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489 results on '"Visvikis, A."'

1. Development and prospective validation of a spatial dose pattern based model predicting acute pulmonary toxicity in patients treated with volumetric arc-therapy for locally advanced lung cancer

Author: V. Bourbonne, Martin Rehn, Dimitris Visvikis, Ulrike Schick, Vincent Jaouen, Julien Bert, François Lucia, and Olivier Pradier
Subjects: medicine.medical_specialty, Lung Neoplasms, Population, computer.software_genre, Voxel, Carcinoma, Non-Small-Cell Lung, Humans, Medicine, Dosimetry, Radiology, Nuclear Medicine and imaging, Prospective cohort study, education, Lung cancer, Proton therapy, Retrospective Studies, education.field_of_study, business.industry, Radiotherapy Planning, Computer-Assisted, Standard treatment, Radiotherapy Dosage, Retrospective cohort study, Hematology, medicine.disease, Oncology, Quality of Life, Radiotherapy, Intensity-Modulated, Radiology, business, computer
Abstract: (Chemo)-radiotherapy is the standard treatment for patients with locally advanced lung cancer (LALC) not accessible to surgery. Despite strict application of dose constraints, acute toxicities such as acute pulmonary toxicity (APT) remain frequent, and may impact treatment's compliance and patients' quality of life. Previously, on a population treated with intensity-modulated photon therapy or passive scattering proton therapy, spatial dose patterns associated with APT were identified in the lower lungs, especially in the posterior right lung. In the present study, we aim to define these spatial dose patterns on a retrospective cohort treated by volumetric-arctherapy (VMAT) and to validate our findings prospectively.For the training cohort, we retrospectively included all patients treated in our institution by VMAT for a LALC between 2015 and 2018. APT was scored according to the CTCAE v4.0 scale. All dose maps were registered to a thorax phantom using a segmentation-based elastic registration. Voxel-based analysis of local dose differences was performed with a non-parametric permutation test accounting for n = 10.000 permutations, producing a 3-dimensional significance maps on which clusters of voxels that exhibited significant dose differences (p 0.05) between the two toxicity groups (APT ≥ grade 2 vs APT grade 2) were identified. A prediction model (Pmap-Model) was then built using a neural network approach and then applied to an observational prospective cohort for validation. The model was evaluated using the Area under the curve (AUC) and the balanced accuracy (Bacc: mean of the sensitivity and specificity).165 and 42 patients were included in the training and validation cohorts, with respective APT rates of 22.4% and 19.1%. In the training cohort, a cluster of voxels (Pmap-region) was identified in the posterior right lung. In the training cohort, the Pmap-Model combining 11 features among which the mean dose to the Pmap-region resulted in an AUC of 0.99 and a Bacc of 99.2 using an 8% probability threshold. Using the same voxel cluster on the validation cohort, the Pmap-model resulted in an AUC of 0.81 and a Bacc of 82.0.Our APT-prediction model was successfully validated in a prospective cohort treated by VMAT. Regional radiosensitivity should be considered in usual lung dose constraints, opening the possibility of easily implementable adaptive dosimetry planning.
Published: 2021

2. Technical note: Surface imaging for real‐time patient positioning in external radiation therapy

Author: Dimitris Visvikis, Souha Nazir, Julien Bert, and Hadi Fayad
Subjects: Surface (mathematics), Computer science, business.industry, Radiotherapy Planning, Computer-Assisted, Radiography, medicine.medical_treatment, Patient positioning, Iterative closest point, General Medicine, Cone-Beam Computed Tomography, Radiotherapy Setup Errors, Patient Positioning, Standard deviation, Radiation therapy, Position (vector), Measured depth, medicine, Humans, business, Radiotherapy, Image-Guided, Biomedical engineering
Abstract: PURPOSE In the last few years, there has been a growing interest in surface imaging for patient positioning in external radiation therapy. The aim of this study is to evaluate the accuracy of daily patient positioning using the Azure Kinect surface imaging. METHODS A total of 50 fractions in 10 patients including lung, pelvic, and head and neck tumors were analyzed in real time. A rigid registration algorithm, based on the iterative closest point (ICP) approach, is employed to estimate the patient position in 6 degrees of freedom (DOF). This position is compared to the reference values obtained by the radiograph imaging. The mean setup error and its standard deviation were calculated for all measured fractions. RESULTS The positioning error showed 1.1 ± 1.1 mm in lateral, 1.8 ± 2.1 mm in longitudinal, and 0.8 ± 1.1 mm in vertical, and 0.3°± 0.4° in yaw, 0.2°± 0.2° in pitch, and 0.2°± 0.2° in roll directions. The larger setup error occurred in pelvic regions. CONCLUSION We have evaluated in a radiotherapy set-up considering different patient anatomical locations, a depth measurement based surface imaging solution for patient positioning considering the 6 DOF couch motion. We showed that the proposed solution allows an accurate patient positioning without the need for patient markings or the use of additional radiation dose.
Published: 2021

3. A deep‐learning‐based prediction model for the biodistribution of 90 Y microspheres in liver radioembolization

Author: Nikolaos D. Papathanasiou, John D. Hazle, George C. Kagadis, Ilias Gatos, Panagiotis Papadimitroulas, Dimitris J. Apostolopoulos, Dimitris Visvikis, Trifon Spyridonidis, Dimitris Plachouris, Kerasia Maria Plachouri, and Ioannis Tzolas
Subjects: Biodistribution, business.industry, General Medicine, computer.software_genre, medicine.disease, Microsphere, Voxel, Absorbed dose, Hepatocellular carcinoma, medicine, Distribution (pharmacology), Nuclear medicine, business, Liver cancer, Radiation treatment planning, computer
Abstract: Background Radioembolization with 90 Y microspheres is a treatment approach for liver cancer. Currently employed dosimetric calculations exhibit low accuracy, lacking consideration of individual patient and tissue characteristics. Purpose The purpose of the present study was to employ DL algorithms to differentiate patterns of pretreatment distribution of 99m Tc-macroaggregated albumin on SPECT/CT and posttreatment distribution of 90 Y microspheres on PET/CT and to accurately predict how the 90 Y-microspheres will be distributed in the liver tissue by radioembolization therapy. Methods Data for 19 patients with liver cancer (10 with hepatocellular carcinoma, 5 with intrahepatic cholangiocarcinoma, 4 with liver metastases) who underwent radioembolization with 90 Y microspheres were used for DL training. We developed a 3D voxel-based variation of the Pix2Pix model, which is a special type of conditional GANs designed to perform image-to-image translation. SPECT and CT scans along with the clinical target volume for each patient were used as inputs, as were their corresponding posttreatment PET scans. The real and predicted absorbed PET doses for the tumor and the whole liver area were compared. Our model was evaluated using the leave-one-out method, and the dose calculations were measured using a tissue-specific dose voxel kernel. Results The comparison of the real and predicted PET/CT scans showed an average absorbed dose difference of 5.42% ± 19.31% and 0.44% ± 1.64% for the tumor and the liver area, respectively. The average absorbed dose differences were 7.98 ± 31.39 Gy and 0.03 ± 0.25 Gy for the tumor and the non-tumor liver parenchyma, respectively. Our model had a general tendency to underpredict the dosimetric results; the largest differences were noticed in one case, where the model underestimated the dose to the tumor area by 56.75% or 72.82 Gy. Conclusions The proposed deep-learning-based pretreatment planning method for liver radioembolization accurately predicted 90 Y microsphere biodistribution. Its combination with a rapid and accurate 3D dosimetry method will render it clinically suitable and could improve patient-specific pretreatment planning. This article is protected by copyright. All rights reserved.
Published: 2021

4. Predicting response to radiotherapy of head and neck squamous cell carcinoma using radiomics from cone-beam CT images

Author: D. Visvikis, François Lucia, V. Bourbonne, Mathieu Hatt, D. Bouzid, G. Goasduff, Florent Tixier, Selima Sellami, Ulrike Schick, and Olivier Pradier
Subjects: Receiver operating characteristic, Squamous Cell Carcinoma of Head and Neck, business.industry, Radiotherapy Planning, Computer-Assisted, medicine.medical_treatment, Head and neck cancer, Hematology, General Medicine, Cone-Beam Computed Tomography, medicine.disease, Head and neck squamous-cell carcinoma, Confidence interval, Standard deviation, Radiation therapy, ROC Curve, Oncology, Head and Neck Neoplasms, Feature (computer vision), Cohort, Humans, Medicine, Radiology, Nuclear Medicine and imaging, business, Nuclear medicine, Retrospective Studies
Abstract: INTRODUCTION Radiotherapy (RT) for head and neck cancer is now guided by cone-beam computed tomography (CBCT). We aim to identify a CBCT radiomic signature predictive of progression to RT. MATERIAL AND METHODS A cohort of 93 patients was split into training (n = 60) and testing (n = 33) sets. A total of 88 features were extracted from the gross tumor volume (GTV) on each CBCT. Receiver operating characteristic (ROC) curves were used to determine the power of each feature at each week of treatment to predict progression to radio(chemo)therapy. Only features with AUC > 0.65 at each week were pre-selected. Absolute differences were calculated between features from each weekly CBCT and baseline CBCT1 images. The smallest detectable change (C = 1.96 × SD, SD being the standard deviation of differences between feature values calculated on CBCT1 and CBCTn) with its confidence interval (95% confidence interval [CI]) was determined for each feature. The features for which the change was larger than C for at least 5% of patients were then selected. A radiomics-based model was built at the time-point that showed the highest AUC and compared with models relying on clinical variables. RESULTS Seven features had an AUC > 0.65 at each week, and six exhibited a change larger than the predefined CI 95%. After exclusion of inter-correlated features, only one parameter remains, Coarseness. Among clinical variable, only hemoglobin value was significant. AUC for predicting the treatment response were 0.78 (p = .006), 0.85 (p
Published: 2021

5. A machine-learning approach based on 409 treatments to predict optimal number of iodine-125 seeds in low-dose-rate prostate brachytherapy

Author: Luc Ollivier, Ulrike Schick, G. Goasduff, Nicolas Boussion, Dimitris Visvikis, Antoine Valeri, Gurvan Dissaux, and Olivier Pradier
Subjects: Original Paper, Mean squared error, business.industry, medicine.medical_treatment, Brachytherapy, machine-learning, Machine learning, computer.software_genre, prostate cancer, Low-Dose Rate Brachytherapy, low-dose-rate brachytherapy, Support vector machine, Oncology, Abacus (architecture), Approximation error, radioactive seeds, Test set, Medicine, Radiology, Nuclear Medicine and imaging, Artificial intelligence, business, computer, Prostate brachytherapy
Abstract: Purpose Low-dose-rate brachytherapy is a key treatment for low-risk or favorable intermediate-risk prostate cancer. The number of radioactive seeds inserted during the procedure depends on prostate volume, and is not easy to predict without pre-planning. Consequently, a large number of unused seeds may be left after treatment. The objective of the present study was to predict the exact number of seeds for future patients using machine learning and a database of 409 treatments. Material and methods Database consisted of 18 dosimetric and efficiency parameters for each of 409 cases. Nine predictive algorithms based on machine-learning were compared in this database, which was divided into training group (80%) and test group (20%). Ten-fold cross-validation was applied to obtain robust statistics. The best algorithm was then used to build an abacus able to predict number of implanted seeds from expected prostate volume only. As an evaluation, the abacus was also applied on an independent series of 38 consecutive patients. Results The best coefficients of determination R2 were given by support vector regression, with values attaining 0.928, 0.948, and 0.968 for training set, test set, and whole set, respectively. In terms of predicted seeds in test group, mean square error, median absolute error, mean absolute error, and maximum error were 2.55, 0.92, 1.21, and 7.29, respectively. The use of obtained abacus in 38 additional patients resulted in saving of 493 seeds (393 vs. 886 remaining seeds). Conclusions Machine-learning-based abacus proposed in this study aims at estimating the necessary number of seeds for future patients according to past experience. This new abacus, based on 409 treatments and successfully tested in 38 new patients, is a good alternative to non-specific recommendations.
Published: 2021

6. The added value of PSMA PET/MR radiomics for prostate cancer staging

Author: Stephan G. Nekolla, Mona Mustafa, Wang Hui, Thomas Amiel, Kristina Schwamborn, Alberto Villagran Asiares, Sylvia Schachoff, Matthias Eiber, Borjana Bogdanovic, Isabel Rauscher, Tobias Maurer, Esteban Lucas Solari, Mathieu Hatt, Wolfgang A. Weber, Dimitris Visvikis, Andrei Gafita, and Nassir Navab
Subjects: Male, 0301 basic medicine, medicine.medical_treatment, Naturwissenschaften, 03 medical and health sciences, Prostate cancer, 0302 clinical medicine, Original Article, Advanced Image Analyses (Radiomics and Artificial Intelligence), PET/MRI, PSMA, Radiomics, Gleason score, medicine, Informatik, Wissen, Systeme, Humans, Effective diffusion coefficient, Radiology, Nuclear Medicine and imaging, Gleason scores, ddc:610, Multiparametric Magnetic Resonance Imaging, Retrospective Studies, Prostatectomy, business.industry, Prostatic Neoplasms, General Medicine, Patient data, medicine.disease, ddc, 030104 developmental biology, 030220 oncology & carcinogenesis, Psma pet, ddc:000, ddc:500, Neoplasm Grading, Prostate cancer staging, business, Nuclear medicine
Abstract: Purpose To evaluate the performance of combined PET and multiparametric MRI (mpMRI) radiomics for the group-wise prediction of postsurgical Gleason scores (psGSs) in primary prostate cancer (PCa) patients. Methods Patients with PCa, who underwent [68 Ga]Ga-PSMA-11 PET/MRI followed by radical prostatectomy, were included in this retrospective analysis (n = 101). Patients were grouped by psGS in three categories: ISUP grades 1–3, ISUP grade 4, and ISUP grade 5. mpMRI images included T1-weighted, T2-weighted, and apparent diffusion coefficient (ADC) map. Whole-prostate segmentations were performed on each modality, and image biomarker standardization initiative (IBSI)-compliant radiomic features were extracted. Nine support vector machine (SVM) models were trained: four single-modality radiomic models (PET, T1w, T2w, ADC); three PET + MRI double-modality models (PET + T1w, PET + T2w, PET + ADC), and two baseline models (one with patient data, one image-based) for comparison. A sixfold stratified cross-validation was performed, and balanced accuracies (bAcc) of the predictions of the best-performing models were reported and compared through Student’s t-tests. The predictions of the best-performing model were compared against biopsy GS (bGS). Results All radiomic models outperformed the baseline models. The best-performing (mean ± stdv [%]) single-modality model was the ADC model (76 ± 6%), although not significantly better (p > 0.05) than other single-modality models (T1w: 72 ± 3%, T2w: 73 ± 2%; PET: 75 ± 5%). The overall best-performing model combined PET + ADC radiomics (82 ± 5%). It significantly outperformed most other double-modality (PET + T1w: 74 ± 5%, p = 0.026; PET + T2w: 71 ± 4%, p = 0.003) and single-modality models (PET: p = 0.042; T1w: p = 0.002; T2w: p = 0.003), except the ADC-only model (p = 0.138). In this initial cohort, the PET + ADC model outperformed bGS overall (82.5% vs 72.4%) in the prediction of psGS. Conclusion All single- and double-modality models outperformed the baseline models, showing their potential in the prediction of GS, even with an unbalanced cohort. The best-performing model included PET + ADC radiomics, suggesting a complementary value of PSMA-PET and ADC radiomics.
Published: 2021

7. Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability

Author: Tove Fall, Anubha Mahajan, Dmitry Shungin, B. Balkau, Gerjan Navis, A. Metspalu, Anneli Pouta, Andrew A. Hicks, Ilja M. Nolte, Ian Ford, Aroon D. Hingorani, Stefan R Bornstein, Anuj Goel, Rona J. Strawbridge, Niek Verweij, Sarah H. Wild, Patricia B. Munroe, T.B. Harris, Jaana Lindström, Johnson Pcd., Nita G. Forouhi, Pierre Meneton, Patricia A. Peyser, Sarin A-P., Andrea Ganna, Timothy M. Frayling, Patrik K. E. Magnusson, Joanne M. Meyer, Yongmei Liu, Jeanette M. Stafford, Christian Herder, L Zudina, Maria G. Stathopoulou, May E. Montasser, Nicholas D. Hastie, Inês Barroso, Schwarz Peh., James B. Meigs, Perttu Salo, George Davey Smith, Gonneke Willemsen, Christopher J. Groves, Erik P A Van Iperen, M. A. Province, Veikko Salomaa, Naveed Sattar, Serena Sanna, Maria Dimitriou, Joop Jukema, Ulrika Krus, Albert V. Smith, Markku Laakso, James F. Wilson, George Nicholson, Loic Yengo, Tatijana Zemunik, Per Eriksson, Harold Snieder, Peter P. Pramstaller, Claudia Langenberg, R. Rauramaa, Alan R. Shuldiner, Pau Navarro, Veronique Vitart, Ross M. Fraser, Aaron Isaacs, C. Lecoeur, Jesper R. Gådin, Jackie F. Price, Letizia Marullo, L.F. Bielak, Sirkka Keinänen-Kiukaanniemi, Amélie Bonnefond, Michael Stumvoll, Alessia Faggian, Anke Tönjes, Tomohiro Tanaka, Wieland Kiess, Harry Campbell, Josée Dupuis, David Altshuler, João Fadista, Winfried März, G K Hovingh, Thomas Illig, Toby Johnson, H Grallert, Kari Stefansson, Reedik Mägi, Palmer Cna., de Geus Ejcn., Martina Müller-Nurasyid, Karen Kapur, Philippe Froguel, Dorret I. Boomsma, Anders Franco-Cereceda, Marcus E. Kleber, Boehnke M, Olga D. Carlson, Ozren Polasek, Andrew P. Morris, Alex S. F. Doney, Najaf Amin, Sara M. Willems, Vilmundur Gudnason, Jose C. Florez, Jeffery R. O'Connell, Nancy L. Pedersen, T. Saaristo, Wolffenbuttel Bhr., M. I. J. Uusitupa, Longda Jiang, Iva Miljkovic, James S. Pankow, Caroline Hayward, Hugh Watkins, Vasiliki Lagou, Johanna Kuusisto, Jaakko Tuomilehto, Alan F. Wright, Josephine M. Egan, Perry Jrb., C M van Duijn, Valeriya Lyssenko, Leif Groop, Stefania Bandinelli, Nigel W. Rayner, Tõnu Esko, Stela McLachlan, Momoko Horikoshi, Eric Boerwinkle, Rick Jansen, Richard N. Bergman, Gudmar Thorleifsson, Lyle J. Palmer, Vilmantas Giedraitis, Peter Kovacs, Nicholas J. Wareham, Luigi Ferrucci, N J Timpson, D Rybin, Anne U. Jackson, Tiinamaija Tuomi, Gonçalo R. Abecasis, Harst Pvd., Meena Kumari, Albert Hofman, Chiara Scapoli, Evelin Mihailov, Josine L. Min, Anders Hamsten, Hottenga J-J., Loos Rjf., Lars Lind, Ulf de Faire, Jaakko Kaprio, Guo Li, Beate St Pourcain, C Gieger, Amanda J. Bennett, Anna Ulrich, Nabila Bouatia-Naji, Satu Männistö, Antigone S. Dimas, Jarvelin M-R., Günther Silbernagel, F Karpe, A. Körner, David S. Siscovick, M Blüher, Rebecca J. Webster, Erik Ingelsson, Susan Campbell, Mika Kivimäki, Laura J. Rasmussen-Torvik, Heikki A. Koistinen, Sophie Visvikis-Siest, Bernhard O. Boehm, Inga Prokopenko, Ping An, Emmanouil T. Dermitzakis, Cecilia M. Lindgren, Kardia Slr., Richa Saxena, Igor Rudan, Richard M. Watanabe, Jian'an Luan, Marika Kaakinen, Shin S-Y., George Dedoussis, Panagiotis Deloukas, Mark I. McCarthy, Barbara Thorand, B.W.J.H. Penninx, Peter Vollenweider, Paul W. Franks, Leena Kinnunen, Markus Perola, Yvonne Boettcher, Timo A. Lakka, Nicole Soranzo, Stavroula Kanoni, Bakker Sjl., Winkelmann Br, The Wellcome Trust Centre for Human Genetics [Oxford], University of Oxford [Oxford], Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Institute of Genomics [Tartu, Estonia], University of Tartu, Vrije Universiteit Amsterdam [Amsterdam] (VU), Vrije Universiteit Medical Centre (VUMC), Helmholtz-Zentrum München (HZM), University of Cambridge [UK] (CAM), Metabolic functional (epi)genomics and molecular mechanisms involved in type 2 diabetes and related diseases - UMR 8199 - UMR 1283 (GI3M), Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre National de la Recherche Scientifique (CNRS), Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Paris (UP), Università degli Studi di Ferrara (UniFE), Boston University [Boston] (BU), VU University Medical Center [Amsterdam], University of Bristol [Bristol], Biomedical Sciences Research Centre Alexander Fleming [Vari, Greece] (BSRC), Imperial College London, Karolinska Institutet [Stockholm], Institute for Molecular Bioscience, University of Queensland [Brisbane], Statens Serum Institut [Copenhagen], Interactions Gène-Environnement en Physiopathologie Cardio-Vasculaire (IGE-PCV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Erasmus University Medical Center [Rotterdam] (Erasmus MC), Cardiovascular Research Institute Maastricht (CARIM), Maastricht University [Maastricht], MRC Institute of Genetics and Molecular Medicine [Edinburgh] (IGMM), University of Edinburgh-Medical Research Council, National Institute on Aging [Baltimore, MD, USA] (NIH), University of Michigan [Ann Arbor], University of Michigan System, University of Maryland School of Medicine, University of Maryland System, The University of Western Australia (UWA), Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Massachusetts General Hospital [Boston], Harvard Medical School [Boston] (HMS), Wake Forest University, National Institute for Health and Welfare [Helsinki], The Wellcome Trust Sanger Institute [Cambridge], University of Iceland [Reykjavik], University of Washington [Seattle], University of Groningen [Groningen], University of Glasgow, William Harvey Research Institute, Barts and the London Medical School, Université de Lausanne (UNIL), deCODE genetics [Reykjavik], Northwestern University Feinberg School of Medicine, Uppsala Universitet [Uppsala], University of Edinburgh, Medical Faculty [Mannheim], University of Graz, Ludwig-Maximilians-Universität München (LMU), Johannes Gutenberg - Universität Mainz (JGU), Institute for Molecular Medicine Finland [Helsinki] (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki-University of Helsinki, Umeå University, Skane University Hospital [Malmo], Lund University [Lund], RIKEN Center for Integrative Medical Sciences [Yokohama] (RIKEN IMS), RIKEN - Institute of Physical and Chemical Research [Japon] (RIKEN), Washington University School of Medicine in St. Louis, Washington University in Saint Louis (WUSTL), Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche [Roma] (CNR), University Medical Center Groningen [Groningen] (UMCG), Universität Leipzig [Leipzig], Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), University of Split, Academic Medical Center - Academisch Medisch Centrum [Amsterdam] (AMC), University of Amsterdam [Amsterdam] (UvA), University of Eastern Finland, Harokopio University of Athens, European Academy Bozen/Bolzano (EURAC), University of Kuopio, German Center for Diabetes Research - Deutsches Zentrum für Diabetesforschung [Neuherberg] (DZD), University of Pittsburgh (PITT), Pennsylvania Commonwealth System of Higher Education (PCSHE), Ninewells Hospital and Medical School [Dundee], University College of London [London] (UCL), University of Essex, Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], University of Helsinki, Leiden University Medical Center (LUMC), Institute of Cardiovascular and Medical Sciences [Glasgow], University of Oulu, The University of Texas Health Science Center at Houston (UTHealth), University of Minnesota Medical School, University of Minnesota System, Keck School of Medicine [Los Angeles], University of Southern California (USC), Amsterdam UMC, University Hospital Carl Gustav Carus [Dresden, Germany], Technische Universität Dresden = Dresden University of Technology (TU Dresden), Lee Kong Chian School of Medicine, Nanyang Technological University (NTU), Universität Heidelberg [Heidelberg], King Abdulaziz University, Pirkanmaa Hospital District, Tampere University Hospital, Cedars-Sinai Medical Center, Danube University Krems, Harvard School of Public Health, 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, Institute of Epidemiology [Neuherberg] (EPI), German Research Center for Environmental Health - Helmholtz Center München (GmbH), Hannover Medical School [Hannover] (MHH), Universität zu Lübeck [Lübeck], University of Exeter, Regeneron Pharmaceuticals [Tarrytown], University of Adelaide, Centre de Recherche des Cordeliers (CRC (UMR_S 872)), Université Pierre et Marie Curie - Paris 6 (UPMC)-Université Paris Descartes - Paris 5 (UPD5)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), National Institute on Aging [Bethesda, USA] (NIA), National Institutes of Health [Bethesda] (NIH), Icahn School of Medicine at Mount Sinai [New York] (MSSM), Harvard T.H. Chan School of Public Health, John Radcliffe Hospital [Oxford University Hospital], Centre Hospitalier Universitaire Vaudois [Lausanne] (CHUV), Université de Genève (UNIGE), Big Data Institute, University of Surrey (UNIS), University of Bergen (UiB), Stanford School of Medicine [Stanford], Stanford Medicine, Stanford University-Stanford University, Stanford Cardiovascular Institute, University of Liverpool, University of Manchester [Manchester], Institute of Biochemistry and Genetics of Ufa Scientific Centre, Russian Academy of Sciences [Moscow] (RAS), Experimental Vascular Medicine, Vascular Medicine, ACS - Atherosclerosis & ischemic syndromes, SIEST, Sofia, PreciDIAB Institute, the holistic approach of personal diabets care - - PreciDIAB2018 - ANR-18-IBHU-0001 - IBHU - VALID, Understanding the dynamic determinants of glucose homeostasis and social capability to promote Healthy and active aging - DYNAHEALTH - - H20202015-04-01 - 2019-03-31 - 633595 - VALID, Beyond the Genetics of Addiction - ADDICTION - - EC:FP7:ERC2011-12-01 - 2017-05-31 - 284167 - VALID, Rise of scientific excellence and collaboration for implementing personalised medicine in Estonia - ePerMed - - H20202016-01-01 - 2018-12-31 - 692145 - VALID, University of Oxford, Helmholtz Zentrum München = German Research Center for Environmental Health, Metabolic functional (epi)genomics and molecular mechanisms involved in type 2 diabetes and related diseases - UMR 8199 - UMR 1283 (EGENODIA (GI3M)), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), Università degli Studi di Ferrara = University of Ferrara (UniFE), Université de Lausanne = University of Lausanne (UNIL), Karl-Franzens-Universität Graz, Johannes Gutenberg - Universität Mainz = Johannes Gutenberg University (JGU), Helsingin yliopisto = Helsingfors universitet = University of Helsinki-Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Helsingin yliopisto = Helsingfors universitet = University of Helsinki, Amsterdam UMC - Amsterdam University Medical Center, Nanyang Technological University [Singapour], Universität zu Lübeck = University of Lübeck [Lübeck], Laboratoire d'Informatique Médicale et Ingénierie des Connaissances en e-Santé (LIMICS), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Université Sorbonne Paris Nord, Université de Genève = University of Geneva (UNIGE), ANR-18-IBHU-0001,PreciDIAB,PreciDIAB Institute, the holistic approach of personal diabets care(2018), European Project: 633595,H2020,H2020-PHC-2014-two-stage,DYNAHEALTH(2015), European Project: 284167,EC:FP7:ERC,ERC-2011-StG_20101124,ADDICTION(2011), European Project: 692145,H2020,H2020-TWINN-2015,ePerMed(2016), Biological Psychology, APH - Health Behaviors & Chronic Diseases, APH - Personalized Medicine, Functional Genomics, APH - Mental Health, Sociology and Social Gerontology, APH - Methodology, Hottenga, Jouke- Jan [0000-0002-5668-2368], Bouatia-Naji, Nabila [0000-0001-5424-2134], Jansen, Rick [0000-0002-3333-6737], Min, Josine L. [0000-0003-4456-9824], Faggian, Alessia [0000-0002-3799-9722], Bonnefond, Amélie [0000-0001-9976-3005], Isaacs, Aaron [0000-0001-5037-4834], Willems, Sara M. [0000-0002-6803-3007], Navarro, Pau [0000-0001-5576-8584], Jackson, Anne U. [0000-0002-9672-2547], Bielak, Lawrence F. [0000-0002-3443-8030], Saxena, Richa [0000-0003-2233-1065], Smith, Albert V. [0000-0003-1942-5845], Verweij, Niek [0000-0002-4303-7685], Goel, Anuj [0000-0003-2307-4021], Johnson, Paul C. D. [0000-0001-6663-7520], Strawbridge, Rona J. [0000-0001-8506-3585], Fall, Tove [0000-0003-2071-5866], Fraser, Ross M. [0000-0003-0488-2592], Kanoni, Stavroula [0000-0002-1691-9615], Giedraitis, Vilmantas [0000-0003-3423-2021], Kleber, Marcus E. [0000-0003-0663-7275], Müller-Nurasyid, Martina [0000-0003-3793-5910], Luan, Jian’an [0000-0003-3137-6337], Sanna, Serena [0000-0002-3768-1749], Nolte, Ilja M. [0000-0001-5047-4077], Zemunik, Tatijana [0000-0001-8120-2891], Kovacs, Peter [0000-0002-0290-5423], Wild, Sarah H. [0000-0001-7824-2569], McLachlan, Stela [0000-0003-0480-6143], Egan, Josephine [0000-0002-8945-0053], Hicks, Andrew A. [0000-0001-6320-0411], Thorand, Barbara [0000-0002-8416-6440], Hingorani, Aroon [0000-0001-8365-0081], Kivimaki, Mika [0000-0002-4699-5627], Koistinen, Heikki A. [0000-0001-7870-070X], Bakker, Stephan J. L. [0000-0003-3356-6791], Palmer, Colin N. A. [0000-0002-6415-6560], Jukema, J. Wouter [0000-0002-3246-8359], Sattar, Naveed [0000-0002-1604-2593], Snieder, Harold [0000-0003-1949-2298], Magnusson, Patrik K. [0000-0002-7315-7899], Blüher, Matthias [0000-0003-0208-2065], Wolffenbuttel, Bruce H. R. [0000-0001-9262-6921], Abecasis, Goncalo R. [0000-0003-1509-1825], Meigs, James B. [0000-0002-2439-2657], Wilson, James F. [0000-0001-5751-9178], Schwarz, Peter E. H. [0000-0001-6317-7880], Boehm, Bernhard O. [0000-0002-2706-7710], Metspalu, Andres [0000-0002-3718-796X], Deloukas, Panos [0000-0001-9251-070X], Körner, Antje [0000-0001-6001-0356], Wareham, Nicholas J. [0000-0003-1422-2993], Langenberg, Claudia [0000-0002-5017-7344], Männistö, Satu [0000-0002-8668-3046], Franks, Paul W. [0000-0002-0520-7604], Hayward, Caroline [0000-0002-9405-9550], Vitart, Veronique [0000-0002-4991-3797], Kaprio, Jaakko [0000-0002-3716-2455], Visvikis-Siest, Sophie [0000-0001-8104-8425], Altshuler, David [0000-0002-7250-4107], Rudan, Igor [0000-0001-6993-6884], van Duijn, Cornelia M. [0000-0002-2374-9204], Pramstaller, Peter P. [0000-0002-9831-8302], Boehnke, Michael [0000-0002-6442-7754], Frayling, Timothy M. [0000-0001-8362-2603], Peyser, Patricia A. [0000-0002-9717-8459], Harst, Pim van der [0000-0002-2713-686X], Smith, George Davey [0000-0002-1407-8314], Forouhi, Nita G. [0000-0002-5041-248X], Loos, Ruth J. F. [0000-0002-8532-5087], Salomaa, Veikko [0000-0001-7563-5324], Soranzo, Nicole [0000-0003-1095-3852], Boomsma, Dorret I. [0000-0002-7099-7972], Groop, Leif [0000-0002-0187-3263], Tuomi, Tiinamaija [0000-0002-8306-6202], Munroe, Patricia B. [0000-0002-4176-2947], Gudnason, Vilmundur [0000-0001-5696-0084], Lecoeur, Cecile [0000-0003-0075-6417], Jarvelin, Marjo-Riitta [0000-0002-2149-0630], Stefansson, Kari [0000-0003-1676-864X], Dermitzakis, Emmanouil T. [0000-0002-9302-6490], Lindgren, Cecilia M. [0000-0002-4903-9374], Froguel, Philippe [0000-0003-2972-0784], Kaakinen, Marika A. [0000-0002-9228-0462], Watanabe, Richard M. [0000-0003-1015-0531], Ingelsson, Erik [0000-0003-2256-6972], Dupuis, Josée [0000-0003-2871-3603], Barroso, Inês [0000-0001-5800-4520], Apollo - University of Cambridge Repository, Epidemiology, University Hospital Mannheim | Universitätsmedizin Mannheim, National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR), Universität Leipzig, Universiteit Leiden, Universität Heidelberg [Heidelberg] = Heidelberg University, Karl-Franzens-Universität [Graz, Autriche], Psychiatry, Amsterdam Neuroscience - Complex Trait Genetics, Amsterdam Neuroscience - Mood, Anxiety, Psychosis, Stress & Sleep, General practice, APH - Digital Health, Groningen Institute for Gastro Intestinal Genetics and Immunology (3GI), Life Course Epidemiology (LCE), Lifestyle Medicine (LM), Groningen Institute for Organ Transplantation (GIOT), Groningen Kidney Center (GKC), Center for Liver, Digestive and Metabolic Diseases (CLDM), Value, Affordability and Sustainability (VALUE), Cardiovascular Centre (CVC), Hottenga, Jouke-Jan [0000-0002-5668-2368], Min, Josine L [0000-0003-4456-9824], Willems, Sara M [0000-0002-6803-3007], Jackson, Anne U [0000-0002-9672-2547], Bielak, Lawrence F [0000-0002-3443-8030], Smith, Albert V [0000-0003-1942-5845], Johnson, Paul CD [0000-0001-6663-7520], Strawbridge, Rona J [0000-0001-8506-3585], Fraser, Ross M [0000-0003-0488-2592], Kleber, Marcus E [0000-0003-0663-7275], Luan, Jian'an [0000-0003-3137-6337], Nolte, Ilja M [0000-0001-5047-4077], Wild, Sarah H [0000-0001-7824-2569], Hicks, Andrew A [0000-0001-6320-0411], Koistinen, Heikki A [0000-0001-7870-070X], Bakker, Stephan JL [0000-0003-3356-6791], Palmer, Colin NA [0000-0002-6415-6560], Jukema, J Wouter [0000-0002-3246-8359], Magnusson, Patrik K [0000-0002-7315-7899], Wolffenbuttel, Bruce HR [0000-0001-9262-6921], Abecasis, Goncalo R [0000-0003-1509-1825], Meigs, James B [0000-0002-2439-2657], Wilson, James F [0000-0001-5751-9178], Schwarz, Peter EH [0000-0001-6317-7880], Boehm, Bernhard O [0000-0002-2706-7710], Wareham, Nicholas J [0000-0003-1422-2993], Franks, Paul W [0000-0002-0520-7604], van Duijn, Cornelia M [0000-0002-2374-9204], Pramstaller, Peter P [0000-0002-9831-8302], Frayling, Timothy M [0000-0001-8362-2603], Peyser, Patricia A [0000-0002-9717-8459], Forouhi, Nita G [0000-0002-5041-248X], Loos, Ruth JF [0000-0002-8532-5087], Boomsma, Dorret I [0000-0002-7099-7972], Munroe, Patricia B [0000-0002-4176-2947], Dermitzakis, Emmanouil T [0000-0002-9302-6490], Lindgren, Cecilia M [0000-0002-4903-9374], Kaakinen, Marika A [0000-0002-9228-0462], Watanabe, Richard M [0000-0003-1015-0531], Tampere University, Tays Research Services, Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC), Fysiologie, RS: FHML MaCSBio, and RS: Carim - B01 Blood proteins & engineering
Subjects: Male, Anorexia Nervosa, medicine.medical_treatment, 45/43, Insulin Resistance/genetics, SUSCEPTIBILITY, Quantitative trait, Impaired glucose tolerance, 0302 clinical medicine, Architecture, LS2_1, IMPUTATION, Insulin, ComputingMilieux_MISCELLANEOUS, Fasting, Publisher Correction, 3. Good health, [SDV] Life Sciences [q-bio], Kruppel-Like Transcription Factors/blood, Endokrinologi och diabetes, Science & Technology - Other Topics, Adult, Blood Glucose, European Continental Ancestry Group, Female, Gene Expression, Genetic Loci, Genome-Wide Association Study, Glucose Intolerance, Humans, Insulin Receptor Substrate Proteins, Insulin Resistance, Kruppel-Like Transcription Factors, Middle Aged, Phenotype, Sex Characteristics, Sex Factors, Waist-Hip Ratio, Sex characteristics, medicine.medical_specialty, Science, 631/208/205/2138, Endocrinology and Diabetes, General Biochemistry, Genetics and Molecular Biology, White People, Gender-differences, 03 medical and health sciences, SDG 3 - Good Health and Well-being, 692/53/2421, GLYCEMIC TRAITS, GENOME-WIDE ASSOCIATION, Glycemic, GENDER-DIFFERENCES, Science & Technology, IDENTIFICATION, 692/699/2743/2815, Blood Glucose/metabolism, Diagnostic markers, medicine.disease, Anorexia Nervosa/blood, 030104 developmental biology, Endocrinology, Glucose, Insulin/blood, Anorexia Nervosa/ethnology, Anorexia Nervosa/genetics, Anorexia Nervosa/physiopathology, Fasting/blood, Glucose Intolerance/blood, Glucose Intolerance/ethnology, Glucose Intolerance/genetics, Glucose Intolerance/physiopathology, Insulin Receptor Substrate Proteins/blood, Insulin Receptor Substrate Proteins/genetics, Kruppel-Like Transcription Factors/genetics, 0301 basic medicine, Identification, [SDV]Life Sciences [q-bio], General Physics and Astronomy, Genome-wide association studies, Waist–hip ratio, LS4_5, RISK, ARCHITECTURE, Multidisciplinary, article, Type 2 diabetes, Multidisciplinary Sciences, MENDELIAN RANDOMIZATION, Pre-diabetes, Medical Genetics, Risk, PATHOPHYSIOLOGY, 030209 endocrinology & metabolism, 3121 Internal medicine, 692/163/2743/137/773, NO, Insulin resistance, Internal medicine, Diabetes mellitus, medicine, Mendelian randomization, 631/208/480, Medicinsk genetik, business.industry, General Chemistry, Impaired fasting glucose, business
Abstract: Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes., Sex differences in fasting glucose and insulin have been identified, but the genetic loci underlying these differences have not. Here, the authors perform a meta-analysis of genome-wide association studies to detect sex-specific and sex-dimorphic loci associated with fasting glucose and insulin.
Published: 2021

8. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants

Author: Zhou, B, Carrillo-Larco, Rm, Danaei, G, Riley, Lm, Paciorek, Cj, Stevens, Ga, Gregg, Ew, Bennett, Je, Solomon, B, Singleton, Rk, Sophiea, Mk, Iurilli, Mlc, Lhoste, Vpf, Cowan, Mj, Savin, S, Woodward, M, Balanova, Y, Cifkova, R, Damasceno, A, Elliott, P, Farzadfar, F, He, J, Ikeda, N, Kengne, Ap, Khang, Yh, Kim, Hc, Laxmaiah, A, Lin, Hh, Maira, Pm, Miranda, Jj, Neuhauser, H, Sundstrom, J, Varghese, C, Widyahening, Is, Zdrojewski, T, Ezzati, M, Abarca-Gomez, L, Abdeen, Za, Rahim, Hfa, Abu-Rmeileh, Nm, Acosta-Cazares, B, Adams, Rj, Aekplakorn, W, Afsana, K, Afzal, S, Agdeppa, Ia, Aghazadeh-Attari, J, Aguilar-Salinas, Ca, Agyemang, C, Ahmad, Na, Ahmadi, A, Ahmadi, N, Ahmadizar, F, Ahmed, Sh, Ahrens, W, Ajlouni, K, Al-Raddadi, R, Alarouj, M, Albuhairan, F, Aldhukair, S, Ali, Mm, Alkandari, A, Alkerwi, A, Allin, K, Aly, E, Amarapurkar, Dn, Amougou, N, Amouyel, P, Andersen, Lb, Anderssen, Sa, Anjana, Rm, Ansari-Moghaddam, A, Ansong, D, Aounallah-Skhiri, H, Araujo, J, Ariansen, I, Aris, T, Arku, Re, Arlappa, N, Aryal, Kk, Aspelund, T, Assah, Fk, Assuncao, Mcf, Auvinen, J, Avdicova, M, Azevedo, A, Azimi-Nezhad, M, Azizi, F, Azmin, M, Babu, Bv, Bahijri, S, Balakrishna, N, Bamoshmoosh, M, Banach, M, Banadinovic, M, Bandosz, P, Banegas, Jr, Baran, J, Barbagallo, Cm, Barcelo, A, Barkat, A, Barreto, M, Barros, Ajd, Barros, Mvg, Bartosiewicz, A, Basit, A, Bastos, Jld, Bata, I, Batieha, Am, Batyrbek, A, Baur, La, Beaglehole, R, Belavendra, A, Ben Romdhane, H, Benet, M, Benson, Ls, Berkinbayev, S, Bernabe-Ortiz, A, Bettiol, H, Bezerra, J, Bhagyalaxmi, A, Bhargava, Sk, Bia, D, Biasch, K, Lele, Ecb, Bikbov, Mm, Bista, B, Bjerregaard, P, Bjertness, E, Bjertness, Mb, Bjorkelund, C, Bloch, Kv, Blokstra, A, Bo, S, Bobak, M, Boeing, H, Boggia, Jg, Boissonnet, Cp, Bojesen, Se, Bongard, V, Bonilla-Vargas, A, Bopp, M, Borghs, H, Bovet, P, Boyer, Cb, Braeckman, L, Brajkovich, I, Branca, F, Breckenkamp, J, Brenner, H, Brewster, Lm, Briceno, Y, Brito, M, Bruno, G, Bueno-de-Mesquita, Hb, Bueno, G, Bugge, A, Burns, C, Bursztyn, M, de Leon, Ac, Cacciottolo, J, Cameron, C, Can, G, Candido, Apc, Capanzana, Mv, Capkova, N, Capuano, E, Capuano, V, Cardoso, Vc, Carlsson, Ac, Carvalho, J, Casanueva, Ff, Censi, L, Cervantes-Loaiza, M, Chadjigeorgiou, Ca, Chamukuttan, S, Chan, Aw, Chan, Q, Chaturvedi, Hk, Chaturvedi, N, Chee, Ml, Chen, Cj, Chen, Ff, Chen, Hs, Chen, Sh, Chen, Zm, Cheng, Cy, Cheraghian, B, Dekkaki, Ic, Chetrit, A, Chien, Kl, Chiolero, A, Chiou, St, Chirita-Emandi, A, Chirlaque, Md, Cho, B, Christensen, K, Christofaro, Dg, Chudek, J, Cinteza, E, Claessens, F, Clarke, J, Clays, E, Cohen, E, Concin, H, Cooper, C, Coppinger, Tc, Costanzo, S, Cottel, D, Cowell, C, Craig, Cl, Crampin, Ac, Crujeiras, Ab, Cruz, Jj, Csilla, S, Cui, Lf, Cureau, Fv, Cuschieri, S, D'Arrigo, G, D'Orsi, E, Dallongeville, J, Dankner, R, Dantoft, Tm, Dauchet, L, Davletov, K, De Backer, G, De Bacquer, D, De Curtis, A, de Gaetano, G, De Henauw, S, de Oliveira, Pd, De Ridder, D, De Smedt, D, Deepa, M, Deev, Ad, Degennaro, V, Delisle, H, Demarest, S, Dennison, E, Deschamps, V, Dhimal, M, Di Castelnuovo, Af, Dias-da-Costa, Js, Diaz, A, Dickerson, Tt, Dika, Z, Djalalinia, S, Htp, Do, Dobson, Aj, Donfrancesco, C, Donoso, Sp, Doring, A, Dorobantu, M, Dorr, M, Doua, K, Dragano, N, Drygas, W, Duante, Ca, Duboz, P, Duda, Rb, Dulskiene, V, Dushpanova, A, Dzakula, A, Dzerve, V, Dziankowska-Zaborszczyk, E, Eddie, R, Eftekhar, E, Eggertsen, R, Eghtesad, S, Eiben, G, Ekelund, U, El-Khateeb, M, El Ati, J, Eldemire-Shearer, D, Eliasen, M, Elosua, R, Erasmus, Rt, Erbel, R, Erem, C, Eriksen, L, Eriksson, Jg, Escobedo-de la Pena, J, Eslami, S, Esmaeili, A, Evans, A, Faeh, D, Fakhretdinova, Aa, Fall, Ch, Faramarzi, E, Farjam, M, Fattahi, Mr, Fawwad, A, Felix-Redondo, Fj, Felix, Sb, Ferguson, Ts, Fernandes, Ra, Fernandez-Berges, D, Ferrante, D, Ferrao, T, Ferrari, M, Ferrario, Mm, Ferreccio, C, Ferreira, Hs, Ferrer, E, Ferrieres, J, Figueiro, Th, Fink, G, Fischer, K, Foo, Lh, Forsner, M, Fouad, Hm, Francis, Dk, Grego, Franco, Frikke-Schmidt, R, Frontera, G, Fuchs, Fd, Fuchs, Sc, Fujita, Y, Fumihiko, M, Furdela, V, Furer, A, Furusawa, T, Gaciong, Z, Galbarczyk, A, Galenkamp, H, Galvano, F, Gao, Jl, Gao, P, Garcia-de-la-Hera, M, Garcia, P, Gareta, D, Garnett, Sp, Gaspoz, Jm, Gasull, M, Gazzinelli, A, Gehring, U, Geleijnse, Jm, George, R, Ghanbari, A, Ghasemi, E, Gheorghe-Fronea, Of, Ghimire, A, Gialluisi, A, Giampaoli, S, Gieger, C, Gill, Tk, Giovannelli, J, Gironella, G, Giwercman, A, Gkiouras, K, Goldberg, M, Goldsmith, Ra, Gomez, Lf, Gomula, A, da Silva, Bgc, Goncalves, H, Goncalves, M, Gonzalez-Chica, Da, Gonzalez-Gross, M, Gonzalez-Rivas, Jp, Gonzalez-Villalpando, C, Gonzalez-Villalpando, Me, Gonzalez, Ar, Gorbea, Mb, Gottrand, F, Graff-Iversen, S, Grafnetter, D, Grajda, A, Grammatikopoulou, Mg, Gregor, Rd, Grodzicki, T, Grosso, G, Gruden, G, Df, Gu, Guan, Op, Gudmundsson, Ef, Gudnason, V, Guerrero, R, Guessous, I, Guimaraes, Al, Gulliford, Mc, Gunnlaugsdottir, J, 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Hernandez, Univ Lorraine, North Karelia Ctr Publ Hlth, Univ Witwatersrand, Inst Med Res, Capital Med Univ, Xinjiang Med Univ, Shanghai Educ Dev Co Ltd, Orebro Univ, St Georges Univ London, Med Univ Vienna, Komfo Anokye Teaching Hosp, Minist Agr & Rural Affairs, Fudan Univ, Shanghai Educ Dev, Univ Cyprus, Niigata Univ, Iran Univ Med Sci, Ctr Estudios Nutr Infantil, Jiangsu Prov Ctr Dis Control & Prevent, Sun Yat Sen Univ, West Kazakhstan Med Univ, Inner Mongolia Med Univ, Éco-Anthropologie (EAE), Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS), Universitas Indonesia (UI ), Montpellier Interdisciplinary center on Sustainable Agri-food systems (Social and nutritional sciences) (UMR MoISA), Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut de Recherche pour le Développement (IRD)-Centre International de Hautes Etudes Agronomiques Méditerranéennes - Institut Agronomique Méditerranéen de Montpellier (CIHEAM-IAMM), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), 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), Interactions Gène-Environnement en Physiopathologie Cardio-Vasculaire (IGE-PCV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Clinicum, Research Programs Unit, Johan Eriksson / Principal Investigator, Department of General Practice and Primary Health Care, University of Helsinki, HUS Helsinki and Uusimaa Hospital District, Department of Public Health, Tampere University, Clinical Medicine, Department of Clinical Chemistry, Eye Centre, Wellcome Trust, Epidemiology, Radiology & Nuclear Medicine, Éco-Anthropologie (EA), Cohortes épidémiologiques en population (CONSTANCES), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay-Université Paris Cité (UPCité), Université Paris Cité (UPCité), Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Centre International de Hautes Études Agronomiques Méditerranéennes (CIHEAM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro - Montpellier SupAgro, 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), Epidemiology and Data Science, APH - Aging & Later Life, APH - Societal Participation & Health, Public and occupational health, APH - Health Behaviors & Chronic Diseases, VU University medical center, APH - Personalized Medicine, Psychiatry, APH - Mental Health, Bin Zhou, Rodrigo M Carrillo-Larco, Goodarz Danaei, Leanne M Riley, Christopher J Paciorek, Gretchen A Stevens, Edward W Gregg, James E Bennett, Bethlehem Solomon, Rosie K Singleton, Marisa K Sophiea, Maria Lc Iurilli, Victor Pf Lhoste, Melanie J Cowan, Stefan Savin, Mark Woodward, Yulia Balanova, Renata Cifkova, Albertino Damasceno, Paul Elliott, Farshad Farzadfar, Jiang He, Nayu Ikeda, Andre P Kengne, Young-Ho Khang, Hyeon Chang Kim, Avula Laxmaiah, Hsien-Ho Lin, Paula Margozzini Maira, J Jaime Miranda, Hannelore Neuhauser, Johan Sundström, Cherian Varghese, Indah S Widyahening, Tomasz Zdrojewski, Leandra Abarca-Gómez, Ziad A Abdeen, Hanan F Abdul Rahim, Niveen M Abu-Rmeileh, Benjamin Acosta-Cazares, Robert J Adams, Wichai Aekplakorn, Kaosar Afsana, Shoaib Afzal, Imelda A Agdeppa, Javad Aghazadeh-Attari, Carlos A Aguilar-Salinas, Charles Agyemang, Noor Ani Ahmad, Ali Ahmadi, Naser Ahmadi, Nastaran Ahmadi, Fariba Ahmadizar, Soheir H Ahmed, Wolfgang Ahrens, Kamel Ajlouni, Rajaa Al-Raddadi, Monira Alarouj, Fadia AlBuhairan, Shahla AlDhukair, Mohamed M Ali, Abdullah Alkandari, Ala'a Alkerwi, Kristine Allin, Eman Aly, Deepak N Amarapurkar, Norbert Amougou, Philippe Amouyel, Lars Bo Andersen, Sigmund A Anderssen, Ranjit Mohan Anjana, Alireza Ansari-Moghaddam, Daniel Ansong, Hajer Aounallah-Skhiri, Joana Araújo, Inger Ariansen, Tahir Aris, Raphael E Arku, Nimmathota Arlappa, Krishna K Aryal, Thor Aspelund, Felix K Assah, Maria Cecília F Assunção, Juha Auvinen, Mária Avdićová, Ana Azevedo, Mohsen Azimi-Nezhad, Fereidoun Azizi, Mehrdad Azmin, Bontha V Babu, Suhad Bahijri, Nagalla Balakrishna, Mohamed Bamoshmoosh, Maciej Banach, Maja Banadinović, Piotr Bandosz, José R Banegas, Joanna Baran, Barbagallo Carlo Maria, Alberto Barceló, Amina Barkat, Marta Barreto, Aluisio Jd Barros, Mauro Virgílio Gomes Barros, Anna Bartosiewicz, Abdul Basit, Joao Luiz D Bastos, Iqbal Bata, Anwar M Batieha, Assembekov Batyrbek, Louise A Baur, Robert Beaglehole, Antonisamy Belavendra, Habiba Ben Romdhane, Mikhail Benet, Lowell S Benson, Salim Berkinbayev, Antonio Bernabe-Ortiz, Gailute Bernotiene, Heloísa Bettiol, Jorge Bezerra, Aroor Bhagyalaxmi, Santosh K Bhargava, Daniel Bia, Katia Biasch, Elysée Claude Bika Lele, Mukharram M Bikbov, Bihungum Bista, Peter Bjerregaard, Espen Bjertness, Marius B Bjertness, Cecilia Björkelund, Katia V Bloch, Anneke Blokstra, Simona Bo, Martin Bobak, Heiner Boeing, Jose G Boggia, Carlos P Boissonnet, Stig E Bojesen, Vanina Bongard, Alice Bonilla-Vargas, Matthias Bopp, Herman Borghs, Pascal Bovet, Christopher B Boyer, Lutgart Braeckman, Imperia Brajkovich, Francesco Branca, Juergen Breckenkamp, Hermann Brenner, Lizzy M Brewster, Yajaira Briceño, Miguel Brito, Graziella Bruno, H Bas Bueno-de-Mesquita, Gloria Bueno, Anna Bugge, Con Burns, Michael Bursztyn, Antonio Cabrera de León, Joseph Cacciottolo, Christine Cameron, Günay Can, Ana Paula C Cândido, Mario V Capanzana, Naděžda Čapková, Eduardo Capuano, Vincenzo Capuano, Viviane C Cardoso, Axel C Carlsson, Joana Carvalho, Felipe F Casanueva, Laura Censi, Marvin Cervantes-Loaiza, Charalambos A Chadjigeorgiou, Snehalatha Chamukuttan, Angelique W Chan, Queenie Chan, Himanshu K Chaturvedi, Nish Chaturvedi, Miao Li Chee, Chien-Jen Chen, Fangfang Chen, Huashuai Chen, Shuohua Chen, Zhengming Chen, Ching-Yu Cheng, Bahman Cheraghian, Imane Cherkaoui Dekkaki, Angela Chetrit, Kuo-Liong Chien, Arnaud Chiolero, Shu-Ti Chiou, Adela Chirita-Emandi, María-Dolores Chirlaque, Belong Cho, Kaare Christensen, Diego G Christofaro, Jerzy Chudek, Eliza Cinteza, Frank Claessens, Janine Clarke, Els Clays, Emmanuel Cohen, Hans Concin, Cyrus Cooper, Tara C Coppinger, Simona Costanzo, Dominique Cottel, Chris Cowell, Cora L Craig, Amelia C Crampin, Ana B Crujeiras, Juan J Cruz, Semánová Csilla, Liufu Cui, Felipe V Cureau, Sarah Cuschieri, Graziella D'Arrigo, Eleonora d'Orsi, Jean Dallongeville, Rachel Dankner, Thomas M Dantoft, Luc Dauchet, Kairat Davletov, Guy De Backer, Dirk De Bacquer, Amalia De Curtis, Giovanni de Gaetano, Stefaan De Henauw, Paula Duarte de Oliveira, David De Ridder, Delphine De Smedt, Mohan Deepa, Alexander D Deev, Vincent Jr DeGennaro, Hélène Delisle, Stefaan Demarest, Elaine Dennison, Valérie Deschamps, Meghnath Dhimal, Augusto F Di Castelnuovo, Juvenal Soares Dias-da-Costa, Alejandro Diaz, Ty T Dickerson, Zivka Dika, Shirin Djalalinia, Ha Tp Do, Annette J Dobson, Chiara Donfrancesco, Silvana P Donoso, Angela Döring, Maria Dorobantu, Marcus Dörr, Kouamelan Doua, Nico Dragano, Wojciech Drygas, Charmaine A Duante, Priscilla Duboz, Rosemary B Duda, Virginija Dulskiene, Anar Dushpanova, Aleksandar Džakula, Vilnis Dzerve, Elzbieta Dziankowska-Zaborszczyk, Ricky Eddie, Ebrahim Eftekhar, Robert Eggertsen, Sareh Eghtesad, Gabriele Eiben, Ulf Ekelund, 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Andre L Guimaraes, Martin C Gulliford, Johanna Gunnlaugsdottir, Marc J Gunter, Prakash C Gupta, Rajeev Gupta, Oye Gureje, Beata Gurzkowska, Laura Gutierrez, Felix Gutzwiller, Seongjun Ha, Farzad Hadaegh, Rosa Haghshenas, Hamid Hakimi, Jytte Halkjær, Ian R Hambleton, Behrooz Hamzeh, Dominique Hange, Abu Am Hanif, Sari Hantunen, Jie Hao, Carla Menêses Hardman, Rachakulla Hari Kumar, Seyed Mohammad Hashemi-Shahri, Jun Hata, Teresa Haugsgjerd, Alison J Hayes, Yuna He, Margit Heier, Marleen Elisabeth Hendriks, Rafael Dos Santos Henrique, Ana Henriques, Leticia Hernandez Cadena, Herqutanto, Sauli Herrala, Ramin Heshmat, Allan G Hill, Sai Yin Ho, Suzanne C Ho, Michael Hobbs, Michelle Holdsworth, Reza Homayounfar, Gonul Horasan Dinc, Andrea Rvr Horimoto, Claudia M Hormiga, Bernardo L Horta, Leila Houti, Christina Howitt, Thein Thein Htay, Aung Soe Htet, Maung Maung Than Htike, Yonghua Hu, José María Huerta, Ilpo Tapani Huhtaniemi, Laetitia Huiart, Martijn Huisman, Abdullatif S Husseini, Inge Huybrechts, Nahla Hwalla, Licia Iacoviello, Anna G Iannone, Mohsen M Ibrahim, Norazizah Ibrahim Wong, M Arfan Ikram, Violeta Iotova, Vilma E Irazola, Takafumi Ishida, Godsent C Isiguzo, Muhammad Islam, Sheikh Mohammed Shariful Islam, Masanori Iwasaki, Rod T Jackson, Jeremy M Jacobs, Hashem Y Jaddou, Tazeen Jafar, Kenneth James, Konrad Jamrozik, Imre Janszky, Edward Janus, Marjo-Riitta Jarvelin, Grazyna Jasienska, Ana Jelaković, Bojan Jelaković, Garry Jennings, Anjani Kumar Jha, Chao Qiang Jiang, Ramon O Jimenez, Karl-Heinz Jöckel, Michel Joffres, Mattias Johansson, Jari J Jokelainen, Jost B Jonas, Torben Jørgensen, Pradeep Joshi, Farahnaz Joukar, Jacek Jóżwiak, Anne Juolevi, Gregor Jurak, Vesna Jureša, Rudolf Kaaks, Anthony Kafatos, Eero O Kajantie, Zhanna Kalmatayeva, Natasa Kalpourtzi, Ofra Kalter-Leibovici, Freja B Kampmann, Srinivasan Kannan, Eva Karaglani, Line L Kårhus, Khem B Karki, Marzieh Katibeh, Joanne Katz, Jussi Kauhanen, Prabhdeep Kaur, Maryam Kavousi, Gyulli M Kazakbaeva, Ulrich Keil, Lital Keinan Boker, Sirkka Keinänen-Kiukaanniemi, Roya Kelishadi, Han Cg Kemper, Maryam Keramati, Alina Kerimkulova, Mathilde Kersting, Timothy Key, Yousef Saleh Khader, Davood Khalili, Kay-Tee Khaw, Bahareh Kheiri, Motahareh Kheradmand, Alireza Khosravi, Ursula Kiechl-Kohlendorfer, Stefan Kiechl, Japhet Killewo, Dong Wook Kim, Jeongseon Kim, Heidi Klakk, Magdalena Klimek, Jurate Klumbiene, Michael Knoflach, Elin Kolle, Patrick Kolsteren, Jukka P Kontto, Raija Korpelainen, Paul Korrovits, Jelena Kos, Seppo Koskinen, Katsuyasu Kouda, Sudhir Kowlessur, Slawomir Koziel, Jana Kratenova, Vilma Kriaucioniene, Peter Lund Kristensen, Steiner Krokstad, Daan Kromhout, Herculina S Kruger, Ruzena Kubinova, Renata Kuciene, Urho M Kujala, Zbigniew Kulaga, R Krishna Kumar, Pawel Kurjata, Yadlapalli S Kusuma, Vladimir Kutsenko, Kari Kuulasmaa, Catherine Kyobutungi, Tiina Laatikainen, Carl Lachat, Youcef Laid, Tai Hing Lam, Orlando Landrove, Vera Lanska, Georg Lappas, Bagher Larijani, Tint Swe Latt, Gwenaëlle Le Coroller, Khanh Le Nguyen Bao, Tuyen D Le, Jeannette Lee, Jeonghee Lee, Nils Lehmann, Terho Lehtimäki, Daniel Lemogoum, Naomi S Levitt, Yanping Li, Christa L Lilly, Wei-Yen Lim, M Fernanda Lima-Costa, Xu Lin, Yi-Ting Lin, Lars Lind, Vijaya Lingam, Allan Linneberg, Lauren Lissner, Mieczyslaw Litwin, Wei-Cheng Lo, Helle-Mai Loit, Esther Lopez-Garcia, Tania Lopez, Paulo A Lotufo, José Eugenio Lozano, Iva Lukačević Lovrenčić, Janice L Lukrafka, Dalia Luksiene, Annamari Lundqvist, Robert Lundqvist, Nuno Lunet, Michala Lustigová, Edyta Luszczki, Guansheng Ma, Jun Ma, George Ll Machado-Coelho, Aristides M Machado-Rodrigues, Enguerran Macia, Luisa M Macieira, Ahmed A Madar, Stefania Maggi, Dianna J Magliano, Emmanuella Magriplis, Gowri Mahasampath, Bernard Maire, Marjeta Majer, Marcia Makdisse, Fatemeh Malekzadeh, Reza Malekzadeh, Rahul Malhotra, Kodavanti Mallikharjuna Rao, Sofia K Malyutina, Lynell V Maniego, Yannis Manios, Jim I Mann, Fariborz Mansour-Ghanaei, Enzo Manzato, Anie Marcil, Staffan B Mårild, Mihalea Marinović Glavić, Pedro Marques-Vidal, Larissa Pruner Marques, Jaume Marrugat, Reynaldo Martorell, Luis P Mascarenhas, Marija Matasin, Ellisiv B Mathiesen, Prashant Mathur, Alicia Matijasevich, Piotr Matlosz, Tandi E Matsha, Christina Mavrogianni, Jean Claude N Mbanya, Anselmo J Mc Donald Posso, Shelly R McFarlane, Stephen T McGarvey, Stela McLachlan, Rachael M McLean, Scott B McLean, Breige A McNulty, Sounnia Mediene Benchekor, Jurate Medzioniene, Parinaz Mehdipour, Kirsten Mehlig, Amir Houshang Mehrparvar, Aline Meirhaeghe, Christa Meisinger, Carlos Mendoza Montano, Ana Maria B Menezes, Geetha R Menon, Alibek Mereke, Indrapal I Meshram, Andres Metspalu, Haakon E Meyer, Jie Mi, Nathalie Michels, Kairit Mikkel, Karolina Milkowska, Jody C Miller, Cláudia S Minderico, G K Mini, Mohammad Reza Mirjalili, Erkin Mirrakhimov, Marjeta Mišigoj-Duraković, Pietro A Modesti, Sahar Saeedi Moghaddam, Bahram Mohajer, Mostafa K 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William A Neal, Azim Nejatizadeh, Ilona Nenko, Martin Neovius, Chung T Nguyen, Nguyen D Nguyen, Quang V Nguyen, Quang Ngoc Nguyen, Ramfis E Nieto-Martínez, Teemu J Niiranen, Yury P Nikitin, Toshiharu Ninomiya, Sania Nishtar, Marina A Njelekela, Marianna Noale, Oscar A Noboa, Ahmad Ali Noorbala, Teresa Norat, Maria Nordendahl, Børge G Nordestgaard, Noto Davide, Natalia Nowak-Szczepanska, Mohannad Al Nsour, Baltazar Nunes, Terence W O'Neill, Dermot O'Reilly, Caleb Ochimana, Eiji Oda, Augustine N Odili, Kyungwon Oh, Kumiko Ohara, Ryutaro Ohtsuka, Valérie Olié, Maria Teresa A Olinto, Isabel O Oliveira, Mohd Azahadi Omar, Altan Onat, Sok King Ong, Lariane M Ono, Pedro Ordunez, Rui Ornelas, Pedro J Ortiz, Clive Osmond, Sergej M Ostojic, Afshin Ostovar, Johanna A Otero, Kim Overvad, Ellis Owusu-Dabo, Fred Michel Paccaud, Cristina Padez, Elena Pahomova, Karina Mary de Paiva, Andrzej Pająk, Domenico Palli, Luigi Palmieri, Wen-Harn Pan, Songhomitra Panda-Jonas, Francesco Panza, Mariela Paoli, Dimitrios Papandreou, Soon-Woo Park, Suyeon Park, Winsome R Parnell, Mahboubeh Parsaeian, Patrick Pasquet, Nikhil D Patel, Halyna Pavlyshyn, Ivan Pećin, Mangesh S Pednekar, João M Pedro, Nasheeta Peer, Sergio Viana Peixoto, Markku Peltonen, Alexandre C Pereira, Karen Gda Peres, Marco A Peres, Annette Peters, Janina Petkeviciene, Niloofar Peykari, Son Thai Pham, Rafael N Pichardo, Iris Pigeot, Hynek Pikhart, Aida Pilav, Lorenza Pilotto, Freda Pitakaka, Aleksandra Piwonska, Andreia N Pizarro, Pedro Plans-Rubió, Ozren Polašek, Miquel Porta, Anil Poudyal, Farhad Pourfarzi, Akram Pourshams, Hossein Poustchi, Rajendra Pradeepa, Alison J Price, Jacqueline F Price, Rui Providencia, Soile E Puhakka, Maria Puiu, Margus Punab, Radwan F Qasrawi, Mostafa Qorbani, Daniel Queiroz, Tran Quoc Bao, Ivana Radić, Ricardas Radisauskas, Salar Rahimikazerooni, Mahfuzar Rahman, Olli Raitakari, Manu Raj, Ellina M Rakhimova, Sudha Ramachandra Rao, Ambady Ramachandran, Elisabete Ramos, Lekhraj Rampal, Sanjay Rampal, Daniel A Rangel Reina, Vayia Rarra, Cassiano Ricardo Rech, Josep Redon, Paul Ferdinand M Reganit, Valéria Regecová, Luis Revilla, Abbas Rezaianzadeh, Robespierre Ribeiro, Elio Riboli, Adrian Richter, Fernando Rigo, Tobias F Rinke de Wit, Raphael M Ritti-Dias, Cynthia Robitaille, Fernando Rodríguez-Artalejo, María Del Cristo Rodriguez-Perez, Laura A Rodríguez-Villamizar, Ulla Roggenbuck, Rosalba Rojas-Martinez, Dora Romaguera, Elisabetta L Romeo, Annika Rosengren, Joel Gr Roy, Adolfo Rubinstein, Jean-Bernard Ruidavets, Blanca Sandra Ruiz-Betancourt, Maria Ruiz-Castell, Iuliia A Rusakova, Paola Russo, Marcin Rutkowski, Charumathi Sabanayagam, Hamideh Sabbaghi, Harshpal S Sachdev, Alireza Sadjadi, Ali Reza Safarpour, Sare Safi, Saeid Safiri, Olfa Saidi, Sibel Sakarya, Nader Saki, Benoit Salanave, Eduardo Salazar Martinez, Diego Salmerón, Veikko Salomaa, Jukka T Salonen, Massimo Salvetti, Jose Sánchez-Abanto, Susana Sans, Diana A Santos, Ina S Santos, Lèlita C Santos, Maria Paula Santos, Rute Santos, Jouko L Saramies, Luis B Sardinha, Giselle Sarganas, Nizal Sarrafzadegan, Thirunavukkarasu Sathish, Kai-Uwe Saum, Savvas Savva, Norie Sawada, Mariana Sbaraini, Marcia Scazufca, Beatriz D Schaan, Herman Schargrodsky, Sabine Schipf, Carsten O Schmidt, Peter Schnohr, Ben Schöttker, Sara Schramm, Constance Schultsz, Aletta E Schutte, Sylvain Sebert, Aye Aye Sein, Abhijit Sen, Idowu O Senbanjo, Sadaf G Sepanlou, Jennifer Servais, Svetlana A Shalnova, Teresa Shamah-Levy, Morteza Shamshirgaran, Coimbatore Subramaniam Shanthirani, Maryam Sharafkhah, Sanjib K Sharma, Jonathan E Shaw, Amaneh Shayanrad, Ali Akbar Shayesteh, Zumin Shi, Kenji Shibuya, Hana Shimizu-Furusawa, Dong Wook Shin, Majid Shirani, Rahman Shiri, Namuna Shrestha, Khairil Si-Ramlee, Alfonso Siani, Rosalynn Siantar, Abla M Sibai, Caroline Ramos de Moura Silva, Diego Augusto Santos Silva, Mary Simon, Judith Simons, Leon A Simons, Michael Sjöström, Jolanta Slowikowska-Hilczer, Przemyslaw Slusarczyk, Liam 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Velasquez-Melendez, Giovanni Veronesi, Wm Monique Verschuren, Roosmarijn Verstraeten, Cesar G Victora, Lucie Viet, Salvador Villalpando, Paolo Vineis, Jesus Vioque, Jyrki K Virtanen, Sophie Visvikis-Siest, Bharathi Viswanathan, Tiina Vlasoff, Peter Vollenweider, Ari Voutilainen, Alisha N Wade, Janette Walton, Elvis Oa Wambiya, Wan Mohamad Wan Bebakar, Wan Nazaimoon Wan Mohamud, Rildo de Souza Wanderley Júnior, Ming-Dong Wang, Ningli Wang, Qian Wang, Xiangjun Wang, Ya Xing Wang, Ying-Wei Wang, S Goya Wannamethee, Nicholas Wareham, Wenbin Wei, Aneta Weres, Bo Werner, Peter H Whincup, Kurt Widhalm, Andrzej Wiecek, Rainford J Wilks, Johann Willeit, Peter Willeit, Emmanuel A Williams, Tom Wilsgaard, Bogdan Wojtyniak, Roy A Wong-McClure, Andrew Wong, Tien Yin Wong, Jean Woo, Frederick C Wu, Shouling Wu, Justyna Wyszynska, Haiquan Xu, Liang Xu, Nor Azwany Yaacob, Weili Yan, Ling Yang, Xiaoguang Yang, Yang Yang, Tabara Yasuharu, Xingwang Ye, Panayiotis K Yiallouros, Moein Yoosefi, Akihiro Yoshihara, San-Lin You, Novie O Younger-Coleman, Ahmad Faudzi Yusoff, Ahmad A Zainuddin, Seyed Rasoul Zakavi, Farhad Zamani, Sabina Zambon, Antonis Zampelas, Maria Elisa Zapata, Ko Ko Zaw, Kristyna Zejglicova, Tajana Zeljkovic Vrkic, Yi Zeng, Luxia Zhang, Zhen-Yu Zhang, Dong Zhao, Ming-Hui Zhao, Shiqi Zhen, Yingfeng Zheng, Bekbolat Zholdin, Dan Zhu, Marie Zins, Emanuel Zitt, Yanina Zocalo, Nada Zoghlami, Julio Zuñiga Cisneros, Majid Ezzati, Sakarya, Sibel (ORCID 0000-0002-9959-6240 & YÖK ID 172028), Zhou, Bin, Carrillo-Larco, Rodrigo M., Danaei, Goodarz, Riley, Leanne M., Paciorek, Christopher J., Stevens, Gretchen A., Gregg, Edward W., Bennett, James E., Solomon, Bethlehem, Singleton, Rosie K., Sophiea, Marisa K., Iurilli, Maria L. 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K., Mirjalili, Mohammad Reza, Mirrakhimov, Erkin, Misigoj-Durakovic, Marjeta, Modesti, Pietro A., Moghaddam, Sahar Saeedi, Mohajer, Bahram, Mohamed, Mostafa K., Mohamed, Shukri F., Mohammad, Kazem, Mohammadi, Mohammad Reza, Mohammadi, Zahra, Mohammadifard, Noushin, Mohammadpourhodki, Reza, Mohan, Viswanathan, Mohanna, Salim, Yusoff, Muhammad Fadhli Mohd, Mohebbi, Iraj, Mohebi, Farnam, Moitry, Marie, Mollehave, Line T., Molnar, Denes, Momenan, Amirabbas, Mondo, Charles K., Monterrubio-Flores, Eric, Monyeki, Kotsedi Daniel K., Moon, Jin Soo, Moosazadeh, Mahmood, Moreira, Leila B., Morejon, Alain, Moreno, Luis A., Morgan, Karen, Moschonis, George, Mossakowska, Malgorzata, Mostafa, Aya, Mostafavi, Seyed-Ali, Mota, Jorge, Motlagh, Mohammad Esmaeel, Motta, Jorge, Andre Moura-dos-Santos, Marcos, Mridha, Malay K., Msyamboza, Kelias P., Mu, Thet Thet, Muhihi, Alfa J., Muiesan, Maria L., Muller-Nurasyid, Martina, Murphy, Neil, Mursu, Jaakko, Musa, Kamarul Imran, Milanovic, Sanja Music, Musil, Vera, Mustafa, Norlaila, Nabipour, Iraj, Naderimagham, Shohreh, Nagel, Gabriele, Naidu, Balkish M., Najafi, Farid, Nakamura, Harunobu, Namesna, Jana, Nang, Ei Ei K., Nangia, Vinay B., Narake, Sameer, Ndiaye, Ndeye Coumba, Neal, William A., Nejatizadeh, Azim, Nenko, Ilona, Neovius, Martin, Neuhauser, Hannelore K., Nguyen, Chung T., Nguyen, Nguyen D., Nguyen, Quang V., Quang Ngoc Nguyen, Nieto-Martinez, Ramfis E., Niiranen, Teemu J., Nikitin, Yury P., Ninomiya, Toshiharu, Nishtar, Sania, Njelekela, Marina A., Noale, Marianna, Noboa, Oscar A., Noorbala, Ahmad Ali, Norat, Teresa, Nordendahl, Maria, Nordestgaard, Borge G., Noto, Davide, Nowak-Szczepanska, Natalia, Al Nsour, Mohannad, Nunes, Baltazar, O'Neill, Terence W., O'Reilly, Dermot, Ochimana, Caleb, Oda, Eiji, Odili, Augustine N., Oh, Kyungwon, Ohara, Kumiko, Ohtsuka, Ryutaro, Olie, Valerie, Olinto, Maria Teresa A., Oliveira, Isabel O., Omar, Mohd Azahadi, Onat, Altan, Ong, Sok King, Ono, Lariane M., Ordunez, Pedro, Ornelas, Rui, Ortiz, Pedro J., Osmond, Clive, Ostojic, Sergej M., Ostovar, Afshin, Otero, Johanna A., Overvad, Kim, Owusu-Dabo, Ellis, Paccaud, Fred Michel, Padez, Cristina, Pahomova, Elena, de Paiva, Karina Mary, Pajak, Andrzej, Palli, Domenico, Palmieri, Luigi, Pan, Wen-Harn, Panda-Jonas, Songhomitra, Panza, Francesco, Paoli, Mariela, Papandreou, Dimitrios, Park, Soon-Woo, Park, Suyeon, Parnell, Winsome R., Parsaeian, Mahboubeh, Pasquet, Patrick, Patel, Nikhil D., Pavlyshyn, Halyna, Pecin, Ivan, Pednekar, Mangesh S., Pedro, Joao M., Peer, Nasheeta, Peixoto, Sergio Viana, Peltonen, Markku, Pereira, Alexandre C., Peres, Karen G. D. A., Peres, Marco A., Peters, Annette, Petkeviciene, Janina, Peykari, Niloofar, Son Thai Pham, Pichardo, Rafael N., Pigeot, Iris, Pikhart, Hynek, Pilav, Aida, Pilotto, Lorenza, Pitakaka, Freda, Piwonska, Aleksandra, Pizarro, Andreia N., Plans-Rubio, Pedro, Polasek, Ozren, Porta, Miquel, Poudyal, Anil, Pourfarzi, Farhad, Pourshams, Akram, Poustchi, Hossein, Pradeepa, Rajendra, Price, Alison J., Price, Jacqueline F., Providencia, Rui, Puhakka, Soile E., Puiu, Maria, Punab, Margus, Qasrawi, Radwan F., Qorbani, Mostafa, Queiroz, Daniel, Tran Quoc Bao, Radic, Ivana, Radisauskas, Ricardas, Rahimikazerooni, Salar, Rahman, Mahfuzar, Raitakari, Olli, Raj, Manu, Rakhimova, Ellina M., Rao, Sudha Ramachandra, Ramachandran, Ambady, Ramos, Elisabete, Rampal, Lekhraj, Rampal, Sanjay, Rangel Reina, Daniel A., Rarra, Vayia, Rech, Cassiano Ricardo, Redon, Josep, Reganit, Paul Ferdinand M., Regecova, Valeria, Revilla, Luis, Rezaianzadeh, Abbas, Ribeiro, Robespierre, Riboli, Elio, Richter, Adrian, Rigo, Fernando, de Wit, Tobias F. Rinke, Ritti-Dias, Raphael M., Robitaille, Cynthia, Rodriguez-Artalejo, Fernando, del Cristo Rodriguez-Perez, Maria, Rodriguez-Villamizar, Laura A., Roggenbuck, Ulla, Rojas-Martinez, Rosalba, Romaguera, Dora, Romeo, Elisabetta L., Rosengren, Annika, Roy, Joel G. R., Rubinstein, Adolfo, Ruidavets, Jean-Bernard, Sandra Ruiz-Betancourt, Blanca, Ruiz-Castell, Maria, Rusakova, Iuliia A., Russo, Paola, Rutkowski, Marcin, Sabanayagam, Charumathi, Sabbaghi, Hamideh, Sachdev, Harshpal S., Sadjadi, Alireza, Safarpour, Ali Reza, Safi, Sare, Safiri, Saeid, Saidi, Olfa, Saki, Nader, Salanave, Benoit, Salazar Martinez, Eduardo, Salmeron, Diego, Salomaa, Veikko, Salonen, Jukka T., Salvetti, Massimo, Sanchez-Abanto, Jose, Sans, Susana, Santos, Diana A., Santos, Ina S., Santos, Lelita C., Santos, Maria Paula, Santos, Rute, Saramies, Jouko L., Sardinha, Luis B., Sarganas, Giselle, Sarrafzadegan, Nizal, Sathish, Thirunavukkarasu, Saum, Kai-Uwe, Savva, Savvas, Sawada, Norie, Sbaraini, Mariana, Scazufca, Marcia, Schaan, Beatriz D., Schargrodsky, Herman, Schipf, Sabine, Schmidt, Carsten O., Schnohr, Peter, Schoettker, Ben, Schramm, Sara, Schultsz, Constance, Schutte, Aletta E., Sebert, Sylvain, Sein, Aye Aye, Sen, Abhijit, Senbanjo, Idowu O., Sepanlou, Sadaf G., Servais, Jennifer, Shalnova, Svetlana A., Shamah-Levy, Teresa, Shamshirgaran, Morteza, Shanthirani, Coimbatore Subramaniam, Sharafkhah, Maryam, Sharma, Sanjib K., Shaw, Jonathan E., Shayanrad, Amaneh, Shayesteh, Ali Akbar, Shi, Zumin, Shibuya, Kenji, Shimizu-Furusawa, Hana, Shin, Dong Wook, Shirani, Majid, Shiri, Rahman, Shrestha, Namuna, Si-Ramlee, Khairil, Siani, Alfonso, Siantar, Rosalynn, Sibai, Abla M., de Moura Silva, Caroline Ramos, Santos Silva, Diego Augusto, Simon, Mary, Simons, Judith, Simons, Leon A., Sjostrom, Michael, Slowikowska-Hilczer, Jolanta, Slusarczyk, Przemyslaw, Smeeth, Liam, So, Hung-Kwan, Soares, Fernanda Cunha, Sobngwi, Eugene, Soderberg, Stefan, Soemantri, Agustinus, Sofat, Reecha, Solfrizzi, Vincenzo, Somi, Mohammad Hossein, Sonestedt, Emily, Song, Yi, Sorensen, Thorkild I. A., Sorgjerd, Elin P., Soric, Maroje, Jerome, Charles Sossa, Soumare, Aicha, Sparboe-Nilsen, Bente, Sparrenberger, Karen, Staessen, Jan A., Starc, Gregor, Stavreski, Bill, Steene-Johannessen, Jostein, Stehle, Peter, Stein, Aryeh D., Stergiou, George S., Stessman, Jochanan, Stieber, Jutta, Stoeckl, Doris, Stocks, Tanja, Stokwiszewski, Jakub, Stronks, Karien, Strufaldi, Maria Wany, Suka, Machi, Sun, Chien-An, Sung, Yn-Tz, Suriyawongpaisal, Paibul, Sy, Rody G., Syddall, Holly E., Sylva, Rene Charles, Szklo, Moyses, Tai, E. Shyong, Tammesoo, Mari-Liis, Tamosiunas, Abdonas, Tan, Eng Joo, Tang, Xun, Tanser, Frank, Tao, Yong, Tarawneh, Mohammed Rasoul, Tarqui-Mamani, Carolina B., Taylor, Anne, Taylor, Julie, Tebar, William R., Tell, Grethe S., Tello, Tania, Tham, Yih Chung, Thankappan, K. R., Theobald, Holger, Theodoridis, Xenophon, Thinggaard, Mikael, Thomas, Nihal, Thorand, Barbara, Thuesen, Betina H., Timmermans, Erik J., Tjandrarini, Dwi H., Tjonneland, Anne, Toft, Ulla, Tolonen, Hanna K., Tolstrup, Janne S., Topbas, Murat, Topor-Madry, Roman, Jose Tormo, Maria, Tornaritis, Michael J., Torrent, Maties, Torres-Collado, Laura, Touloumi, Giota, Traissac, Pierre, Triantafyllou, Areti, Trichopoulos, Dimitrios, Trichopoulou, Antonia, Trinh, Oanh T. H., Trivedi, Atul, Tshepo, Lechaba, Tsugane, Shoichiro, Tuliakova, Azaliia M., Tulloch-Reid, Marshall K., Tullu, Fikru, Tuomainen, Tomi-Pekka, Tuomilehto, Jaakko, Turley, Maria L., Twig, Gilad, Tynelius, Per, Tzourio, Christophe, Ueda, Peter, Ugel, Eunice, Ulmer, Hanno, Uusitalo, Hannu M. T., Valdivia, Gonzalo, Valvi, Damaskini, van Dam, Rob M., van den Born, Bert-Jan, Van der Heyden, Johan, van der Schouw, Yvonne T., Van Herck, Koen, Hoang Van Minh, Van Schoor, Natasja M., van Valkengoed, Irene G. M., van Zutphen, Elisabeth M., Vanuzzo, Diego, Varbo, Anette, Vasan, Senthil K., Vega, Tomas, Veidebaum, Toomas, Velasquez-Melendez, Gustavo, Veronesi, Giovanni, Verschuren, W. M. Monique, Verstraeten, Roosmarijn, Victora, Cesar G., Viet, Lucie, Villalpando, Salvador, Vineis, Paolo, Vioque, Jesus, Virtanen, Jyrki K., Visvikis-Siest, Sophie, Viswanathan, Bharathi, Vlasoff, Tiina, Vollenweider, Peter, Voutilainen, Ari, Wade, Alisha N., Walton, Janette, Wambiya, Elvis O. A., Bebakar, Wan Mohamad Wan, Mohamud, Wan Nazaimoon Wan, Wanderley Junior, Rildo de Souza, Wang, Ming-Dong, Wang, Ningli, Wang, Qian, Wang, Xiangjun, Wang, Ya Xing, Wang, Ying-Wei, Wannamethee, S. Goya, Wareham, Nicholas, Wei, Wenbin, Weres, Aneta, Werner, Bo, Whincup, Peter H., Widhalm, Kurt, Wiecek, Andrzej, Wilks, Rainford J., Willeit, Johann, Willeit, Peter, Williams, Emmanuel A., Wilsgaard, Tom, Wojtyniak, Bogdan, Wong-McClure, Roy A., Wong, Andrew, Wong, Tien Yin, Woo, Jean, Wu, Frederick C., Wu, Shouling, Wyszynska, Justyna, Xu, Haiquan, Xu, Liang, Yaacob, Nor Azwany, Yan, Weili, Yang, Ling, Yang, Xiaoguang, Yang, Yang, Yasuharu, Tabara, Ye, Xingwang, Yiallouros, Panayiotis K., Yoosefi, Moein, Yoshihara, Akihiro, You, San-Lin, Younger-Coleman, Novie O., Yusoff, Ahmad Faudzi, Zainuddin, Ahmad A., Zakavi, Seyed Rasoul, Zamani, Farhad, Zambon, Sabina, Zampelas, Antonis, Elisa Zapata, Maria, Zaw, Ko Ko, Zejglicova, Kristyna, Vrkic, Tajana Zeljkovic, Zeng, Yi, Zhang, Luxia, Zhao, Dong, Zhao, Ming-Hui, Zhen, Shiqi, Zheng, Yingfeng, Zholdin, Bekbolat, Zhu, Dan, Zins, Marie, Zitt, Emanuel, Zocalo, Yanina, Zoghlami, Nada, Zuniga Cisneros, Julio., School of Medicine, ACS - Diabetes & metabolism, APH - Global Health, Pulmonology, Medical Informatics, Adult Psychiatry, Global Health, APH - Quality of Care, APH - Methodology, Vascular Medicine, ACS - Atherosclerosis & ischemic syndromes, Anesthesiology, Graduate School, and ACS - Heart failure & arrhythmias
Subjects: Male, Latin Americans, Nutrition and Disease, Epidemiology, [SDV]Life Sciences [q-bio], Medizin, BLOOD-PRESSURE, 030204 cardiovascular system & hematology, Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants, Hypertension, Prevalence, Control, Tretament, GUIDELINES, Global Health, Worldwide trends, 0302 clinical medicine, Hypertension prevalence, Voeding en Ziekte, Medicine and Health Sciences, kohonnut verenpaine, Medicine, 030212 general & internal medicine, Prevention and Control, 11 Medical and Health Sciences, ComputingMilieux_MISCELLANEOUS, education.field_of_study, food and beverages, Public Health, Global Health, Social Medicine and Epidemiology, General Medicine, Noncommunicable diseases, Period prevalence, Middle Aged, kansainvälinen vertailu, 3142 Public health care science, environmental and occupational health, 3. Good health, MIDDLE-INCOME, Pooled analysis, SYSTEMATIC ANALYSIS, INCOME COUNTRIES, ADULTS, PREVENTION, MANAGEMENT, ADHERENCE, DIAGNOSIS, Western europe, [SDE]Environmental Sciences, Hypertension/diagnosis, NCD Risk Factor Collaboration (NCD-RisC), Female, B990 Subjects Allied to Medicine not elsewhere classified, Life Sciences & Biomedicine, Adult, health-care, esiintyvyys, Central asia, Population, Nursing, 3121 Internal medicine, 03 medical and health sciences, Medicine, General & Internal, Drug Therapy, General & Internal Medicine, Life Science, Humans, ddc:610, education, Antihypertensive Agents, VLAG, Aged, Science & Technology, Antihypertensive Agents/therapeutic use, business.industry, Omvårdnad, fungi, General and internal medicine, Estados de Saúde e de Doença, Taking medication, Treatment, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, Blood pressure, Faculdade de Ciências Sociais, 3121 General medicine, internal medicine and other clinical medicine, lääkehoito, 1182 Biochemistry, cell and molecular biology, business, Demography
Abstract: Background: hypertension can be detected at the primary health-care level and low-cost treatments can effectively control hypertension. We aimed to measure the prevalence of hypertension and progress in its detection, treatment, and control from 1990 to 2019 for 200 countries and territories. Methods: we used data from 1990 to 2019 on people aged 30-79 years from population-representative studies with measurement of blood pressure and data on blood pressure treatment. We defined hypertension as having systolic blood pressure 140 mm Hg or greater, diastolic blood pressure 90 mm Hg or greater, or taking medication for hypertension. We applied a Bayesian hierarchical model to estimate the prevalence of hypertension and the proportion of people with hypertension who had a previous diagnosis (detection), who were taking medication for hypertension (treatment), and whose hypertension was controlled to below 140/90 mm Hg (control). The model allowed for trends over time to be non-linear and to vary by age. Findings: the number of people aged 30-79 years with hypertension doubled from 1990 to 2019, from 331 (95% credible interval 306-359) million women and 317 (292-344) million men in 1990 to 626 (584-668) million women and 652 (604-698) million men in 2019, despite stable global age-standardised prevalence. In 2019, age-standardised hypertension prevalence was lowest in Canada and Peru for both men and women; in Taiwan, South Korea, Japan, and some countries in western Europe including Switzerland, Spain, and the UK for women; and in several low-income and middle-income countries such as Eritrea, Bangladesh, Ethiopia, and Solomon Islands for men. Hypertension prevalence surpassed 50% for women in two countries and men in nine countries, in central and eastern Europe, central Asia, Oceania, and Latin America. Globally, 59% (55-62) of women and 49% (46-52) of men with hypertension reported a previous diagnosis of hypertension in 2019, and 47% (43-51) of women and 38% (35-41) of men were treated. Control rates among people with hypertension in 2019 were 23% (20-27) for women and 18% (16-21) for men. In 2019, treatment and control rates were highest in South Korea, Canada, and Iceland (treatment >70%; control >50%), followed by the USA, Costa Rica, Germany, Portugal, and Taiwan. Treatment rates were less than 25% for women and less than 20% for men in Nepal, Indonesia, and some countries in sub-Saharan Africa and Oceania. Control rates were below 10% for women and men in these countries and for men in some countries in north Africa, central and south Asia, and eastern Europe. Treatment and control rates have improved in most countries since 1990, but we found little change in most countries in sub-Saharan Africa and Oceania. Improvements were largest in high-income countries, central Europe, and some upper-middle-income and recently high-income countries including Costa Rica, Taiwan, Kazakhstan, South Africa, Brazil, Chile, Turkey, and Iran. Interpretation Improvements in the detection, treatment, and control of hypertension have varied substantially across countries, with some middle-income countries now outperforming most high-income nations. The dual approach of reducing hypertension prevalence through primary prevention and enhancing its treatment and control is achievable not only in high-income countries but also in low-income and middle-income settings., British Heart Foundation Centre of Research Excellence Grant; World Health Organization (WHO); Abdul Latif Jameel Institute for Disease and Emergency Analytics Fellowship
Published: 2021

9. Simultaneous Mapping of Vasculature, Hypoxia, and Proliferation Using Dynamic Susceptibility Contrast MRI, 18F-FMISO PET, and 18F-FLT PET in Relation to Contrast Enhancement in Newly Diagnosed Glioblastoma

Author: Samuel Valable, Solène Collet, Jean-Marc Constans, Mathieu Hatt, Jean-Sébastien Guillamo, Cécile Perrio, Ararat Chakhoyan, Dimitris Visvikis, Emmanuèle Lechapt-Zalcman, Stéphane Guillouet, J M Derlon, Myriam Bernaudin, and David Hassanein Berro
Subjects: Contrast enhancement, business.industry, media_common.quotation_subject, Newly diagnosed, 18f fmiso, Hypoxia (medical), medicine.disease, 030218 nuclear medicine & medical imaging, Peripheral, 03 medical and health sciences, 0302 clinical medicine, 030220 oncology & carcinogenesis, medicine, Contrast (vision), Radiology, Nuclear Medicine and imaging, medicine.symptom, business, Nuclear medicine, Dynamic susceptibility, media_common, Glioblastoma
Abstract: Conventional MRI plays a key role in the management of patients with high grade glioma but multiparametric MRI and PET tracers could provide further information to better characterize the tumor metabolism and heterogeneity, by identifying the regions having a high risk of recurrence. In this study, we focused on the proliferation, hypervascularization and hypoxia, all factors considered as factors of poor prognosis. They were assessed by measuring the uptake of 18F-FLT, the rCBV maps and the uptake of 18F-FMISO, respectively. For each modality, the volumes and high uptake sub-volumes (hotspots) were semi-automatically segmented and compared to contrast enhancement (CE) volume on T1w-Gd images, commonly used in the management of patient with glioblastoma. Methods: DSC MRI (31 patients), 18F-FLT PET (20 patients) and/or 18F-FMISO PET (20 patients), for a total of 31 patients, were performed on pre-operative glioblastoma patients. Volumes and hotspots were segmented on SUV maps for 18F-FLT (using FLAB) and 18F-FMISO (using mean contralateral + 3.3 SD) PET and on rCBV maps (using mean contralateral + 1.96 SD) for DSC MRI and overlaid on T1w-Gd images. For each modality, the percentage of peripheral volumes and the peripheral hotspot outside the CE volume were calculated. Results: All tumors showed high proliferation, hypervascularization and hypoxic regions. Images also showed pronounced heterogeneity of both tracers uptake and rCBV maps, within each individual case. Overlaid volumes on T1w-Gd images showed that some proliferative, hypervascularization and hypoxic regions extended beyond the CE volume but with marked differences between patients. The ranges of peripheral volume outside the CE volume were [1.6% - 155.5 %], [1.5% - 89.5%] and [3.1% - 78.0%] for 18F-FLT, rCBV and 18F-FMISO respectively. All patients had hyperproliferative hotspots outside CE volume, whereas hotspots of hypervasculature and hypoxia were mainly detected within the enhancing region. Conclusion: The spatial analysis of the multiparametric maps with the segmented volumes and hotspots provides valuable information to optimize the management and treatment of the patients with glioblastoma.
Published: 2021

10. Accuracy of noncontact surface imaging for tidal volume and respiratory rate measurements in the ICU

Author: Erwan L'Her, Victoire Pateau, Dimitris Visvikis, and Souha Nazir
Subjects: P-SILI, Respiratory rate, VILI, medicine.medical_treatment, Health Informatics, Respiratory monitoring, Lung injury, Critical Care and Intensive Care Medicine, Tidal volume, 03 medical and health sciences, 0302 clinical medicine, Respiratory Rate, 030202 anesthesiology, medicine, Humans, Respiratory system, Original Research, Mechanical ventilation, business.industry, Time-of-flight, Respiration, 030208 emergency & critical care medicine, Respiration, Artificial, Intensive Care Units, Anesthesiology and Pain Medicine, Breathing, business, Biomedical engineering, Volume (compression)
Abstract: Tidal volume monitoring may help minimize lung injury during respiratory assistance. Surface imaging using time-of-flight camera is a new, non-invasive, non-contact, radiation-free, and easy-to-use technique that enables tidal volume and respiratory rate measurements. The objectives of the study were to determine the accuracy of Time-of-Flight volume (VTTOF) and respiratory rate (RRTOF) measurements at the bedside, and to validate its application for spontaneously breathing patients under high flow nasal canula. Data analysis was performed within the ReaSTOC data-warehousing project (ClinicalTrials.gov identifier NCT02893462). All data were recorded using standard monitoring devices, and the computerized medical file. Time-of-flight technique used a Kinect V2 (Microsoft, Redmond, WA, USA) to acquire the distance information, based on measuring the phase delay between the emitted light-wave and received backscattered signals. 44 patients (32 under mechanical ventilation; 12 under high-flow nasal canula) were recorded. High correlation (r = 0.84; p 8 mL/kg) and low ventilation in 6 patients (tidal volume
Published: 2021

11. Heterogeneous NLRP3 inflammasome signature in circulating myeloid cells as a biomarker of COVID-19 severity

Author: Géraldine Gonfrier, Laurent Yvan-Charvet, Laurent Bailly, Julie Contenti, Christelle Pomares-Estran, David Chirio, Laurent Boyer, Céline Loubatier, Orane Visvikis, Michel Carles, Océane Dufies, Patrick Munro, Arnaud Jacquel, Cedric Torre, Sébastien Vitale, Anne Doye, Valérie Giordanengo, Johan Courjon, Stoyan Ivanov, Jean Dellamonica, Romain Lotte, Patrick Auberger, and Alexandre Robert
Subjects: 0301 basic medicine, Clinical Trials and Observations, Inflammasomes, Stimulation, 03 medical and health sciences, 0302 clinical medicine, Immune system, NLR Family, Pyrin Domain-Containing 3 Protein, medicine, Humans, Myeloid Cells, Prospective Studies, Prospective cohort study, Innate immune system, business.industry, SARS-CoV-2, Case-control study, COVID-19, Inflammasome, Hematology, Middle Aged, Peripheral, 030104 developmental biology, 030220 oncology & carcinogenesis, Case-Control Studies, Immunology, Biomarker (medicine), business, Biomarkers, medicine.drug
Abstract: Key Points Measurement of NLRP3 inflammasome activation in the blood of patients reveals an impaired immature neutrophil response in severe COVID-19. Inflammasome signature analysis in circulating myeloid cells allows COVID-19 patients to be stratified and predicts evolution., Dysregulated immune response is the key factor leading to unfavorable coronavirus disease 2019 (COVID-19) outcome. Depending on the pathogen-associated molecular pattern, the NLRP3 inflammasome can play a crucial role during innate immunity activation. To date, studies describing the NLRP3 response during severe acute respiratory syndrome coronavirus 2 infection in patients are lacking. We prospectively monitored caspase-1 activation levels in peripheral myeloid cells from healthy donors and patients with mild to critical COVID-19. The caspase-1 activation potential in response to NLRP3 inflammasome stimulation was opposed between nonclassical monocytes and CD66b+CD16dim granulocytes in severe and critical COVID-19 patients. Unexpectedly, the CD66b+CD16dim granulocytes had decreased nigericin-triggered caspase-1 activation potential associated with an increased percentage of NLRP3 inflammasome impaired immature neutrophils and a loss of eosinophils in the blood. In patients who recovered from COVID-19, nigericin-triggered caspase-1 activation potential in CD66b+CD16dim cells was restored and the proportion of immature neutrophils was similar to control. Here, we reveal that NLRP3 inflammasome activation potential differs among myeloid cells and could be used as a biomarker of a COVID-19 patient’s evolution. This assay could be a useful tool to predict patient outcome. This trial was registered at www.clinicaltrials.gov as #NCT04385017., Visual Abstract
Published: 2021

12. Convolutional neural networks for PET functional volume fully automatic segmentation: development and validation in a multi-center setting

Author: Frédéric Kridelka, François Lucia, Joanne Alfieri, Ramon Rovira, Ulrike Schick, Augustin Mervoyer, Pierre Lovinfosse, Marta Ferreira, Roland Hustinx, Andrei Iantsen, Marjolein Decuypere, Olivier Pradier, Philippe Robin, Pietro Andrea Bonaffini, Ingrid Masson, Caroline Reinhold, Caroline Rousseau, Dimitris Visvikis, Vincent Jaouen, and Mathieu Hatt
Subjects: Computer science, Bayesian probability, Context (language use), Convolutional neural network, Fuzzy logic, 030218 nuclear medicine & medical imaging, Upsampling, 03 medical and health sciences, 0302 clinical medicine, Segmentation, Prior probability, Image Processing, Computer-Assisted, Humans, Radiology, Nuclear Medicine and imaging, Ground truth, business.industry, Pattern recognition, Bayes Theorem, General Medicine, U-Net, PET, 030220 oncology & carcinogenesis, Positron-Emission Tomography, Cervical cancer, Original Article, Artificial intelligence, Neural Networks, Computer, business, Algorithms
Abstract: PurposeIn this work, we addressed fully automatic determination of tumor functional uptake from positron emission tomography (PET) images without relying on other image modalities or additional prior constraints, in the context of multicenter images with heterogeneous characteristics.MethodsIn cervical cancer, an additional challenge is the location of the tumor uptake near or even stuck to the bladder. PET datasets of 232 patients from five institutions were exploited. To avoid unreliable manual delineations, the ground truth was generated with a semi-automated approach: a volume containing the tumor and excluding the bladder was first manually determined, then a well-validated, semi-automated approach relying on the Fuzzy locally Adaptive Bayesian (FLAB) algorithm was applied to generate the ground truth. Our model built on the U-Net architecture incorporates residual blocks with concurrent spatial squeeze and excitation modules, as well as learnable non-linear downsampling and upsampling blocks. Experiments relied on cross-validation (four institutions for training and validation, and the fifth for testing).ResultsThe model achieved good Dice similarity coefficient (DSC) with little variability across institutions (0.80 ± 0.03), with higher recall (0.90 ± 0.05) than precision (0.75 ± 0.05) and improved results over the standard U-Net (DSC 0.77 ± 0.05, recall 0.87 ± 0.02, precision 0.74 ± 0.08). Both vastly outperformed a fixed threshold at 40% of SUVmax (DSC 0.33 ± 0.15, recall 0.52 ± 0.17, precision 0.30 ± 0.16). In all cases, the model could determine the tumor uptake without including the bladder. Neither shape priors nor anatomical information was required to achieve efficient training.ConclusionThe proposed method could facilitate the deployment of a fully automated radiomics pipeline in such a challenging multicenter context.
Published: 2021

13. National trends in total cholesterol obscure heterogeneous changes in HDL and non-HDL cholesterol and total-to-HDL cholesterol ratio : a pooled analysis of 458 population-based studies in Asian and Western countries

Author: NCD Risk Factor Collaboration (NCD-RisC), [missing], Taddei, Cristina, Jackson, Rod, Zhou, Bin, Bixby, Honor, Danaei, Goodarz, Di Cesare, Mariachiara, Kuulasmaa, Kari, Hajifathalian, Kaveh, Bentham, James, Bennett, James E, Aekplakorn, Wichai, Cifkova, Renata, Dallongeville, Jean, DeBacquer, Dirk, Giampaoli, Simona, Gudnason, Vilmundur, Khang, Young-Ho, Laatikainen, Tiina, Mann, JimI, Marques-Vidal, Pedro, Mensah, George A, Müller-Nurasyid, Martina, Ninomiya, Toshiharu, Petkeviciene, Janina, Rodríguez-Artalejo, Fernando, Servais, Jennifer, Söderberg, Stefan, Stavreski, Bill, Wilsgaard, Tom, Zdrojewski, Tomasz, Zhao, Dong, Stevens, Gretchen A, Savin, Stefan, Cowan, Melanie J, Riley, Leanne M, Ezzati, Majid, Adams, Robert J, Ahrens, Wolfgang, Amouyel, Philippe, Amuzu, Antoinette, Anderssen, Sigmund A, Ariansen, Inger, Arveiler, Dominique, Aspelund, Thor, Auvinen, Juha, Avdicová, Mária, Banach, Maciej, Bandosz, Piotr, Banegas, José R, Barbagallo, Carlo M, Bata, Iqbal, Baur, Louise A, Beaglehole, Robert, Bernotiene, Gailute, Bi, Yufang, Bienek, Asako, Björkelund, Cecilia, Bo, Simona, Boehm, Bernhard O, Bonaccio, Marialaura, Bongard, Vanina, Borchini, Rossana, Borghs, Herman, Breckenkamp, Juergen, Brenner, Hermann, Bruno, Graziella, Busch, Markus A, Cabrera de León, Antonio, Capuano, Vincenzo, Casanueva, Felipe F, Casas, Juan-Pablo, Caserta, Carmelo A, Censi, Laura, Chen, Fangfang, Chen, Shuohua, Chirlaque, María-Dolores, Cho, Belong, Cho, Yumi, Chudek, Jerzy, Claessens, Frank, Clarke, Janine, Clays, Els, Cooper, Cyrus, Costanzo, Simona, Cottel, Dominique, Cowell, Chris, Crujeiras, Ana B, Cui, Liufu, D'Arrigo, Graziella, Dauchet, Luc, De Backer, Guy, De Bacquer, Dirk, de Gaetano, Giovanni, De Henauw, Stefaan, De Smedt, Delphine, Dennison, Elaine, Deschamps, Valérie, DiCastelnuovo, Augusto, Dobson, Annette J, Donfrancesco, Chiara, Döring, Angela, Drygas, Wojciech, Du, Yong, Dziankowska-Zaborszczyk, Elzbieta, Eggertsen, Robert, Ekelund, Ulf, Elosua, Roberto, Eriksson, Johan G, Evans, Alun, Faeh, David, Felix-Redondo, Francisco J, Fernández-Bergés, Daniel, Ferrari, Marika, Ferrieres, Jean, Finn, Joseph D, Forslund, Ann-Sofie, Forsner, Maria, Frontera, Guillermo, Fujita, Yuki, Gaciong, Zbigniew, Galvano, Fabio, Gao, Jingli, Garcia-de-la-Hera, Manoli, Garnett, Sarah P, Gaspoz, Jean-Michel, Gasull, Magda, Gates, Louise, Gianfagna, Francesco, Gill, Tiffany K, Giovannelli, Jonathan, Goltzman, David, GonzalezGross, Marcela, Gottrand, Frederic, Graff-Iversen, Sidsel, Grafnetter, Dušan, Gregor, Ronald D, Grodzicki, Tomasz, Grosso, Giuseppe, Gruden, Grabriella, Gu, Dongfeng, Guallar-Castillón, Pilar, Gudmundsson, Elias F, Guessous, Idris, Gunnlaugsdottir, Johanna, Gutzwiller, Felix, Hardy, Rebecca, Hata, Jun, Haugsgjerd, Teresa, Hayes, Alison J, He, Jiang, He, Yuna, Herrala, Sauli, TapaniHihtaniemi, Ilpo, Hobbs, Michael, Hopman, Wilma M, MaríaHuerta, José, Huybrechts, Inge, Iacoviello, Licia, Iannone, Anna G, Ikeda, Nayu, Iwasaki, Masanori, Jamrozik, Konrad, Janszky, Imre, Jarvelin, Marjo-Riitta, Jasienska, Grazyna, Jennings, Garry, Jeong, Seung-lyeal, QiangJiang, Chao, Joffres, Michel, Jokelainen, Jari J, Jonas, Jost B, Jóźwiak, Jacek, Kajantie, Eero O, Kauhanen, Jussi, Keil, Ulrich, Keinänen-Kiukaanniemi, Sirkka, Kersting, Mathilde, Kiechl-Kohlendorfer, Ursula, Kiechl, Stefan, Kim, Jeongseon, Kim, Yeon-Yong, Klumbiene, Jurate, Knoflach, Michael, Ko, Stephanie, Kolle, Elin, Korpelainen, Raija, Koskinen, Seppo, Kouda, Katsuyasu, Kratzer, Wolfgang, Kriemler, Susi, Krokstad, Steinar, Kujala, Urho M, Kurjata, Pawel, HingLam, Tai, Lanska, Vera, Lappas, Georg, Laugsand, Lars E, Lee, Jeonghee, Lehtimäki, Terho, Li, Yanping, Lilly, Christa L, Lin, Xu, Lind, Lars, Lissner, Lauren, Liu, Jing, Lopez-Garcia, Esther, Lorbeer, Roberto, EugenioLozano, José, Luksiene, Dalia, Lundqvist, Annamari, Lundqvist, Robert, Lytsy, Per, Ma, Guansheng, Machi, Suka, Maggi, Stefania, Magliano, Dianna J, Manzato, Enzo, Mathiesen, Ellisiv B, McLachlan, Stela, McLean, Rachael M, McLean, Scott B, Meirhaeghe, Aline, Meisinger, Christa, Metcalf, Patricia, Mi, Jie, Miller, Jody C, Moreno, Luis A, Morin, Suzanne, Mossakowska, Malgorzata, Muiesan, Maria L, Mursu, Jaakko, Nakamura, Harunobu, Námešná, Jana, Nauck, Matthias, MariaNavarrete-Muñoz, Eva, Neal, William A, Nenko, Ilona, Niiranen, Teemu J, Ning, Guang, Noale, Marianna, Norie, Sawada, Noto, Davide, O'Neill, Terence, O'Reilly, Dermot, Oh, Kyungwon, Olafsson, Örn, MichelPaccaud, Fred, Pajak, Andrzej, Palmieri, Luigi, Panza, Francesco, Parnell, Winsome R, Peltonen, Markku, Peters, Annette, Petersmann, Astrid, Pigeot, Iris, Pilotto, Lorenza, Piwonska, Aleksandra, Plans-Rubió, Pedro, Porta, Miquel, Price, Jacqueline F, Puder, Jardena J, Puhakka, Soile E, Radisauskas, Ricardas, Raitakari, Olli, Ramos, Rafel, Redon, Josep, Rigo, Fernando, Rodriguez-Perez, MaríadelCristo, Romaguera, Dora, Ronkainen, Kimmo, Rosengren, Annika, Roy, Joel G R, Ruidavets, Jean-Bernard, Rutkowski, Marcin, Salanave, Benoit, Salmerón, Diego, Salomaa, Veikko, Salonen, Jukka T, Salvetti, Massimo, Sans, Susana, Saramies, Jouko L, Saum, Kai-Uwe, Scheidt-Nave, Christa, Schienkiewitz, Anja, Schipf, Sabine, Schmidt, Carsten O, Schöttker, Ben, Sebert, Sylvain, Sen, Abhijit, Shaw, Jonathan E, Shibuya, Kenji, WookShin, Dong, Shiri, Rahman, Simons, Judith, Simons, Leon A, Sjöström, Michael, Slowikowska-Hilczer, Jolanta, Slusarczyk, Przemyslaw, Solfrizzi, Vincenzo, Sonestedt, Emily, Soumare, Aicha, Staessen, Jan A, Stathopoulou, Maria G, Steene-Johannessen, Jostein, Stehle, Peter, Stieber, Jutta, Stöckl, Doris, Stokwiszewski, Jakub, Sundström, Johan, Suriyawongpaisal, Paibul, Tamosiunas, Abdonas, JooTan, Eng, Taylor, Anne, Tell, Grethe, Thijs, Lutgarde, Tolonen, HannaK, Topór-Madry, Roman, JoséTormo, María, Torrent, Maties, Tsugane, Shoichiro, Tuomainen, Tomi-Pekka, Tuomilehto, Jaakko, Tzourio, Christophe, Uusitalo, Hannu M T, Van Herck, Koen, Vanderschueren, Dirk, Vanuzzo, Diego, Vatten, Lars, Vega, Tomas, Veronesi, Giovanni, Vioque, Jesus, Virtanen, JyrkiK, Visvikis-Siest, Sophie, Vollenweider, Peter, Voutilainen, Sari, Vrijheid, Martine, Wagner, Aline, Wagner, Anne, Wang, Ming-Dong, Wang, Qian, XingWang, Ya, Wannamethee, S Goya, Wei, Wenbin, Whincup, Peter H, Wiecek, Andrzej, Willeit, Johann, Willeit, Peter, Wojtyniak, Bogdan, Wong, Andrew, Woodward, Mark, GiwercmanWu, Aleksander, Wu, Frederick C, Wu, Shouling, Xu, Haiquan, Xu, Liang, Yan, Weili, Yang, Xiaoguang, Ye, Xingwang, Yoshihara, Akihiro, Zambon, Sabina, Zhao, Wenhua, Imperial College London, University of Auckland [Auckland], Harvard T.H. Chan School of Public Health, Middlesex University, National Institute for Health and Welfare [Helsinki], Weill Medical College of Cornell University [New York], University of Kent [Canterbury], Mahidol University [Bangkok], Charles University [Prague] (CU), Epidémiologie des maladies chroniques : impact des interactions gène environnement sur la santé des populations, Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille, Droit et Santé, Universiteit Gent = Ghent University [Belgium] (UGENT), Istituto Superiore di Sanita [Rome], Icelandic Heart Association [Kopavogur, Iceland] (IHA), Seoul National University [Seoul] (SNU), University of Otago [Dunedin, Nouvelle-Zélande], Centre Hospitalier Universitaire Vaudois [Lausanne] (CHUV), Helmholtz-Zentrum München (HZM), Kyushu University [Fukuoka], Universidad Autonoma de Madrid (UAM), CIBER de Epidemiología y Salud Pública (CIBERESP), Umeå University, The Arctic University of Norway (UiT), Medical University of Gdańsk, Capital University of Medical Sciences [Beijing] (CUMS), Organisation Mondiale de la Santé / World Health Organization Office (OMS / WHO), University of Adelaide, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Leibniz Association, Facteurs de Risque et Déterminants Moléculaires des Maladies liées au Vieillissement - U 1167 (RID-AGE), Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), London School of Hygiene and Tropical Medicine (LSHTM), Norwegian School of Sport Sciences = Norges idrettshøgskole [Oslo] (NIH), Norwegian Institute of Public Health [Oslo] (NIPH), Université de Strasbourg (UNISTRA), University of Iceland [Reykjavik], University of Oulu, Regional Authority of Public Health [Slovaquia] (RAPH), Ministry of Health of the Slovak Republic [Slovaquia], Medical University of Łódź (MUL), Università degli studi di Palermo - University of Palermo, Dalhousie University [Halifax], The University of Sydney, Public Health Agency of Canada, University of Gothenburg (GU), University of Turin, Nanyang Technological University [Singapour], Istituto Neurologico Mediterraneo (NEUROMED I.R.C.C.S.), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome]-Università degli studi di Napoli Federico II, Faculté de Médecine [Rangueil], Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse], Universität Bielefeld = Bielefeld University, German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Robert Koch Institute [Berlin] (RKI), Universidad de La Laguna [Tenerife - SP] (ULL), Universidade de Santiago de Compostela [Spain] (USC ), University College of London [London] (UCL), University of Silesia in Katowice, Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), University of Southampton, Biomolécules et inflammation pulmonaire, Service d'Epidémiologie et de Santé Publique [Lille], Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Santé publique France - French National Public Health Agency [Saint-Maurice, France], University of Queensland [Brisbane], IMIM-Hospital del Mar, Generalitat de Catalunya, Queen's University [Belfast] (QUB), Universität Zürich [Zürich] = University of Zurich (UZH), Epidémiologie et analyses en santé publique : risques, maladies chroniques et handicaps (LEASP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, University of Manchester [Manchester], Kindai University, Medical University of Warsaw - Poland, Università degli studi di Catania [Catania], Geneva University Hospital (HUG), Universitá degli Studi dell’Insubria, McGill University = Université McGill [Montréal, Canada], Universidad Politécnica de Madrid (UPM), Lille Inflammation Research International Center - U 995 (LIRIC), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Institut Pasteur de Lille, Réseau International des Instituts Pasteur (RIIP)-Réseau International des Instituts Pasteur (RIIP), Institute for Clinical and Experimental Medicine (IKEM), Uniwersytet Jagielloński w Krakowie = Jagiellonian University (UJ), University of Bergen (UiB), Tulane University, Chinese Center for Disease Control and Prevention, Oulu University Hospital [Oulu], The University of Western Australia (UWA), Centre International de Recherche contre le Cancer - International Agency for Research on Cancer (CIRC - IARC), Niigata University, Norwegian University of Science and Technology (NTNU), Simon Fraser University (SFU.ca), University of Eastern Finland, Westfälische Wilhelms-Universität Münster (WWU), Innsbruck Medical University [Austria] (IMU), Universitätsklinikum Ulm - University Hospital of Ulm, University of Jyväskylä (JYU), The University of Hong Kong (HKU), Sahlgrenska Academy at University of Gothenburg [Göteborg], Tampere University Hospital, University of Tampere [Finland], West Virginia University [Morgantown], University of Chinese Academy of Sciences [Beijing] (UCAS), Uppsala Universitet [Uppsala], Universität Greifswald - University of Greifswald, Peking University [Shenzhen], The Jikei University School of Medicine, Baker Heart and Diabetes Institute (AUSTRALIA), Universita degli Studi di Padova, University of Edinburgh, University of Zaragoza - Universidad de Zaragoza [Zaragoza], International Institute of Molecular and Cell Biology [Warsaw] (IIMCB), Università degli Studi di Brescia [Brescia], Konan University [Kobe, Japan], University of Turku, Shanghai Jiao Tong University School of Medicine, Department of Epidemiology and Biostatistics, Imperial College London-School of public health, The University of Hong Kong (HKU)-The University of Hong Kong (HKU), Department of Emergency and Cardiovascular Medicine, Sahlgrenska Academy, Department of Epidemiology, Health Economics and Public Health, UMR 558 INSERM, Université de Toulouse, Centre Hospitalier Universitaire de Toulouse, Unité de Surveillance et d'Epidémiologie Nutritionnelle (USEN), Conservatoire National des Arts et Métiers [CNAM] (CNAM)-Institut de Veille Sanitaire (INVS)-Université Paris 13 (UP13), Department of Epidemiology, Murcia Regional Health Council, Murcia, Spain, parent, Department of Chronic Disease Prevention, Institute for plasma research, Institute for Plasma Research, Department of Biosciences and Nutrition, Karolinska Institutet [Stockholm], Department of Reproductive Endocrinology, University of Bari Aldo Moro (UNIBA), Nutritional Epidemiology, Department of Clinical Sciences, Clinical Research Center, Lund University [Lund]-Lund University [Lund], Neuroépidémiologie, Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Santé et de la Recherche Médicale (INSERM), Applied Food Science, Department of Medical Sciences and Uppsala Clinical Research Center, Uppsala University Hospital, Research Unit Hypertension and Cardiovascular Epidemiology [Louvain, Belgique], Studies Coordinating Centre [Louvain, Belgique], Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven)-Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), National Cancer Center, University of Kuopio, Epidémiologie cardiovasculaire et métabolique, Université Paris-Sud - Paris 11 (UP11)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de Santé Publique, d'Epidémiologie et de Développement (ISPED), Université Bordeaux Segalen - Bordeaux 2, Tampere University, Department of Andrology and Endocrinology, Katholieke Universiteit Leuven, Norwegian University of Science and Technology [Trondheim] (NTNU), Departamento de Salud Pública, Universidad Miguel Hernández [Elche] (UMH), Interactions Gène-Environnement en Physiopathologie Cardio-Vasculaire (IGE-PCV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Department of Internal Medicine, School of Public Health and Clinical Nutrition, Epidemiologia Ambiental, Centre for Research in Environmental Epidemiology (CREAL)-Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP) of Pamplona-Institut Hospital del Mar d'Investigacions Mèdiques (IMIM), Department of Epidemiology and Public Health, EA 3430, Laboratoire Chrono-environnement - CNRS - UBFC (UMR 6249) (LCE), Centre National de la Recherche Scientifique (CNRS)-Université de Franche-Comté (UFC), Université Bourgogne Franche-Comté [COMUE] (UBFC)-Université Bourgogne Franche-Comté [COMUE] (UBFC), Medical University of Silesia (SUM), University of Innsbruck, National Institute of Hygiene Warsaw, The Georges Institute for International Health, Naval Research Laboratory (NRL), Grant numbers 101506/Z/13/Z and Research Training Fellowship 203616/Z/16/Z, Ministry of Health of the Czech Republic (grant number 15-27109A), UAM. Departamento de Medicina Preventiva y Salud Pública y Microbiología, HUS Children and Adolescents, Department of Public Health, Universidad Autónoma de Madrid (UAM), Università degli studi di Torino = University of Turin (UNITO), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM), Westfälische Wilhelms-Universität Münster = University of Münster (WWU), CHU Toulouse [Toulouse], Université Paris 13 (UP13)-Institut de Veille Sanitaire (INVS)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université (HESAM)-HESAM Université (HESAM), Taddei, C, Zhou, B, Bixby, H, Danaei, G, Di Cesare, M, Kuulasmaa, K, Hajifathalian, K, Bentham, J, Bennett, JE, Aekplakorn, W, Cifkova, R, Dallongeville, J, DeBacquer, D, Giampaoli, S, Gudnason, V, Khang, Y-O, Laatikainen, T, Mann, JI, Marques-Vidal, P, Mensah, GA, Müller-Nurasyid, M, Ninomiya, T, Petkeviciene, J, Rodríguez-Artalejo, F, Servais, J, Söderberg, S, Stavreski, B, Wilsgaard, T, Zdrojewski, T, Zhao, D, Stevens, GA, Savin, S, Cowan, Mj, Riley, LM, Ezzati, M, Adams, R, Ahrens, W, Amouyel, P, Amuzu, A, Anderssen, SA, Ariansen, I, Arveiler, D, Aspelund, T, Auvinen, A, Avdicová, M, Banach, M, Bandosz, P, Banegas. JR, Barbagallo, CM, Bata, I, Baur, LA, Beaglehole, R, Bennet, JE, Bernotiene, G, Bi, Y, Bienek, A, Björkelund, C, Bo, S, Boehm, BO, Bonaccio, M, Bongard, V, Borchini, R, Borghs, H, Breckenkamp, J, Brenner, H, Bruno, G, Busch, MA, CabreradeLeón, A, Capuano, V, Casanueva, FF, Casas, J-P, Caserta, CA, Censi, L, Chen, F, Chen, S, Chirlaque, M-D, Cho, B, Cho, Y, Chudek, J, Claessens, F, Clarke, J, Clays, E, Cooper, C, Costanzo, S, Cottel, D, Cowell C, Crujeiras, AB, Cui, L, D'Arrigo, G, Dauchet, L, De Backer, G, De Bacquer, D, de Gaetano, G, De Henauw, S, De Smedt, D, Dennison, E, Desschamps, V, Di Castelnuovo, A, Dobson, AJ, Donfrancesco, C, Döring, A, Doua, K, Drygas, W, Du, Y, Dziankowska-Zaborszczyk, E, Eggertsen, R, Ekelund, U, Elousa, R, Eriksson, JG, Evans, A, Faeh, D, Felix-Redondo, FJ, Fernández-Bergés, D, Ferrari, M, Ferrieres, J, Finn, JD, Forslund, A-S, Forsner, M, Frontera, G, Fujita, Y, Gaciong, Z, Galvano, F, Gao, J, Garcia-de-la-Hera, M, Garnett, SP, Gaspoz, J-M, Gasul, L, Gates, L, Gianfagna, F, Gill, TK, Giovannelli, J, Goltzman, D, GonzalezGross, M, Gottrand, F, Graff-Iversen, S, Grafnetter, D, Gregor, RD, Grodzicki, T, Grosso, G, Gruden, G, Gu, D, Guallar-Castillón, P, Gudmundsson, EF, Guessous, I, Gunnlaugsdottir J, Gutzwiller, F, Hardy, R, Hata, J, Haugsgjerd, T, Hayes, AJ, He, H, He, Y, Herrala, S, Hihtaniemi, IP, Hobbs, M, Hopman, WM, Huerta, JM, Huybrechts, I, Iacoviello, L, Iannone, AG, Ikeda, N, Iwasaki, M, Jackson, R, Jamrozi, K, Janszky, I, Jarvelin, M-R, Jasienska, G, Jennings, G, Jeong, S-L, Jiang, CQ, Joffres, M, Jokelainen, JJ, Jonas, JB, Jóźwiak, J, Kajantie, EO, Kauhanen, K, Keil, U, Keinänen-Kiukaanniemi, S, Kersting, M, Khang, Y-H, Kiechl-Kohlendorfer, U, Kiechl, S, Kim, J, Kim, Y-Y, Klumbiene, J, Knoflach, M, Ko, S, Kolle, E, Korpelainen, R, Koskinen, S, Kouda, K, Kratzer, W, Kriemler, S, Krokstad, S, Kujala,UM, Kurjata, P, Lam, TH, Lanska, V, Lappas , G, Laugsand, LE, Lee, J, Lehtimäki, T, Li, Y, Lilly, C, Lin, X, Lind, L, Lissner, L, Liu, J, Lopez-Garcia, E, Lorbeer, R, Lozano, JE, Luksiene, D, Lundqvist, A, Lundqvist, R, Lytsy, P, Ma, G, Machi, S, Maggi, S, Magliano, DJ, Manzato, E, Mathiesen, EB, McLachlan, S, McLean, RM, Meirhaeghe, A, Metcalf, P, Mi, JM, Miller, JC, Moreno, LA, Morin, S, Mossakowska, M, Muiesan, ML, Mursu, J, Nakamura, H, Námešná, J, Navarrete-Muñoz, EM, Neal, WA, Nenko, I, Niiranen, T, Ning, G, Noale, M, Norie, S, Noto, Davide, O’Neill, T, O'Reilly, D, Oh, K, Olafsson, O, Paccaud, F, Pajak, A, Palmieri, L, Panza, F, Parnell, WR, Peltonen, M, Peters, A, Petersmann, A, P Pigeot, I, Pilotto, L, Piwonska, A, Pedro Plans-Rubió, P, Porta, M, Price, JF, Puder, JJ, Puhakka, SE, Radisauskas, R, Raitakari, O, Ramos, R, Redon, J, Rigo, F, Rodriguez-Perez, MdC, Romaguera, D, Ronkainen, K, Rosengren, A, Roy, JGR, Ruidavets, JB, Rutkowski, M, Salanave, B, Salmeron, D, Salomaa, V, Salonen, JT, Salvetti, M, Sans, S, Saramies, JL, Saum, K-U, Scheidt-Nave, C, Schienkiewitz, A, Schipf, S, Schmidt, CO, Schottker, B, Sebert S, Sen, A, Shaw, JE, Shibuya, K, Shin, DO, Shiri, R, Simons, J, Simons, LA, Sjostrom, M, Slowikowska-Hilczer, J, Slusarczyk, P, Solfrizzi, V, Sonestedt, E, Soumare, A, Staessen, JA, Stathopoulou, MG, Steene-Johannessen, J, Stehle, P, Stieber, J, Stöckl, D, Stokwiszewski, J, Sundström, J, Suriyawongpaisal, P, Tamosiunas, A, Tan, E, Taylor, A, Tell, G, Thijs, L, Tolonen, HK, Topór-Madry, R, Tormo, MJ, Torrent, M, Tsugane, S, Tuomainen, T-P, Tuomilehto, J, Tzourio, C, Uusitalo, HMT, Van Herck, K, Vanderschueren, D, Vanuzzo, D, Vatten, L, Vega, T, Veronesi, G, Vioque, P, Virtanen, JK, Visvikis-Siest, S, Vollenweider, P, Voutilainen, S, Vrijheid, M, Wagner, A, Wang, M-D, Wang, Q, Wang YX, Wannamethee, SG, Wei, W, Whincup, PH, Wiecek, A, Willeit, J, Willeit, P, Wojtyniak, B, Wong, A, Woodward, M, Wu, FC, JianFeng Wu, JF, Wu, SL, Xu, H, Xu, L, Yan, W, Yang, X, Ye, X, Yoshihara, A, Zambon, S, Zhao, W, NCD Risk Factor Collaboration (NCD-RisC), Jackson, R., Zhou, B., Bixby, H., Danaei, G., Di Cesare, M., Kuulasmaa, K., Hajifathalian, K., Bentham, J., Bennett, J.E., Aekplakorn, W., Cifkova, R., Dallongeville, J., DeBacquer, D., Giampaoli, S., Gudnason, V., Khang, Y.H., Laatikainen, T., Mann, J., Marques-Vidal, P., Mensah, G.A., Müller-Nurasyid, M., Ninomiya, T., Petkeviciene, J., Rodríguez-Artalejo, F., Servais, J., Söderberg, S., Stavreski, B., Wilsgaard, T., Zdrojewski, T., Zhao, D., Stevens, G.A., Savin, S., Cowan, M.J., Riley, L.M., Ezzati, M., Adams, R.J., Ahrens, W., Amouyel, P., Amuzu, A., Anderssen, S.A., Ariansen, I., Arveiler, D., Aspelund, T., Auvinen, J., Avdicová, M., Banach, M., Bandosz, P., Banegas, J.R., Barbagallo, C.M., Bata, I., Baur, L.A., Beaglehole, R., Bernotiene, G., Bi, Y., Bienek, A., Björkelund, C., Bo, S., Boehm, B.O., Bonaccio, M., Bongard, V., Borchini, R., Borghs, H., Breckenkamp, J., Brenner, H., Bruno, G., Busch, M.A., Cabrera de León, A., Capuano, V., Casanueva, F.F., Casas, J.P., Caserta, C.A., Censi, L., Chen, F., Chen, 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Superiore di Sanità (ISS), Helmholtz Zentrum München = German Research Center for Environmental Health, Kyushu University, The Arctic University of Norway [Tromsø, Norway] (UiT), Università degli Studi di Roma 'La Sapienza' = Sapienza University [Rome] (UNIROMA)-University of Naples Federico II = Università degli studi di Napoli Federico II, Université de Toulouse (UT)-Université de Toulouse (UT)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de la Santé et de la Recherche Médicale (INSERM), Università degli studi di Catania = University of Catania (Unict), Universitá degli Studi dell’Insubria = University of Insubria [Varese] (Uninsubria), Innsbruck Medical University = Medizinische Universität Innsbruck (IMU), Università degli Studi di Padova = University of Padua (Unipd), Università degli Studi di Brescia = University of Brescia (UniBs), Service Epidémiologie clinique et santé publique [CHU Toulouse], Pôle Santé publique et médecine publique [CHU Toulouse], Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse), 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), Università degli studi di Bari Aldo Moro = University of Bari Aldo Moro (UNIBA), Laboratoire Chrono-environnement (UMR 6249) (LCE), Leopold Franzens Universität Innsbruck - University of Innsbruck, National Institute of Public Health - National Institute of Hygiene [Poland], Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Université de Lille-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Pasteur de Lille, Medical University of Silesia, Katowice, University of Helsinki, National Institute for Health and Welfare, University of Helsinki, University of Helsinki, and University of Helsinki, Finnish Institute of Occupational Health
Subjects: Male, gupo de ascendencia continental asiática, Settore MED/09 - Medicina Interna, Total Cholesterol, Ldl Cholesterol, Hdl Cholesterol, Blood Lipids, Multi-country Study, Epidemiology, kolesteroli, humanos, Blood lipids, BLOOD-PRESSURE, 030204 cardiovascular system & hematology, triglicéridos, chemistry.chemical_compound, 0302 clinical medicine, High-density lipoprotein, HDL cholesterol, Medicine and Health Sciences, CARDIOVASCULAR RISK-FACTORS, Medicine, 030212 general & internal medicine, mediana edad, lípidos, Public, Environmental & Occupational Health, 2. Zero hunger, anciano, education.field_of_study, Age Factors, Public Health, Global Health, Social Medicine and Epidemiology, General Medicine, Middle Aged, colesterol, adulto, kansainvälinen vertailu, Lipids, 3142 Public health care science, environmental and occupational health, 3. Good health, Europe, Cholesterol, Population Surveillance, LDL cholesterol, SERUM-LIPIDS, Female, lipids (amino acids, peptides, and proteins), Life Sciences & Biomedicine, Adult, Asian Continental Ancestry Group, Canada, Total cholesterol, blood lipids, multi-country study, medicine.medical_specialty, Asia, Medicina, European Continental Ancestry Group, Population, Nursing, HDL-kolesteroli, White People, DIETARY-FAT, 03 medical and health sciences, Sex Factors, Asian People, kansanterveys, vigilancia de la población, Humans, CORONARY-HEART-DISEASE, ddc:610, LDL-kolesteroli, education, Triglycerides, METAANALYSIS, Aged, INDIVIDUAL DATA, Science & Technology, business.industry, Omvårdnad, blood lipid, Cholesterol, HDL, Ecological study, nutritional and metabolic diseases, Cholesterol, LDL, United States, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, Cholesterol/blood, Lipids/blood, Population Surveillance/methods, Triglycerides/blood, chemistry, WORLDWIDE TRENDS, Multi-country study, grupo de ascendencia continental europea, business, HIGH-DENSITY-LIPOPROTEIN, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology, Demography, Lipoprotein
Abstract: Artículo con numerosos autores. Sólo quedan reflejados el primero, los pertenecientes a la UAM y el colectivo, Background: Although high-density lipoprotein (HDL) and non-HDL cholesterol have opposite associations with coronary heart disease, multi-country reports of lipid trends only use total cholesterol (TC). Our aim was to compare trends in total, HDL and non-HDL cholesterol and the total-to-HDL cholesterol ratio in Asian and Western countries. Methods: We pooled 458 population-based studies with 82.1 million participants in 23 Asian and Western countries. We estimated changes in mean total, HDL and non-HDL cholesterol and mean total-to-HDL cholesterol ratio by country, sex and age group. Results: Since ∼1980, mean TC increased in Asian countries. In Japan and South Korea, the TC rise was due to rising HDL cholesterol, which increased by up to 0.17 mmol/L per decade in Japanese women; in China, it was due to rising non-HDL cholesterol. TC declined in Western countries, except in Polish men. The decline was largest in Finland and Norway, at ∼0.4 mmol/L per decade. The decline in TC in most Western countries was the net effect of an increase in HDL cholesterol and a decline in non-HDL cholesterol, with the HDL cholesterol increase largest in New Zealand and Switzerland. Mean total-to-HDL cholesterol ratio declined in Japan, South Korea and most Western countries, by as much as ∼0.7 per decade in Swiss men (equivalent to ∼26% decline in coronary heart disease risk per decade). The ratio increased in China. Conclusions: HDL cholesterol has risen and the total-to-HDL cholesterol ratio has declined in many Western countries, Japan and South Korea, with only a weak correlation with changes in TC or non-HDL cholesterol., This work was supported by the Wellcome Trust (grant numbers 101506/Z/13/Z and Research Training Fellowship 203616/Z/16/Z). R.C. acknowledges funding from the Ministry of Health of the Czech Republic (grant number 15-27109A)
Published: 2020

14. Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants

Author: Rodriguez-Martinez, Andrea, Zhou, Bin, Sophiea, Marisa K, Bentham, James, Paciorek, Christopher J, Iurilli, Maria LC, Carrillo-Larco, Rodrigo M, Bennett, James E, Di Cesare, Mariachiara, Taddei, Cristina, Bixby, Honor, Stevens, Gretchen A, Riley, Leanne M, Cowan, Melanie J, Savin, Stefan, Danaei, Goodarz, Chirita-Emandi, Adela, Kengne, Andre P, Khang, Young-Ho, Laxmaiah, Avula, Malekzadeh, Reza, Miranda, J Jaime, Moon, Jin Soo, Popovic, Stevo R, Sorensen, Thorkild IA, Soric, Maroje, Starc, Gregor, Zainuddin, Ahmad A, Gregg, Edward W, Bhutta, Zulfiqar A, Black, Robert, Ezzati, Majid, Abarca-Gomez, Leandra, Abdeen, Ziad A, Abdrakhmanova, Shynar, Ghaffar, Suhaila Abdul, Rahim, Hanan F Abdul, Abu-Rmeileh, Niveen M, Garba, Jamila Abubakar, Acosta-Cazares, Benjamin, Adams, Robert J, Aekplakorn, Wichai, Afsana, Kaosar, Afzal, Shoaib, Agdeppa, Imelda A, Aghazadeh-Attari, Javad, Aguilar-Salinas, Carlos A, Agyemang, Charles, Ahmad, Mohamad Hasnan, Ahmad, Noor Ani, Ahmadi, Ali, Ahmadi, Naser, Ahmed, Soheir H, Ahrens, Wolfgang, Aitmurzaeva, Gulmira, Ajlouni, Kamel, Al-Hazzaa, Hazzaa M, Al-Othman, Amani Rashed, Al-Raddadi, Rajaa, Alarouj, Monira, AlBuhairan, Fadia, AlDhukair, Shahla, Ali, Mohamed M, Alkandari, Abdullah, Alkerwi, Ala'a, Allin, Kristine, Alvarez-Pedrerol, Mar, Aly, Eman, Amarapurkar, Deepak N, Amiri, Parisa, Amougou, Norbert, Amouyel, Philippe, Andersen, Lars Bo, Anderssen, Sigmund A, Angquist, Lars, Anjana, Ranjit Mohan, Ansari-Moghaddam, Alireza, Aounallah-Skhiri, Hajer, Araujo, Joana, Ariansen, Inger, Aris, Tahir, Arku, Raphael E, Arlappa, Nimmathota, Aryal, Krishna K, Aspelund, Thor, Assah, Felix K, Assuncao, Maria Cecilia F, Aung, May Soe, Auvinen, Juha, Avdicova, Maria, Azevedo, Ana, Azimi-Nezhad, Mohsen, Azizi, Fereidoun, Azmin, Mehrdad, Babu, Bontha V, Jorgensen, Maja Baeksgaard, Baharudin, Azli, Bahijri, Suhad, Baker, Jennifer L, Balakrishna, Nagalla, Bamoshmoosh, Mohamed, Banach, Maciej, Bandosz, Piotr, Banegas, Jose R, Baran, Joanna, 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Zampelas, Antonis, Zamrazilova, Hana, Zapata, Maria Elisa, Zargar, Abdul Hamid, Zaw, Ko Ko, Zdrojewski, Tomasz, Vrkic, Tajana Zeljkovic, Zeng, Yi, Zhang, Luxia, Zhang, Zhen-Yu, Zhao, Dong, Zhao, Ming-Hui, Zhao, Wenhua, Zhen, Shiqi, Zheng, Wei, Zheng, Yingfeng, Zholdin, Bekbolat, Zhou, Maigeng, Zhu, Dan, Zocalo, Yanina, Cisneros, Julio Zuniga, Zuziak, Monika, Faculdade de Ciências da Nutrição e Alimentação, Instituto de Saúde Pública da Universidade do Porto, Interactions Gène-Environnement en Physiopathologie Cardio-Vasculaire (IGE-PCV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Environnement, Santé, Sociétés (ESS), Centre National de la Recherche Scientifique (CNRS), European Project: 774548, Reproductive Origins of Adult Health and Disease (ROAHD), Rodriguez-Martinez A, Zhou B, Sophiea MK, Bentham J, Paciorek CJ, Iurilli ML, Carrillo-Larco RM, Bennett JE, Di Cesare M, Taddei C, Bixby H, Stevens GA, Riley LM, Cowan MJ, Savin S, 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Baharudin A, Bahijri S, Baker JL, Balakrishna N, Bamoshmoosh M, Banach M, Bandosz P, Banegas JR, Baran J, Barbagallo CM, Barceló A, Barkat A, Barros AJ, Barros MVG, Basit A, Bastos JLD, Bata I, Batieha AM, Batista RL, Battakova Z, Batyrbek A, Baur LA, Beaglehole R, Bel-Serrat S, Belavendra A, Ben Romdhane H, Benedics J, Benet M, Berkinbayev S, Bernabe-Ortiz A, Bernotiene G, Bettiol H, Bezerra J, Bhagyalaxmi A, Bharadwaj S, Bhargava SK, Bi H, Bi Y, Bia D, Bika Lele EC, Bikbov MM, Bista B, Bjelica DJ, Bjerregaard P, Bjertness E, Bjertness MB, Björkelund C, Bloch KV, Blokstra A, Bo S, Bobak M, Boddy LM, Boehm BO, Boeing H, Boggia JG, Bogova E, Boissonnet CP, Bojesen SE, Bonaccio M, Bongard V, Bonilla-Vargas A, Bopp M, Borghs H, Bovet P, Braeckevelt L, Braeckman L, Bragt MC, Brajkovich I, Branca F, Breckenkamp J, Breda J, Brenner H, Brewster LM, Brian GR, Brinduse L, Brophy S, Bruno G, Bueno-de-Mesquita HB, Bugge A, Buoncristiano M, Burazeri G, Burns C, Cabrera de León A, Cacciottolo J, Cai H, Cama T, Cameron C, Camolas J, Can G, Cândido APC, Cañete F, Capanzana MV, Capková N, Capuano E, Capuano V, Cardol M, Cardoso VC, Carlsson AC, Carmuega E, Carvalho J, Casajús JA, Casanueva FF, Celikcan E, Censi L, Cervantes-Loaiza M, Cesar JA, Chamukuttan S, Chan AW, Chan Q, Chaturvedi HK, Chaturvedi N, Che Abdul Rahim N, Chen C-J, Chen F, Chen H, Chen S, Chen Z, Cheng C-Y, Cheraghian B, Chetrit A, Chikova-Iscener E, Chiolero A, Chiou S-T, Chirlaque M-D, Cho B, Christensen K, Christofaro DG, Chudek J, Cifkova R, Cilia M, Cinteza E, Claessens F, Clarke J, Clays E, Cohen E, Concin H, Confortin SC, Cooper C, Coppinger TC, Corpeleijn E, Costanzo S, Cottel D, Cowell C, Craig CL, Crampin AC, Crujeiras AB, Csilla S, Cucu AM, Cui L, Cureau FV, D'Arrigo G, d'Orsi E, Dacica L, Dal Re Saavedra MÁ, Dallongeville J, Damasceno A, Damsgaard CT, Dankner R, Dantoft TM, Dasgupta P, Dastgiri S, Dauchet L, Davletov K, De Backer G, De Bacquer D, de Gaetano G, De Henauw S, de Oliveira PD, De Ridder D, De Ridder K, de Rooij SR, De Smedt D, Deepa M, Deev AD, DeGennaro V, Jr, Dehghan A, Delisle H, Delpeuch F, Demarest S, Dennison E, Deren K, Deschamps V, Dhana K, Dhimal M, Di Castelnuovo AF, Dias-da-Costa JS, Díaz-Sánchez ME, Diaz A, Dika Z, Djalalinia S, Djordjic V, Do HT, Dobson AJ, Donati MB, Donfrancesco C, Donoso SP, Döring A, Dorobantu M, Dorosty AR, Doua K, Drygas W, Duan JL, Duante CA, Duboz P, Duda RB, Duleva V, Dulskiene V, Dumith SC, Dushpanova A, Dzerve V, Dziankowska-Zaborszczyk E, Eddie R, Eftekhar E, Egbagbe EE, Eggertsen R, Eghtesad S, Eiben G, Ekelund U, El-Khateeb M, El Ati J, Eldemire-Shearer D, Eliasen M, Elliott P, Engle-Stone R, Enguerran M, Erasmus RT, Erbel R, Erem C, Eriksen L, Eriksson JG, Escobedo-de la Peña J, Eslami S, Esmaeili A, Evans A, Faeh D, Fakhretdinova AA, Fall CH, Faramarzi E, Farjam M, Farrugia Sant'Angelo V, Farzadfar F, Fattahi MR, Fawwad A, Felix-Redondo FJ, Ferguson TS, Fernandes RA, Fernández-Bergés D, Ferrante D, Ferrao T, Ferrari M, Ferrario MM, Ferreccio C, Ferrer E, Ferrieres J, Figueiró TH, Fijalkowska A, Fink G, Fischer K, Föger B, Foo LH, Forsner M, Fouad HM, Francis DK, Franco MDC, Franco OH, Frikke-Schmidt R, Frontera G, Fuchs FD, Fuchs SC, Fujiati II, Fujita Y, Fumihiko M, Furusawa T, Gaciong Z, Gafencu M, Galbarczyk A, Galenkamp H, Galeone D, Galfo M, Galvano F, Gao J, Garcia-de-la-Hera M, García-Solano M, Gareta D, Garnett SP, Gaspoz J-M, Gasull M, Gaya ACA, Gaya AR, Gazzinelli A, Gehring U, Geiger H, Geleijnse JM, Ghanbari A, Ghasemi E, Gheorghe-Fronea O-F, Giampaoli S, Gianfagna F, Gill TK, Giovannelli J, Gironella G, Giwercman A, Gkiouras K, Godos J, Gogen S, Goldsmith RA, Goltzman D, Gómez SF, Gomula A, Goncalves Cordeiro da Silva B, Gonçalves H, Gonzalez-Chica DA, Gonzalez-Gross M, González-Leon M, González-Rivas JP, González-Villalpando C, González-Villalpando M-E, Gonzalez AR, Gottrand F, Graça AP, Graff-Iversen S, Grafnetter D, Grajda A, Grammatikopoulou MG, Gregor RD, Grodzicki T, Grøholt EK, Grøntved A, Grosso G, Gruden G, Gu D, Gualdi-Russo E, Guallar-Castillón P, Gualtieri A, Gudmundsson EF, Gudnason V, Guerrero R, Guessous I, Guimaraes AL, Gulliford MC, Gunnlaugsdottir J, Gunter MJ, Guo X-H, Guo Y, Gupta PC, Gupta R, Gureje O, Gurzkowska B, Gutiérrez-González E, Gutierrez L, Gutzwiller F, Ha S, Hadaegh F, Hadjigeorgiou CA, Haghshenas R, Hakimi H, Halkjær J, Hambleton IR, Hamzeh B, Hange D, Hanif AA, Hantunen S, Hari Kumar R, Hashemi-Shahri SM, Hassapidou M, Hata J, Haugsgjerd T, Hayes AJ, He J, He Y, He Y, Heidinger-Felso R, Heinen M, Hejgaard T, Hendriks ME, Henrique RDS, Henriques A, Hernandez Cadena L, Herrala S, Herrera VM, Herter-Aeberli I, Heshmat R, Hill AG, Ho SY, Ho SC, Hobbs M, Hofman A, Holden Bergh I, Holdsworth M, Homayounfar R, Homs C, Hopman WM, Horimoto AR, Hormiga CM, Horta BL, Houti L, Howitt C, Htay TT, Htet AS, Htike MMT, Hu Y, Huerta JM, Huhtaniemi IT, Huidumac Petrescu C, Husseini A, Huu CN, Huybrechts I, Hwalla N, Hyska J, Iacoviello L, Ibarluzea JM, Ibrahim MM, Ibrahim Wong N, Ikeda N, Ikram MA, Iotova V, Irazola VE, Ishida T, Islam M, Islam SMS, Iwasaki M, Jackson RT, Jacobs JM, Jaddou HY, Jafar T, James K, Jamil KM, Jamrozik K, Janszky I, Janus E, Jarani J, Jarvelin M-R, Jasienska G, Jelakovic A, Jelakovic B, Jennings G, Jha AK, Jiang CQ, Jimenez RO, Jöckel K-H, Joffres M, Johansson M, Jokelainen JJ, Jonas JB, Jørgensen T, Joshi P, Joukar F, Jovic DP, Józwiak JJ, Juolevi A, Jurak G, Jurca Simina I, Juresa V, Kaaks R, Kaducu FO, Kafatos A, Kajantie EO, Kalmatayeva Z, Kalter-Leibovici O, Kameli Y, Kanala KR, Kannan S, Kapantais E, Karki KB, Katibeh M, Katz J, Katzmarzyk PT, Kauhanen J, Kaur P, Kavousi M, Kazakbaeva GM, Keil U, Keinan Boker L, Keinänen-Kiukaanniemi S, Kelishadi R, Kelleher C, Kemper HC, Keramati M, Kerimkulova A, Kersting M, Key T, Khader YS, Khalili D, Khaw K-T, Kheiri B, Kheradmand M, Khosravi A, Khouw IM, Kiechl-Kohlendorfer U, Kiechl S, Killewo J, Kim DW, Kim HC, Kim J, Kindblom JM, Klakk H, Klimek M, Klimont J, Klumbiene J, Knoflach M, Koirala B, Kolle E, Kolsteren P, König J, Korpelainen R, Korrovits P, Korzycka M, Kos J, Koskinen S, Kouda K, Kovacs VA, Kowlessur S, Koziel S, Kratzer W, Kriemler S, Kristensen PL, Krokstad S, Kromhout D, Krtalic B, Kruger HS, Kubinova R, Kuciene R, Kujala UM, Kujundzic E, Kulaga Z, Kumar RK, Kunešová M, Kurjata P, Kusuma YS, Kuulasmaa K, Kyobutungi C, La QN, Laamiri FZ, Laatikainen T, Lachat C, Laid Y, Lam TH, Lambrinou C-P, Landais E, Lanska V, Lappas G, Larijani B, Latt TS, Lauria L, Lazo-Porras M, Le Nguyen Bao K, Le Port A, Le TD, Lee J, Lee J, Lee PH, Lehmann N, Lehtimäki T, Lemogoum D, Levitt NS, Li Y, Liivak M, Lilly CL, Lim W-Y, Lima-Costa MF, Lin H-H, Lin X, Lin Y-T, Lind L, Linneberg A, Lissner L, Litwin M, Liu J, Liu L, Lo W-C, Loit H-M, Long KQ, Lopes L, Lopes O, Lopez-Garcia E, Lopez T, Lotufo PA, Lozano JE, Lukrafka JL, Luksiene D, Lundqvist A, Lundqvist R, Lunet N, Lunogelo C, Lustigová M, Luszczki E, Ma G, Ma J, Ma X, Machado-Coelho GL, Machado-Rodrigues AM, Machi S, Macieira LM, Madar AA, Maggi S, Magliano DJ, Magnacca S, Magriplis E, Mahasampath G, Maire B, Majer M, Makdisse M, Mäki P, Malekzadeh F, Malhotra R, Mallikharjuna Rao K, Malyutina SK, Maniego LV, Manios Y, Mann JI, Mansour-Ghanaei F, Manzato E, Margozzini P, Markaki A, Markey O, Markidou Ioannidou E, Marques-Vidal P, Marques LP, Marrugat J, Martin-Prevel Y, Martin R, Martorell R, Martos E, Marventano S, Mascarenhas LP, Masoodi SR, Mathiesen EB, Mathur P, Matijasevich A, Matsha TE, Mavrogianni C, Mazur A, Mbanya JCN, McFarlane SR, McGarvey ST, McKee M, McLachlan S, McLean RM, McLean SB, McNulty BA, Mediene-Benchekor S, Medzioniene J, Mehdipour P, Mehlig K, Mehrparvar AH, Meirhaeghe A, Meisfjord J, Meisinger C, Menezes AMB, Menon GR, Mensink GB, Menzano MT, Mereke A, Meshram II, Metspalu A, Mi J, Michaelsen KF, Michels N, Mikkel K, Milkowska K, Miller JC, Minderico CS, Mini GK, Miquel JF, Mirjalili MR, Mirkopoulou D, Mirrakhimov E, Mišigoj-Durakovic M, Mistretta A, Mocanu V, Modesti PA, Moghaddam SS, Mohajer B, Mohamed MK, Mohamed SF, Mohammad K, Mohammadi Z, Mohammadifard N, Mohammadpourhodki R, Mohan V, Mohanna S, Mohd Yusoff MF, Mohebbi I, Mohebi F, Moitry M, Molbo D, Møllehave LT, Møller NC, Molnár D, Momenan A, Mondo CK, Monroy-Valle M, Monterrubio-Flores E, Monyeki KDK, Moosazadeh M, Moreira LB, Morejon A, Moreno LA, Morgan K, Morin SN, Mortensen EL, Moschonis G, Mossakowska M, Mostafa A, Mota-Pinto A, Mota J, Motlagh ME, Motta J, Moura-dos-Santos MA, Mridha MK, Msyamboza KP, Mu TT, Muc M, Mugoša B, Muiesan ML, Mukhtorova P, Müller-Nurasyid M, Murphy N, Mursu J, Murtagh EM, Musa KI, Music Milanovic S, Musil V, Mustafa N, Nabipour I, Naderimagham S, Nagel G, Naidu BM, Najafi F, Nakamura H, Námešná J, Nang EEK, Nangia VB, Nankap M, Narake S, Nardone P, Nauck M, Neal WA, Nejatizadeh A, Nelis K, Nelis L, Nenko I, Neovius M, Nervi F, Nguyen CT, Nguyen D, Nguyen QN, Nieto-Martínez RE, Nikitin YP, Ning G, Ninomiya T, Nishtar S, Noale M, Noboa OA, Nogueira H, Norat T, Nordendahl M, Nordestgaard BG, Noto D, Nowak-Szczepanska N, Nsour MA, Nuhoglu I, Nurk E, O'Neill TW, O'Reilly D, Obreja G, Ochimana C, Ochoa-Avilés AM, Oda E, Oh K, Ohara K, Ohlsson C, Ohtsuka R, Olafsson Ö, Olinto MTA, Oliveira IO, Omar MA, Onat A, Ong SK, Ono LM, Ordunez P, Ornelas R, Ortiz AP, Ortiz PJ, Osler M, Osmond C, Ostojic SM, Ostovar A, Otero JA, Overvad K, Owusu-Dabo E, Paccaud FM, Padez C, Pagkalos I, Pahomova E, Paiva KMD, Pajak A, Palli D, Palloni A, Palmieri L, Pan W-H, Panda-Jonas S, Pandey A, Panza F, Papandreou D, Park S-W, Park S, Parnell WR, Parsaeian M, Pascanu IM, Pasquet P, Patel ND, Pednekar MS, Peer N, Peixoto SV, Peltonen M, Pereira AC, Peres MA, Pérez-Farinós N, Pérez CM, Peterkova V, Peters A, Petersmann A, Petkeviciene J, Petrauskiene A, Pettenuzzo E, Peykari N, Pham ST, Pichardo RN, Pierannunzio D, Pigeot I, Pikhart H, Pilav A, Pilotto L, Pistelli F, Pitakaka F, Piwonska A, Pizarro AN, Plans-Rubió P, Poh BK, Pohlabeln H, Pop RM, Porta M, Posch G, Poudyal A, Poulimeneas D, Pouraram H, Pourfarzi F, Pourshams A, Poustchi H, Pradeepa R, Price AJ, Price JF, Providencia R, Puder JJ, Pudule I, Puhakka SE, Puiu M, Punab M, Qasrawi RF, Qorbani M, Quoc Bao T, Radic I, Radisauskas R, Rahimikazerooni S, Rahman M, Rahman M, Raitakari O, Raj M, Rakhimova E, Rakhmatulloev S, Rakovac I, Ramachandra Rao S, Ramachandran A, Ramke J, Ramos E, Ramos R, Rampal L, Rampal S, Rarra V, Rascon-Pacheco RA, Rasmussen M, Rech CR, Redon J, Reganit PFM, Regecová V, Revilla L, Rezaianzadeh A, Ribas-Barba L, Ribeiro R, Riboli E, Richter A, Rigo F, Rinaldo N, Rinke de Wit TF, Rito A, Ritti-Dias RM, Rivera JA, Robitaille C, Roccaldo R, Rodrigues D, Rodríguez-Artalejo F, Rodriguez-Perez MDC, Rodríguez-Villamizar LA, Roggenbuck U, Rojas-Martinez R, Rojroongwasinkul N, Romaguera D, Romeo EL, Rosario RV, Rosengren A, Rouse I, Roy JG, Rubinstein A, Rühli FJ, Ruidavets J-B, Ruiz-Betancourt BS, Ruiz Moreno E, Rusakova IA, Russell Jonsson K, 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Shrestha N, Si-Ramlee K, Siani A, Siantar R, Sibai AM, Silva AM, Silva DAS, Simon M, Simons J, Simons LA, Sjöberg A, Sjöström M, Skodje G, Slowikowska-Hilczer J, Slusarczyk P, Smeeth L, So H-K, Soares FC, Sobek G, Sobngwi E, Sodemann M, Söderberg S, Soekatri MY, Soemantri A, Sofat R, Solfrizzi V, Somi MH, Sonestedt E, Song Y, Sørgjerd EP, Sossa Jérome C, Soto-Rojas VE, Soumaré A, Sovic S, Sparboe-Nilsen B, Sparrenberger K, Spinelli A, Spiroski I, Staessen JA, Stamm H, Stathopoulou MG, Staub K, Stavreski B, Steene-Johannessen J, Stehle P, Stein AD, Stergiou GS, Stessman J, Stevanovic R, Stieber J, Stöckl D, Stocks T, Stokwiszewski J, Stoyanova E, Stratton G, Stronks K, Strufaldi MW, Sturua L, Suárez-Medina R, Suka M, Sun C-A, Sundström J, Sung Y-T, Sunyer J, Suriyawongpaisal P, Swinburn BA, Sy RG, Syddall HE, Sylva RC, Szklo M, Szponar L, Tai ES, Tammesoo M-L, Tamosiunas A, Tan EJ, Tang X, Tanser F, Tao Y, Tarawneh MR, Tarp J, Tarqui-Mamani CB, Taxová Braunerová R, Taylor A, Taylor J, 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Vladulescu M, Vlasoff T, Vocanec D, Völzke H, Voutilainen A, Voutilainen S, Vrijheid M, Vrijkotte TG, Wade AN, Wagner A, Waldhör T, Walton J, Wambiya EO, Wan Bebakar WM, Wan Mohamud WN, Wanderley Júnior RDS, Wang M-D, Wang N, Wang Q, Wang X, Wang YX, Wang Y-W, Wannamethee SG, Wareham N, Weber A, Wedderkopp N, Weerasekera D, Weghuber D, Wei W, Weres A, Werner B, Whincup PH, Widhalm K, Widyahening IS, Wiecek A, Wilks RJ, Willeit J, Willeit P, Williams J, Wilsgaard T, Wojtyniak B, Wong-McClure RA, Wong A, Wong JE, Wong TY, Woo J, Woodward M, Wu FC, Wu J, Wu LJ, Wu S, Xu H, Xu L, Yaacob NA, Yamborisut U, Yan W, Yang L, Yang X, Yang Y, Yardim N, Yaseri M, Yasuharu T, Ye X, Yiallouros PK, Yoosefi M, Yoshihara A, You QS, You S-L, Younger-Coleman NO, Yusof SM, Yusoff AF, Zaccagni L, Zafiropulos V, Zakavi SR, Zamani F, Zambon S, Zampelas A, Zamrazilová H, Zapata ME, Zargar AH, Zaw KK, Zdrojewski T, Zeljkovic Vrkic T, Zeng Y, Zhang L, Zhang Z-Y, Zhao D, Zhao M-H, Zhao W, Zhen S, Zheng W, Zheng Y, Zholdin B, Zhou M, Zhu D, Zocalo Y, Zuñiga Cisneros J, Zuziak M, Ezzati M, Tampere University, Clinical Medicine, Department of Clinical Chemistry, Eye Centre, NCD Risk Factor Collaboration (NCD-RisC), ACS - Diabetes & metabolism, APH - Global Health, APH - Personalized Medicine, Public and occupational health, Epidemiology and Data Science, APH - Aging & Later Life, APH - Health Behaviors & Chronic Diseases, ARD - Amsterdam Reproduction and Development, Adult Psychiatry, Global Health, APH - Methodology, APH - Quality of Care, ACS - Atherosclerosis & ischemic syndromes, ACS - Heart failure & arrhythmias, Rodriguez-Martinez A., Zhou B., Sophiea M.K., Bentham J., Paciorek C.J., Iurilli M.L., Carrillo-Larco R.M., Bennett J.E., Di Cesare M., Taddei C., Bixby H., Stevens G.A., Riley L.M., Cowan M.J., Savin S., Danaei G., Chirita-Emandi A., Kengne A.P., Khang Y.-H., Laxmaiah A., Malekzadeh R., Miranda J.J., Moon J.S., Popovic S.R., Sorensen T.I., Soric M., Starc G., Zainuddin A.A., Gregg 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Uysal N., Vaitkeviciute J., Valdivia G., Vale S., Valvi D., van Dam R.M., Van der Heyden J., van der Schouw Y.T., Van Herck K., Van Minh H., van Valkengoed I.G., Vanderschueren D., Vanuzzo D., Varbo A., Varela-Moreiras G., Varona-Perez P., Vasan S.K., Vega T., Veidebaum T., Velasquez-Melendez G., Velika B., Veronesi G., Verschuren W.M., Victora C.G., Viegi G., Viet L., Villalpando S., Vineis P., Vioque J., Virtanen J.K., Visser M., Visvikis-Siest S., Viswanathan B., Vladulescu M., Vlasoff T., Vocanec D., Volzke H., Voutilainen A., Voutilainen S., Vrijheid M., Vrijkotte T.G., Wade A.N., Wagner A., Waldhor T., Walton J., Wambiya E.O., Wan Bebakar W.M., Wan Mohamud W.N., Wanderley Junior R.D.S., Wang M.-D., Wang N., Wang Q., Wang X., Wang Y.X., Wang Y.-W., Wannamethee S.G., Wareham N., Weber A., Wedderkopp N., Weerasekera D., Weghuber D., Wei W., Weres A., Werner B., Whincup P.H., Widhalm K., Widyahening I.S., Wiecek A., Wilks R.J., Willeit J., Willeit P., Williams J., Wilsgaard T., Wojtyniak B., Wong-McClure R.A., Wong A., Wong J.E., Wong T.Y., Woo J., Woodward M., Wu F.C., Wu J., Wu L.J., Wu S., Xu H., Xu L., Yaacob N.A., Yamborisut U., Yan W., Yang L., Yang X., Yang Y., Yardim N., Yaseri M., Yasuharu T., Ye X., Yiallouros P.K., Yoosefi M., Yoshihara A., You Q.S., You S.-L., Younger-Coleman N.O., Yusof S.M., Yusoff A.F., Zaccagni L., Zafiropulos V., Zakavi S.R., Zamani F., Zambon S., Zampelas A., Zamrazilova H., Zapata M.E., Zargar A.H., Zaw K.K., Zdrojewski T., Zeljkovic Vrkic T., Zeng Y., Zhang L., Zhang Z.-Y., Zhao D., Zhao M.-H., Zhao W., Zhen S., Zheng W., Zheng Y., Zholdin B., Zhou M., Zhu D., Zocalo Y., Zuniga Cisneros J., Zuziak M., and Ezzati M.
Subjects: Male, body-mass index, ADULTHOOD, Adolescents, pituuskasvu, Pediatrics, Body Mass Index, 0302 clinical medicine, Child Development, nuoret, Public health surveillance, Medicine, Health Status Indicators, 10. No inequality, Child, 11 Medical and Health Sciences, Body mass index, ComputingMilieux_MISCELLANEOUS, education.field_of_study, VDP::Medisinske Fag: 700::Helsefag: 800::Samfunnsmedisin, sosialmedisin: 801, General Medicine, Body mass indexes, kansainvälinen vertailu, 3. Good health, Geography, Health, 030220 oncology & carcinogenesis, Child, Preschool, Medical and Health sciences, purl.org/becyt/ford/3 [https], medicine.medical_specialty, School-aged adolescents, Socio-culturale, lapset (ikäryhmät), Nursing, territories, ravinto, purl.org/becyt/ford/3.3 [https], 03 medical and health sciences, School Children, SDG 3 - Good Health and Well-being, SYSTEMATIC ANALYSIS, Humans, school-aged children and adolescents, Montenegro, education, Science & Technology, Omvårdnad, Health sciences, Medical and Health sciences, Ciências médicas e da saúde, Bayes Theorem, Anthropometry, Adolescent Development, medicine.disease, TRENDS, Height and Body-mass Index, Faculdade de Ciências Sociais, UNDERNUTRITION, Height index trajectories, Height, body mass index, children , epidemiology, risk factors, growth, Stature, Demography, Settore MED/09 - Medicina Interna, Internationality, [SDV]Life Sciences [q-bio], 030204 cardiovascular system & hematology, Body-mass index trajectories, Epidemiology, Medicine and Health Sciences, risk factors, countries, EPIDEMIOLOGY, height, children, adolescents, BMI, 030212 general & internal medicine, painoindeksi, Child development, 2. Zero hunger, Medicine(all), School age child, obestity children cardiovascular, Population Health, 1. No poverty, Pediatrik, Public Health, Global Health, Social Medicine and Epidemiology, 3142 Public health care science, environmental and occupational health, Pooled analysis, NUTRITION, Female, medicine.symptom, pooled analysis, Life Sciences & Biomedicine, terveys, height, BMI, nutrition, health, children, adolescents, Adolescent, growth, Population, body-mass, Population based, Body-mass index, Young Adult, Medicine, General & Internal, Meta-Analysis as Topic, General & Internal Medicine, parasitic diseases, Weight gain, School-aged childrens, Age trajectories, business.industry, Height, Weight, Body Height, Folkhälsovetenskap, global hälsa, socialmedicin och epidemiologi, Malnutrition, ONSET, Ciências da Saúde, Ciências médicas e da saúde, School-aged children, VDP::Medical disciplines: 700::Health sciences: 800::Community medicine, Social medicine: 801, business, terveysriskit, Estilos de Vida e Impacto na Saúde
Abstract: BACKGROUND: Comparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents., METHODS: For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence., FINDINGS: We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls., INTERPRETATION: The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks., Wellcome Trust, AstraZeneca Young Health Programme, EU., peer-reviewed
Published: 2020

15. Surface imaging for real‐time patient respiratory function assessment in intensive care

Author: Victoire Pateau, J. F. Clement, Julien Bert, Erwan L'Her, Souha Nazir, Hadi Fayad, and Dimitris Visvikis
Subjects: medicine.medical_specialty, Critical Care, Respiratory rate, Context (language use), Respiratory monitoring, 030218 nuclear medicine & medical imaging, law.invention, 03 medical and health sciences, 0302 clinical medicine, law, Intensive care, Tidal Volume, Humans, Medicine, Respiratory function, Tidal volume, Monitoring, Physiologic, business.industry, Respiration, Continuous monitoring, General Medicine, Intensive care unit, Intensive Care Units, 030220 oncology & carcinogenesis, Emergency medicine, business
Abstract: PURPOSE Monitoring of physiological parameters is a major concern in Intensive Care Units (ICU) given their role in the assessment of vital organ function. Within this context, one issue is the lack of efficient noncontact techniques for respiratory monitoring. In this paper, we present a novel noncontact solution for real-time respiratory monitoring and function assessment of ICU patients. METHODS The proposed system uses a Time-of-Flight depth sensor to analyze the patient's chest wall morphological changes in order to estimate multiple respiratory function parameters. The automatic detection of the patient's torso is also proposed using a deep neural network model trained on the COCO dataset. The evaluation of the proposed system was performed on a mannequin and on 16 mechanically ventilated patients (a total of 216 recordings) admitted in the ICU of the Brest University Hospital. RESULTS The estimation of respiratory parameters (respiratory rate and tidal volume) showed high correlation with the reference method (r = 0.99; P
Published: 2020

16. Design and Kinematics of a Comanipulated Robot Dedicated to Prostate Brachytherapy

Author: Julien Bert, Aziza Ben Halima, Dimitris Visvikis, Mozert Djohossou, and Antoine Valérie
Subjects: 0209 industrial biotechnology, Robot kinematics, business.industry, Computer science, General Mathematics, medicine.medical_treatment, Brachytherapy, Robotics, 02 engineering and technology, Kinematics, Computer Science Applications, 03 medical and health sciences, 020901 industrial engineering & automation, 0302 clinical medicine, Control and Systems Engineering, 030220 oncology & carcinogenesis, medicine, Robot, Artificial intelligence, business, Software, Delta robot, Prostate brachytherapy, Simulation, Haptic technology
Abstract: SUMMARYIn brachytherapy, the manual implantation of seeds is not accurate leading to side effects and limiting the use of new procedures. Robotics solutions have to be fully suitable for medical applications especially considering the operating room. This paper investigates a delta robot solution for improving the accuracy of the prostate brachytherapy procedure by proposing a compact and lightweight robot. In addition, the design was thought as a comanipulated robot for a better acceptability and human–machine interaction. The robot kinematics and singularities were determined and the theoretical capability in term of resolution and force feedback was evaluated. A prototype was built in order to experimentally measure the capability of this first prototype.
Published: 2020

17. Cardiotoxic Profile and Arterial Stiffness of Adjuvant Chemotherapy for Colorectal Cancer

Author: Christodoulos Stefanadis, Christos Georgakopoulos, C. Vlachopoulos, Stella Kyvelou, P Pietri, K Manousou, Dimitris Tousoulis, D Pektasides, and A Visvikis
Subjects: 0301 basic medicine, Oncology, Cardiotoxicity, Chemotherapy, medicine.medical_specialty, Colorectal cancer, business.industry, medicine.medical_treatment, Cancer, medicine.disease, 03 medical and health sciences, 030104 developmental biology, 0302 clinical medicine, FOLFOX, 030220 oncology & carcinogenesis, Internal medicine, medicine, Arterial stiffness, business, Prospective cohort study, Pulse wave velocity, medicine.drug
Abstract: Background and Purpose Even though new cancer therapies have improved the overall survival, in some cases they have been associated with adverse effects, including increased cardiotoxicity. The purpose of the present study was to assess the cardiovascular effects of adjuvant chemotherapy for colorectal cancer and mainly the impact on arterial stiffness indices. Material and Methods A total of 70 patients with non-metastatic colorectal cancer who were treated either with FOLFOX (n=16) or with XELOX (n=54) adjuvant chemotherapy were included in the study. All patients were subjected to full cardiovascular evaluation at the beginning and the end of chemotherapy. Arterial stiffness was assessed by means of pulse wave velocity (PWV) and augmentation index (Aix) and full laboratory examinations were conducted prior to, and soon after, the termination of chemotherapy. Results Patients exhibited significantly higher levels of carotid-radial PWV, carotid femoral RWV and Aix post-chemotherapy (p
Published: 2020

18. Genetic Variations of VEGFA Gene Are Associated With Infiltration of Adjacent Tissues and the Clinical Outcome of Patients With Nasopharyngeal Carcinoma

Author: George Fountzilas, Elizabeth Psoma, Paris Kosmidis, Foteinos-Ioannis Dimitrakopoulos, Kyriaki Papadopoulou, Dimitra Rontogianni, Georgia-Angeliki Koliou, Anastasios Visvikis, Anna Kalogera-Fountzila, Jannis Constantinidis, and Mattheos Bobos
Subjects: endocrine system, Cancer Research, Endothelin receptor type A, business.industry, Single-nucleotide polymorphism, General Medicine, medicine.disease, Nibrin, Andrology, 03 medical and health sciences, Vascular endothelial growth factor A, 0302 clinical medicine, Oncology, Nasopharyngeal carcinoma, 030220 oncology & carcinogenesis, Genotype, Medicine, Clinical significance, business, Pterygoid Muscles
Abstract: BACKGROUND/AIM The aim of the present study was to investigate the clinical significance of 7 single nucleotide polymorphisms (SNPs) of vascular endothelial growth factor A (VEGFA), endothelin receptor type A (EDNRA), nibrin (NBS1) and Fas cell surface death receptor (FAS) genes in patients with nasopharyngeal carcinoma (NPC). PATIENTS AND METHODS Genomic DNA was extracted from the peripheral blood specimens of 60 patients. Genotyping of 4 VEGFA polymorphisms, namely VEGFA -1154 G/A (rs1570360), -2578 A/C (rs699947), -1498 C/T (rs833061) and +936 C/T (rs3025039), as well as EDNRA SNP p.His323 (rs5333), NBS1 p.E185Q (rs1805794) and FAS -671 A/G (rs1800682) was performed by using Sanger sequencing. RESULTS The VEGFA +936 CC genotype was more frequent in tumors with bilateral infiltration of pterygoid plates compared to those with ipsilateral (76.9% vs. 69.6%, p=0.008) and no infiltration of pterygoid plates (76.9% vs. 68.8%, p=0.023). VEGFA -2578, VEGFA -1154 and VEGFA +936 were significantly associated with infiltration to the pterygoid processes (p=0.011, p=0.041 and p=0.032, respectively). EDNRA H323H TT genotype was marginally associated with infiltration to the ipsilateral medial pterygoid muscles (p=0.045). A trend towards longer overall survival was observed for VEGFA -2578 CC as compared to AC (HR=0.24, p=0.060). CONCLUSION The studied VEGFA SNPs seem to be associated with the local extension of the NPC and maybe with the clinical outcome of this patient group.
Published: 2020

19. Prediction of recurrence after surgery in colorectal cancer patients using radiomics from diagnostic contrast-enhanced computed tomography: a two-center study

Author: Denis Pezet, Dimitris Visvikis, Bogdan Badic, Vincent Jaouen, Karine Poirot, Ronrick Da-Ano, Johan Gagnière, Mathieu Hatt, Benoit Magnin, Institut Pascal (IP), Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne (UCA)-Institut national polytechnique Clermont Auvergne (INP Clermont Auvergne), and Université Clermont Auvergne (UCA)-Université Clermont Auvergne (UCA)
Subjects: medicine.medical_specialty, Multivariate statistics, Support Vector Machine, Colorectal cancer, Context (language use), Feature selection, Disease-Free Survival, 030218 nuclear medicine & medical imaging, Machine Learning, 03 medical and health sciences, 0302 clinical medicine, Lasso (statistics), [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, medicine, Adjuvant therapy, Humans, Radiology, Nuclear Medicine and imaging, ComputingMilieux_MISCELLANEOUS, Retrospective Studies, Neuroradiology, business.industry, General Medicine, medicine.disease, 030220 oncology & carcinogenesis, Cohort, Radiology, Colorectal Neoplasms, Tomography, X-Ray Computed, business
Abstract: To assess the value of contrast-enhanced (CE) diagnostic CT scans characterized through radiomics as predictors of recurrence for patients with stage II and III colorectal cancer in a two-center context. This study included 193 patients diagnosed with stage II and III colorectal adenocarcinoma from 1 July 2008 to 15 March 2017 in two different French University Hospitals. To compensate for the variability in two-center data, a statistical harmonization method Bootstrapped ComBat (B-ComBat) was used. Models predicting disease-free survival (DFS) were built using 3 different machine learning (ML): (1) multivariate regression (MR) with 10-fold cross-validation after feature selection based on least absolute shrinkage and selection operator (LASSO), (2) random forest (RF), and (3) support vector machine (SVM), both with embedded feature selection. The performance for both balanced and 95% sensitivity models was systematically higher after our proposed B-ComBat harmonization compared to the use of the original untransformed data. The most clinically relevant performance was achieved by the multivariate regression model combining a clinical variable (postoperative chemotherapy) with two radiomics shape descriptors (compactness and least axis length) with a BAcc of 0.78 and an MCC of 0.6 associated with a required sensitivity of 95%. The resulting stratification in terms of DFS was significant (p = 0.00021), especially compared to the use of unharmonized original data (p = 0.17). Radiomics models derived from contrast-enhanced CT could be trained and validated in a two-center cohort with a good predictive performance of recurrence in stage II et III colorectal cancer patients. • Adjuvant therapy decision in colorectal cancer can be a challenge in medical oncology. • Radiomics models, derived from diagnostic CT, trained and validated in a two-center cohort, could predict recurrence in stage II and III colorectal cancer patients. • Identifying patients with a low risk of recurrence, these models could facilitate treatment optimization and avoid unnecessary treatment.
Published: 2022
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20. Number and Replating Capacity of Endothelial Colony‐Forming Cells are Telomere Length Dependent: Implication for Human Atherogenesis

Author: Simon Toupance, Stéphanie Simonici, Carlos Labat, Chloé Dumoulin, Tsung‐Po Lai, Cécile Lakomy, Véronique Regnault, Patrick Lacolley, Françoise Dignat George, Florence Sabatier, Abraham Aviv, Athanase Benetos, Sylvie Gautier, Ghassan Watfa, Huguette Louis, Mélanie Folio, Margaux Schmitt, Oualid Ayad, Agnes Didier, Sandrine Vauthier, Nelly François, Sophie Visvikis‐Siest, Maria G. Stathopolou, Masayuki Kimura, Pascal M. Rossi, Patricia Béranger, Serguei Malikov, Nicla Settembre, Jacques Hubert, Luc Frimat, Baptiste Bertrand, Mourad Boufi, Xavier Flecher, Nicolas Sadoul, Pascal Eschwege, Michèle Kessler, Irene P. Tzanetakou, Ilias P. Doulamis, Panagiotis Konstantopoulos, Aspasia Tzani, Marilina Korou, Anastasios Gkogkos, Konstantinos Perreas, Evangelos Menenakos, Georgios Samanidis, Michail Vasiloglou‐Gkanis, Jeremy D. Kark, Simon Verhulst, Défaillance Cardiovasculaire Aiguë et Chronique (DCAC), Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Pôle Maladies du Vieillissement, Gérontologie et Soins Palliatif [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Centre recherche en CardioVasculaire et Nutrition = Center for CardioVascular and Nutrition research (C2VN), Aix Marseille Université (AMU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Rutgers New Jersey Medical School (NJMS), Rutgers University System (Rutgers), This work was supported by the French National Research Agency (Agence Nationale de Recherche ‐ANR), Translationnelle: N°ID RCB: 2014‐A00298‐39: 2014‐2017 and partially supported by the French PIA project « Lorraine Université d’Excellence », reference ANR‐15‐IDEX‐04‐LUE, the Investments for the Future Program under grant agreement No. ANR‐15‐RHU‐0004 and the Contrats de Plan État‐Région (CPER)–Innovations Technologiques, Modélisation et Médecine Personnalisée (ITM2P) 2015‐2020. Aviv research is supported by National Institutes of Health grants R01HD071180, R01HL116446, R01HL13840., TELARTA consortium †: Sylvie Gautier, Ghassan Watfa, Huguette Louis, Mélanie Folio, Margaux Schmitt, Oualid Ayad, Agnes Didier, Sandrine Vauthier, Nelly François, Sophie Visvikis-Siest, Maria G Stathopolou, Masayuki Kimura, Pascal M Rossi, Patricia Béranger, Serguei Malikov, Nicla Settembre, Jacques Hubert, Luc Frimat, Baptiste Bertrand, Mourad Boufi, Xavier Flecher, Nicolas Sadoul, Pascal Eschwege, Michèle Kessler, Irene P Tzanetakou, Ilias P Doulamis, Panagiotis Konstantopoulos, Aspasia Tzani, Marilina Korou, Anastasios Gkogkos, Konstantinos Perreas, Evangelos Menenakos, Georgios Samanidis, Michail Vasiloglou-Gkanis, Jeremy D Kark, Simon Verhulst, ANR-15-RHUS-0004,FIGHT-HF,Combattre l'insuffisance cardiaque(2015), ANR-15-IDEX-0004,LUE,Isite LUE(2015), Université de Lorraine (UL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), CHRU-Nancy, Pôle 'Maladies du Vieillissement, Gérontologie et Soins Palliatif, DE CARVALHO, Philippe, Combattre l'insuffisance cardiaque - - FIGHT-HF2015 - ANR-15-RHUS-0004 - RHUS - VALID, ISITE - Isite LUE - - LUE2015 - ANR-15-IDEX-0004 - IDEX - VALID, and Rutgers, The State University of New Jersey [New Brunswick] (RU)
Subjects: Adult, Male, [SDV]Life Sciences [q-bio], Endothelial repair, Neovascularization, Physiologic, 030204 cardiovascular system & hematology, Endothelial progenitor cell, 03 medical and health sciences, Young Adult, 0302 clinical medicine, endothelial progenitor cell, Vascular Biology, Genetics, Medicine, Humans, Cells, Cultured, 030304 developmental biology, Original Research, Aged, Cell Proliferation, Endothelial Progenitor Cells, Retrospective Studies, Aged, 80 and over, 0303 health sciences, telomere, Atherosclerotic cardiovascular disease, business.industry, aging, Telomere Homeostasis, Middle Aged, Atherosclerosis, Telomere, [SDV] Life Sciences [q-bio], Cancer research, Female, Cardiology and Cardiovascular Medicine, business
Abstract: Background Short leukocyte telomere length (TL) is associated with atherosclerotic cardiovascular disease. Endothelial repair plays a key role in the development of atherosclerosis. The objective was to examine associations between TL and proliferative dynamics of endothelial colony‐forming cells (ECFCs), which behave as progenitor cells displaying endothelial repair activity. Methods and Results To isolate ECFCs, we performed a clonogenic assay on blood samples from 116 participants (aged 24–94 years) in the TELARTA (Telomere in Arterial Aging) cohort study. We detected no ECFC clones in 29 (group 1), clones with no replating capacity in other 29 (group 2), and clones with replating capacity in the additional 58 (group 3). Leukocyte TL was measured by Southern blotting and ECFCs (ECFC‐TL). Age‐ and sex‐adjusted leukocyte TL (mean±SEM) was the shortest in group 1 (6.51±0.13 kb), longer in group 2 (6.69±0.13 kb), and the longest in group 3 (6.78±0.09 kb) ( P P P r =0.82; P Conclusions Individuals with longer telomeres display a higher number of self‐renewing ECFCs. Our results also indicate that leukocyte TL, as a proxy of TL dynamics in ECFCs, could be used as a surrogate marker of endothelial repair capacity in clinical and laboratory practice because of easy accessibility of leukocytes. Registration URL: https://www.clinicaltrials.gov ; Unique identifier: NCT02176941.
Published: 2021

21. Position paper of the EACVI and EANM on artificial intelligence applications in multimodality cardiovascular imaging using SPECT/CT, PET/CT, and cardiac CT

Author: Oliver Gaemperli, Paola Anna Erba, Antti Saraste, Michelle C. Williams, Alessia Gimelli, Piotr J. Slomka, Christoph Rischpler, Roland Hustinx, Marc R. Dweck, Hein J. Verberne, Andor W. J. M. Glaudemans, Bernard Cosyns, Márton Kolossváry, Panagiotis Georgoulias, Luis Eduardo Juarez-Orozco, Ivana Išgum, Gilbert Habib, Mark Lubberink, Riemer H. J. A. Slart, Olivier Gheysens, Dimitris Visvikis, Fabien Hyafil, Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE), Translational Immunology Groningen (TRIGR), Cardiovascular Centre (CVC), IvI Research (FNWI), UCL - SSS/IREC/SLUC - Pôle St.-Luc, UCL - (SLuc) Centre du cancer, UCL - (SLuc) Service de médecine nucléaire, Clinical sciences, Cardio-vascular diseases, Cardiology, Slart, R, Williams, M, Juarez-Orozco, L, Rischpler, C, Dweck, M, Glaudemans, A, Gimelli, A, Georgoulias, P, Gheysens, O, Gaemperli, O, Habib, G, Hustinx, R, Cosyns, B, Verberne, H, Hyafil, F, Erba, P, Lubberink, M, Slomka, P, Isgum, I, Visvikis, D, Kolossvary, M, Saraste, A, University Medical Center Groningen [Groningen] (UMCG), University of Twente, University of Edinburgh, Utrecht University [Utrecht], University of Groningen [Groningen], Universität Duisburg-Essen = University of Duisburg-Essen [Essen], Fondazione Toscana Gabriele Monasterio, University Hospital of Larissa, Cliniques Universitaires Saint-Luc [Bruxelles], Université Catholique de Louvain = Catholic University of Louvain (UCL), Hirslanden Medical Center, Microbes évolution phylogénie et infections (MEPHI), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Centre National de la Recherche Scientifique (CNRS), Hôpital de la Timone [CHU - APHM] (TIMONE), Institut Hospitalier Universitaire Méditerranée Infection (IHU Marseille), GIGA [Université Liège], Université de Liège, Universitair Ziekenhuis [Brussels, Belgium], University of Amsterdam [Amsterdam] (UvA), Hôpital Européen Georges Pompidou [APHP] (HEGP), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO), Paris-Centre de Recherche Cardiovasculaire (PARCC (UMR_S 970/ U970)), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris Cité (UPCité), University of Pisa - Università di Pisa, Uppsala University, Uppsala University Hospital, Cedars-Sinai Medical Center, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Semmelweis University [Budapest], University of Turku, Turku University Hospital (TYKS), Radiology and Nuclear Medicine, ACS - Amsterdam Cardiovascular Sciences, Biomedical Engineering and Physics, ACS - Atherosclerosis & ischemic syndromes, ANS - Brain Imaging, and ACS - Heart failure & arrhythmias
Subjects: medicine.medical_specialty, Medizin, 030204 cardiovascular system & hematology, Guidelines, Cardiovascular, Multimodality imaging, 030218 nuclear medicine & medical imaging, Multimodality, 03 medical and health sciences, 0302 clinical medicine, [SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system, [SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases, Artificial Intelligence, Positron Emission Tomography Computed Tomography, Machine learning, medicine, Humans, [SDV.MP.PAR]Life Sciences [q-bio]/Microbiology and Parasitology/Parasitology, Radiology, Nuclear Medicine and imaging, Medical physics, Position paper, Deep learning, Positron-Emission Tomography, Tomography, Emission-Computed, Single-Photon, Tomography, X-Ray Computed, Nuclear Medicine, Tomography, [SDV.MHEP.ME]Life Sciences [q-bio]/Human health and pathology/Emerging diseases, PET-CT, medicine.diagnostic_test, business.industry, Coronary computed tomography angiography, General Medicine, [SDV.MP.BAC]Life Sciences [q-bio]/Microbiology and Parasitology/Bacteriology, X-Ray Computed, Functional imaging, Positron emission tomography, [SDV.MP.VIR]Life Sciences [q-bio]/Microbiology and Parasitology/Virology, Radiologi och bildbehandling, Applications of artificial intelligence, Emission-Computed, Cardiology and Cardiovascular Medicine, business, Emission computed tomography, Radiology, Nuclear Medicine and Medical Imaging, Single-Photon
Abstract: In daily clinical practice, clinicians integrate available data to ascertain the diagnostic and prognostic probability of a disease or clinical outcome for their patients. For patients with suspected or known cardiovascular disease, several anatomical and functional imaging techniques are commonly performed to aid this endeavor, including coronary computed tomography angiography (CCTA) and nuclear cardiology imaging. Continuous improvement in positron emission tomography (PET), single-photon emission computed tomography (SPECT), and CT hardware and software has resulted in improved diagnostic performance and wide implementation of these imaging techniques in daily clinical practice. However, the human ability to interpret, quantify, and integrate these data sets is limited. The identification of novel markers and application of machine learning (ML) algorithms, including deep learning (DL) to cardiovascular imaging techniques will further improve diagnosis and prognostication for patients with cardiovascular diseases. The goal of this position paper of the European Association of Nuclear Medicine (EANM) and the European Association of Cardiovascular Imaging (EACVI) is to provide an overview of the general concepts behind modern machine learning-based artificial intelligence, highlights currently prefered methods, practices, and computational models, and proposes new strategies to support the clinical application of ML in the field of cardiovascular imaging using nuclear cardiology (hybrid) and CT techniques.
Published: 2021

22. Comparison of radiomic pre-processing steps in the reproducible prediction of disease free survival across multi-scanners/centers

Author: François Lucia, Dimitris Visvikis, Roland Hustinx, Claire Bernard, Philippe Robin, Frédéric Kridelka, Mathieu Hatt, Ralph T.H. Leijenaar, Johanne Hermesse, Patrick E. Meyer, Caroline Rousseau, Ulrike Schick, Caroline Reinhold, Philippe Lambin, Pierre Lovinfosse, Marjolein Decuypere, and Marta Ferreira
Subjects: Disease free survival, medicine.medical_specialty, business.industry, Medicine, Radiology, business
Abstract: Background Features reproducibility and the generalizability of the models are currently among the most important limitations when integrating radiomics into the clinics. Radiomic features are sensitive to imaging acquisition protocols, reconstruction algorithms and parameters, as well as by the different steps of the usual radiomics workflow. We propose a framework for comparing the reproducibility of different pre-processing steps in PET/CT radiomic analysis in the prediction of disease free survival (DFS) across multi-scanners/centers. Results We evaluated and compared the prediction performance of several models that differ in i) the type of intensity discretization, ii) feature selection method, iii) features type i.e, original or tumour to liver ratio radiomic features (OR or TLR). We trained our models using data from one scanner/center and tested on two external scanner/centers. Our results show that there is a low reproducibility in predictions across scanners and discretization methods. Despite of this, TLR based models were generally more robust than OR. Maximum relevance minimum redundancy (MRMR) forward feature selection with Pearson correlation was the feature selection method that had the best mean area under the precision recall curve when using it combining the features from all discretization’s bin’s number (D_All_FBN) with TLR features for two of the four classifiers. Conclusion We evaluated and compared the prediction performance of several models in a data set containing hundred fifty-eight patients with locally advanced cervical cancer (LACC) from three distinct scanners. In our cohort of LAAC patients pre-processing of radiomic features in [18F]FDG PET affects DFS predictions performances across scanners and combining the D_All_FBN TLR approach with the MRMR forward Pearson feature selection method might help increasing robustness of radiomic studies.
Published: 2021

23. Evaluation of conventional and deep learning based image harmonization methods in radiomics studies

Author: Olivier Gallinato, Clément Hognon, Florent Tixier, Thierry Colin, Vincent Jaouen, and Dimitris Visvikis
Subjects: Radiological and Ultrasound Technology, Wilcoxon signed-rank test, Computer science, business.industry, Deep learning, Paired difference test, Histogram matching, Feature selection, Pattern recognition, Image segmentation, Magnetic Resonance Imaging, Cohort Studies, Deep Learning, Lasso (statistics), Histogram, Humans, Radiology, Nuclear Medicine and imaging, Artificial intelligence, business
Abstract: Objective. To evaluate the impact of image harmonization on outcome prediction models using radiomics. Approach. 234 patients from the Brain Tumor Image Segmentation Benchmark (BRATS) dataset with T1 MRI were enrolled in this study. Images were harmonized to a reference image using histogram matching (HHM) and a generative adversarial network (GAN)-based method (HGAN). 88 radiomics features were extracted on HHM, HGAN and original (HNONE) images. Wilcoxon paired test was used to identify features significantly impacted by the harmonization protocol used. Radiomic prediction models were built using feature selection with the Least Absolute Shrinkage and Selection Operator (LASSO) and Kaplan–Meier analysis. Main results. More than 50% of the features (49/88) were statistically modified by the harmonization with HHM and 55 with HGAN (adjusted p-value none, 62% for HHM and 71% for HGAN). Both image-based harmonization methods allowed to split patients into two groups with significantly different survival (pGAN images, we were also able to build and validate a model using only features impacted by the harmonization (median survivals of 189 versus 437 days, p = 0.006) Significance. Data harmonization in a multi-institutional cohort allows to recover the predictive value of some radiomics features that was lost due to differences in the image properties across centers. In terms of ability to build survival prediction models in the BRATS dataset, the loss of power from impacted histogram and heterogeneity features was compensated by the selection of additional shape features. The harmonization using a GAN-based approach outperformed the histogram matching technique, supporting the interest for the development of new advanced harmonization techniques for radiomic analysis purposes.
Published: 2021

24. Advances in PET/CT Imaging

Author: Guy Bormans, Florent Tixier, Christian Vanhove, Andrej Doma, Ivana Zagar, Elisabetta Cerudelli, Igor Iskra, Ian Armstrong, Tyler Middlebrooks, Donatienne Van Weehaeghe, Frederik Cleeren, Michel Koole, and Dimitris Visvikis
Subjects: business.industry, Pet ct imaging, Nuclear medicine, business
Published: 2021

25. Tumor Genotyping and Homologous Recombination Repair Gene Variants in Patients With Epithelial Ovarian Cancer: Is Pathogenic Enough?

Author: Elena Fountzilas, Vassiliki Kotoula, Georgia-Angeliki Koliou, Michalis Liontos, Kyriaki Papadopoulou, Eleni Giannoulatou, Alexios Papanikolaou, Ioannis Tikas, Sofia Chrisafi, Davide Mauri, Kyriakos Chatzopoulos, Florentia Fostira, Dimitrios Pectasides, Georgios Oikonomopoulos, Dimitra Aivazi, Angeliki Andrikopoulou, Anastasios Visvikis, Gerasimos Aravantinos, Flora Zagouri, and George Fountzilas
Subjects: Oncology, Cancer Research, medicine.medical_specialty, endocrine system diseases, BRCA, homologous recombination repair, Internal medicine, medicine, Clinical endpoint, In patient, predictive, Gene, Genotyping, co-mutation, RC254-282, Original Research, business.industry, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Clinical trial, Serous fluid, biomarker, Biomarker (medicine), Homologous recombination, business, prognostic
Abstract: Our hypothesis was that the predictive accuracy of pathogenic variants in genes participating in the homologous recombination repair (HRR) system in patients with epithelial ovarian cancer (EOC) could be improved by considering additional next-generation sequencing (NGS) metrics. NGS genotyping was performed in tumor tissue, retrospectively and prospectively collected from patients with EOC, diagnosed from 8/1998 to 10/2016. Variants were considered clonal when variant allele frequencies corresponded to >25%. The primary endpoint was overall survival (OS). This study included 501 patients with EOC, predominantly with high-grade serous (75.2%) and advanced stage tumors (81.7%); median age was 58 years (22-84). Pathogenic and clonal pathogenic variants in HRR and/or TP53 genes were identified in 72.8% and 66.5% tumors, respectively. With a median follow-up of 123.9 months, the presence of either pathogenic or clonal pathogenic HRR-only variants was associated with longer OS compared to HRR/TP53 co-mutation (HR=0.54; 95% CI, 0.34-0.87, Wald’s p=0.012 and HR=0.45; 95% CI, 0.27-0.78, Wald’s p=0.004, respectively). However, only the presence of clonal HRR-only variants was independently associated with improved OS (HR=0.55; 95% CI, 0.32-0.94, p=0.030). Variant clonality and co-occuring TP53 variants affect the predictive value of HRR pathogenic variants for platinum agents in patients with EOC.Clinical Trial Registration[ClinicalTrials.gov], identifier [NCT04716374].
Published: 2021

26. Fluorine-18 fluorodeoxyglucose positron emission tomography-based textural features for prediction of event prone carotid atherosclerotic plaques

Author: Pavlos Kafouris, George M. Spyrou, Iosif Koutagiar, Constantinos Anagnostopoulos, Dimitrios Visvikis, and Alexandros Georgakopoulos
Subjects: Fluorodeoxyglucose, Fluorine-18-fluorodeoxyglucose, medicine.diagnostic_test, Receiver operating characteristic, business.industry, medicine.medical_treatment, 030204 cardiovascular system & hematology, medicine.disease_cause, Logistic regression, Vulnerable plaque, 030218 nuclear medicine & medical imaging, Correlation, 03 medical and health sciences, 0302 clinical medicine, Positron emission tomography, Medicine, Radiology, Nuclear Medicine and imaging, Cardiology and Cardiovascular Medicine, business, Nuclear medicine, Endarterectomy, medicine.drug
Abstract: Texture analysis has been increasingly used in the field of positron emission tomography (PET)/computed tomography (CT) imaging with Fluorine-18 fluorodeoxyglucose (18F-FDG), aiming at assessing tumor heterogeneity. The purpose of the present study is to examine the feasibility of performing texture analysis in carotid arteries, investigate the value of textural features as predictors of potential plaque vulnerability using as reference standards histological and immunohistochemical data and compare their performance with conventional uptake measurements. 67 different 18F-FDG PET-based textural features were extracted from carotid images of 21 patients with high-grade carotid stenosis undergoing endarterectomy. To identify the more reliable predictors, univariate logistic regression analysis was performed. The accuracy was satisfactory in case of an Area Under the Receiver Operating Characteristic (ROC) curve (AUC) ≥ 0.80. First measure of information correlation (AUC = 0.87, P
Published: 2019

27. Intracranial migration of intraocular silicone oil following repetitive head trauma

Author: Soham J. Patel, George A. Visvikis, Han Zhong, Christina M. Bianchi, and Allen R. Wolfe
Subjects: inorganic chemicals, lcsh:Medical physics. Medical radiology. Nuclear medicine, medicine.medical_specialty, lcsh:R895-920, Intracranial hemorrhage, Trauma, complex mixtures, 030218 nuclear medicine & medical imaging, Head trauma, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Silicone oil, Ophthalmology, medicine, Radiology, Nuclear Medicine and imaging, PACS, picture archiving and communication system, business.industry, technology, industry, and agriculture, Retinal detachment, medicine.disease, equipment and supplies, MRI - Magnetic resonance imaging, CT, computed tomography, stomatognathic diseases, chemistry, Neuroradiology, Vitreous chamber, Tamponade, business, MRI, magnetic resonance imaging, 030217 neurology & neurosurgery
Abstract: Intraocular injection of silicone oil as a tamponade agent is a commonly used technique for the treatment of retinal detachment. An incompletely understood phenomenon which can occur after injection is the migration of silicone oil from the vitreous chamber to the intracranial space. Because the appearance of silicone oil can mimic hemorrhage or other pathologies on CT and MRI, careful comparison with prior studies is necessary to avoid unnecessary follow-up studies. We report a case of intracranial migration of intraocular silicone oil following repetitive head trauma. Keywords: Silicone oil, Intracranial hemorrhage, Trauma, Retinal detachment
Published: 2019

28. Radiomics: Data Are Also Images

Author: Bogdan Badic, Florent Tixier, Catherine Cheze Le Rest, Mathieu Hatt, Ulrike Schick, and Dimitris Visvikis
Subjects: Diagnostic Imaging, Computer science, media_common.quotation_subject, Field (computer science), 030218 nuclear medicine & medical imaging, Machine Learning, 03 medical and health sciences, Deep Learning, 0302 clinical medicine, Radiomics, Nuclear medicine imaging, Image Interpretation, Computer-Assisted, Image Processing, Computer-Assisted, Humans, Radiology, Nuclear Medicine and imaging, Segmentation, Quality (business), media_common, business.industry, Deep learning, Automation, Data science, Feature (computer vision), Positron-Emission Tomography, 030220 oncology & carcinogenesis, Artificial intelligence, Nuclear Medicine, business
Abstract: The aim of this review is to provide readers with an update on the state of the art, pitfalls, solutions for those pitfalls, future perspectives, and challenges in the quickly evolving field of radiomics in nuclear medicine imaging and associated oncology applications. The main pitfalls were identified in study design, data acquisition, segmentation, feature calculation, and modeling; however, in most cases, potential solutions are available and existing recommendations should be followed to improve the overall quality and reproducibility of published radiomics studies. The techniques from the field of deep learning have some potential to provide solutions, especially in terms of automation. Some important challenges remain to be addressed but, overall, striking advances have been made in the field in the last 5 y.
Published: 2019

29. Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications

Author: Mathieu Hatt, Catherine Cheze Le Rest, Vincent Jaouen, and Dimitris Visvikis
Subjects: business.industry, Computer science, Deep learning, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, General Medicine, Iterative reconstruction, Field (computer science), Molecular Imaging, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Deep Learning, 0302 clinical medicine, Computer Science::Computer Vision and Pattern Recognition, 030220 oncology & carcinogenesis, Image Processing, Computer-Assisted, Medical imaging, Key (cryptography), Humans, Radiology, Nuclear Medicine and imaging, Segmentation, Artificial intelligence, Nuclear Medicine, Focus (optics), business, Predictive modelling
Abstract: Techniques from the field of artificial intelligence, and more specifically machine (deep) learning methods, have been core components of most recent developments in the field of medical imaging. They are already being exploited or are being considered to tackle most tasks, including image reconstruction, processing (denoising, segmentation), analysis and predictive modelling. In this review we introduce and define these key concepts and discuss how the techniques from this field can be applied to nuclear medicine imaging applications with a particular focus on radio(geno)mics.
Published: 2019

30. Initial Results of the MINDView PET Insert Inside the 3T mMR

Author: S. Sanchez, Markus Schwaiger, C. Correcher, Juan V. Catret, A. Iborra, Filomeno Sanchez, Gabriel Canizares, Edwin J. Pincay, Liczandro Hernandez, L. F. Vidal, Laura Moliner, Sebastian Aussenhofer, Jorge Cabello, Andrea Gonzalez-Montoro, Antonio González, Thibaut Merlin, Julio Barbera, Efthymios Lamprou, Jose M. Benlloch, Dimitris Visvikis, and Santiago Jimenez-Serrano
Subjects: Physics, Scintillation, business.industry, Center (category theory), Scintillator, Atomic and Molecular Physics, and Optics, Imaging phantom, Lyso, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Optics, Silicon photomultiplier, 030220 oncology & carcinogenesis, Radiology, Nuclear Medicine and imaging, Sensitivity (control systems), business, Instrumentation, Image resolution
Abstract: Hybrid molecular and anatomical imaging devices, especially when simultaneously working, have shown to provide advantages over sequential acquisitions. In particular, we present in this preliminary study, the working performance of a brain positron emission tomography (PET) insert, within a 3T magnetic resonance imaging (MRI) system. To our knowledge, this is the largest PET system based on monolithic LYSO blocks. It consists of 60 scintillator blocks of $50\ \text{mm} \times 50\ \text{mm} \times 20\ \text{mm}$ arranged in 3 rings of 20 detector modules each. An effective field of view (FOV) of 240 mm in diameter and 154 mm axially is defined. The crystals included a retroreflector layer at the entrance face, and are coupled to custom arrays of $12 \times 12$ silicon photomultipliers (SiPM), $3\ \text{mm} \times 3\ \text{mm}$ each. Frontend electronics provide ${X}$ and ${Y}$ projections of the scintillation light by sampling each row and column of the SiPM arrays. The insert is thermally stabilized by using temperature-controlled air to about 27 °C. The PET insert has been installed at the Klinikum rechts der Isar (Munich) and tested within the whole-body Siemens biograph mMR, a 3T MRI combined with a PET scanner. A system sensitivity of almost 7% for an energy window of 350–650 keV was measured using a small size source at the center of the FOV (CFOV). Current system evaluation showed a spatial resolution at the CFOV of 1.7 mm using iterative algorithms, being below 2 mm within a centered diameter of 120 mm. Rods of a small Derenzo phantom of 2.5 mm were clearly resolved, independently of all tested MRI sequences including echo-planar imaging, ultrashort time echo, MPrage, and T2-flair or arterial spin labeling. The PET insert did not show any count rate degradation also under those sequences for a variety of MR imaging acquisitions.
Published: 2019

31. Comparison of Radiomics Models Built Through Machine Learning in a Multicentric Context With Independent Testing: Identical Data, Similar Algorithms, Different Methodologies

Author: Taman Upadhaya, Martin Vallières, Avishek Chatterjee, François Lucia, Augustin Mervoyer, Pietro Andrea Bonaffini, Catherine Cheze Le Rest, Ingrid Masson, Jan Seuntjens, Ulrike Schick, Caroline Reinhold, Dimitris Visvikis, and Mathieu Hatt
Subjects: business.industry, Computer science, Feature extraction, Local failure, High dimensional, Machine learning, computer.software_genre, Atomic and Molecular Physics, and Optics, Random forest, Radiomics, Robustness (computer science), Statistical analyses, Radiology, Nuclear Medicine and imaging, Artificial intelligence, business, Instrumentation, computer, Algorithm, Model building
Abstract: Machine learning techniques are becoming increasingly popular in radiomics studies. They can handle high dimensional sets of radiomics features with higher robustness than usual statistical analyses, by capturing complex interactions between features themselves and between feature combinations and clinical endpoints under investigation in order to build efficient prognostic/predictive models. However, there is no “one fits all” solution and deciding which algorithm is the most accurate for a given application is not always straightforward. In this paper, to keep a realistic perspective on various emerging clinical applications based on radiomics, we performed an evaluation of the popular random forest classifier for predicting local failure in cervix cancer exploiting identical data, but relying on different methodologies to select and combine features of interest. The main objective was to demonstrate various challenges of model building and tuning for radiomics applications. The results obtained in the present work could provide general guidelines to assist in the practical development of radiomics-based models.
Published: 2019

32. Abstract P4-08-13: Prognostic significance of CD8+ tumor-infiltrating lymphocytes (TILs) in patients with early breast cancer (EBC) treated with dose-dense sequential adjuvant chemotherapy (dds-CT). An observational study (ACTRN12616001043426)

Author: A. Psyrri, Alexandra Papoudou-Bai, D.G. Pectasides, Helen Gogas, Georgios Pentheroudakis, Sofia Chrisafi, I. Nicolaou, Maria Sotiropoulou, Gerassimos Aravantinos, C Petraki, C. Christodoulou, Flora Zagouri, Angelos Koutras, Anna Batistatou, G-A Koliou, Triantafyllia Koletsa, G. Fountzilas, Helen P. Kourea, Mattheos Bobos, Petroula Arapantoni-Dadioti, Vasiliki Kotoula, Ioannis Efstratiou, and A. Visvikis
Subjects: Oncology, Cancer Research, medicine.medical_specialty, Stromal cell, Tumor-infiltrating lymphocytes, business.industry, medicine.medical_treatment, Cancer, medicine.disease, Radiation therapy, Breast cancer, Docetaxel, Trastuzumab, Internal medicine, medicine, business, medicine.drug, Epirubicin
Abstract: Background - aim: Information on the prognostic role of cytotoxic CD8+ T cells in the era of modern adjuvant CT is limited. The primary objective of the present report is to assess the prognostic impact of CD8+ cells in patients with intermediate or high-risk EBC (T1-3N1-2M0) treated with dds-CT. Secondary endpoints are safety, disease-free survival (DFS) and overall survival (OS). Patients and Methods: Patients (N=1,000) were treated with 4 cycles of Epirubicin, 75mg/m2, and Cyclophophamide, 600mg/m2 every 2 weeks followed by 4 cycles of Docetaxel (D), 100mg/m2 every 3 weeks with G-CSF support in all cycles. Trastuzumab was initiated concurrently with D and continued for a total of 1 year. Hormonal and radiation therapy were given post CT, as indicated. Formalin-fixed paraffin-embedded tumors were available for 642 patients (64.2%) and were centrally assessed for immunohistochemical subtypes (IHC4; N=526), stromal TILs density by morphology (N=636), as well as stromal and intratumoral cytotoxic CD8+ T cell numbers (N=554). TILs and CD8+ were assessed as continuous variables for associations and as 10% increments for outcome. Results: In total, 901/1,000 pts (90.1%) completed 8 cycles of CT. Severe (gradeIII-IV) toxicitiesincludedneutropenia (5.6%), leucopenia (3.6%), lymphopenia (2.1%), hand-footsyndrome (2.1%), and hepatotoxicity (1.8%). Febrileneutropenia occurred in 1.6% of the patients. The 5-year DFS and OS rates were 89.5% and 93.1%, respectively. Luminal A tumors were classified in 26.2%, Luminal B in 35.2%, luminal HER2 in 9.5%; HER2-enriched in 7.2%; and, triple-negative (TNBC) in 21.9% of informative patients. Among subtypes, stromal TILs density was higher in HER2-enriched and TNBC (p Conclusions: In this study, dds-CT was well tolerated and active in patients with EBC. We confirm the presence of morphologically assessed higher TILs density, and of higher cytotoxic CD8+ T cell numbers in hormone receptor negative EBC, as well as the favorable prognostic impact of higher stromal TILs density in TNBC. In comparison to stromal TILs density, higher stromal CD8+ may confer favorable prognosis irrespectively of EBC subtype. Stromal CD8+ seems to be a marker worth further standardizing for reporting on immune cell infiltrates in EBC. Citation Format: Kourea HP, Koletsa T, Kotoula V, Koliou G-A, Batistatou A, Pentheroudakis G, Arapantoni-Dadioti P, Zagouri F, Bobos M, Sotiropoulou M, Papoudou-Bai A, Chrisafi S, Efstratiou I, Aravantinos G, Nicolaou I, Gogas H, Visvikis A, Christodoulou C, Petraki C, Koutras A, Psyrri A, Pectasides D, Fountzilas G. Prognostic significance of CD8+ tumor-infiltrating lymphocytes (TILs) in patients with early breast cancer (EBC) treated with dose-dense sequential adjuvant chemotherapy (dds-CT). An observational study (ACTRN12616001043426) [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr P4-08-13.
Published: 2019

33. Radiomics Analysis of 3D Dose Distributions to Predict Toxicity of Radiotherapy for Cervical Cancer

Author: Olivier Pradier, Florent Tixier, V. Bourbonne, Dimitris Visvikis, Mathieu Hatt, Julien Bert, François Lucia, Dorothy M Gujral, Vincent Jaouen, Ulrike Schick, O. Miranda, Gurvan Dissaux, and Dominique Gouders
Subjects: medicine.medical_specialty, cervical cancer, medicine.medical_treatment, Brachytherapy, Medicine (miscellaneous), Rectum, NTCP, Article, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, medicine, radiotherapy, Cervical cancer, business.industry, Standard treatment, Area under the curve, toxicity, Common Terminology Criteria for Adverse Events, medicine.disease, Radiation therapy, medicine.anatomical_structure, radiomics, 030220 oncology & carcinogenesis, Medicine, Radiology, business, Chemoradiotherapy
Abstract: Standard treatment for locally advanced cervical cancer (LACC) is chemoradiotherapy followed by brachytherapy. Despite radiation therapy advances, the toxicity rate remains significant. In this study, we compared the prediction of toxicity events after radiotherapy for locally advanced cervical cancer (LACC), based on either dose-volume histogram (DVH) parameters or the use of a radiomics approach applied to dose maps at the voxel level. Toxicity scores using the Common Terminology Criteria for Adverse Events (CTCAE v4), spatial dose distributions, and usual clinical predictors for the toxicity of 102 patients treated with chemoradiotherapy followed by brachytherapy for LACC were used in this study. In addition to usual DVH parameters, 91 radiomic features were extracted from rectum, bladder and vaginal 3D dose distributions, after discretization into a fixed bin width of 1 Gy. They were evaluated for predictive modelling of rectal, genitourinary (GU) and vaginal toxicities (grade ≥ 2). Logistic Normal Tissue Complication Probability (NTCP) models were derived using clinical parameters only or combinations of clinical, DVH and radiomics. For rectal acute/late toxicities, the area under the curve (AUC) using clinical parameters was 0.53/0.65, which increased to 0.66/0.63, and 0.76/0.87, with the addition of DVH or radiomics parameters, respectively. For GU acute/late toxicities, the AUC increased from 0.55/0.56 (clinical only) to 0.84/0.90 (+DVH) and 0.83/0.96 (clinical + DVH + radiomics). For vaginal acute/late toxicities, the AUC increased from 0.51/0.57 (clinical only) to 0.58/0.72 (+DVH) and 0.82/0.89 (clinical + DVH + radiomics). The predictive performance of NTCP models based on radiomics features was higher than the commonly used clinical and DVH parameters. Dosimetric radiomics analysis is a promising tool for NTCP modelling in radiotherapy.
Published: 2021

34. Effect of Mastiha supplementation on NAFLD: The MAST4HEALTH Randomised, Controlled Trial

Author: Panos Deloukas, Milan Vukic, Ilias Smyrnioudis, Charalampia Amerikanou, Lucia Cesarini, George Dedoussis, Mladen Bjelan, Iraklis Varlamis, M. Pilar Francino, Francisco J. Roig, Stavroula Kanoni, Beatriz Soriano, Giuseppe D'Auria, Maria Giovanna Trivella, Sofia Mouchti, John Victor Lamont, Aimo Kannt, Maria Halabalaki, Milica Medic-Stojanoska, Andriana C. Kaliora, Carlos Llorens, Natasa Milic, Miriam Marascio, Fernando Marascio, Jonica Campolo, Angela Barone, Sophie Visvikis-Siest, Aristea Gioxari, Rajarshi Banerjee, Amalia Gastaldelli, Eleni V Mikropoulou, María José Gosalbes, Stefan Stojanoski, Maria G. Stathopoulou, Harokopio University of Athens, William Harvey Research Institute, Barts and the London Medical School, University of Novi Sad, Fundación para el Fomento de la Investigación Sanitaria y Biomédica de la Comunitat Valenciana [Espagne] (FISABIO), CIBER de Epidemiología y Salud Pública (CIBERESP), Interactions Gène-Environnement en Physiopathologie Cardio-Vasculaire (IGE-PCV), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), Biotechvana [Valencia, Spain], National and Kapodistrian University of Athens (NKUA), SANOFI (Research and Development), Research and Development, Goethe-Universität Frankfurt am Main, Randox Laboratories Limited [Crumlin, Irlande du Nord], Universidad San Jorge, Queen Mary University of London (QMUL), European Commission, European Research Council, and Publica
Subjects: Male, 0301 basic medicine, NAFLD/NASH, [SDV]Life Sciences [q-bio], MathematicsofComputing_GENERAL, Gut flora, Gastroenterology, Body Mass Index, law.invention, Placebos, metabolomics, Liver disease, Randomized controlled trial, Non-alcoholic Fatty Liver Disease, Fibrosis, law, Nonalcoholic fatty liver disease, microbiota dysbiosis, ComputingMilieux_MISCELLANEOUS, Greece, biology, Mastic Resin, Mastiha, NASH, TheoryofComputation_GENERAL, Middle Aged, 3. Good health, Italy, Liver, Female, Serbia, MRI, Biotechnology, Adult, medicine.medical_specialty, Placebo, 03 medical and health sciences, Double-Blind Method, NAFLD, Internal medicine, medicine, Humans, Obesity, Aged, 030109 nutrition & dietetics, business.industry, medicine.disease, biology.organism_classification, Gastrointestinal Microbiome, Clinical trial, 030104 developmental biology, Dietary Supplements, Dysbiosis, ComputingMilieux_COMPUTERSANDSOCIETY, business, Food Science
Abstract: On behalf of MAST4HEALTH consortium: et al., [Scope]: Nonalcoholic fatty liver disease (NAFLD) is the most common liver disease with poor therapeutic strategies. Mastiha possesses antioxidant/anti-inflammatory and lipid-lowering properties. The authors investigate the effectiveness of Mastiha as a nonpharmacological intervention in NAFLD., [Methods and Results]: Ninety-eight patients with NAFLD in three countries (Greece, Italy, Serbia) are randomly allocated to either Mastiha or Placebo for 6 months, as part of a multicenter, randomized, double-blind, placebo-controlled, parallel-group clinical trial. The authors assess NAFLD severity via magnetic resonance imaging (MRI) scanning and LiverMultiScan technique and evaluate the effectiveness of Mastiha through medical, anthropometric, biochemical, metabolomic, and microbiota assessment. Mastiha is not superior to Placebo on changes in iron-corrected T1 (cT1) and Liver Inflammation Fibrosis score (LIF) in entire patient population; however, after BMI stratification (BMI ≤ 35 kg m-2 and BMI > 35 kg m-2), severely obese patients show an improvement in cT1 and LIF in Mastiha versus Placebo. Mastiha increases dissimilarity of gut microbiota, as shown by the Bray-Curtis index, downregulates Flavonifractor, a known inflammatory taxon and decreases Lysophosphatidylcholines-(LysoPC) 18:1, Lysophosphatidylethanolamines-(LysoPE) 18:1, and cholic acid compared to Placebo., [Conclusion]: Mastiha supplementation improves microbiota dysbiosis and lipid metabolite levels in patients with NAFLD, although it reduces parameters of liver inflammation/fibrosis only in severely obese patients., This project was funded by the European Union’s Horizon 2020 researchand innovation program MAST4HEALTH under the Marie Skłodowska-Curie grant agreement no. 691042.
Published: 2021

35. Business risks analysis in shipping and traditional risk management strategies

Author: Manolis G. Kavussanos, Dimitris A. Tsouknidis, and Ilias D. Visvikis
Subjects: Risk analysis (engineering), business.industry, Business risks, business, Risk management
Published: 2021

36. Freight Derivatives and Risk Management in Shipping

Author: Manolis G. Kavussanos, Dimitris A. Tsouknidis, and Ilias D. Visvikis
Subjects: Finance, business.industry, business, Risk management
Published: 2021

37. Bunker price derivatives

Author: Ilias D. Visvikis, Manolis G. Kavussanos, and Dimitris A. Tsouknidis
Subjects: Bunker, business.industry, Financial economics, Economics, Futures market, Fuel oil, business, Crude oil, Hedge (finance), Oil futures, Price risk, Risk management
Abstract: This chapter outlines the use of “modern” methods of risk management with the use of bunker derivatives for hedging the fluctuations in bunker prices. It presents an overview of the bunker market. The chapter considers key economic variables affecting bunker prices. It examines the Over-The-Counter (OTC) forward bunker agreements as a tool for hedging bunker price risk. The chapter focuses on the market of bunker fuel oil futures, including early efforts, the practice of cross-hedging and the current bunker futures market. It discusses bunker swaps and bunker options as important tools to hedge bunker price risk, respectively. Bunker fuel is the residue of the distillation process of crude oil, i.e. after all the higher components have been evaporated or boiled off. The demand for bunkers comes from shipowners and vessel operators, including charterers, who have a vessel on a time-charter agreement.
Published: 2021

38. PSMA PET/MR radiomics to improve postsurgical Gleason score prediction in prostate cancer

Author: Sylvia Schachoff, M Eiber, SG Nekolla, WA Weber, A Gafita, Esteban Lucas Solari, D. Visvikis, and Mathieu Hatt
Subjects: Oncology, medicine.medical_specialty, Prostate cancer, Radiomics, business.industry, Psma pet, Internal medicine, medicine, medicine.disease, business
Published: 2021

39. [18F]FDG PET radiomics to predict disease-free survival in cervical cancer: a multi-scanner/center study with external validation

Author: François Lucia, Philippe Robin, Ulrike Schick, Frédéric Kridelka, Johanne Hermesse, Caroline Reinhold, Dimitris Visvikis, Philippe Lambin, Patrick E. Meyer, Marjolein Decuypere, Roland Hustinx, Marta Ferreira, Mathieu Hatt, Claire Bernard, Ralph T.H. Leijenaar, Caroline Rousseau, Pierre Lovinfosse, GIGA [Université Liège], Université de Liège, Centre Hospitalier Universitaire de Liège (CHU-Liège), Nuclear Oncology (CRCINA-ÉQUIPE 13), 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), CRLCC René Gauducheau, Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), McGill University Health Center [Montreal] (MUHC), Groupe d'Etude de la Thrombose de Bretagne Occidentale (GETBO), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO)-Université de Brest (UBO), Maastricht University Medical Centre (MUMC), Maastricht University [Maastricht], European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 766276, Bernardo, Elizabeth, Precision Medicine, RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy, and Beeldvorming
Subjects: [18F]FDG PET/CT, Scanner, medicine.medical_specialty, Disease-free survival, [F-18]FDG PET, Feature selection, [SDV.CAN]Life Sciences [q-bio]/Cancer, Cross-validation, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Text mining, [SDV.CAN] Life Sciences [q-bio]/Cancer, Machine learning, medicine, Radiology, Nuclear Medicine and imaging, Bootstrapping (statistics), Cervical cancer, Reproducibility, Radiomics, business.industry, Proportional hazards model, General Medicine, medicine.disease, 3. Good health, 030220 oncology & carcinogenesis, Original Article, Radiology, business, CT
Abstract: Purpose To test the performances of native and tumour to liver ratio (TLR) radiomic features extracted from pre-treatment 2-[18F] fluoro-2-deoxy-D-glucose ([18F]FDG) PET/CT and combined with machine learning (ML) for predicting cancer recurrence in patients with locally advanced cervical cancer (LACC). Methods One hundred fifty-eight patients with LACC from multiple centers were retrospectively included in the study. Tumours were segmented using the Fuzzy Local Adaptive Bayesian (FLAB) algorithm. Radiomic features were extracted from the tumours and from regions drawn over the normal liver. Cox proportional hazard model was used to test statistical significance of clinical and radiomic features. Fivefold cross validation was used to tune the number of features. Seven different feature selection methods and four classifiers were tested. The models with the selected features were trained using bootstrapping and tested in data from each scanner independently. Reproducibility of radiomics features, clinical data added value and effect of ComBat-based harmonisation were evaluated across scanners. Results After a median follow-up of 23 months, 29% of the patients recurred. No individual radiomic or clinical features were significantly associated with cancer recurrence. The best model was obtained using 10 TLR features combined with clinical information. The area under the curve (AUC), F1-score, precision and recall were respectively 0.78 (0.67–0.88), 0.49 (0.25–0.67), 0.42 (0.25–0.60) and 0.63 (0.20–0.80). ComBat did not improve the predictive performance of the best models. Both the TLR and the native models performance varied across scanners used in the test set. Conclusion [18F]FDG PET radiomic features combined with ML add relevant information to the standard clinical parameters in terms of LACC patient’s outcome but remain subject to variability across PET/CT devices.
Published: 2021

40. Genetic Variation in the Vascular Endothelial Growth Factor (VEGFA) Gene at rs13207351 Is Associated with Overall Survival of Patients with Head and Neck Cancer

Author: Kyriaki Papadopoulou, Konstantinos Vlachtsis, Nikolaos Angouridakis, Angelos Koutras, George Fountzilas, Amanda Psyrri, Angelos Nikolaou, Konstantinos Markou, Vassiliki Kotoula, Paris Kosmidis, Ilias Karasmanis, Georgia-Angeliki Koliou, Foteinos-Ioannis Dimitrakopoulos, and Anastasios Visvikis
Subjects: 0301 basic medicine, Oncology, VEGFA, Cancer Research, medicine.medical_specialty, nasopharyngeal cancer, Single-nucleotide polymorphism, lcsh:RC254-282, NBS1, Article, 03 medical and health sciences, chemistry.chemical_compound, 0302 clinical medicine, Internal medicine, medicine, Clinical significance, Allele, business.industry, Hazard ratio, Head and neck cancer, Cancer, FAS, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, medicine.disease, Vascular endothelial growth factor, Vascular endothelial growth factor A, 030104 developmental biology, chemistry, 030220 oncology & carcinogenesis, head and neck cancer, business, endothelin, SNPs
Abstract: Simple Summary Angiogenesis and apoptosis play a pivotal role in the pathogenesis and clinical course not only of nasopharyngeal cancer (NPC), but also of other subgroups of head and neck cancer (HNC), such as laryngeal cancer. Thus, the aim of this study was to investigate the clinical significance of genetic polymorphisms in four pivotal angiogenesis- and apoptosis-related genes (VEGFA, FAS, EDNRA and NBS1) in HNC patients. Thirty-four genetic variants located in the studied genes were assessed. Two of them (VEGFA rs13207351 and FAS rs2234768) were associated with overall survival for patients with laryngeal cancer and NPC, respectively, with VEGFA rs13207351 showing the most promise for its prognostic value in the subgroup of laryngeal cancer patients. This study suggests that genetic variations in angiogenesis- and apoptosis-related genes may be useful in the management of HNC patients. Abstract Head and neck cancer (HNC) is a significantly heterogeneous disease and includes malignancies arising from different anatomical sites, such as nasopharyngeal cancer (NPC) and laryngeal cancer (LC). In the current study, polymorphisms located in angiogenesis- and apoptosis-related genes (VEGFA, FAS, EDNRA and NBS1) were evaluated regarding their clinical significance in HNC patients. In total, 333 HNC patients were enrolled in this study and 34 variants located on the aforementioned genes were genotyped via Sanger sequencing. LC patients, homozygous A for VEGFA rs13207351, had shorter overall survival (OS) as opposed to homozygous G (Hazard ratio (HR) = 2.06, Wald’s p = 0.017) upon adjustment for age, disease stage, and surgery. Following the dominant model, LC patients carrying the A allele had a marginally significantly higher risk for death (HR = 1.72, p = 0.059). NPC patients heterozygous (CT) for FAS rs2234768 had a marginal but significantly higher risk of death compared to those with homozygosity for the T allele (HR = 2.22, p = 0.056). In conclusion, rs13207351 (VEGFA) and rs2234768 (FAS) polymorphisms seem to have prognostic significance in HNC, with VEGFA rs13207351 showing the most promise in this subgroup of LC patients.
Published: 2021
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41. Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization

Author: Panagiotis Papadimitroulas, Wistan Marchadour, Laurent Gaubert, Dimitris Plachouris, Dimitris Visvikis, Lennart Brocki, Mathieu Hatt, Franck Vermet, Vasilis Eleftheriadis, George C. Kagadis, Neo Christopher Chung, University of Patras [Patras], University of Warsaw (UW), University of California [Los Angeles] (UCLA), University of California, Laboratoire de Traitement de l'Information Medicale (LaTIM), Institut National de la Santé et de la Recherche Médicale (INSERM)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Laboratoire de Mathématiques de Bretagne Atlantique (LMBA), Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), and BIOEMTECH
Subjects: Computer science, Feature extraction, Biophysics, General Physics and Astronomy, Image processing, Convolutional neural network, Field (computer science), 030218 nuclear medicine & medical imaging, Personalization, 03 medical and health sciences, 0302 clinical medicine, Artificial Intelligence, Machine learning, Image Processing, Computer-Assisted, Multicenter Studies as Topic, Radiology, Nuclear Medicine and imaging, Interpretability, [INFO]Computer Science [cs], Radiomics, Data curation, business.industry, Deep learning, General Medicine, Explainability, 3. Good health, 030220 oncology & carcinogenesis, Neural Networks, Computer, Artificial intelligence, business
Abstract: International audience; Over the last decade there has been an extensive evolution in the Artificial Intelligence (AI) field. Modern radiation oncology is based on the exploitation of advanced computational methods aiming to personalization and high diagnostic and therapeutic precision. The quantity of the available imaging data and the increased developments of Machine Learning (ML), particularly Deep Learning (DL), triggered the research on uncovering "hidden" biomarkers and quantitative features from anatomical and functional medical images. Deep Neural Networks (DNN) have achieved outstanding performance and broad implementation in image processing tasks. Lately, DNNs have been considered for radiomics and their potentials for explainable AI (XAI) may help classification and prediction in clinical practice. However, most of them are using limited datasets and lack generalized applicability. In this study we review the basics of radiomics feature extraction, DNNs in image analysis, and major interpretability methods that help enable explainable AI. Furthermore, we discuss the crucial requirement of multicenter recruitment of large datasets, increasing the biomarkers variability, so as to establish the potential clinical value of radiomics and the development of robust explainable AI models.
Published: 2021

42. Dosimetric Validation of a GAN-Based Pseudo-CT Generation for MRI-Only Stereotactic Brain Radiotherapy

Author: Julien Bert, V. Bourbonne, Vincent Jaouen, Dimitris Visvikis, François Lucia, Ulrike Schick, Olivier Pradier, Clément Hognon, Nicolas Boussion, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), IMT Atlantique (IMT Atlantique), and Jaouen, Vincent
Subjects: Pinnacle, Cancer Research, Planning target volume, Brain radiotherapy, [SDV.CAN]Life Sciences [q-bio]/Cancer, lcsh:RC254-282, Article, 030218 nuclear medicine & medical imaging, Stereotactic radiotherapy, 03 medical and health sciences, 0302 clinical medicine, [SDV.CAN] Life Sciences [q-bio]/Cancer, brain metastases, medicine, Initial treatment, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, medicine.diagnostic_test, business.industry, Philips healthcare, Magnetic resonance imaging, lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens, Confidence interval, 3. Good health, GAN, Oncology, 030220 oncology & carcinogenesis, stereotactic radiotherapy, business, Nuclear medicine, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, MRI
Abstract: Simple Summary Stereotactic radiotherapy (SRT) has become widely accepted as a treatment of choice for patients with a small number of brain metastases that are of an acceptable size. A magnetic resonance imaging (MRI)-only workflow could shorten the planning time and reduce the risk of misalignment in this treatment. Given the absence of a calibrated electronic density in MRI, we successfully compared generative adversarial network (GAN)-generated computed tomography (CT) scans from diagnostic brain MRIs with initial CT scans for the planning of brain stereotactic radiotherapy, finding a high similarity between the planning CT and the synthetic CT for both the organs at risk and the target volumes. Abstract Purpose: Stereotactic radiotherapy (SRT) has become widely accepted as a treatment of choice for patients with a small number of brain metastases that are of an acceptable size, allowing for better target dose conformity, resulting in high local control rates and better sparing of organs at risk. An MRI-only workflow could reduce the risk of misalignment between magnetic resonance imaging (MRI) brain studies and computed tomography (CT) scanning for SRT planning, while shortening delays in planning. Given the absence of a calibrated electronic density in MRI, we aimed to assess the equivalence of synthetic CTs generated by a generative adversarial network (GAN) for planning in the brain SRT setting. Methods: All patients with available MRIs and treated with intra-cranial SRT for brain metastases from 2014 to 2018 in our institution were included. After co-registration between the diagnostic MRI and the planning CT, a synthetic CT was generated using a 2D-GAN (2D U-Net). Using the initial treatment plan (Pinnacle v9.10, Philips Healthcare), dosimetric comparison was performed using main dose-volume histogram (DVH) endpoints in respect to ICRU 91 guidelines (Dmax, Dmean, D2%, D50%, D98%) as well as local and global gamma analysis with 1%/1 mm, 2%/1 mm and 2%/2 mm criteria and a 10% threshold to the maximum dose. t-test analysis was used for comparison between the two cohorts (initial and synthetic dose maps). Results: 184 patients were included, with 290 treated brain metastases. The mean number of treated lesions per patient was 1 (range 1–6) and the median planning target volume (PTV) was 6.44 cc (range 0.12–45.41). Local and global gamma passing rates (2%/2 mm) were 99.1 CI95% (98.1–99.4) and 99.7 CI95% (99.6–99.7) respectively (CI: confidence interval). DVHs were comparable, with no significant statistical differences regarding ICRU 91′s endpoints. Conclusions: Our study is the first to compare GAN-generated CT scans from diagnostic brain MRIs with initial CT scans for the planning of brain stereotactic radiotherapy. We found high similarity between the planning CT and the synthetic CT for both the organs at risk and the target volumes. Prospective validation is under investigation at our institution.
Published: 2021

43. Development and Validation of a Spatial Dose Pattern Based Model Predicting Acute Pulmonary Toxicity in Patients Treated With Volumetric Arc-Therapy for Locally Advanced Lung Cancer

Author: Vincent Jaouen, V. Bourbonne, Olivier Pradier, D. Visvikis, Ulrike Schick, François Lucia, and Mathieu Hatt
Subjects: Cancer Research, medicine.medical_specialty, education.field_of_study, Radiation, business.industry, Standard treatment, Population, Area under the curve, Retrospective cohort study, medicine.disease, computer.software_genre, Oncology, Voxel, medicine, Radiology, Nuclear Medicine and imaging, Radiology, education, Lung cancer, business, Prospective cohort study, Proton therapy, computer
Abstract: PURPOSE/OBJECTIVE(S): (Chemo)-radiotherapy is the standard treatment for patients with locally advanced lung cancer (LALC) not accessible to surgery. Despite strict application of dose constraints, acute toxicities such as acute pulmonary toxicity (APT) remain frequent, and may impact treatment's compliance and patients' quality of life. Previously, on a population treated with intensity-modulated photon therapy or passive scattering proton therapy, spatial dose patterns associated with APT were identified in the lower lungs, especially in the lower right lung. In the present study, we aim to define these spatial dose patterns on a retrospective cohort treated by volumetric-arc therapy (VMAT) and to validate our findings prospectively. MATERIALS/METHODS For the training cohort, we retrospectively included all patients treated in our institution by VMAT for a LALC between 2015 and 2018. APT was scored according to the CTCAE v4.0 scale. All dose maps were registered to a thorax phantom using a segmentation-based elastic registration. Voxel-based analysis of local dose differences was performed with a non-parametric permutation test accounting for n = 10.000 permutations, producing a 3-dimensional significance maps on which clusters of voxels that exhibited significant dose differences (P < 0.05) between the two toxicity groups (APT ≥ grade 2 vs APT < grade 2) were identified. A dose cut-off was defined by maximizing the Youden-Index derived from the Receiver Operative Curve. For the validation cohort, we applied the predefined spatial dose pattern and the dose cut-off to an observational prospective cohort. The model was evaluated using the Area under the curve (AUC) and the balanced accuracy (Bacc: mean of the sensitivity and specificity). RESULTS 167 and 42 patients were included in the training and validation cohorts, with respective APT rates of 22.2% and 21.4%. In the training cohort, a cluster of voxels was identified in the lower right lung with a cut off of 30.3 Gy for the Dmean(mean-dose). This spatial-based model resulted in an AUC of 0.69 and a Bacc of 0.71. Using the same voxel cluster and the Dmeancut-off on the validation cohort, the model resulted in an AUC of 0.74 and a Bacc of 0.69. CONCLUSION Our model corroborates previous findings regarding the possible role of the lower right lung regarding the development of APT and successfully evaluates its robustness in a prospective cohort treated by VMAT. Regional radiosensitivity should be considered in usual lung dose constraints.
Published: 2021

44. Modèle radiomique prédictif du cancer de la prostate cliniquement significatif chez les patients porteurs d’une lésion PI-RADS 3

Author: D. Visvikis, A. Fourcade, Georges Fournier, Charles Deruelle, F. Tixier, L. Doucet, V. Tissot, Antoine Valeri, T. Nguyen, A. Zambon, C. Payrard-Starck, and K. Saout
Subjects: Gynecology, medicine.medical_specialty, business.industry, Urology, medicine, business
Published: 2021

45. Radiomics analysis of 3D dose distributions to predict toxicity of radiotherapy for lung cancer

Author: D. Visvikis, Gurvan Dissaux, François Lucia, Ulrike Schick, Vincent Jaouen, R. Da-ano, Olivier Pradier, V. Bourbonne, Julien Bert, Mathieu Hatt, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), CCSD, Accord Elsevier, Institut National de la Santé et de la Recherche Médicale (INSERM)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-Université de Brest (UBO)-Institut Brestois Santé Agro Matière (IBSAM)
Subjects: medicine.medical_specialty, Lung Neoplasms, medicine.medical_treatment, [SDV]Life Sciences [q-bio], Youden's J statistic, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Esophagus, 0302 clinical medicine, Radiomics, Carcinoma, Non-Small-Cell Lung, Humans, Medicine, Radiology, Nuclear Medicine and imaging, Lung cancer, Retrospective Studies, Dose spatial distribution, Lung, business.industry, Radiotherapy Dosage, Hematology, Gold standard (test), medicine.disease, 3. Good health, Radiation therapy, [SDV] Life Sciences [q-bio], medicine.anatomical_structure, Oncology, 030220 oncology & carcinogenesis, Toxicity, Radiology, Toxicities prediction, business
Abstract: International audience; Purpose: (Chemo)-radiotherapy (RT) is the gold standard treatment for patients with locally advanced lung cancer non accessible for surgery. However, current toxicity prediction models rely on clinical and dose volume histograms (DVHs) and remain unsufficient. The goal of this work is to investigate the added predictive value of the radiomics approach applied to dose maps regarding acute and late toxicities in both the lungs and esophagus.Methods: Acute and late toxicities scored using the CTCAE v4.0 were retrospectively collected on patients treated with RT in our institution. Radiomic features were extracted from 3D dose maps considering Gy values as grey-levels in images. DVH and usual clinical factors were also considered. Three toxicity prediction models (clinical only, clinical + DVH and combined, i.e., including clinical + DVH + radiomics) were incrementally trained using a neural network on 70% of the patients for prediction of grade ≥2 acute and late pulmonary toxicities (APT/LPT) and grade ≥2 acute esophageal toxicities (AET). After bootstrapping (n = 1000), optimal cut-off values were determined based on the Youden Index. The trained models were then evaluated in the remaining 30% of patients using balanced accuracy (BAcc).Results: 167 patients were treated from 2015 to 2018: 78% non small-cell lung cancers, 14% small-cell lung cancers and 8% other histology with a median age at treatment of 66 years. Respectively, 22.2%, 16.8% and 30.0% experienced APT, LPT and AET. In the training set (n = 117), the corresponding BAcc for clinical only/clinical + DVH/combined were 0.68/0.79/0.92, 0.66/0.77/0.87 and 0.68/0.73/0.84. In the testing evaluation (n = 50), these trained models obtained a corresponding BAcc of 0.69/0.69/0.92, 0.76/0.80/0.89 and 0.58/0.73/0.72.Conclusion: In patients with a lung cancer treated with RT, radiomic features extracted from 3D dose maps seem to surpass usual models based on clinical factors and DVHs for the prediction of APT and LPT.
Published: 2021

46. Fast Monte Carlo-based Inverse Planning for Prostate Brachytherapy by Using Deep Learning

Author: Dimitris Visvikis, Julien Bert, Ulrike Schick, Antoine Valeri, Mateo Villa, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), CHRU de Brest, service de chirurgie urologique et de la transplantation reinale (CHU - BREST - Urologie), Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), and CHRU Brest - Service de radiothérapie (CHU - BREST - Radiothérapie)
Subjects: Computer science, business.industry, medicine.medical_treatment, Deep learning, Monte Carlo method, Brachytherapy, Inverse, Atomic and Molecular Physics, and Optics, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, Deep Learning, 030220 oncology & carcinogenesis, Dosimetry, medicine, [PHYS.PHYS.PHYS-MED-PH]Physics [physics]/Physics [physics]/Medical Physics [physics.med-ph], Radiology, Nuclear Medicine and imaging, [INFO]Computer Science [cs], Artificial intelligence, business, Instrumentation, Algorithm, Prostate brachytherapy, Inverse Planning
Abstract: International audience; Inverse planning is an essential tool for optimizing the delivered radiation dose on low-dose-rate (LDR) prostate brachytherapy. Clinical inverse planning systems use the TG-43 dose computation formalism in order to perform a fast optimization. However, this method is an approximation that often leads to a dose overestimation, resulting on sub-optimal plans. Alternatively, Monte Carlo simulation (MCS) can be used to obtain an accurate dose distribution, but considerably increasing the estimation time. We propose a fast inverse planning method for LDR prostate brachytherapy that uses a deep convolutional neural network (DCNN) trained on a GPU-based MCS generated database to estimate the dose distribution on the prostate and organs at risk. Segmentations of the organs and seeds' positions are given as the DCNN input. The mean percent error on the test set was-1.19±0.94% within the prostate. The DCNN was used to estimate the dosimetric parameters in each organ for every configuration of the optimization loop. The dosimetric parameters of the final DCNN-based brachytherapy plans were in good agreement compared to the same plans recalculated with a full MCS. The proposed inverse planning based on DCNN was capable to reach an equivalent level of accuracy with Monte Carlo with a runtime in less than 1 min using conventional GPU card.
Published: 2021

47. Comparison and Fusion of Machine Learning Algorithms for Prospective Validation of PET/CT Radiomic Features Prognostic Value in Stage II-III Non-Small Cell Lung Cancer

Author: Taman Upadhaya, Dimitris Visvikis, Catherine Cheze Le Rest, Mathieu Hatt, Olena Tankyevych, and Shima Sepehri
Subjects: Clinical Biochemistry, Feature selection, Context (language use), Machine learning, computer.software_genre, Article, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, 0302 clinical medicine, ComputingMethodologies_SYMBOLICANDALGEBRAICMANIPULATION, Clinical endpoint, Medicine, Stage (cooking), Prospective cohort study, non-small cell lung cancer, lcsh:R5-920, business.industry, Random forest, Support vector machine, machine learning, Binary classification, radiomics, 030220 oncology & carcinogenesis, Artificial intelligence, prognosis, lcsh:Medicine (General), business, computer, Algorithm, prospective study
Abstract: Machine learning (ML) algorithms for selecting and combining radiomic features into multiparametric prediction models have become popular, however, it has been shown that large variations in performance can be obtained by relying on different approaches. The purpose of this study was to evaluate the potential benefit of combining different algorithms into an improved consensus for the final prediction, as it has been shown in other fields. Methods: The evaluation was carried out in the context of the use of radiomics from 18F-FDG PET/CT images for predicting outcome in stage II-III Non-Small Cell Lung Cancer. A cohort of 138 patients was exploited for the present analysis. Eighty-seven patients had been previously recruited retrospectively for another study and were used here for training and internal validation. We also used data from prospectively recruited patients (n = 51) for testing. Three different machine learning pipelines relying on embedded feature selection were trained to predict overall survival (OS) as a binary classification: Support Vector machines (SVMs), Random Forests (RFs), and Logistic Regression (LR). Two different clinical endpoints were investigated: median OS or OS shorter than 6 months. The fusion of the three approaches was implemented using two different strategies: majority voting on the binary outputs or averaging of the output probabilities. Results: Our results confirm previous findings, highlighting that different ML pipelines select different sets of features and reach different classification performances (accuracy in the testing set ranging between 63% and 67% for median OS, and between 75% and 80% for OS &lt, 6 months). Generating a consensus improved the performance for both endpoints, with the probabilities averaging strategy outperforming the majority voting (accuracy of 78% vs. 71% for median OS and 89 vs. 84% for OS &lt, 6 months). Overall, the performance of these radiomic-based models outperformed the standard clinical staging in both endpoints (accuracy of 58% and 53% accuracy in the testing set for each endpoint). Conclusion: Although obtained in a small cohort of patients, our results suggest that a consensus of machine learning algorithms can improve performance in the context of radiomics. The resulting prognostic stratification in the prospective testing cohort is higher than when relying on the clinical stage. This could be of interest for clinical practice as it could help to identify patients with higher risk amongst stage II and III patients, who could benefit from intensified treatment and/or more frequent follow-up after treatment.
Published: 2021

48. A Pseudo-TOF Image Reconstruction Approach for Three-Gamma Small Animal Imaging

Author: Clotilde Canot, Yuwei Zhu, Sara Diglio, Thibaut Merlin, Y. Xing, E. Morteau, Alexandre Bousse, D. Thers, N. Beaupère, Dimitris Visvikis, Jean-Pierre Cussonneau, A. Iborra Carreres, D. Giovagnoli, Julien Masbou, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université européenne de Bretagne - European University of Brittany (UEB)-Université de Brest (UBO)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest), Laboratoire de physique subatomique et des technologies associées (SUBATECH), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)
Subjects: Physics, [PHYS]Physics [physics], Photon, Physics::Instrumentation and Detectors, business.industry, Image quality, Astrophysics::High Energy Astrophysical Phenomena, Physics::Medical Physics, Compton scattering, Iterative reconstruction, Atomic and Molecular Physics, and Optics, Imaging phantom, Optics, Positron, Radiology, Nuclear Medicine and imaging, Tomography, business, Instrumentation, Image resolution
Abstract: In this article, we propose a novel image reconstruction technique for three-gamma (3- $ {\gamma }$ ) imaging systems that aims at reaching high image quality with a low statistics. The proposed approach is based on the utilization of a ( $ {\beta +}$ , ${\gamma }$ ) emitter and a liquid xenon (LXe) camera, the Xenon Medical Imaging System 2 (XEMIS2), that is currently being manufactured. The third gamma emitted is more energetic than the two 511-keV photons and interacts mostly through Compton scattering in LXE; therefore, we utilize the interactions’ position and the deposited energy to define a Compton cone that gives the direction from where the third gamma was emitted. The intersection point between the cone and the two coincidence photons’ lines of response (LOR) is the center of the probability distribution function (PDF), a resolution model similar to that of time-of-flight (TOF)-positron emission tomography (PET). The third gamma’s information is used to narrow the LOR portion employed during reconstruction. In this article, we show reconstruction results from simulation using highly realistic Geant4 Application for Emission Tomography (GATE) Monte Carlo simulation of the imaging device and a NEMA-like phantom.
Published: 2021

49. Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images

Author: Dimitris Visvikis, Mathieu Hatt, and Andrei Iantsen
Subjects: Normalization (statistics), medicine.diagnostic_test, Computer science, business.industry, Normalization (image processing), Computed tomography, Context (language use), Image segmentation, medicine.disease, Primary tumor, Radiomics, Positron emission tomography, medicine, Segmentation, Head and neck, Nuclear medicine, business
Abstract: Development of robust and accurate fully automated methods for medical image segmentation is crucial in clinical practice and radiomics studies. In this work, we contributed an automated approach for Head and Neck (H&N) primary tumor segmentation in combined positron emission tomography / computed tomography (PET/CT) images in the context of the MICCAI 2020 Head and Neck Tumor segmentation challenge (HECKTOR). Our model was designed on the U-Net architecture with residual layers and supplemented with Squeeze-and-Excitation Normalization. The described method achieved competitive results in cross-validation (DSC 0.745, precision 0.760, recall 0.789) performed on different centers, as well as on the test set (DSC 0.759, precision 0.833, recall 0.740) that allowed us to win first prize in the HECKTOR challenge among 21 participating teams. The full implementation based on PyTorch and the trained models are available at this https URL
Published: 2021

50. Squeeze-and-Excitation Normalization for Brain Tumor Segmentation

Author: Vincent Jaouen, Dimitris Visvikis, Mathieu Hatt, and Andrei Iantsen
Subjects: medicine.diagnostic_test, business.industry, Computer science, Normalization (image processing), Pattern recognition, Magnetic resonance imaging, Dice, Context (language use), medicine.disease, Glioma, Medical imaging, medicine, Segmentation, Artificial intelligence, business, Brain tumor segmentation
Abstract: In this paper we described our approach for glioma segmentation in multi-sequence magnetic resonance imaging (MRI) in the context of the MICCAI 2020 Brain Tumor Segmentation Challenge (BraTS). We proposed an architecture based on U-Net with a new computational unit termed “SE Norm” that brought significant improvements in segmentation quality. Our approach obtained competitive results on the validation (Dice scores of 0.780, 0.911, 0.863) and test (Dice scores of 0.805, 0.887, 0.843) sets for the enhanced tumor, whole tumor and tumor core sub-regions. The full implementation and trained models are available at https://github.com/iantsen/brats.
Published: 2021

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