cette nouvelle version de l’expertise. Elle présente aussi l’analyse des données sur deux substances actives et une famille de pesticides : le glyphosate, le chlordécone et les fongicides inhibiteurs de la succinate déshydrogénase.
Published: 2021

22. MRI-Based Radiomics Input for Prediction of 2-Year Disease Recurrence in Anal Squamous Cell Carcinoma

Author: Christelle De La Fouchardiere, Arnaud Hocquelet, Véronique Vendrely, Emilie Barbier, Louis-Arnaud Bazire, Thomas Charleux, Meher Ben Abdelghani, Xavier Mirabel, Ariane Darut-Jouve, Wulfran Cacheux, Thomas Aparicio, Delphine Argo-Leignel, Nicolas Giraud, Pierre-Luc Etienne, Nicolas Magné, Hervé Trillaud, Olivier Saut, Gilles Breysacher, Philippe Ronchin, Alexandre Tessier, Claire Lemanski, Côme Lepage, Astrid Lièvre, Hôpital Haut-Lévêque [CHU Bordeaux], CHU Bordeaux [Bordeaux], Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Hopital Saint-Louis [AP-HP] (AP-HP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Centre Azuréen de Cancérologie [Mougins, France], Institut Curie [Paris], Fédération Francophone de Cancérologie Digestive (FFCD), Institut du Cancer de Montpellier (ICM), Centre Régional de Lutte contre le Cancer Oscar Lambret [Lille] (UNICANCER/Lille), Université Lille Nord de France (COMUE)-UNICANCER, Centre Armoricain de Radiothérapie, d'Imagerie médicale et d'Oncologie [Plérin, Saint-Brieuc] (CARIO), Hôpital Sud [CHU Rennes], CHU Pontchaillou [Rennes], Hôpital privé Pays de Savoie, Institut de Cancérologie de Bourgogne, Centre Léon Bérard [Lyon], Hôpitaux Civils de Colmar, Groupe Hospitalier Bretagne Sud (GHBS), Centre Hospitalier Annecy-Genevois [Saint-Julien-en-Genevois], Institut de Cancérologie de la Loire Lucien Neuwirth, Centre Hospitalier Universitaire de Saint-Etienne (CHU de Saint-Etienne), Institut de Cancérologie de Strasbourg Europe (ICANS), CHU Dijon, Centre Hospitalier Universitaire de Dijon - Hôpital François Mitterrand (CHU Dijon), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Université de Lille-UNICANCER, and Centre Hospitalier Universitaire de Saint-Etienne [CHU Saint-Etienne] (CHU ST-E)
Subjects: Cancer Research, medicine.medical_specialty, Multivariate analysis, anal cancer, precision medicine, [SDV.CAN]Life Sciences [q-bio]/Cancer, Logistic regression, lcsh:RC254-282, Article, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, medicine, Anal cancer, magnetic resonance imaging, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], Univariate analysis, Proportional hazards model, business.industry, Standard treatment, Univariate, Anal Squamous Cell Carcinoma, medicine.disease, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 3. Good health, machine learning, Oncology, radiomics, prediction medicine, 030220 oncology & carcinogenesis, Radiology, business
Abstract: Simple Summary Exclusive chemo-radiotherapy (CRT) is the standard treatment for non-metastatic anal squamous cell carcinomas. Identifying novel prognostic factors could help to improve CRT outcomes, notably for locally advanced diseases where relapses still occur in around 35% of patients. In this study, we aim to assess the potential value of a pre-therapeutic MRI radiomic analysis added to standard clinical variables in order to build a logistic regression model predicting 2-year recurrence after CRT. In a population of 82 patients randomly divided in training (n = 54) and testing (n = 28) sets, after selection of optimal variables, a model using two radiomic (FirstOrder_Entropy and GLCM_JointEnergy) and two clinical (tumor size and CRT length) features was able to predict the 2-year recurrence with good performances in the testing set. Radiomic biomarkers provided valuable additional and independent information added to clinical data, and could help contribute to identify high risk patients amenable to treatment intensification with view of personalized medicine. Abstract Purpose: Chemo-radiotherapy (CRT) is the standard treatment for non-metastatic anal squamous cell carcinomas (ASCC). Despite excellent results for T1-2 stages, relapses still occur in around 35% of locally advanced tumors. Recent strategies focus on treatment intensification, but could benefit from a better patient selection. Our goal was to assess the prognostic value of pre-therapeutic MRI radiomics on 2-year disease control (DC). Methods: We retrospectively selected patients with non-metastatic ASCC treated at the CHU Bordeaux and in the French FFCD0904 multicentric trial. Radiomic features were extracted from T2-weighted pre-therapeutic MRI delineated sequences. After random division between training and testing sets on a 2:1 ratio, univariate and multivariate analysis were performed on the training cohort to select optimal features. The correlation with 2-year DC was assessed using logistic regression models, with AUC and accuracy as performance gauges, and the prediction of disease-free survival using Cox regression and Kaplan-Meier analysis. Results: A total of 82 patients were randomized in the training (n = 54) and testing sets (n = 28). At 2 years, 24 patients (29%) presented relapse. In the training set, two clinical (tumor size and CRT length) and two radiomic features (FirstOrder_Entropy and GLCM_JointEnergy) were associated with disease control in univariate analysis and included in the model. The clinical model was outperformed by the mixed (clinical and radiomic) model in both the training (AUC 0.758 versus 0.825, accuracy of 75.9% versus 87%) and testing (AUC 0.714 versus 0.898, accuracy of 78.6% versus 85.7%) sets, which led to distinctive high and low risk of disease relapse groups (HR 8.60, p = 0.005). Conclusion: A mixed model with two clinical and two radiomic features was predictive of 2-year disease control after CRT and could contribute to identify high risk patients amenable to treatment intensification with view of personalized medicine.
Published: 2021

23. Impact of CT-based body composition parameters at baseline, their early changes and response in metastatic cancer patients treated with immune checkpoint inhibitors

Author: Sophie Cousin, Amandine Crombé, Maud Toulmonde, Michèle Kind, Antoine Italiano, Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux]
Subjects: Male, Oncology, Multivariate statistics, medicine.medical_specialty, medicine.medical_treatment, [SDV]Life Sciences [q-bio], Population, Adipose tissue, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Neoplasms, Internal medicine, medicine, Humans, Radiology, Nuclear Medicine and imaging, Progression-free survival, education, Immune Checkpoint Inhibitors, education.field_of_study, Proportional hazards model, business.industry, Cancer, General Medicine, Immunotherapy, Middle Aged, Prognosis, medicine.disease, 3. Good health, 030220 oncology & carcinogenesis, Sarcopenia, Body Composition, Female, Tomography, X-Ray Computed, business
Abstract: Purpose CT-based Body-composition (BC) parameters correlate with the patients’ outcome in metastatic cancer patients treated with chemotherapies or targeted therapies. Our aim was to investigate similar associations regarding immune checkpoint inhibitor (CPI). Methods Patients were consecutively included as they were treated with CPI at our institution for a metastatic solid cancer with baseline CT-scan (CT0) and early evaluation CT-scan (CT1, 2 months later). At each evaluation, the areas corresponding to psoas muscles alone, skeletal muscle, subcutaneous, visceral and total adipose tissues at L3 vertebral level were extracted and weighted by height2, providing PMI, SMI, SATI, VATI and TATI, respectively, and their changes (Δt-) from the first day of treatment to CT1. Correlations between continuous BC-parameters and progression free survival (PFS) were evaluated in men, women and whole population with univariate Cox regressions. After dichotomizing the BC-parameters per whole-population and sex-specific tertiles, uni- and multivariate Cox models were built to identify independent predictors of the PFS. Results Between December 2013 and December 2016, 117 patients were included (55 women, mean age: 62.4) and 78 showed a progression (median PFS = 125 days, 95 %CI = 87–117). Changes in BC-parameters did not depend on sex. None of the baseline BC-parameters correlated with PFS while Δt-PMI and Δt-SATI did (multivariate HR = 2.41, p = 0.0008 and HR = 2.82, p = 0.0004, respectively). Conclusions The occurrence of subcutaneous adipopenia and sarcopenia after beginning CPI treatment, estimated through Δt-SATI and Δt-PMI, correlated with higher risk of progression.
Published: 2020

24. Modélisation mathématique du transport des nanoparticules dans les tumeurs

Author: Vaghi, Cristina, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux, Sébastien Benzekry, Clair Poignard, Raphaëlle Fanciullino, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux]
Subjects: Modèles non linéaires à effets mixtes, Antibody-Nanoconjugates, Pharmacocinétique, Pharmacodynamique, Nonlinear mixed-Effects modeling, Pharmacodynamic modeling, Nanoparticules, Pharmacokinetic, Two-Scale asymptotic expansion, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA], Développement asymptotique double-Échelle
Abstract: Nanomedicine offers promising and innovative tools to treat cancer. Recently, liposomes conjugated with an antibody were developed to target breast cancer cells while sparing healthy tissues from the toxicity of the chemotherapy. These nanoparticles are called antibody-nanoconjugates (ANCs) and are currently tested in a preclinical trial. However, the pharmacokinetics, biodistribution, and efficacy of these nanoparticles are not well known and could be improved. Mathematical modeling can help in understanding the intratumor penetration of the nanoparticles and in quantifying the treatment efficacy.Pharmacokinetic-pharmacodynamic modeling evaluates the dose-response relationship in vivo and can be used to optimize the therapy schedule. Here, we described several biological processes using ordinary differential equations: (i) the untreated tumor growth with a novel reduced Gompertz model, (ii) the nanoparticle biodistribution using a two-compartment pharmacokinetic model, and (iii) the therapeutic response with a resistance model. All the models were validated against experimental data in the statistical framework of nonlinear mixed-effects modeling, which models simultaneously the dynamic of the population and the inter-individual variability.Furthermore, we derived a spatial mathematical model with the two-scale asymptotic expansion method to describe the fluid and nanoparticle transport within the tumor tissue. This approach allowed us to evaluate the barriers that impair a homogeneous distribution of nanoparticles at the tumor site. Moreover, we propose a computational framework to predict tumor accumulation of nanoparticles using individual imaging data.; La nanomédecine offre des perspectives ambitieuses pour le traitement du cancer. Récemment, des liposomes conjugués à des anticorps spécifiques ont été développés pour cibler les cellules tumorales du cancer au sein, en réduisant la toxicité de la chimiothérapie dans les tissus sains. Ces nanoparticules, appelées ANC (pour antibody nano-conjugate), sont actuellement testées dans une phase préclinique. Cependant, la pharmacocinétique, la biodistribution et l'efficacité de ces nanoparticules ne sont pas bien caracterisées quantitativement et pourrait être ameliorées. La modélisation mathématique peut aider à mieux comprendre la dynamique de la pénétration des ANC dans la tumeur et à améliorer l'efficacité du traitement.La modélisation pharmacocinétique-pharmacodynamique permet d'évaluer la réponse du traitement extit{in vivo} en fonction de la dose injectée. Dans ce travail, nous avons décrit plusieurs phénomènes biologiques avec des équations differentielles ordinaires : (i) la croissance tumorale avec un nouveau modèle réduit de Gompertz, (ii) la biodistribution des nanoparticules avec un modèle pharmacocinétique à deux compartiments, et (iii) la réponse au traitement avec un modèle de résistance. Tous les modèles ont été calibrés dans le cadre des modèles non linéaires à effets mixtes, qui décrivent la dynamique globale de la population ainsi que la variabilité individuelle.De plus, nous avons dérivé un modèle mathématique spatial avec la technique de développement asymptotique double-échelle pour décrire le transport des fluides et des nanoparticules dans le tissu tumoral. Cette méthodologie nous permet d'évaluer les barrières microscopiques qui empêchent une distribution homogène des ANC dans la tumeur. Finalement, nous proposons un schéma computationnel pour prédire l'accumulation des nanoparticules à partir des données individuels d'imagerie.
Published: 2020

25. Intensity Harmonization Techniques Influence Radiomics Features and Radiomics-based Predictions in Sarcoma Patients

Author: Olivier Saut, François Le Loarer, Antoine Italiano, Michèle Kind, Xavier Buy, David Fadli, Amandine Crombé, Institut Bergonié [Bordeaux], UNICANCER, Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux (UB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Adult, Male, medicine.medical_specialty, Science, Soft Tissue Neoplasms, [SDV.CAN]Life Sciences [q-bio]/Cancer, Article, 030218 nuclear medicine & medical imaging, Young Adult, 03 medical and health sciences, 0302 clinical medicine, Radiomics, Image Interpretation, Computer-Assisted, Humans, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], Medicine, Aged, Aged, 80 and over, Multidisciplinary, business.industry, Reproducibility of Results, Sarcoma, Middle Aged, Prognosis, medicine.disease, Magnetic Resonance Imaging, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Progression-Free Survival, Computational biology and bioinformatics, Intensity (physics), Radiography, Oncology, 030220 oncology & carcinogenesis, Female, Supervised Machine Learning, Radiology, business, Biomarkers
Abstract: Intensity harmonization techniques (IHT) are mandatory to homogenize multicentric MRIs before any quantitative analysis because signal intensities (SI) do not have standardized units. Radiomics combine quantification of tumors’ radiological phenotype with machine-learning to improve predictive models, such as metastastic-relapse-free survival (MFS) for sarcoma patients. We post-processed the initial T2-weighted-imaging of 70 sarcoma patients by using 5 IHTs and extracting 45 radiomics features (RFs), namely: classical standardization (IHTstd), standardization per adipose tissue SIs (IHTfat), histogram-matching with a patient histogram (IHTHM.1), with the average histogram of the population (IHTHM.All) and plus ComBat method (IHTHM.All.C), which provided 5 radiomics datasets in addition to the original radiomics dataset without IHT (No-IHT). We found that using IHTs significantly influenced all RFs values (p-values: std, IHTHM.All, and IHTHM.All.C datasets significantly correlated with MFS in multivariate Cox models (p = 0.02, 0.007, 0.004 and 0.02, respectively). We built radiomics-based supervised models to predict metastatic relapse at 2-years with a training set of 50 patients. The models performances varied markedly depending on the IHT in the validation set (range of AUROC from 0.688 with IHTstd to 0.823 with IHTHM.1). Hence, the use of intensity harmonization and the related technique should be carefully detailed in radiomics post-processing pipelines as it can profoundly affect the reproducibility of analyses.
Published: 2020

26. Automated landmarking for insects morphometric analysis using deep neural networks

Author: Marie Beurton-Aimar, Akka Zemmari, Nicolas Parisey, Van Linh Le, Alexia Marie, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut de Génétique, Environnement et Protection des Plantes (IGEPP), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-AGROCAMPUS OUEST, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Université de Rennes (UR)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), and Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-INSTITUT AGRO Agrocampus Ouest
Subjects: 0106 biological sciences, Computer science, [SDV]Life Sciences [q-bio], ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Convolutional neural network, 010603 evolutionary biology, 01 natural sciences, Layer (object-oriented design), Divergence (statistics), Ecology, Evolution, Behavior and Systematics, Landmark, Ecology, business.industry, Landmarks, 010604 marine biology & hydrobiology, Applied Mathematics, Ecological Modeling, Deep learning, Morphometry, Pattern recognition, Modular design, Computer Science Applications, Data set, Computational Theory and Mathematics, Modeling and Simulation, Deep neural networks, Artificial intelligence, business
Abstract: International audience; Landmarks are one of the important concepts in morphometry analysis. They are anatomical points that can be located consistently (e.g., corner of the eyes) and used to establish correspondence or divergence among morphologies of biological or non-biological specimens. Currently, the landmarks are mostly positioned manually by entomologists on numerical images. In this work, we propose a method to automatically predict the landmarks on entomological images based on Deep Learning methods, more specifically by using Convolutional Neural Network (CNN). We propose a CNN architecture, EB-Net, which is built in a modular way the concept of "Elementary Blocks", each made up of usual layer types of CNN. After using a custom data augmentation procedure, the network has been trained and tested on a data set of different anatomical part of carabids (pronotum, head and elytra). In this numerical experiment, we have generated two strategies to evaluate the network and to improve the obtained results: training from scratch or applying a fine-tuning step. The predicted landmark coordinates have been compared to the coordinates of the manual landmarks provided by the biologists. The statistical analysis of the distances between predicted and manual coordinates has shown that our predictions can replace efficiently manual landmarking and allows to propose automatization of such operation.
Published: 2020

27. AssemblyNet: A large ensemble of CNNs for 3D whole brain MRI segmentation

Author: José V. Manjón, Vinh-Thong Ta, Boris Mansencal, Baudouin Denis de Senneville, Vincent Lepetit, Michaël Clément, Rémi Giraud, Pierrick Coupé, Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Patch-based processing for medical and natural images (PICTURA), Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)-Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de l'intégration, du matériau au système (IMS), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Institut Polytechnique de Bordeaux (Bordeaux INP), ITACA, Universitat Politècnica de València (UPV), This work benefited from the support of the project DeepvolBrain of the French National Research Agency (ANR-18-CE45-0013). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57 for the BigDataBrain project. Moreover, we thank the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX- 03- 02, HL-MRI Project), Cluster of excellence CPU and the CNRS/INSERM for the DeepMultiBrain project. This study has been also supported by the DPI2017-87743-R grant from the Spanish Ministerio de Economia, Industria Competitividad. The authors gratefully acknowledge the support of NVIDIA Corporation with their donation of the TITAN Xp GPU used in this research., ANR-18-CE45-0013,DeepVolBrain,Apprentissage profond pour la volumétrie cérébrale : vers le BigData en neuroscience(2018), ANR-10-LABX-0057,TRAIL,Translational Research and Advanced Imaging Laboratory(2010), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux]
Subjects: FOS: Computer and information sciences, Computer Science - Machine Learning, Computer science, Computer Vision and Pattern Recognition (cs.CV), Cognitive Neuroscience, Computer Science - Computer Vision and Pattern Recognition, Convolutional neural network, 050105 experimental psychology, Machine Learning (cs.LG), lcsh:RC321-571, 03 medical and health sciences, 0302 clinical medicine, Segmentation, Robustness (computer science), Image Processing, Computer-Assisted, FOS: Electrical engineering, electronic engineering, information engineering, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Brain mri, Humans, Brain segmentation, 0501 psychology and cognitive sciences, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, business.industry, Deep learning, Image and Video Processing (eess.IV), 05 social sciences, Brain, Pattern recognition, Electrical Engineering and Systems Science - Image and Video Processing, Magnetic Resonance Imaging, 3. Good health, Neurology, FISICA APLICADA, Artificial intelligence, business, Software, 030217 neurology & neurosurgery
Abstract: [EN] Whole brain segmentation of fine-grained structures using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images. To address this problem, previous DL methods proposed to use a single convolution neural network (CNN) or few independent CNNs. In this paper, we present a novel ensemble method based on a large number of CNNs processing different overlapping brain areas. Inspired by parliamentary decision-making systems, we propose a framework called AssemblyNet, made of two "assemblies" of U-Nets. Such a parliamentary system is capable of dealing with complex decisions, unseen problem and reaching a relevant consensus. AssemblyNet introduces sharing of knowledge among neighboring U-Nets, an "amendment" procedure made by the second assembly at higher-resolution to refine the decision taken by the first one, and a final decision obtained by majority voting. During our validation, AssemblyNet showed competitive performance compared to state-of-the-art methods such as U-Net, Joint label fusion and SLANT. Moreover, we investigated the scan-rescan consistency and the robustness to disease effects of our method. These experiences demonstrated the reliability of AssemblyNet. Finally, we showed the interest of using semi-supervised learning to improve the performance of our method., This work benefited from the support of the project DeepvolBrain of the French National Research Agency (ANR-18-CE45-0013). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57 for the BigDataBrain project. Moreover, we thank the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-0 3-02, HL-MRI Project), Cluster of excellence CPU and the CNRS/INSERM for the DeepMultiBrain project. This study has been also supported by the DPI2017-87743-R grant from the Spanish Ministerio de Economia, Industria Competitividad. The C-MIND data used in the preparation of this article were obtained from the C-MIND Data Repository (accessed in Feb 2015) created by the C-MIND study of Normal Brain Development. This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by Cincinnati Children's Hospital Medical Center and UCLA and supported by the National Institute of Child Health and Human Development (Contract #s HHSN275200900018C). The NDAR data used in the preparation of this manuscript were obtained from the NIH-supported National Database for Autism Research (NDAR). This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by the Brain Development Cooperative Group and supported by the National Institute of Child Health and Human Development, the National Institute on Drug Abuse, the National Institute of Mental Health, and the National Institute of Neurological Disorders and Stroke (Contract #s N01- HD02-3343, N01-MH9-0002, and N01-NS-9-2314, -2315, -2316, -2317, -2319 and -2320). A listing of the participating sites and a complete listing of the study investigators can be found at http://pediatricmri.nih.gov/nihpd/info/participating_centers.html. The ADNI data used in the preparation of this manuscript were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). ADNI data are disseminated by the Laboratory for NeuroImaging at the University of California, Los Angeles. This research was also supported by NIH grants P30AG010129, K01 AG030514 and the Dana Foundation. The OASIS data used in the preparation of this manuscript were obtained from the OASIS project funded by grants P50 AG05681, P01 AG03991, R01 AG021910, P50 MH071616, U24 RR021382, R01 MH56584. The AIBL data used in the preparation of this manuscript were obtained from the AIBL study of ageing funded by the Common-wealth Scientific Industrial Research Organization (CSIRO; a publicly funded government research organization), Science Industry Endowment Fund, National Health and Medical Research Council of Australia (project grant 1011689), Alzheimer's Association, Alzheimer's Drug Discovery Foundation, and an anonymous foundation. The ICBM data used in the preparation of this manuscript were supported by Human Brain Project grant PO1MHO52176-11 (ICBM, P.I. Dr John Mazziotta) and Canadian Institutes of Health Research grant MOP-34996. The IXI data used in the preparation of this manuscript were supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) GR/S21533/02. ABIDE primary support for the work by Adriana Di Martino was provided by the NIMH (K23MH087770) and the Leon Levy Foundation. Primary support for the work by Michael P. Milham and the INDI team was provided by gifts from Joseph P. Healy and the Stavros Niarchos Foundation to the Child Mind Institute, as well as by an NIMH award to MPM (R03MH096321).
Published: 2020

28. Quantitative investigation of dose accumulation errors from intra-fraction motion in MRgRT for prostate cancer

Author: Cornel Zachiu, L S Bosma, Mario Ries, B. Denis de Senneville, Bas W. Raaymakers, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux]
Subjects: Male, Computer science, medicine.medical_treatment, Motion-compensated dose accumulation, Signal-To-Noise Ratio, Radiation, dose reconstruction, 030218 nuclear medicine & medical imaging, Motion, 03 medical and health sciences, Prostate cancer, Imaging, Three-Dimensional, 0302 clinical medicine, Prostate, Image Processing, Computer-Assisted, medicine, Humans, Radiology, Nuclear Medicine and imaging, Fraction (mathematics), Image warping, Dose delivery, Radiological and Ultrasound Technology, medicine.diagnostic_test, Radiotherapy Planning, Computer-Assisted, Rectum, Prostatic Neoplasms, Reproducibility of Results, Magnetic resonance imaging, Gold standard (test), intra-fraction plan adaption, medicine.disease, Magnetic Resonance Imaging, 3. Good health, Visualization, Radiation therapy, medicine.anatomical_structure, MRguided radiotherapy, 030220 oncology & carcinogenesis, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, Radiotherapy, Image-Guided, Biomedical engineering
Abstract: Accurate spatial dose delivery in radiotherapy is frequently complicated due to changes in the patient’s internal anatomy during and in-between therapy segments. The recent introduction of hybrid MRI radiotherapy systems allows unequaled soft-tissue visualization during radiation delivery and can be used for dose reconstruction to quantify the impact of motion. To this end, knowledge of anatomical deformations obtained from continuous monitoring during treatment has to be combined with information on the spatio-temporal dose delivery to perform motion-compensated dose accumulation (MCDA). Here, the influence of the choice of deformable image registration algorithm, dose warping strategy, and magnetic resonance image resolution and signal-to-noise-ratio on the resulting MCDA is investigated. For a quantitative investigation, four 4D MRI-datasets representing typical patient observed motion patterns are generated using finite element modeling and serve as a gold standard. Energy delivery is simulated intra-fractionally in the deformed image space and, subsequently, MCDA-processed. Finally, the results are substantiated by comparing MCDA strategies on clinically acquired patient data. It is shown that MCDA is needed for correct quantitative dose reconstruction. For prostate treatments, using the energy per mass transfer dose warping strategy has the largest influence on decreasing dose estimation errors.
Published: 2020

29. Real data calibration and floating potential model in the context of electroporation

Author: Corridore, Sergio, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux, Clair Poignard, Annabelle Collin, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], and STAR, ABES
Subjects: Stratégie de calibration numérique, Elliptic problem, Asymptotic Analysis, Bioimpedance, Analyse asymptotique, [MATH.MATH-NA] Mathematics [math]/Numerical Analysis [math.NA], [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Bioimpédance, Electroporation, Électroporation, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, Problèmes elliptiques, Numerical Calibration Strategy, [MATH.MATH-NA]Mathematics [math]/Numerical Analysis [math.NA]
Abstract: Electroporation is a complex phenomenon that occurs when biological tissues are subjected to electric pulses. It makes it possible to either kill directly the cells in the target region (as for example a tumor) or to introduce molecules into living cells. Even though the phenomenon has been discovered for several decades, it is still incompletely understood. Several bioelectrical engineering strategies have been developed to improve the knowledge of the membrane response to electric stimulation by bioimpedance measurements. Bioimpedance measurements is a powerful tool from electrical engineering to track the electrical properties changes in biological tissues and cells. However the quantification of such impedance changes in terms of dielectric and conductive properties of the biological is far from trivial. This is due the addition of complex bioelectrical phenomena such as the electrode polarization, system calibration, and in addition the lack of accurate electrical model of biological samples.The aim of this thesis consists in proposing a modeling of the bioimpedance measures in a 4-electrode system, in the context of electroporation. On the one hand, the work consists in deriving an effective electrical circuit of the biological and to fit its parameters thanks to the 4-electrode system. The fitting is far from trivial since the ``measured data'' have been already pre-filtered by the 3 measurements, but due to the complexity of the experimental set-up and the complexity of biological electrical properties the calibration leads to large error. To overcome this issue, a new calibration is proposed to minimise the error on the filtered data. Then, advanced calibration procedure is proposed to investigate the impact of electroporation on the effective conductance and capacitance of cell membranes.On the other, we investigate the asymptotic analysis problem of floating potential. Indeed, it is well-known in quasi-electrostatics that highly conductive materials behave like an equipotential and a nonlocal boundary condition is imposed, so-called floating potential. This floating potential problem consists in solving Poisson equation with a constant Dirichlet boundary condition, which is fixed by the condition that the total current on the boundary vanishes, meaning that no current flows out of the domain. Thanks to an asymptotic analysis, the accuracy of such floating potential is derived, and an improvement is proposed to account for the geometry of the needles. This is particularly crucial in electroporation, when high amplitude electric fields are applied. Finally, we validate this model by comparing the bioimpedances obtained with the PDE simulations with the measured bioimpedances., L'électroporation est un phénomène complexe qui se produit lorsque des tissus biologiques sont soumis à des impulsions électriques. L'électroporation permet de tuer les cellules d'une tumeur ou d'introduire des molécules dans les cellules en augmentant la perméabilité de leur membrane. Même si le phénomène a été découvert il y a plusieurs décennies, de nombreuses questions persistent. Plusieurs stratégies d'ingénierie bioélectrique ont été développées pour améliorer la connaissance de la réponse membranaire à la stimulation électrique par des mesures de bioimpédance. Les mesures de bioimpédance sont un outil puissant pour suivre les changements des propriétés électriques dans les tissus et dans les cellules biologiques. Cependant, la quantification de ces changements d'impédance en termes de propriétés diélectriques et conductrices est loin d'être triviale. En effet les phénomènes bioélectriques complexes tels que la polarisation des électrodes, l'étalonnage du système ainsi que l'absence de modèle électrique précis compliquent la procédure. L'objectif de cette thèse est de proposer une modélisation des mesures de bioimpédance dans un système à 4 électrodes, dans le cadre de l'électroporation. Dans une première partie, le travail consiste à dériver un circuit électrique modélisant l'impédance du système et à adapter ses paramètres grâce aux données d'impédance issues des mesures. L'ajustement est loin d'être facile puisque les `` données mesurées '' ont déjà été pré-traitées par une étape de calibration qui en raison de la complexité de la configuration expérimentale et des propriétés électriques biologiques, conduit à une erreur importante. Pour surmonter ce problème, une nouvelle stratégie de calibration est proposée et permet de minimiser l'erreur sur les données calibrées. Ensuite, une procédure d'estimation paramétrique du circuit électrique est proposée afin d'étudier l'impact de l'électroporation sur la conductance et la capacité effectives des membranes cellulaires.Dans une deuxième partie, nous proposons une analyse asymptotique du potentiel flottant. En effet, il est bien connu en quasi-électrostatique que les matériaux hautement conducteurs se comportent comme une équipotentielle et qu'une condition aux limites non locale est imposée, appelée potentiel flottant. Ce problème de potentiel flottant consiste à résoudre l'équation de Poisson avec une condition aux limites de Dirichlet constante. Cette condition de Dirichlet qui est une inconnue du système va être fixée par la condition que le courant total sur la frontière s'annule c'est-à-dire qu'aucun courant ne sort du domaine. Grâce à une analyse asymptotique, un potentiel flottant est obtenue, et nous proposons une amélioration permettant de prendre en compte la géométrie des électrodes. Ceci est particulièrement crucial en électroporation, lorsque des champs électriques de haute amplitude sont appliqués. Enfin, nous validons ce modèle en comparant les bioimpédances obtenues avec les simulations du potentiel flottant aux bioimpédances calibrées issues des mesures.
Published: 2020

30. Mechanistic Learning for Combinatorial Strategies With Immuno-oncology Drugs: Can Model-Informed Designs Help Investigators?

Author: Joseph Ciccolini, Fabrice Barlesi, Sebastien Benzekry, Dominique Barbolosi, Nicolas André, Simulation and Modeling of Adaptive Response for Therapeutics in Cancer (SMARTc), Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Hôpital de la Timone [CHU - APHM] (TIMONE), Institut Gustave Roussy (IGR), Direction de la recherche clinique [Gustave Roussy], Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Benzekry, Sebastien, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Cancer Research, Computer science, Big data, [SDV.CAN]Life Sciences [q-bio]/Cancer, Machine learning, computer.software_genre, 03 medical and health sciences, 0302 clinical medicine, [SDV.CAN] Life Sciences [q-bio]/Cancer, [STAT.AP] Statistics [stat]/Applications [stat.AP], Set (psychology), 030304 developmental biology, 0303 health sciences, [STAT.AP]Statistics [stat]/Applications [stat.AP], business.industry, Statistical model, Predictive analytics, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Pharmacometrics, 3. Good health, Oncology, Drug development, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, 030220 oncology & carcinogenesis, [SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Artificial intelligence, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, business, Oncology drugs, computer, [PHYS.PHYS.PHYS-DATA-AN] Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an], [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an], Systems pharmacology
Abstract: International audience; The amount of 'big' data generated in clinical oncology, whether from molecular, imaging, pharmacological or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive algorithms and simulations are required, with applications for diagnosis, prognosis, drug development or prediction of the response to therapy. Such mathematical and computational constructs can be subdivided into two broad classes: biologically agnostic, statistical models using artificial intelligence techniques, and physiologically-based, mechanistic models. In this review, recent advances in the applications of such methods in clinical oncology are outlined. These include machine learning applied to big data (omics, imaging or electronic health records), pharmacometrics, quantitative systems pharmacology, tumor size kinetics, and metastasis modeling. Focus is set on studies with high potential of clinical translation, as well as applied to cancer immunotherapy. Perspectives are given in terms of combinations of the two approaches: 'mechanistic learning'.
Published: 2020

31. Deciphering the response and resistance to immunecheckpoint inhibitors in lung cancer with artificial intelligence-based analysis: the pioneer and quantic joint-projects

Author: Ciccolini, Joseph, Benzekry, Sébastien, Barlesi, Fabrice, Simulation and Modeling of Adaptive Response for Therapeutics in Cancer (SMARTc), Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Institut Gustave Roussy (IGR), Direction de la recherche clinique [Gustave Roussy], PIONeeRis supported by French National Research Agency (grant# ANR-17-RHUS-0007) and is a partnership between AMU, APHM, AstraZeneca, Centre Léon Bérard, CNRS, HalioDx, ImCheck Therapeutics, Innate Pharma, Inserm, Institut Paoli Calmettes with an APHM sponsoring.QUANTIC is funded by ITMO Cancer AVIESAN and French Institut National du Cancer (grant #19CM148-00)., ANR-17-RHUS-0007,PIONEER,Precision Immuno-Oncology for advanced Non small cell lung cancer patients with PD-1 ICI Resistance(2017), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: [STAT.AP]Statistics [stat]/Applications [stat.AP], [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, [SDV.CAN]Life Sciences [q-bio]/Cancer, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an]
Abstract: International audience; Despite striking results, clinical outcome with immune checkpoint inhibitors remains too often uncertain. This joint-project aims at generating dense longitudinal data in lung cancer patients undergoing anti-PD1 or anti-PDL1 therapy, alone or in combination with other anticancer agents. Mathematical modelling with mechanistic learning algorithms will be used next to decipher the mechanisms underlying response or resistance to immunotherapy. Ultimately, this project should help to better understand the mechanisms underlying resistance to immune checkpoint inhibitors and identify a serial of actionable items to increase the efficacy of immunotherapy.
Published: 2020

32. Physiological changes may dominate the electrical properties of liver during reversible electroporation: Measurements and modelling

Author: Lluis M. Mir, Damien Voyer, Clair Poignard, Tomás García-Sánchez, Universitat Pompeu Fabra [Barcelona] (UPF), Aspects métaboliques et systémiques de l'oncogénèse pour de nouvelles approches thérapeutiques (METSY), Institut Gustave Roussy (IGR)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), EIGSI La Rochelle (EIGSI ), Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), This work was partially funded by the ITMO Cancer in the frame of the Plan Cancer 2014–2019 (projects PC201515 and PC201615) and ‘‘La Ligue contre le Cancer' postdoctoral fellowship program.The authors thank the funding support of the CNRS, Gustave Roussy, Univ. Paris-Sud and Université Paris-Saclay, and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux]
Subjects: Work (thermodynamics), Materials science, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, Electrochemotherapy, Biophysics, 02 engineering and technology, Conductivity, Models, Biological, 01 natural sciences, Mice, Electric Impedance, Electrochemistry, Animals, Electrical measurements, Sensitivity (control systems), Physical and Theoretical Chemistry, Electrical impedance, Pulse (signal processing), Electroporation, 010401 analytical chemistry, General Medicine, 021001 nanoscience & nanotechnology, 3. Good health, 0104 chemical sciences, Liver, Dielectric Spectroscopy, Electrode, [SDV.IB]Life Sciences [q-bio]/Bioengineering, 0210 nano-technology, Biological system
Abstract: This study presents electrical measurements (both conductivity during the pulses and impedance spectroscopy before and after) performed in liver tissue of mice during electroporation with classical electrochemotherapy conditions (8 pulses of 100 µs duration). A four-needle electrode arrangement inserted in the tissue was used for the measurements. The undesirable effects of the four-electrode geometry, notably concerning its sensitivity, were quantified and discussed showing how the electrode geometry chosen for the measurements can impact the results. Numerical modelling was applied to the information collected during the pulse, and to the impedance spectra acquired before and after the pulses sequence. Our results show that the numerical results were not consistent, suggesting that other collateral phenomena not considered in the model are at work during electroporation in vivo. We show how the modification in the volume of the intra and extra cellular media, likely caused by the vascular lock effect, could at least partially explain the recorded impedance evolution. In the present study we demonstrate the significant impact that physiological effects have on impedance changes following electroporation at the tissue scale and the potential need of introducing them into the numerical models. The code for the numerical model is publicly available at https://gitlab.inria.fr/poignard/4-electrode-system. This work was partially funded by the ITMO Cancer in the frame of the Plan Cancer 2014–2019 (projects PC201515 an PC201615) and “La Ligue contre le Cancer” postdoctoral fellowship program. The authors thank the funding support of the CNRS, Gustave Roussy, Univ. Paris-Sud and Université Paris-Saclay. The authors declare no conflict of interest. DV and CP are grateful to Annabelle Collin, Assistant Professor at Bordeaux INP, for helpful discussions and advices on the implementation of the numerical method.
Published: 2020

33. Deep correction of breathing-related artifacts in real-time MR-thermometry

Author: Mario Ries, B. Denis de Senneville, Pierrick Coupé, L. Facq, Chrit T. W. Moonen, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), University Medical Center [Utrecht], Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Accuracy and precision, Computer Science - Machine Learning, Mr thermometry, Computer science, medicine.medical_treatment, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, FOS: Physical sciences, Health Informatics, Thermometry, Deep neural network, Convolutional neural network, 030218 nuclear medicine & medical imaging, Machine Learning (cs.LG), 03 medical and health sciences, Motion, 0302 clinical medicine, medicine, Humans, Radiology, Nuclear Medicine and imaging, Computer vision, Interventional procedures, Physiological motion, Real-time systems, Proton resonance frequency, Radiological and Ultrasound Technology, business.industry, Deep learning, Respiration, Computer Graphics and Computer-Aided Design, Physics - Medical Physics, Magnetic Resonance Imaging, High-intensity focused ultrasound, Motion artifacts, Artifact suppression, MR-thermometry, Computer Vision and Pattern Recognition, Artificial intelligence, Medical Physics (physics.med-ph), business, Artifacts, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery
Abstract: Real-time MR-imaging has been clinically adapted for monitoring thermal therapies since it can provide on-the-fly temperature maps simultaneously with anatomical information. However, proton resonance frequency based thermometry of moving targets remains challenging since temperature artifacts are induced by the respiratory as well as physiological motion. If left uncorrected, these artifacts lead to severe errors in temperature estimates and impair therapy guidance. In this study, we evaluated deep learning for on-line correction of motion related errors in abdominal MR-thermometry. For this, a convolutional neural network (CNN) was designed to learn the apparent temperature perturbation from images acquired during a preparative learning stage prior to hyperthermia. The input of the designed CNN is the most recent magnitude image and no surrogate of motion is needed. During the subsequent hyperthermia procedure, the recent magnitude image is used as an input for the CNN-model in order to generate an on-line correction for the current temperature map. The method's artifact suppression performance was evaluated on 12 free breathing volunteers and was found robust and artifact-free in all examined cases. Furthermore, thermometric precision and accuracy was assessed for in vivo ablation using high intensity focused ultrasound. All calculations involved at the different stages of the proposed workflow were designed to be compatible with the clinical time constraints of a therapeutic procedure., 21 pages, 9 figures, 1 table
Published: 2020

34. Development and applications of radiomics approaches to improve diagnostic and prognostic management for patients with soft-tissue sarcomas

Author: Crombé, Amandine, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux, Olivier Saut, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux]
Subjects: Predictive modelling, Oncologie, Intratumoral heterogeneity, Imagerie dynamique de perfusion, Dynamic-contrast enhanced MRI, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Soft-tissue sarcomas, Magnetic resonance imaging, Oncology, Imagerie par résonance magnétique, Modélisation prédictive, Hétérogénéité intra-tumorale, Sarcomes des tissus mous
Abstract: Soft-tissue sarcomas (STS) are malignant ubiquitous mesenchymal tumors that are characterized by their heterogeneity at several levels, i.e. in terms of clinical presentation, radiological presentation, histology, molecular features and prognosis. Magnetic resonance imaging (MRI) with a contrast-agent injection is the imaging of reference for these tumors. MRI enables to perform the local staging, the evaluation of response to treatment, to plan the surgery and to look for local relapse. Furthermore, MRI can access non-invasively to the whole tumor in situ and in vivo which is complementary to histopathological and molecular analyses requiring invasive biopsy samples at risk of sampling bias. However, no imaging biomarker dedicated to STS has been validated so far. Meanwhile, technical innovations have been developed, namely: (i) alternative imaging modalities or MRI sequences that can quantify intratumoral physiopathological phenomenon; (ii) image analysis tools that can quantify radiological phenotypes better than human’s eyes through hundreds of textural and shape quantitative features (named radiomics features); and (iii) mathematical algorithms that can integrate all these information into predictive models (: machine-learning). Radiomics approaches correspond to the development of predictive models based on machine-learning algorithms and radiomics features, eventually combined with other clinical, pathological and molecular features. The aim of this thesis was to put these innovations into practice and to optimize them in order to improve the diagnostic and therapeutic managements of patients with STS.In the first part, we combined radiological and radiomics features extracted from the baseline structural MRIs of patients with a locally-advanced subtype of STS in order to build a radiomics signature that could help to identify patients with higher risk of metastatic relapse and may benefit from neoadjuvant treatments. In the second part, we elaborated a model based on the early changes in intratumoral heterogeneity (: delta-radiomics) on structural MRIs of patients with locally-advanced high-grade STS treated with neoadjuvant chemotherapy, in order to rapidly identify patients who do not respond to treatment and would benefit from early therapeutic adjustments. In the last part, we tried to better identify and control potential bias in radiomics approaches in order to optimize the predictive models based on radiomics features.; Les sarcomes des tissus mous (STM) sont des tumeurs malignes mésenchymateuses ubiquitaires hétérogènes en terme de présentations cliniques, radiologiques, histologiques, moléculaires et pronostiques. L’imagerie de référence des STM est l’IRM avec injection de produit de contraste qu’il s’agisse du bilan initial, de l’évaluation de la réponse aux traitements, de la planification préopératoire ou de la recherche de rechute locale. De plus, l’IRM permet d’accéder à la tumeur en place, in vivo, dans sa globalité et de manière non invasive, en complément des analyses anatomo-pathologiques et moléculaires qui nécessitent des prélèvements invasifs ne correspondant qu’à une infime fraction du volume tumoral. Cependant, aucun biomarqueur radiologique n’a été validé dans la prise en charge des STM. Parallèlement, se sont développés (i) d’autres modalités et séquences d’imagerie quantitative permettant d’aboutir à une quantification de phénomène physiopathologique intratumoraux, (ii) des techniques d’analyse d’image permettant de quantifier les phénotypes radiologiques au-delà de ce que peut voir l’œil humain à travers de multiples indicateurs de texture et de forme (: indices radiomics), et (iii) des outils d’analyses mathématiques (: algorithme de machine-learning) permettant d’intégrer et trier toutes ces informations dans des modèles prédictifs. Les approches radiomics correspondent au développement de modèles prédictifs basés sur ces algorithmes et ces indices radiomics. L’objectif de cette thèse est de mettre en application ces innovations et de les optimiser pour améliorer la prise en charge des patients atteints de STM. Pour cela, trois grands axes ont été développés. Dans une première partie, nous avons cherché à améliorer la prédiction du pronostic de patients atteints de certains sarcomes en combinant approches radiologiques classiques et approches radiomics sur leur IRM initiale, avec comme potentielle application de mieux identifier les patients à haut risque de rechute métastatique. Dans une deuxième partie, nous avons construit un modèle basé sur l’évolution précoce de l’hétérogénéité intratumorale ( : delta-radiomics) de patients atteints de STM traités par chimiothérapie néoadjuvante afin d’identifier les patients n’y répondant pas favorablement et qui pourraient bénéficier d’adaptations thérapeutiques anticipées. Dans une troisième et dernière partie, nous avons cherché à identifier et mieux contrôler les biais potentiels des approches radiomics afin, in fine, d’optimiser les modélisations prédictives basées sur les indices radiomics.
Published: 2020

35. Artificial Intelligence and Mechanistic Modeling for Clinical Decision Making in Oncology

Author: Sebastien Benzekry, Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Computer science, Big data, Clinical Decision-Making, [SDV.CAN]Life Sciences [q-bio]/Cancer, Medical Oncology, 030226 pharmacology & pharmacy, Decision Support Techniques, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 03 medical and health sciences, 0302 clinical medicine, Clinical decision making, Artificial Intelligence, Neoplasms, Data Mining, Electronic Health Records, Humans, Pharmacology (medical), Diagnosis, Computer-Assisted, Set (psychology), Pharmacology, [STAT.AP]Statistics [stat]/Applications [stat.AP], business.industry, Statistical model, Genomics, Predictive analytics, Models, Theoretical, Decision Support Systems, Clinical, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Pharmacometrics, 3. Good health, Drug development, 030220 oncology & carcinogenesis, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Artificial intelligence, Immunotherapy, business, [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an], Systems pharmacology
Abstract: The amount of "big" data generated in clinical oncology, whether from molecular, imaging, pharmacological, or biological origin, brings novel challenges. To mine efficiently this source of information, mathematical models able to produce predictive algorithms and simulations are required, with applications for diagnosis, prognosis, drug development, or prediction of the response to therapy. Such mathematical and computational constructs can be subdivided into two broad classes: biologically agnostic, statistical models using artificial intelligence techniques, and physiologically based, mechanistic models. In this review, recent advances in the applications of such methods in clinical oncology are outlined. These include machine learning applied to big data (omics, imaging, or electronic health records), pharmacometrics and quantitative systems pharmacology, as well as tumor kinetics and metastasis modeling. Focus is set on studies with high potential of clinical translation, and particular attention is given to cancer immunotherapy. Perspectives are given in terms of combinations of the two approaches: "mechanistic learning."
Published: 2020

36. Numerical modelling challenges for clinical electroporation ablation technique of liver tumors

Author: Olivier Seror, Olivier Gallinato, Baudouin Denis de Senneville, Clair Poignard, Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Paris 13 (UP13), This study has been carried out within the frame of the LABEX TRAIL, ANR-10-LABX-0057 with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the 'Investments for the future' Programme IdEx (ANR-10-IDEX-03-02). The authors are supported by the INCA Aviesan Plan Cancer project DYNAMO (ref. PC201615)and NUMEP (ref. PC201615), ANR-10-IDEX-0003,IDEX BORDEAUX,Initiative d'excellence de l'Université de Bordeaux(2010), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], and NEPA
Subjects: Computer science, Applied Mathematics, Electroporation, medicine.medical_treatment, Electrical model, Numerical modeling, Computational tissue electroporation, Ablation, Tumor ablation, 030218 nuclear medicine & medical imaging, Mathematics Subject Classification.35J15, 35J87, 92B, Clinical Practice, 03 medical and health sciences, Nonlinear electrical tissue mode, 0302 clinical medicine, 030220 oncology & carcinogenesis, Modeling and Simulation, medicine, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], Clinical imaging, Clinical electroporation ablation, Biomedical engineering
Abstract: International audience; Electroporation ablation is a promising non surgical and minimally invasive technique of tumor ablation, for which no monitoring is currently available. In this paper, we present the recent advances and challenges on the numerical modeling of clinical electroporation ablation of liver tumors. In particular, we show that a nonlinear static electrical model of tissue combined with clinical imaging can give crucial information of the electric field distribution in the clinical configuration. We conclude the paper by presenting some important questions that have to be addressed for an effective impact of computational modeling in clinical practice of electroporation ablation. Mathematics Subject Classification. 35J15, 35J87, 92B.
Published: 2020

37. Population modeling of tumor growth curves and the reduced Gompertz model improve prediction of the age of experimental tumors

Author: Cristina, Vaghi, Anne, Rodallec, Raphaëlle, Fanciullino, Joseph, Ciccolini, Jonathan P, Mochel, Michalis, Mastri, Clair, Poignard, John M L, Ebos, Sébastien, Benzekry, Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Simulation and Modeling of Adaptive Response for Therapeutics in Cancer (SMARTc), Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Iowa State University (ISU), Roswell Park Cancer Institute [Buffalo], Bodescot, Myriam, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Bergonié - CRLCC Bordeaux, Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Aix Marseille Université (AMU), Laboratoire de Pharmacocinétique et Toxicologie [Marseille], Hôpital de la Timone [CHU - APHM] (TIMONE), and Roswell Park Cancer Institute [Buffalo] (RPCI)
Subjects: QH301-705.5, [SCCO.COMP]Cognitive science/Computer science, [SDV.CAN]Life Sciences [q-bio]/Cancer, Research and Analysis Methods, Lung and Intrathoracic Tumors, Mice, [SDV.CAN] Life Sciences [q-bio]/Cancer, Diagnostic Medicine, [SCCO.COMP] Cognitive science/Computer science, Breast Tumors, Breast Cancer, Medicine and Health Sciences, Cancer Detection and Diagnosis, Animals, Computer Simulation, Biology (General), Cell Proliferation, Applied Mathematics, Simulation and Modeling, Cancers and Neoplasms, Bayes Theorem, Neoplasms, Experimental, Animal Models, Probability Theory, Disease Models, Animal, Oncology, Experimental Organism Systems, Physical Sciences, Animal Studies, Secondary Lung Tumors, Mathematics, Algorithms, Research Article, Statistical Distributions
Abstract: Tumor growth curves are classically modeled by means of ordinary differential equations. In analyzing the Gompertz model several studies have reported a striking correlation between the two parameters of the model, which could be used to reduce the dimensionality and improve predictive power. We analyzed tumor growth kinetics within the statistical framework of nonlinear mixed-effects (population approach). This allowed the simultaneous modeling of tumor dynamics and inter-animal variability. Experimental data comprised three animal models of breast and lung cancers, with 833 measurements in 94 animals. Candidate models of tumor growth included the exponential, logistic and Gompertz models. The exponential and—more notably—logistic models failed to describe the experimental data whereas the Gompertz model generated very good fits. The previously reported population-level correlation between the Gompertz parameters was further confirmed in our analysis (R2 > 0.92 in all groups). Combining this structural correlation with rigorous population parameter estimation, we propose a reduced Gompertz function consisting of a single individual parameter (and one population parameter). Leveraging the population approach using Bayesian inference, we estimated times of tumor initiation using three late measurement timepoints. The reduced Gompertz model was found to exhibit the best results, with drastic improvements when using Bayesian inference as compared to likelihood maximization alone, for both accuracy and precision. Specifically, mean accuracy (prediction error) was 12.2% versus 78% and mean precision (width of the 95% prediction interval) was 15.6 days versus 210 days, for the breast cancer cell line. These results demonstrate the superior predictive power of the reduced Gompertz model, especially when combined with Bayesian estimation. They offer possible clinical perspectives for personalized prediction of the age of a tumor from limited data at diagnosis. The code and data used in our analysis are publicly available at https://github.com/cristinavaghi/plumky., Author summary Mathematical models for tumor growth kinetics have been widely used since several decades but mostly fitted to individual or average growth curves. Here we compared three classical models (exponential, logistic and Gompertz) using a population approach, which accounts for inter-animal variability. The exponential and the logistic models failed to fit the experimental data while the Gompertz model showed excellent descriptive power. Moreover, the strong correlation between the two parameters of the Gompertz equation motivated a simplification of the model, the reduced Gompertz model, with a single individual parameter and equal descriptive power. Combining the mixed-effects approach with Bayesian inference, we predicted the age of individual tumors with only few late measurements. Thanks to its simplicity, the reduced Gompertz model showed superior predictive power. Although our method remains to be extended to clinical data, these results are promising for the personalized estimation of the age of a tumor from limited measurements at diagnosis.
Published: 2020

38. Cell migration in complex environments: chemotaxis and topographical obstacles

Author: Lamis Sabbagh, Nicolas Meunier, Alessandro Cucchi, Laurent Navoret, Christèle Etchegaray, Mathématiques Appliquées Paris 5 (MAP5 - UMR 8145), Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Université Paris Descartes - Paris 5 (UPD5), Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Bergonié - CRLCC Bordeaux, Laboratoire de Mathématiques et Modélisation d'Evry (LaMME), Centre National de la Recherche Scientifique (CNRS)-ENSIIE-Université d'Évry-Val-d'Essonne (UEVE)-Institut National de la Recherche Agronomique (INRA), Institut de Recherche Mathématique Avancée (IRMA), Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Institut Montpelliérain Alexander Grothendieck (IMAG), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), The present research was carried out within the scope of the 2018 CEMRACS Summer Program. The authors acknowledge support from SMAI (French Society of Applied and Industrial Mathematics), PEPS,MI from CNRS and from the MESOPROBIO ERC., Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université d'Évry-Val-d'Essonne (UEVE)-ENSIIE-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Université Paris Descartes - Paris 5 (UPD5)-Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS), Université d'Évry-Val-d'Essonne (UEVE)-ENSIIE-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Institut National des Sciences Mathématiques et de leurs Interactions (INSMI)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux]
Subjects: Physics, T57-57.97, 0303 health sciences, Asymptotic analysis, Applied mathematics. Quantitative methods, Dynamics (mechanics), Cell migration, Signal, Action (physics), [MATH.MATH-PR]Mathematics [math]/Probability [math.PR], 03 medical and health sciences, Stochastic differential equation, 0302 clinical medicine, QA1-939, Constant (mathematics), Biological system, Mathematics, 030217 neurology & neurosurgery, Intracellular, 030304 developmental biology
Abstract: Cell migration is a complex phenomenon that plays an important role in many biological processes. Our aim here is to build and study models of reduced complexity to describe some aspects of cell motility in tissues. Precisely, we study the impact of some biochemical and mechanical cues on the cell dynamics in a 2D framework. For that purpose, we model the cell as an active particle with a velocity solution to a particular Stochastic Differential Equation that describes the intracellular dynamics as well as the presence of some biochemical cues. In the 1D case, an asymptotic analysis puts to light a transition between migration dominated by the cell’s internal activity and migration dominated by an external signal. In a second step, we use the contact algorithm introduced in [15,18] to describe the cell dynamics in an environment with obstacles. In the 2D case, we study how a cell submitted to a constant directional force that mimics the action of chemoattractant, behaves in the presence of obstacles. We numerically observe the existence of a velocity value that the cell can not exceed even if the directional force intensity increases. We find that this threshold value depends on the number of obstacles. Our result confirms a result that was already observed in a discrete framework in [3,4].
Published: 2020

39. Numerical Modeling of Floating Potentials in Electrokinetic Problems Using an Asymptotic Method

Author: Clair Poignard, Sergio Corridore, Riccardo Scorretti, Annabelle Collin, Damien Voyer, EIGSI La Rochelle (EIGSI ), Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Ampère, Département Bioingénierie (BioIng), Ampère (AMPERE), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-École Centrale de Lyon (ECL), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-École Centrale de Lyon (ECL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 010302 applied physics, 4-electrode system, floating potential, Computer science, Computation, Numerical analysis, [SPI.NRJ]Engineering Sciences [physics]/Electric power, 01 natural sciences, Measure (mathematics), Electronic, Optical and Magnetic Materials, Nonlinear system, Electrokinetic phenomena, Cascade, 0103 physical sciences, Applied mathematics, nonlinear problem, Electric potential, Electrical and Electronic Engineering, Perfect conductor, Asymptotic method
Abstract: International audience; Floating potentials appear in electrokinetic problems when isolated high conductive materials are included in a dielectric or weakly conductive ambient medium. The large contrast of conductivities generates numerical issues that make hard the computation of the electric potential. The paper proposes a rigorous numerical method to tackle such kind of problems. Interestingly, a correction to the case of perfect conductor is given in order to improve the accuracy of the computation. The method involves a cascade of two elementary problems set respectively in the ambient medium and in the high conductive inclusions. An example is proposed with a 4-electrode system designed to both induce electroporation in a biological tissue sample and measure the resulting impedance. The approach is extended to a nonlinear problem by taking advantage of the iterative scheme that is necessarily applied in this case.
Published: 2020

40. Spatial mechanistic modeling for prediction of the growth of asymptomatic meningioma

Author: COLLIN, Annabelle, COPOL, Cédrick, PIANET, Vivien, COLIN, Thierry, ENGELHARDT, Julien, KANTOR, Guy, LOISEAU, Hugues, SAUT, Olivier, TATON, Benjamin, Institut Polytechnique de Bordeaux (Bordeaux INP), Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), SOPHiA GENETICS [Pessac], Groupe hospitalier Pellegrin, Institut Bergonié [Bordeaux], UNICANCER, Imagerie moléculaire et thérapies innovantes en oncologie (IMOTION), Université de Bordeaux (UB), This study was supported by the French Laboratory of Excellence TRAILANR-10-LABX-57.The authors would like to thank the POPRA program which is supported by the Conseil régional Nouvelle-Aquitaine and the European Funds FEDER., Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER, and This study was supported by the French Laboratory of Excellence TRAILANR-10-LABX-57.The authors would like to thank the POPRA program which issupported by the Conseil r ́egional Nouvelle-Aquitaine and the European FundsFEDER.
Subjects: PDE Modeling, Tumor Growth, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Inverse problem, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], [SDV.CAN]Life Sciences [q-bio]/Cancer, [INFO.INFO-NA]Computer Science [cs]/Numerical Analysis [cs.NA], Meningiomas, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation
Abstract: International audience; Mathematical modeling of tumor growth draws interest from the medical community as they have the potential to improve patients' care and the use of public health resources. The main objectives of this work are to model the growth of meningiomas-slow-growing benign tumors requiring extended imaging follow-up-and to predict tumor volume and shape at a later desired time using only two time examinations. We propose two variants of a 3D partial differential system of equations (PDE) which yield after a spatial integration systems of ordinary differential equations (ODE) that relate tumor volume with time. Estimation of models parameters is a crucial step for obtaining a personalized model for a patient that can be used for descriptive or predictive purposes. As PDE and ODE systems share the same parameters, they are both estimated by fitting the ODE systems to the tumor volumes obtained from MRI examinations acquired at different times. A population approach allows to compensate for sparse sampling times and measurement uncertainties by constraining the variability of the parameters in the population. Description capabilities of the models are investigated in 40 patients with benign asymptomatic meningiomas who had had at least 3 surveillance MRI examinations. The two models can fit to the data accurately and more realistically than a naive linear regression. Prediction performances are validated for 33 patients using a population approach. Mean relative errors in volume predictions are less than 10% with ODE systems versus 12.5% with the naive linear model using only two time examinations. Concerning the shape, the mean Sørensen-Dice coefficients are 85% with the PDE systems in a subset of 10 representative patients.
Published: 2020

41. Chloroquine for COVID-19 Infection

Author: Nicholas Moore, Bodescot, Myriam, Plateforme Bordeaux PharmacoEpi [Bordeaux] (BPE), Centre d'Investigation Clinique [Bordeaux], Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Université de Bordeaux (UB)-CHU Bordeaux [Bordeaux]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Université de Bordeaux (UB)-CHU Bordeaux [Bordeaux]-Institut National de la Santé et de la Recherche Médicale (INSERM), Bordeaux population health (BPH), and Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Subjects: 2019-20 coronavirus outbreak, [SDV.SP.MED] Life Sciences [q-bio]/Pharmaceutical sciences/Medication, Coronavirus disease 2019 (COVID-19), Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Pharmacology toxicology, Pneumonia, Viral, Toxicology, 030226 pharmacology & pharmacy, 03 medical and health sciences, Betacoronavirus, 0302 clinical medicine, [SDV.SP.MED]Life Sciences [q-bio]/Pharmaceutical sciences/Medication, Chloroquine, Pandemic, medicine, Humans, Pharmacology (medical), Pandemics, ComputingMilieux_MISCELLANEOUS, 030304 developmental biology, Pharmacology, [SDV.MP.VIR] Life Sciences [q-bio]/Microbiology and Parasitology/Virology, [SDV.MHEP.ME] Life Sciences [q-bio]/Human health and pathology/Emerging diseases, PharmacoEpi-Drugs, 0303 health sciences, [SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases, biology, business.industry, SARS-CoV-2, COVID-19, medicine.disease, biology.organism_classification, Virology, COVID-19 Drug Treatment, Pneumonia, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, business, Coronavirus Infections, medicine.drug
Abstract: International audience
Published: 2020

42. Patch-based field-of-view matching in multi-modal images for electroporation-based ablations

Author: Luc Lafitte, Clair Poignard, Baudouin Denis de Senneville, Olivier Sutter, Mario Ries, Antoine Petit, Cornel Zachiu, Olivier Seror, Rémi Giraud, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Laboratoire de l'intégration, du matériau au système (IMS), Centre National de la Recherche Scientifique (CNRS)-Institut Polytechnique de Bordeaux-Université Sciences et Technologies - Bordeaux 1, University Medical Center [Utrecht], Hôpital Jean Verdier [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), The authors thank the Laboratory of Excellence TRAIL ANR-10-LABX-57 for funding. This study has been carried out with the financial support of the French National Research Agency (ANR) in the frame of the 'Investments for the future' Programme IdEx Bordeaux-CPU (ANR-10-IDEX-03-02). This research has been partly granted by the Plan Cancer project NUMEP (Inserm 11099), led by C.P., ANR-10-IDEX-0003,IDEX BORDEAUX,Initiative d'excellence de l'Université de Bordeaux(2010), Université Sciences et Technologies - Bordeaux 1-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Department of Radiology [Utrecht], Hôpital Avicenne [Bobigny], Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Sciences et Technologies - Bordeaux 1 (UB)-Institut Polytechnique de Bordeaux-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], and NEPA
Subjects: FOS: Computer and information sciences, Cone beam computed tomography, Similarity (geometry), Multi-modal image registration, Matching (graph theory), Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Image registration, Health Informatics, Computed tomography, Field of view, computer.software_genre, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Imaging, Three-Dimensional, Voxel, FOS: Electrical engineering, electronic engineering, information engineering, medicine, Image Processing, Computer-Assisted, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], Radiology, Nuclear Medicine and imaging, Computer vision, Interventional procedures, Computer Science - Performance, Radiological and Ultrasound Technology, medicine.diagnostic_test, business.industry, Image and Video Processing (eess.IV), Magnetic resonance imaging, Cone-Beam Computed Tomography, Electrical Engineering and Systems Science - Image and Video Processing, Computer Graphics and Computer-Aided Design, Performance (cs.PF), Electroporation, Metric (mathematics), Computer Vision and Pattern Recognition, Artificial intelligence, Patch-based matching, business, Tomography, X-Ray Computed, computer, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, 030217 neurology & neurosurgery, Algorithms
Abstract: Various multi-modal imaging sensors are currently involved at different steps of an interventional therapeutic work-flow. Cone beam computed tomography (CBCT), computed tomography (CT) or Magnetic Resonance (MR) images thereby provides complementary functional and/or structural information of the targeted region and organs at risk. Merging this information relies on a correct spatial alignment of the observed anatomy between the acquired images. This can be achieved by the means of multi-modal deformable image registration (DIR), demonstrated to be capable of estimating dense and elastic deformations between images acquired by multiple imaging devices. However, due to the typically different field-of-view (FOV) sampled across the various imaging modalities, such algorithms may severely fail in finding a satisfactory solution. In the current study we propose a new fast method to align the FOV in multi-modal 3D medical images. To this end, a patch-based approach is introduced and combined with a state-of-the-art multi-modal image similarity metric in order to cope with multi-modal medical images. The occurrence of estimated patch shifts is computed for each spatial direction and the shift value with maximum occurrence is selected and used to adjust the image field-of-view. We show that a regional registration approach using voxel patches provides a good structural compromise between the voxel-wise and "global shifts" approaches. The method was thereby beneficial for CT to CBCT and MRI to CBCT registration tasks, especially when highly different image FOVs are involved. Besides, the benefit of the method for CT to CBCT and MRI to CBCT image registration is analyzed, including the impact of artifacts generated by percutaneous needle insertions. Additionally, the computational needs are demonstrated to be compatible with clinical constraints in the practical case of on-line procedures., 22 pages, 9 figures
Published: 2020

43. RegQCNET: Deep quality control for image-to-template brain MRI affine registration

Author: Baudouin Denis de Senneville, José V. Manjón, Pierrick Coupé, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), ITACA, Universitat Politècnica de València (UPV), Laboratoire Bordelais de Recherche en Informatique (LaBRI), Université de Bordeaux (UB)-Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB), This work benefited from the support of the project DeepvolBrain of the French National Research Agency (ANR-18-CE45-0013). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57 for the BigDataBrain project.Moreover, we thank the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), Cluster of excellence CPU and the CNRS/INSERM for the DeepMultiBrain project. This study has been also supported by the DPI2017-87743-R grant from the Spanish Ministerio de Economia, Industria Competitividad. Theauthors gratefully acknowledge the support of NVIDIA Corporation with their donationof a TITAN X GPU used in this research., ANR-18-CE45-0013,DeepVolBrain,Apprentissage profond pour la volumétrie cérébrale : vers le BigData en neuroscience(2018), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], and Université de Bordeaux (UB)-École Nationale Supérieure d'Électronique, Informatique et Radiocommunications de Bordeaux (ENSEIRB)-Centre National de la Recherche Scientifique (CNRS)
Subjects: FOS: Computer and information sciences, Quality Control, Digital image correlation, Computer Science - Machine Learning, Support Vector Machine, Computer science, Computer Vision and Pattern Recognition (cs.CV), Computer Science - Computer Vision and Pattern Recognition, Image processing, Deep Neural Network, Deep neural network, Convolutional neural network, Machine Learning (cs.LG), 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Robustness (computer science), FOS: Electrical engineering, electronic engineering, information engineering, Image Processing, Computer-Assisted, Brain segmentation, Humans, Image-to-template registration, Radiology, Nuclear Medicine and imaging, Radiological and Ultrasound Technology, business.industry, Deep learning, Image and Video Processing (eess.IV), Brain, Quality control, Pattern recognition, Bayes Theorem, Electrical Engineering and Systems Science - Image and Video Processing, Magnetic Resonance Imaging, Feature (computer vision), 030220 oncology & carcinogenesis, FISICA APLICADA, Affine transformation, Artificial intelligence, Neural Networks, Computer, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: [EN] Affine registration of one or several brain image(s) onto a common reference space is a necessary prerequisite for many image processing tasks, such as brain segmentation or functional analysis. Manual assessment of registration quality is a tedious and time-consuming task, especially in studies comprising a large amount of data. Automated and reliable quality control (QC) becomes mandatory. Moreover, the computation time of the QC must be also compatible with the processing of massive datasets. Therefore, automated deep neural network approaches have emerged as a method of choice to automatically assess registration quality. In the current study, a compact 3D convolutional neural network, referred to as RegQCNET, is introduced to quantitatively predict the amplitude of an affine registration mismatch between a registered image and a reference template. This quantitative estimation of registration error is expressed using the metric unit system. Therefore, a meaningful task-specific threshold can be manually or automatically defined in order to distinguish between usable and non-usable images. The robustness of the proposed RegQCNET is first analyzed on lifespan brain images undergoing various simulated spatial transformations and intensity variations between training and testing. Secondly, the potential of RegQCNET to classify images as usable or non-usable is evaluated using both manual and automatic thresholds. During our experiments, automatic thresholds are estimated using several computer-assisted classification models (logistic regression, support vector machine, Naive Bayes and random forest) through cross-validation. To this end we use an expert's visual QC estimated on a lifespan cohort of 3953 brains. Finally, the RegQCNET accuracy is compared to usual image features such as image correlation coefficient and mutual information. The results show that the proposed deep learning QC is robust, fast and accurate at estimating affine registration error in the processing pipeline., The experiments presented in this paper were carried out using the PlaFRIM experimental testbed, supported by Inria, CNRS (LABRI and IMB), Universite de Bordeaux, Bordeaux INP and Conseil Regional d'Aquitaine (see https://www.plafrim.fr/). This work benefited from the support of the project DeepvolBrain of the French National Research Agency (ANR-18-CE45-0013). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57 for the BigDataBrain project. Moreover, we thank the Investments for the Future Program IdEx Bordeaux (ANR-10-IDEX-03-02, HL-MRI Project), the Cluster of Excellence CPU and the CNRS/INSERM for the DeepMultiBrain project. This study has been also supported by a DPI2017-87743-R grant from the Spanish Ministerio de Economia, Industria Competitividad. The authors gratefully acknowledge the support of NVIDIA Corporation with their donation of a TITAN X GPU used in this research.
Published: 2020
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44. Prédiction de l'efficacité de l'immunothérapie dans le cancer bronchique à partir de données cliniques et biologiques simples : apport de l'intelligence artificielle

Author: S. Chaleat, Sebastien Benzekry, M. Grangeon, Laurent Greillier, Dominique Barbolosi, Fabrice Barlesi, Pascale Tomasini, Assistance Publique - Hôpitaux de Marseille (APHM), Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Simulation and Modeling of Adaptive Response for Therapeutics in Cancer (SMARTc), Centre de Recherche en Cancérologie de Marseille (CRCM), Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Aix Marseille Université (AMU)-Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Benzekry, Sebastien, Service d'oncologie multidisciplinaire innovations thérapeutiques [Hôpital Nord - APHM], Assistance Publique - Hôpitaux de Marseille (APHM)- Hôpital Nord [CHU - APHM], Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Pulmonary and Respiratory Medicine, [STAT.AP]Statistics [stat]/Applications [stat.AP], [SDV.CAN]Life Sciences [q-bio]/Cancer, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, 3. Good health, 03 medical and health sciences, 0302 clinical medicine, 030228 respiratory system, [SDV.CAN] Life Sciences [q-bio]/Cancer, [STAT.AP] Statistics [stat]/Applications [stat.AP], [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, 030220 oncology & carcinogenesis, [SDV.SP.PHARMA] Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, [SDV.SP.PHARMA]Life Sciences [q-bio]/Pharmaceutical sciences/Pharmacology, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, [INFO.INFO-MO] Computer Science [cs]/Modeling and Simulation, [PHYS.PHYS.PHYS-DATA-AN] Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an], ComputingMilieux_MISCELLANEOUS, [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an]
Abstract: Introduction Malgre la place grandissante des inhibiteurs de check-point immunitaire (ICIs) dans le cancer bronchique non a petites cellules (CBNPC), le taux de reponse a ce type de traitement est faible et il existe un risque non negligeable de toxicites specifiques. Aussi, determiner des facteurs predictifs de reponse a l’immunotherapie est essentielle et pouvoir le faire a partir de donnees facilement accessibles au clinicien serait une aide precieuse. L’objectif de cette etude a ete de developper, grâce a l’intelligence artificielle (IA), un outil d’aide a la selection des patients qui pourraient tirer beneficie d’un traitement par ICIs, et ce a partir de caracteristiques cliniques et biologiques simples. Methodes Il s’agit d’une etude de cohorte observationnelle, retrospective de patients souffrant de CBNPC, ayant recu au moins un cycle d’ICI entre avril 2013 et novembre 2017. Les variables etudiees etaient les caracteristiques demographiques, cliniques, hematologiques, therapeutiques et evolutives. Les donnees clinicobiologiques ont ete correlees associees au taux de reponse objective (TRO) et au taux de controle de la maladie (TCM) par regression logistique, et a la survie (survie sans progression [SSP] et survie globale [SG]) par modele de Cox. Resultats Parmi les 350 patients inclus dans cette cohorte, l’âge median etait de 62 ans, 66 % etaient des hommes. 26 % avaient un performance status (PS) ≥ 2. Le TRO etait de 16 %, le TCM etait de 53 %, la SSP mediane etait de 3 mois et la SG mediane etait de 13,7 mois. En analyse multivariee, le PS ≥ 2 et l’IMC etaient significativement correles au TRO (respectivement odds ratio [OR] 0,078, p = 0,002 et 1,09, p = 0,001). Pour le TCM, il avait une correlation significative avec le PS ≥ 2, l’hemoglobine et le rapport neutrophiles/lymphocytes (NLR) (respectivement OR a 0,32, p Conclusion Grâce a l’IA, la combinaison de caracteristiques cliniques et hematologiques basiques pourrait conduire a une prediction de l’efficacite des ICIs a l’echelle individuelle, et ainsi integrer le processus de decision therapeutique a l’heure de la medecine de precision. L’algorithme developpe ici necessitera neanmoins une validation dans une cohorte independante de patients.
Published: 2020

45. Survival criterion for a population subject to selection and mutations ; Application to temporally piecewise constant environments

Author: Christèle Etchegaray, Manon Costa, Sepideh Mirrahimi, Institut de Mathématiques de Toulouse UMR5219 (IMT), Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), The first and last authors are grateful or partial funding from the chaire Modélisation Mathématique et Biodiversité of Véolia Environment - École Polytechnique - Museum National d’Histoire Naturelle - Fondation X. The last author is also grateful for partial funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 639638), held by Vincent Calvez, Université Toulouse Capitole (UT Capitole), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National des Sciences Appliquées - Toulouse (INSA Toulouse), Institut National des Sciences Appliquées (INSA)-Université de Toulouse (UT)-Institut National des Sciences Appliquées (INSA)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre National de la Recherche Scientifique (CNRS), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Toulouse 1 Capitole (UT1), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), and Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)
Subjects: 35K55, 35B40, 35D40, 35R09, 92D15, Hamilton-Jacobi equation with constraint, media_common.quotation_subject, Population, Adaptive evolution, Mathematics::Analysis of PDEs, 01 natural sciences, Competition (biology), Mathematics - Analysis of PDEs, FOS: Mathematics, Applied mathematics, Quantitative Biology::Populations and Evolution, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], Limit (mathematics), 0101 mathematics, education, Selection (genetic algorithm), media_common, education.field_of_study, Applied Mathematics, 010102 general mathematics, General Engineering, General Medicine, Dirac concentrations, 010101 applied mathematics, Constraint (information theory), Computational Mathematics, Piecewise, Parabolic integro-differential equations, Adaptation, Constant (mathematics), General Economics, Econometrics and Finance, Analysis, Analysis of PDEs (math.AP)
Abstract: International audience; We study a parabolic Lotka-Volterra type equation that describes the evolution of a population structured by a phenotypic trait, under the effects of mutations, and competition for resources modelled by a nonlocal feedback. The limit of small mutations is characterized by a Hamilton-Jacobi equation with constraint that describes the concentration of the population on some traits. This result was already established in [PB08, BMP09, LMP11] in a constant environment, when the asymptotic persistence of the population was ensured. Here, we relax the assumptions on the growth rate and the initia data to extend the study to situations where the population goes extinct at the limit. For that purpose, we provide conditions on the initial data for the asymptotic fate of the population. Finally, we show how this study for a constant environment allows to consider temporally piecewise constant environments. This applies to several applications in biology such as the adaptation to a pharmacological treatment, and the interaction between two populations evolving on different ecological timescales.
Published: 2020

46. Machine Learning and Mechanistic Modeling for Prediction of Metastatic Relapse in Early-Stage Breast Cancer

Author: Sebastien Benzekry, Cynthia Perier, Carine Bellera, Mélanie Prague, Gaëtan MacGrogan, Chiara Nicolò, Olivier Saut, Prague, Mélanie, Modélisation Mathématique pour l'Oncologie (MONC), Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Statistics In System biology and Translational Medicine (SISTM), Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)- Bordeaux population health (BPH), Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Bordeaux (UB)-Institut de Santé Publique, d'Épidémiologie et de Développement (ISPED)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Bergonié [Bordeaux], UNICANCER, This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)
Subjects: 0301 basic medicine, Oncology, [SDV]Life Sciences [q-bio], 0302 clinical medicine, Breast cancer, [MATH.MATH-ST]Mathematics [math]/Statistics [math.ST], Stage (cooking), Young adult, Neoplasm Metastasis, [MATH.MATH-ST] Mathematics [math]/Statistics [math.ST], ComputingMilieux_MISCELLANEOUS, Metastatic relapse, Aged, 80 and over, Age Factors, EPICENE, General Medicine, Middle Aged, Tumor Burden, SISTM, Survival Rate, [SDV] Life Sciences [q-bio], 030220 oncology & carcinogenesis, Predictive value of tests, Mechanistic model, Female, Algorithms, Adult, medicine.medical_specialty, [MATH.MATH-DS]Mathematics [math]/Dynamical Systems [math.DS], [MATH.MATH-DS] Mathematics [math]/Dynamical Systems [math.DS], Breast Neoplasms, [SDV.CAN]Life Sciences [q-bio]/Cancer, 03 medical and health sciences, Young Adult, Text mining, [SDV.CAN] Life Sciences [q-bio]/Cancer, Predictive Value of Tests, Internal medicine, Machine learning, medicine, Biomarkers, Tumor, Humans, Computer Simulation, Survival rate, Survival analysis, Aged, Neoplasm Staging, business.industry, medicine.disease, 030104 developmental biology, [SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie, Neoplasm staging, [SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie, Neoplasm Recurrence, Local, business
Abstract: PURPOSE For patients with early-stage breast cancer, predicting the risk of metastatic relapse is of crucial importance. Existing predictive models rely on agnostic survival analysis statistical tools (eg, Cox regression). Here we define and evaluate the predictive ability of a mechanistic model for time to distant metastatic relapse. METHODS The data we used for our model consisted of 642 patients with 21 clinicopathologic variables. A mechanistic model was developed on the basis of two intrinsic mechanisms of metastatic progression: growth (parameter α) and dissemination (parameter μ). Population statistical distributions of the parameters were inferred using mixed-effects modeling. A random survival forest analysis was used to select a minimal set of five covariates with the best predictive power. These were further considered to individually predict the model parameters by using a backward selection approach. Predictive performances were compared with classic Cox regression and machine learning algorithms. RESULTS The mechanistic model was able to accurately fit the data. Covariate analysis revealed statistically significant association of Ki67 expression with α ( P = .001) and EGFR expression with μ ( P = .009). The model achieved a c-index of 0.65 (95% CI, 0.60 to 0.71) in cross-validation and had predictive performance similar to that of random survival forest (95% CI, 0.66 to 0.69) and Cox regression (95% CI, 0.62 to 0.67) as well as machine learning classification algorithms. CONCLUSION By providing informative estimates of the invisible metastatic burden at the time of diagnosis and forward simulations of metastatic growth, the proposed model could be used as a personalized prediction tool for routine management of patients with breast cancer.
Published: 2020

47. A Continuum Mechanics Model of Enzyme-Based Tissue Degradation in Cancer Therapies

Author: Clair Poignard, Manon Deville, Roberto Natalini, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Consiglio Nazionale delle Ricerche [Roma] (CNR), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Plan Cancer DYNAMO 201515, Plan Cancer NUMEP 201615, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux]
Subjects: 0301 basic medicine, Mathematics Subject Classification (2000) 65M06, 65M12, 92C37, Stereochemistry, General Mathematics, Immunology, Poromechanics, Enzyme Therapy, Models, Biological, General Biochemistry, Genetics and Molecular Biology, 03 medical and health sciences, 0302 clinical medicine, Neoplasms, Tissue degradation, Pressure, Extracellular, Animals, Humans, [MATH.MATH-AP]Mathematics [math]/Analysis of PDEs [math.AP], Computer Simulation, General Environmental Science, Interstitial fluid pressure, Pharmacology, chemistry.chemical_classification, Drug distribution in tissue, Continuum mechanics, ECM degradation, General Neuroscience, Extracellular Fluid, Mathematical Concepts, Biological tissue, Penetration (firestop), Poroelasticity, Elasticity, Biomechanical Phenomena, Extracellular Matrix, 030104 developmental biology, Enzyme, Nonlinear Dynamics, Computational Theory and Mathematics, chemistry, Mathematical biology, 030220 oncology & carcinogenesis, Linear Models, Biophysics, General Agricultural and Biological Sciences, Porosity, Algorithms
Abstract: International audience; We propose a mathematical model to describe enzyme-based tissue degradation in cancer therapies. The proposed model combines the poroelastic theory of mixtures with the transport of enzymes or drugs in the extracellular space. The effect of the matrix degrading enzymes on the tissue composition and its mechanical response are accounted for. Numerical simulations in 1D, 2D and ax-isymmetric (3D) configurations show how an injection of matrix degrading enzymes alters the porosity of a biological tissue. We eventually exhibit numerically the main consequences of a matrix degrading enzyme pretreatment in the framework of chemotherapy: the removal of the diffusive hindrance to the penetration of therapeutic molecules in tumors and the reduction of interstitial fluid pressure which improves transcapillary transport. Both effects are consistent with previous biological observations.
Published: 2018

48. Dose- and time-dependence of the host-mediated response to paclitaxel therapy: a mathematical modeling approach

Author: Dror Alishekevitz, Sebastien Benzekry, Madeleine Benguigui, Yuval Shaked, Michael Timaner, Ziv Raviv, Dvir Shechter, Rappaport faculty of Medicine, Technion - Israel Institute of Technology [Haifa], Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux]
Subjects: 0301 basic medicine, medicine.medical_treatment, [SDV.CAN]Life Sciences [q-bio]/Cancer, invasion and migration, Bioinformatics, chemotherapy, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Pharmacokinetics, metronomic chemotherapy, medicine, Progenitor cell, Chemotherapy, business.industry, Cancer, medicine.disease, Metronomic Chemotherapy, host effects, 3. Good health, 030104 developmental biology, Oncology, Paclitaxel, chemistry, 030220 oncology & carcinogenesis, Pharmacodynamics, business, Host (network), mathematical models, Research Paper
Abstract: International audience; It has recently been suggested that pro-tumorigenic host-mediated processes induced in response to chemotherapy counteract the anti-tumor activity of therapy, and thereby decrease net therapeutic outcome. Here we use experimental data to formulate a mathematical model describing the host response to different doses of paclitaxel (PTX) chemotherapy as well as the duration of the response. Three previously described host-mediated effects are used as readouts for the host response to therapy. These include the levels of circulating endothelial progenitor cells in peripheral blood and the effect of plasma derived from PTX-treated mice on migratory and invasive properties of tumor cells in vitro. A first set of mathematical models, based on basic principles of pharmacokinetics/pharmacodynamics, did not appropriately describe the dose-dependence and duration of the host response regarding the effects on invasion. We therefore provide an alternative mathematical model with a dose-dependent threshold, instead of a concentration-dependent one, that describes better the data. This model is integrated into a global model defining all three host-mediated effects. It not only precisely describes the data, but also correctly predicts host-mediated effects at different doses as well as the duration of the host response. This mathematical model may serve as a tool to predict the host response to chemotherapy in cancer patients, and therefore may be used to design chemotherapy regimens with improved therapeutic outcome by minimizing host mediated effects.
Published: 2017

49. A novel adaptive needle insertion sequencing for robotic, single needle MR-guided high-dose-rate prostate brachytherapy

Author: B Denis de Senneville, Marinus A. Moerland, M Maenhout, Guillaume Leopold Theodorus Frederik Hautvast, J J W Lagendijk, Dirk Binnekamp, M Borot de Battisti, University Medical Center [Utrecht], Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Philips Medical Systems International BV, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux]
Subjects: Male, medicine.medical_specialty, Time Factors, medicine.medical_treatment, Brachytherapy, Radiation Dosage, Patient Positioning, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Prostate cancer, 0302 clinical medicine, Robotic Surgical Procedures, Prostate, Journal Article, Humans, Medicine, Radiology, Nuclear Medicine and imaging, ComputingMilieux_MISCELLANEOUS, Radiological and Ultrasound Technology, business.industry, Prostatic Neoplasms, Radiotherapy Dosage, medicine.disease, Magnetic Resonance Imaging, 3. Good health, Surgery, Radiation therapy, medicine.anatomical_structure, Needles, 030220 oncology & carcinogenesis, Needle insertion, business, Dose rate, Nuclear medicine, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Mri guided, Prostate brachytherapy, Radiotherapy, Image-Guided
Abstract: MR-guided high-dose-rate (HDR) brachytherapy has gained increasing interest as a treatment for patients with localized prostate cancer because of the superior value of MRI for tumor and surrounding tissues localization. To enable needle insertion into the prostate with the patient in the MR bore, a single needle MR-compatible robotic system involving needle-by-needle dose delivery has been developed at our institution. Throughout the intervention, dose delivery may be impaired by: (1) sub-optimal needle positioning caused by e.g. needle bending, (2) intra-operative internal organ motion such as prostate rotations or swelling, or intra-procedural rectum or bladder filling. This may result in failure to reach clinical constraints. To assess the first aforementioned challenge, a recent study from our research group demonstrated that the deposited dose may be greatly improved by real-time adaptive planning with feedback on the actual needle positioning. However, the needle insertion sequence is left to the doctor and therefore, this may result in sub-optimal dose delivery. In this manuscript, a new method is proposed to determine and update automatically the needle insertion sequence. This strategy is based on the determination of the most sensitive needle track. The sensitivity of a needle track is defined as its impact on the dose distribution in case of sub-optimal positioning. A stochastic criterion is thus presented to determine each needle track sensitivity based on needle insertion simulations. To assess the proposed sequencing strategy, HDR prostate brachytherapy was simulated on 11 patients with varying number of needle insertions. Sub-optimal needle positioning was simulated at each insertion (modeled by typical random angulation errors). In 91% of the scenarios, the dose distribution improved when the needle was inserted into the most compared to the least sensitive needle track. The computation time for sequencing was less than 6 s per needle track. The proposed needle insertion sequencing can therefore assist in delivering an optimal dose in HDR prostate brachytherapy.
Published: 2017

50. Analyse quantitative des données de routine clinique pour le pronostic précoce en oncologie

Author: Perier, Cynthia, Institut de Mathématiques de Bordeaux (IMB), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), Modélisation Mathématique pour l'Oncologie (MONC), Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux], UNICANCER-UNICANCER-Inria Bordeaux - Sud-Ouest, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Université de Bordeaux, Olivier Saut, Baudouin Denis de Senneville, Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS), and Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Université Bordeaux Segalen - Bordeaux 2-Université Sciences et Technologies - Bordeaux 1 (UB)-Université de Bordeaux (UB)-Institut Polytechnique de Bordeaux (Bordeaux INP)-Centre National de la Recherche Scientifique (CNRS)-Institut Bergonié [Bordeaux]
Subjects: Radiomics, Apprentissage statistique, Oncologie, Traitement d'image, [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation, Analyse de textures, Oncology, Texture analysis, Image processing, [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV], Machine learning, Radiomique, MRI, IRM
Abstract: Tumor shape and texture evolution may highlight internal modifications resulting from the progression of cancer. In this work, we want to study the contribution of delta-radiomics features to cancer-evolution prediction. Our goal is to provide a complete pipeline from the 3D reconstruction of the volume of interest to the prediction of its evolution, using routinely acquired data only.To this end, we first analyse a subset of MRI(-extracted) radiomics biomarquers in order to determine conditions that ensure their robustness. Then, we determine the prerequisites of features reliability and explore the impact of both reconstruction and image processing (rescaling, grey-level normalization). A first clinical study emphasizes some statistically-relevant MRI radiomics features associated with event-free survival in anal carcinoma.We then develop machine-learning models to improve our results.Radiomics and machine learning approaches were then combined in a study on high grade soft tissu sarcoma (STS). Combining Radiomics and machine-learning approaches in a study on high-grade soft tissue sarcoma, we find out that a T2-MRI delta-radiomic signature with only three features is enough to construct a classifier able to predict the STS histological response to neoadjuvant chemotherapy. Our ML pipeline is then trained and tested on a middle-size clinical dataset in order to predict early metastatic relapse of patients with breast cancer. This classification model is then compared to the relapsing time predicted by the mechanistic model.Finally we discuss the contribution of deep-learning techniques to extend our pipeline with tumor automatic segmentation or edema detection.; L'évolution de la texture ou de la forme d'une tumeur à l'imagerie médicale reflète les modifications internes dues à la progression (naturelle ou sous traitement) d'une lésion tumorale. Dans ces travaux nous avons souhaité étudier l'apport des caractéristiques delta-radiomiques pour prédire l'évolution de la maladie. Nous cherchons à fournir un pipeline complet de la reconstruction des lésions à la prédiction, en utilisant seulement les données obtenues en routine clinique.Tout d'abord, nous avons étudié un sous ensemble de marqueurs radiomiques calculés sur IRM, en cherchant à établir quelles conditions sont nécessaires pour assurer leur robustesse. Des jeux de données artificiels et cliniques nous permettent d'évaluer l'impact de la reconstruction 3D des zones d'intérêt et celui du traitement de l'image.Une première analyse d'un cas clinique met en évidence des descripteurs de texture statistiquement associés à la survie sans évènement de patients atteints d'un carcinome du canal anal dès le diagnostic.Dans un second temps, nous avons développé des modèles d'apprentissage statistique. Une seconde étude clinique révèle qu'une signature radiomique IRM en T2 à trois paramètres apprise par un modèle de forêts aléatoires donne des résultats prometteurs pour prédire la réponse histologique des sarcomes des tissus mous à la chimiothérapie néoadjuvante.Le pipeline d'apprentissage est ensuite testé sur un jeu de données de taille moyenne sans images, dans le but cette fois de prédire la rechute métastatique à court terme de patientes atteinte d'un cancer du sein. La classification des patientes est ensuite comparée à la prédiction du temps de rechute fournie par un modèle mécanistique de l'évolution des lésions.Enfin nous discutons de l'apport des techniques plus avancées de l'apprentissage statistique pour étendre l'automatisation de notre chaîne de traitement (segmentation automatique des tumeurs, analyse quantitative de l'oedème péri-tumoral).
Published: 2019

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