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1. Use of prostate systematic and targeted biopsy on the basis of multiparametric MRI in biopsy-naive patients (MRI-FIRST): a prospective, multicentre, paired diagnostic study

Author

Athivada Soto Thammavong, Raphaële Renard-Penna, Gilles Pasticier, Jean-Philippe Fendler, Flavie Bratan, Romain Boutier, Hervé Lang, Pascal Eschwege, Jochen Walz, Arnault Viller, Myriam Decaussin-Petrucci, Alain Ruffion, Nicolas Barry Delongchamps, Y. Lebras, François Cornud, Pierre Mozer, Jean-Michel Correas, Olivier Rouvière, Laurent Lemaitre, Michel Claudon, Paul C. Moldovan, Marine Dubreuil-Chambardel, Fanny Cros, Pierre Colin, Bernard Malavaud, Sebastien Crouzet, Hocine Habchi, Ahmed Khairoune, F Lefèvre, Anne-Marie Schott, Nicolas Grenier, G. Pagnoux, S. Brunelle, Marc Colombel, Laurent Magaud, Laurent Remontet, Philippe Camparo, Philippe Puech, François Cornelis, Florence Mège-Lechevallier, Muriel Rabilloud, Pierre Nevoux, E. Potiron, Daniel Portalez, Marc-Olivier Timsit, Nicolas Mottet, Gaelle Fiard, Jean-Luc Descotes, Catherine Roy, Philippe Hallouin, Hospices Civils de Lyon (HCL), Therapies Interventionnelles Assistees Par l'Image et la Simulation, Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille, Droit et Santé, Thérapies Laser Assistées par l'Image pour l'Oncologie - U 1189 (ONCO-THAI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille-Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Université de Lille, Service d'urologie et transplantation rénales [CHU Pitié-Salpétrière], CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Département de Radiologie adultes [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy)-Université de Lorraine (UL), Imagerie Adaptative Diagnostique et Interventionnelle (IADI), Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lorraine (UL), CHU Strasbourg, Centre Hospitalier Lyon Sud [CHU - HCL] (CHLS), Service de Biostatistiques [Lyon], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Hôpital Edouard Herriot [CHU - HCL], Centre de Recherche en Cancérologie de Lyon (UNICANCER/CRCL), Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre Léon Bérard [Lyon], Centre Léon Bérard [Lyon], Service d'urologie [Centre Hospitalier Lyon Sud - HCL], Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Centre de recherche cerveau et cognition (CERCO), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut des sciences du cerveau de Toulouse. (ISCT), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse - Jean Jaurès (UT2J)-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Biostatistiques santé, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Imagerie moléculaire et fonctionnelle: de la physiologie à la thérapie, Université Bordeaux Segalen - Bordeaux 2-IFR8-Centre National de la Recherche Scientifique (CNRS), Centre de Recherches en Cancérologie de Toulouse (CRCT), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Hospices Civils de Lyon, Departement de Neurologie (HCL), Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC), CHU Amiens-Picardie, Service de radiologie [CHU Necker], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université de Bordeaux (UB), Service Maladies infectieuses et tropicales [AP-HP Hôpital Cochin], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Cochin [AP-HP], Gestes Medico-chirurgicaux Assistés par Ordinateur (TIMC-IMAG-GMCAO), Techniques de l'Ingénierie Médicale et de la Complexité - Informatique, Mathématiques et Applications, Grenoble - UMR 5525 (TIMC-IMAG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), Centre Hospitalier Universitaire [Grenoble] (CHU), Service d'Urologie [CHRU Nancy], Centre Hospitalier Régional Universitaire de Nancy (CHRU Nancy), Centre hospitalier Saint Joseph - Saint Luc [Lyon], Centre Hospitalier Universitaire de Reims (CHU Reims), CHU Necker - Enfants Malades [AP-HP], Service d'urologie [Mondor], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Henri Mondor-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), CHU Bordeaux [Bordeaux], Centre d'Energétique et de Thermique de Lyon (CETHIL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Institut Universitaire du Cancer de Toulouse - Oncopole (IUCT Oncopole - UMR 1037), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM), Service d'urologie, andrologie et transplantation rénale, Université Bordeaux Segalen - Bordeaux 2-CHU Bordeaux [Bordeaux]-Groupe hospitalier Pellegrin, Service d'Hématologie [IUCT Toulouse], Université Fédérale Toulouse Midi-Pyrénées-Institut Universitaire du Cancer de Toulouse - Oncopole (IUCT Oncopole - UMR 1037), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM), Université Lille 2 - Faculté de Médecine, Centre Hospitalier Universitaire de Saint-Etienne (CHU de Saint-Etienne), 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), Université Paris Descartes - Paris 5 (UPD5), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille), Therapies Interventionnelles Assistees Par l'Image et la Simulation - U 703 (Thiais), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Institut des sciences du cerveau de Toulouse. (ISCT), Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Université de Toulouse (UT)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse - Jean Jaurès (UT2J), Université de Toulouse (UT)-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), MRI-FIRST Investigators: Nicolas Barry Delongchamps, Romain Boutier, Flavie Bratan, Serge Brunelle, Philippe Camparo, Pierre Colin, Jean-Michel Corréas, François Cornélis, François Cornud, Fanny Cros, Jean-Luc Descotes, Pascal Eschwege, Gaelle Fiard, Jean-Philippe Fendler, Hocine Habchi, Philippe Hallouin, Ahmed Khairoune, Hervé Lang, Yann Lebras, Frédéric Lefèvre, Bernard Malavaud, Paul Cezar Moldovan, Nicolas Mottet, Pierre Mozer, Pierre Nevoux, Gaele Pagnoux, Gilles Pasticier, Daniel Portalez, Eric Potiron, Athivada Soto Thammavong, Marc-Olivier Timsit, Arnault Viller, Jochen Walz, Thérapies Laser Assistées par l'Image pour l'Oncologie (ONCO-THAI), Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Pitié-Salpêtrière [APHP], Université de Lorraine (UL), Centre de Recherche en Cancérologie de Lyon (CRCL), Université de Lyon-Université de Lyon-Centre Léon Bérard [Lyon]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Necker - Enfants Malades [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-CHU Cochin [AP-HP], Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-IMAG-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-IMAG-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Assistance publique - Hôpitaux de Paris (AP-HP) (APHP)-Hôpital Henri Mondor-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Hôpitaux Universitaires Paris Ouest - Hôpitaux Universitaires Île de France Ouest (HUPO)-Assistance publique - Hôpitaux de Paris (AP-HP) (APHP), Centre Hospitalier Régional Universitaire [Lille] (CHRU Lille)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université de Lille, Service d'Urologie [CHU Pitié-Salpêtrière], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-CHU Toulouse [Toulouse]-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre Hospitalier Universitaire de Toulouse (CHU Toulouse)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), and CCSD, Accord Elsevier

Subjects
Adult, Image-Guided Biopsy, Male, medicine.medical_specialty, Adolescent, [SDV]Life Sciences [q-bio], 030232 urology & nephrology, Prostatitis, Young Adult, 03 medical and health sciences, Prostate cancer, 0302 clinical medicine, Prostate, Biopsy, medicine, Humans, Prospective Studies, Multiparametric Magnetic Resonance Imaging, Stage (cooking), Prospective cohort study, Ultrasonography, Interventional, Aged, medicine.diagnostic_test, Urinary retention, business.industry, Prostatic Neoplasms, Cancer, Middle Aged, Prostate-Specific Antigen, medicine.disease, 3. Good health, [SDV] Life Sciences [q-bio], medicine.anatomical_structure, Oncology, 030220 oncology & carcinogenesis, Radiology, medicine.symptom, business
Abstract

Summary Background Whether multiparametric MRI improves the detection of clinically significant prostate cancer and avoids the need for systematic biopsy in biopsy-naive patients remains controversial. We aimed to investigate whether using this approach before biopsy would improve detection of clinically significant prostate cancer in biopsy-naive patients. Methods In this prospective, multicentre, paired diagnostic study, done at 16 centres in France, we enrolled patients aged 18–75 years with prostate-specific antigen concentrations of 20 ng/mL or less, and with stage T2c or lower prostate cancer. Eligible patients had been referred for prostate multiparametric MRI before a first set of prostate biopsies, with a planned interval of less than 3 months between MRI and biopsies. An operator masked to multiparametric MRI results did a systematic biopsy by obtaining 12 systematic cores and up to two cores targeting hypoechoic lesions. In the same patient, another operator targeted up to two lesions seen on MRI with a Likert score of 3 or higher (three cores per lesion) using targeted biopsy based on multiparametric MRI findings. Patients with negative multiparametric MRI (Likert score ≤2) had systematic biopsy only. The primary outcome was the detection of clinically significant prostate cancer of International Society of Urological Pathology grade group 2 or higher (csPCa-A), analysed in all patients who received both systematic and targeted biopsies and whose results from both were available for pathological central review, including patients who had protocol deviations. This study is registered with ClinicalTrials.gov, number NCT02485379, and is closed to new participants. Findings Between July 15, 2015, and Aug 11, 2016, we enrolled 275 patients. 24 (9%) were excluded from the analysis. 53 (21%) of 251 analysed patients had negative (Likert ≤2) multiparametric MRI. csPCa-A was detected in 94 (37%) of 251 patients. 13 (14%) of these 94 patients were diagnosed by systematic biopsy only, 19 (20%) by targeted biopsy only, and 62 (66%) by both techniques. Detection of csPCa-A by systematic biopsy (29·9%, 95% CI 24·3–36·0) and targeted biopsy (32·3%, 26·5–38·4) did not differ significantly (p=0·38). csPCa-A would have been missed in 5·2% (95% CI 2·8–8·7) of patients had systematic biopsy not been done, and in 7·6% (4·6–11·6) of patients had targeted biopsy not been done. Four grade 3 post-biopsy adverse events were reported (3 cases of prostatitis, and 1 case of urinary retention with haematuria). Interpretation There was no difference between systematic biopsy and targeted biopsy in the detection of ISUP grade group 2 or higher prostate cancer; however, this detection was improved by combining both techniques and both techniques showed substantial added value. Thus, obtaining a multiparametric MRI before biopsy in biopsy-naive patients can improve the detection of clinically significant prostate cancer but does not seem to avoid the need for systematic biopsy. Funding French National Cancer Institute.

Published
2019
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2. What Influences the Way Radiologists Express Themselves in Their Reports? A Quantitative Assessment Using Natural Language Processing

Author

Amandine Crombé, Mylène Seux, Flavie Bratan, Jean-François Bergerot, Nathan Banaste, Vivien Thomson, Jean-Christophe Lecomte, and Guillaume Gorincour

Subjects
Radiological and Ultrasound Technology, Radiologists, Humans, Radiology, Nuclear Medicine and imaging, Radiology, Tomography, X-Ray Computed, Computer Science Applications, Natural Language Processing, Retrospective Studies
Abstract

Although using standardized reports is encouraged, most emergency radiological reports in France remain in free-text format that can be mined with natural language processing for epidemiological purposes, activity monitoring or data collection. These reports are obtained under various on-call conditions by radiologists with various backgrounds. Our aim was to investigate what influences the radiologists' written expressions. To do so, this retrospective multicentric study included 30,227 emergency radiological reports of computed tomography scans and magnetic resonance imaging involving exactly one body region, only with pathological findings, interpreted from 2019-09-01 to 2020-02-28 by 165 radiologists. After text pre-processing, one-word tokenization and use of dictionaries for stop words, polarity, sentiment and uncertainty, 11 variables depicting the structure and content of words and sentences in the reports were extracted and summarized to 3 principal components capturing 93.7% of the dataset variance. In multivariate analysis, the 1

Published
2021

3. The accuracy of teleradiologists in diagnosing COVID-19 based on a French multicentric emergency cohort

Author

Nicolas Favard, François Petitpierre, Basile Porta, Grégoire Bouquet, Alexandre Ben Cheikh, Emile Youssof, Florian Alonzo-Lacroix, Julien Balique, Thomas Ayoub, Nathan Banaste, Hubert Nivet, Vivien Thomson, Jean-François Bergerot, Guillaume Gorincour, Flavie Bratan, Amandine Crombé, Paul Schuster, Charles Mastier, Laurent Pourriol, and Alban Chazot

Subjects
Adult, medicine.medical_specialty, Teleradiology, Prevalence, Sensitivity and Specificity, 030218 nuclear medicine & medical imaging, Hospital, 03 medical and health sciences, 0302 clinical medicine, Cohen's kappa, medicine, Humans, Radiology, Nuclear Medicine and imaging, Prospective Studies, Medical diagnosis, Prospective cohort study, Neuroradiology, medicine.diagnostic_test, SARS-CoV-2, business.industry, Tomography, X-ray computed, COVID-19, Interventional radiology, General Medicine, Polymerase chain reaction, Radiology Nuclear Medicine and imaging, 030220 oncology & carcinogenesis, Cohort, Chest, Radiology, Coronavirus Infections, Emergency Service, Hospital, business, Emergency service, Kappa
Abstract

Objectives To evaluate the accuracy of diagnoses of COVID-19 based on chest CT as well as inter-observer agreement between teleradiologists during on-call duty and senior radiologists in suspected COVID-19 patients. Materials and methods From March 13, 2020, to April 14, 2020, consecutive suspected COVID-19 adult patients who underwent both an RT-PCR test and chest CT from 15 hospitals were included in this prospective study. Chest CTs were immediately interpreted by the on-call teleradiologist and were systematically blind reviewed by a senior radiologist. Readings were categorised using a five-point scale: (1) normal; (2) non-infectious findings; (3) infectious findings but not consistent with COVID-19 infection; (4) consistent with COVID-19 infection; and (5) typical appearance of COVID-19 infection. The diagnostic accuracy of chest CT and inter-observer agreement using the kappa coefficient were evaluated over the study period. Results In total, 513 patients were enrolled, of whom 244/513 (47.6%) tested positive for RT-PCR. First readings were scored 4 or 5 in 225/244 (92%) RT-PCR+ patients, and between 1 and 3 in 201/269 (74.7%) RT-PCR− patients. The data were highly consistent (weighted kappa = 0.87) and correlated with RT-PCR (p < 0.001, AUC1st-reading = 0.89, AUC2nd-reading = 0.93). The negative predictive value for scores of 4 or 5 was 0.91–0.92, and the PPV for a score of 5 was 0.89–0.96 at the first and second readings, respectively. Diagnostic accuracy was consistent over the study period, irrespective of a variable prevalence rate. Conclusion Chest CT demonstrated high diagnostic accuracy with strong inter-observer agreement between on-call teleradiologists with varying degrees of experience and senior radiologists over the study period. Key Points • The accuracy of readings by on-call teleradiologists, relative to second readings by senior radiologists, demonstrated a sensitivity of 0.75–0.79, specificity of 0.92–0.97, NPV of 0.80–0.83, and PPV of 0.89–0.96, based on “typical appearance,” as predictive of RT-PCR+. • Inter-observer agreement between the first reading in the emergency setting and the second reading by the senior emergency teleradiologist was excellent (weighted kappa = 0.87). Electronic supplementary material The online version of this article (10.1007/s00330-020-07345-z) contains supplementary material, which is available to authorized users.

Published
2020
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4. Practical Clinical and Radiological Models to Diagnose COVID-19 on Chest CT in a French Multicentric Emergency Population

Author

Flavie Bratan, Amandine Crombé, Paul Schuster, Vivien Thomson, Alice H. Berger, Alban Chazot, Grégoire Bouquet, Nicolas Favard, Nathan Banaste, Hubert Nivet, Florian Alonzo-Lacroix, Charles Mastier, Basile Porta, François Petitpierre, Julien Balique, Guillaume Gorincour, Laurent Pourriol, Jean-François Bergerot, Emile Youssof, and Alexandre Ben Cheikh

Subjects
medicine.medical_specialty, education.field_of_study, Coronavirus disease 2019 (COVID-19), business.industry, Radiological weapon, Population, medicine, Chest ct, Radiology, business, education
Abstract

Our aim was to develop practical models built with simple clinical-radiological features to facilitate COVID-19 diagnosis. To do so, 513 consecutive adult patients suspected of having COVID-19 from 15 emergency departments from 03/13/2020 to 04/14/2020 were included (244 [47.6%] with a positive RT-PCR). Chest CTs were immediately and prospectively analysed by on-call teleradiologists (OCTR) and systematically reviewed within one week by another senior teleradiologist. Each OCTR reading was concluded using a 5-point scale: normal, non-infectious, infectious non-COVID-19, indeterminate and highly suspicious of COVID-19. The senior reading reported the lesions’ semiology, distribution, extent and differential diagnoses. Multivariate stepwise logistic regression (Step-LR) and classification tree (CART) models to predict a positive RT-PCR were trained on 412 patients, validated on an independent cohort of 101 patients and compared with the OCTR performances (295 and 71 with available clinical data, respectively). Regarding models elaborated on radiological variables alone, best performances were reached with the CART model (i.e., AUC=0.92 versus 0.88 for OCTR) while step-LR provided the highest AUC with clinical-radiological variables (0.93 versus 0.86 for OCTR). Hence, these two simple models, depending on the availability of clinical data, could be used by any radiologist to support their conclusion in case of COVID-19 suspicion.

Published
2020
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5. Practical clinical and radiological models to diagnose COVID-19 based on a multicentric teleradiological emergency chest CT cohort

Author

Nicolas Favard, Jean-François Bergerot, Alban Chazot, Grégoire Bouquet, François Petitpierre, Julien Balique, Nathan Banaste, Emile Youssof, Hubert Nivet, Vivien Thomson, Florian Alonzo-Lacroix, Amandine Crombé, Basile Porta, Alice H. Berger, G. Gorincour, Flavie Bratan, Charles Mastier, Paul Schuster, Laurent Pourriol, and Alexandre Ben Cheikh

Subjects
Male, medicine.medical_specialty, Teleradiology, Mathematics and computing, Radiography, Science, Diseases, Sensitivity and Specificity, Article, 030218 nuclear medicine & medical imaging, Cohort Studies, 03 medical and health sciences, 0302 clinical medicine, medicine, Humans, Medical diagnosis, Multidisciplinary, Receiver operating characteristic, business.industry, SARS-CoV-2, Health care, COVID-19, Stepwise regression, Semiology, Risk factors, 030220 oncology & carcinogenesis, Radiological weapon, Cohort, Medicine, Female, Radiography, Thoracic, Radiology, business, Cohort study
Abstract

Our aim was to develop practical models built with simple clinical and radiological features to help diagnosing Coronavirus disease 2019 [COVID-19] in a real-life emergency cohort. To do so, 513 consecutive adult patients suspected of having COVID-19 from 15 emergency departments from 2020-03-13 to 2020-04-14 were included as long as chest CT-scans and real-time polymerase chain reaction (RT-PCR) results were available (244 [47.6%] with a positive RT-PCR). Immediately after their acquisition, the chest CTs were prospectively interpreted by on-call teleradiologists (OCTRs) and systematically reviewed within one week by another senior teleradiologist. Each OCTR reading was concluded using a 5-point scale: normal, non-infectious, infectious non-COVID-19, indeterminate and highly suspicious of COVID-19. The senior reading reported the lesions’ semiology, distribution, extent and differential diagnoses. After pre-filtering clinical and radiological features through univariate Chi-2, Fisher or Student t-tests (as appropriate), multivariate stepwise logistic regression (Step-LR) and classification tree (CART) models to predict a positive RT-PCR were trained on 412 patients, validated on an independent cohort of 101 patients and compared with the OCTR performances (295 and 71 with available clinical data, respectively) through area under the receiver operating characteristics curves (AUC). Regarding models elaborated on radiological variables alone, best performances were reached with the CART model (i.e., AUC = 0.92 [versus 0.88 for OCTR], sensitivity = 0.77, specificity = 0.94) while step-LR provided the highest AUC with clinical-radiological variables (AUC = 0.93 [versus 0.86 for OCTR], sensitivity = 0.82, specificity = 0.91). Hence, these two simple models, depending on the availability of clinical data, provided high performances to diagnose positive RT-PCR and could be used by any radiologist to support, modulate and communicate their conclusion in case of COVID-19 suspicion. Practically, using clinical and radiological variables (GGO, fever, presence of fibrotic bands, presence of diffuse lesions, predominant peripheral distribution) can accurately predict RT-PCR status.

Published
2020

6. Stiffness of benign and malignant prostate tissue measured by shear-wave elastography: a preliminary study

Author

Olivier Rouvière, Florence Mège-Lechevallier, Marc Colombel, Sebastien Crouzet, Christelle Melodelima, Rémi Souchon, Thomas Sanzalone, G. Pagnoux, Au Hoang Dinh, and Flavie Bratan

Subjects
Male, medicine.medical_specialty, medicine.medical_treatment, Prostatic Hyperplasia, 030232 urology & nephrology, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Elasticity Imaging Techniques, 0302 clinical medicine, Prostate, medicine, Humans, Radiology, Nuclear Medicine and imaging, Prospective Studies, Aged, Neuroradiology, Prostatectomy, medicine.diagnostic_test, business.industry, Ultrasound, Prostatic Neoplasms, Stiffness, General Medicine, Middle Aged, Prostate-Specific Antigen, musculoskeletal system, Sagittal plane, body regions, medicine.anatomical_structure, Multivariate Analysis, Elastography, Radiology, medicine.symptom, business
Abstract

To measure benign and malignant prostate tissue stiffness using shear-wave elastography (SWE). Thirty consecutive patients underwent transrectal SWE in the axial and sagittal planes before prostatectomy. After reviewing prostatectomy specimens, two radiologists measured stiffness in regions corresponding to cancers, lateral and median benign peripheral zone (PZ) and benign transition zone (TZ). Cancers were stiffer than benign PZ and TZ. All tissue classes were stiffer on sagittal than on axial imaging, in TZ than in PZ, and in median PZ than in lateral PZ. At multivariate analysis, the nature of tissue (benign or malignant; P

Published
2016
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7. Characterization of Prostate Cancer with Gleason Score of at Least 7 by Using Quantitative Multiparametric MR Imaging: Validation of a Computer-aided Diagnosis System in Patients Referred for Prostate Biopsy

Author

Au Hoang Dinh, Alain Ruffion, G. Pagnoux, Olivier Rouvière, Christelle Melodelima, Marc Colombel, Flavie Bratan, Florence Mège-Lechevallier, Rémi Souchon, Paul C. Moldovan, and Sebastien Crouzet

Subjects
Male, medicine.medical_specialty, Prostate biopsy, Population, Sensitivity and Specificity, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Prostate cancer, 0302 clinical medicine, Biopsy, Image Interpretation, Computer-Assisted, medicine, Humans, Radiology, Nuclear Medicine and imaging, In patient, Diagnosis, Computer-Assisted, Prospective Studies, education, Aged, education.field_of_study, medicine.diagnostic_test, business.industry, Prostate, Middle Aged, medicine.disease, Image Enhancement, Mr imaging, Predictive value, Magnetic Resonance Imaging, ROC Curve, Computer-aided diagnosis, 030220 oncology & carcinogenesis, Area Under Curve, Radiology, Neoplasm Grading, business
Abstract

Purpose To determine the performance of a computer-aided diagnosis (CAD) system trained at characterizing cancers in the peripheral zone (PZ) with a Gleason score of at least 7 in patients referred for multiparametric magnetic resonance (MR) imaging before prostate biopsy. Materials and Methods Two institutional review board-approved prospective databases of patients who underwent multiparametric MR imaging before prostatectomy (database 1) or systematic and targeted biopsy (database 2) were retrospectively used. All patients gave informed consent for inclusion in the databases. A CAD combining the 10th percentile of the apparent diffusion coefficient and the time to peak of enhancement was trained to detect cancers in the PZ with a Gleason score of at least 7 in 106 patients from database 1. The CAD was tested in 129 different patients from database 2. All targeted lesions were prospectively scored at biopsy by using a five-level Likert score. The CAD scores were retrospectively calculated. Biopsy results were used as the reference standard. Areas under the receiver operating characteristic curves (AUCs) were computed for CAD and Likert scores by using binormal smoothing for per-lesion and per-lobe analyses, and a density function for per-patient analysis. Results The CAD outperformed the Likert score in the overall population and all subgroups, except in the transition zone. The difference was statistically significant for the overall population (AUC, 0.95 [95% confidence interval {CI}: 0.90, 0.98] vs 0.88 [95% CI: 0.68, 0.96]; P = .02) at per-patient analysis, and for less-experienced radiologists (1 year) at per-lesion (AUC, 0.90 [95% CI: 0.81, 0.95] vs 0.83 [95% CI: 0.73, 0.90]; P = .04) and per-lobe (AUC, 0.92 [95% CI: 0.80, 0.96] vs 0.84 [95% CI: 0.72, 0.91]; P = .04) analysis. Conclusion The CAD outperformed the Likert score prospectively assigned at biopsy in characterizing cancers with a Gleason score of at least 7.

Published
2018

8. Value of prostate multiparametric magnetic resonance imaging for predicting biopsy results in first or repeat biopsy

Author

T. Sanzalone, Florence Mège-Lechevallier, Muriel Rabilloud, Marc Colombel, Flavie Bratan, Hocine Habchi, Sebastien Crouzet, G. Pagnoux, Olivier Rouvière, and A. Paye

Subjects
Male, medicine.medical_specialty, Prostate biopsy, Biopsy, Predictive Value of Tests, Prostate, medicine, Humans, Radiology, Nuclear Medicine and imaging, Prospective Studies, Multiparametric Magnetic Resonance Imaging, Aged, Receiver operating characteristic, medicine.diagnostic_test, Repeat biopsy, business.industry, Prostatic Neoplasms, Cancer, General Medicine, Middle Aged, medicine.disease, Magnetic Resonance Imaging, Surgery, SSS, medicine.anatomical_structure, Radiology, business
Abstract

Aim To assess multiparametric magnetic resonance imaging (mp-MRI) in predicting prostate biopsy results. Materials and methods Patients who underwent mp-MRI prior to prostate biopsy were prospectively included. The prostate was subdivided into 14 sectors and mp-MRI findings assessed using a five-level subjective suspicion score (SSS). Biopsy included targeted samples of abnormal sectors and systematic samples of normal peripheral zone sectors. Results Two hundred and eighty-eight patients were included [153 biopsy naive, 135 with negative ( n = 51) or positive ( n = 84) prior biopsy]. Biopsy was positive in 168 patients. mp-MRI area under the receiver operating characteristic (ROC) curve (AUC) was 69.1% (95% CI: 67.1–70.9%), 72.5% (95% CI: 69.5–76%), and 73.8% (95% CI: 68.3–79.3%) at per sector, per lobe, and per patient analysis, respectively. At the per sector level, the AUC was significantly larger if detection was limited to cancers with a Gleason score of ≥7 (72.6%; 95% CI: 69.8–75.8%; p 0.01) or ≥8 (87.1%; 95% CI: 78.3–95.7%; p 0.01). mp-MRI performance was significantly influenced by prostate volume ( p = 0.02), the presence of a concordant hypoechoic area ( p 0.001), but not by prostate-specific antigen (PSA) value, status of prior biopsy, or radiologists' experience. SSS was significantly associated with the Gleason score in true-positive lobes and patients ( p 0.0001). Using a SSS threshold of ≥3, cancer was missed in 13/102 lobes and 4/72 patients with cancers of Gleason score ≥7. Conclusion mp-MRI provides a good detection of cancers with a Gleason score of ≥7 in candidates suitable for prostate biopsy.

Published
2014
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9. Can pre- and postoperative magnetic resonance imaging predict recurrence-free survival after whole-gland high-intensity focused ablation for prostate cancer?

Author

Florence Mège-Lechevallier, Hélène Tonoli-Catez, Rémy Rosset, Flavie Bratan, Albert Gelet, Sebastien Crouzet, and Olivier Rouvière

Subjects
Male, medicine.medical_specialty, medicine.medical_treatment, Brachytherapy, 030232 urology & nephrology, Salvage therapy, 030218 nuclear medicine & medical imaging, Lesion, 03 medical and health sciences, Prostate cancer, 0302 clinical medicine, Prostate, medicine, Humans, Radiology, Nuclear Medicine and imaging, Postoperative Period, Neoplasm Metastasis, Ultrasound, High-Intensity Focused, Transrectal, Neuroradiology, Aged, Retrospective Studies, Salvage Therapy, medicine.diagnostic_test, business.industry, Prostatic Neoplasms, Magnetic resonance imaging, General Medicine, Middle Aged, Prostate-Specific Antigen, medicine.disease, Prognosis, Magnetic Resonance Imaging, Prostate-specific antigen, medicine.anatomical_structure, Treatment Outcome, High-Intensity Focused Ultrasound Ablation, Radiology, medicine.symptom, Neoplasm Grading, Neoplasm Recurrence, Local, business
Abstract

Our aim was to assess whether magnetic resonance imaging (MRI) features predict recurrence-free survival (RFS) after prostate cancer high-intensity focused ultrasound (HIFU) ablation. We retrospectively selected 81 patients who underwent (i) whole-gland HIFU ablation between 2007 and 2011 as first-line therapy or salvage treatment after radiotherapy or brachytherapy, and (ii) pre- and postoperative MRI. On preoperative imaging, two senior (R1, R2) and one junior (R3) readers assessed the number of sectors invaded by the lesion with the highest Likert score (dominant lesion) using a 27-sector diagram. On postoperative imaging, readers assessed destruction of the dominant lesion using a three-level score. Multivariate analysis included the number of sectors invaded by the dominant lesion, its Likert and destruction scores, the pre-HIFU prostate-specific antigen (PSA) level, Gleason score, and the clinical setting (primary/salvage). The most significant predictor was the number of prostate sectors invaded by the dominant lesion for R2 and R3 (p≤0.001) and the destruction score of the dominant lesion for R1 (p = 0.011). The pre-HIFU PSA level was an independent predictor for R2 (p = 0.014), but with only marginal significance for R1 (p = 0.059) and R3 (p = 0.053). The dominant lesion’s size and destruction assessed by MRI provide independent prognostic information compared with usual predictors. • The size of the MR-dominant lesion significantly influences post-HIFU recurrence-free survival. • The destruction score of the MR-dominant lesion predicts post-HIFU recurrence-free survival. • Patients with a completely devascularized MR-dominant lesion show better recurrence-free survival • Pre- and post-HIFU MRI provide prognostic information independent of usual predictors.

Published
2016

10. Detection of locally radio-recurrent prostate cancer at multiparametric MRI: Can dynamic contrast-enhanced imaging be omitted?

Author

T. Vitry, Albert Gelet, Olivier Rouvière, Flavie Bratan, Sebastien Crouzet, F. Alonzo, and Christelle Melodelima

Subjects
Male, medicine.medical_specialty, Prostate biopsy, Contrast Media, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Prostate cancer, 0302 clinical medicine, Prostate, Biopsy, medicine, Humans, Radiology, Nuclear Medicine and imaging, Multiparametric Magnetic Resonance Imaging, Aged, Retrospective Studies, Radiological and Ultrasound Technology, medicine.diagnostic_test, Receiver operating characteristic, business.industry, Prostatic Neoplasms, Magnetic resonance imaging, General Medicine, medicine.disease, Magnetic Resonance Imaging, medicine.anatomical_structure, 030220 oncology & carcinogenesis, Dynamic contrast-enhanced MRI, Radiology, Neoplasm Recurrence, Local, business
Abstract

Objective The goal of this study was to assess the added value of dynamic contrast-enhanced (DCE) imaging in detecting locally radio-recurrent prostate cancer using multiparametric magnetic resonance imaging (mpMRI) at 3 Tesla (T). Materials and methods We retrospectively analyzed 45 patients with rising prostate-specific antigen level after prostate radiotherapy who underwent mpMRI [T2-weighted (T2w), diffusion-weighted (Dw) and DCE imaging] at 3 T before prostate biopsy. Four readers assigned a 5-level Likert score of cancer likelihood in 8 prostate sectors (6 sextants, 2 seminal vesicles) on T2w + Dw and T2w + Dw + DCE images. Biopsy results were used as the standard of reference. Results T2w + Dw and T2w + Dw + DCE imaging had similar areas under the receiver operating characteristic curves on per-sector (0.87–0.89 vs. 0.87–0.89; P = 0.19–0.78) and per-lobe (0.82–0.94 vs. 0.80–0.91; P = 0.21–0.84) analysis. Using a Likert score ≥ 2/5 for diagnosis threshold, T2w + Dw + DCE imaging showed non-significantly higher sensitivities on per-sector (0.56–0.72 vs. 0.52–0.73, P = 0.34–0.69) and per-lobe (0.80–0.90 vs. 0.73–0.88; P = 0.63–0.99) analysis. It also showed non-significantly lower specificities on per-sector (0.74–0.89 vs. 0.82–0.89; P = 0.09–0.99) and per-lobe (0.48–0.81 vs. 0.61–0.84; P = 0.10–0.99) analysis. Weighted kappa values were respectively 0.57–0.70 and 0.55–0.66 for T2w + Dw and T2w + Dw + DCE imaging at the sector level, and 0.66–0.83 and 0.58–0.85 at the lobe level. Conclusion The use of DCE MR imaging tends to increase sensitivity and decrease specificity for all readers, but the differences are not significant.

Published
2015

11. Characterization of Prostate lesions as Benign or Malignant at Multiparametric MR imaging: Comparison of Three Scoring Systems in Patients Treated with Radical Prostatectomy

Author

Florence Mège-Lechevallier, Tiphaine Vaché, Muriel Rabilloud, Olivier Rouvière, Sylvain Roche, Flavie Bratan, Biostatistiques santé, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), and Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Male, medicine.medical_specialty, medicine.medical_treatment, [SDV]Life Sciences [q-bio], Malignancy, 030218 nuclear medicine & medical imaging, Diagnosis, Differential, 03 medical and health sciences, Prostate cancer, 0302 clinical medicine, Neoplasm Recurrence, Prostate, Area under curve, medicine, Humans, Radiology, Nuclear Medicine and imaging, In patient, Aged, Observer Variation, Prostatectomy, business.industry, Prostatic Neoplasms, Middle Aged, medicine.disease, Magnetic Resonance Imaging, Mr imaging, medicine.anatomical_structure, Area Under Curve, 030220 oncology & carcinogenesis, Radiology, Neoplasm Recurrence, Local, business
Abstract

To compare the subjective Likert score to the Prostate Imaging Reporting and Data System (PIRADS) and morphology-location-signal intensity (MLS) scores for categorization of prostate lesions as benign or malignant at multiparametric magnetic resonance (MR) imaging.Two hundred fifteen patients who underwent T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced multiparametric MR imaging of the prostate before radical prostatectomy were included in a prospective database after they signed the institutional review board-approved forms. Senior readers 1 and 2 prospectively noted the location, shape, and signal intensity of lesions on MR images from individual pulse sequences and scored each for likelihood of malignancy by using a Likert scale (range, 1-5). A junior reader (reader 3) retrospectively reviewed the database and did the same analysis. The MLS score (range, 1-13) was computed by using the readers' descriptions of the lesions. Then, the three readers again scored the lesions they described by using the PIRADS score (range, 3-15). MLS and PIRADS scores were compared with the Likert score by using their areas under the receiver operating characteristic curves.Areas under the receiver operating characteristic curves of the Likert, MLS, and PIRADS scores were 0.81, 0.77 (P = .03), and 0.75 (P = .01) for reader 1; 0.88, 0.74 (P.0001), and 0.76 (P.0001) for reader 2; and 0.81, 0.78 (P = .23), and 0.75 (P = .01) for reader 3. For diagnosing cancers with Gleason scores greater than or equal to 7, the Likert score was significantly more accurate than the others, except for the MLS score for reader 3. Weighted κ values were 0.470-0.524, 0.405-0.430, and 0.378-0.441 for the Likert, MLS, and PIRADS scores, respectively.The Likert score allowed significantly more accurate categorization of prostate lesions on MR images than did the MLS and PIRADS scores.

Published
2014

12. Prostate Focal Peripheral Zone Lesions: Characterization at Multiparametric MR Imaging--Influence of a Computer-aided Diagnosis System

Author

Emilie Niaf, Florence Mège-Lechevallier, Muriel Rabilloud, Flavie Bratan, Carole Lartizien, Olivier Rouvière, Laurent Roche, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé ( CREATIS ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Hospices Civils de Lyon ( HCL ) -Université Jean Monnet [Saint-Étienne] ( UJM ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ), Application des ultrasons à la thérapie ( LabTAU ), Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale ( INSERM ), 2 - Images et Modèles, Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Hospices Civils de Lyon ( HCL ) -Université Jean Monnet [Saint-Étienne] ( UJM ) -Institut National de la Santé et de la Recherche Médicale ( INSERM ) -Centre National de la Recherche Scientifique ( CNRS ) -Université Claude Bernard Lyon 1 ( UCBL ), Département de médecine nucléaire, Hospices Civils de Lyon ( HCL ), Laboratoire de Biométrie et Biologie Evolutive ( LBBE ), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ), Department of Urinary and Vascular Radiology, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Application des ultrasons à la thérapie (LabTAU), Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM), Images et Modèles, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Hospices Civils de Lyon (HCL), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Biostatistiques santé, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL)

Subjects
Male, medicine.medical_specialty, medicine.medical_treatment, Contrast Media, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, Diagnosis, Differential, Meglumine, Prostate, Area under curve, Organometallic Compounds, Humans, Medicine, Radiology, Nuclear Medicine and imaging, Diagnosis, Computer-Assisted, Prospective Studies, Aged, Observer Variation, Prostatectomy, business.industry, Prostatic Neoplasms, Middle Aged, Prostate-Specific Antigen, Magnetic Resonance Imaging, Mr imaging, Peripheral zone, Diffusion Magnetic Resonance Imaging, medicine.anatomical_structure, ROC Curve, Computer-aided diagnosis, Area Under Curve, Preoperative Period, Radiology, Observer variation, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract

International audience; Purpose To assess the impact of a computer-aided diagnosis (CAD) system in the characterization of focal prostate lesions at multiparametric magnetic resonance (MR) imaging. Materials and Methods Formal institutional review board approval was not required. Thirty consecutive 1.5-T multiparametric MR imaging studies (with T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced imaging) obtained before radical prostatectomy in patients between September 2008 and February 2010 were reviewed. Twelve readers assessed the likelihood of malignancy of 88 predefined peripheral zone lesions by using a five-level (level, 0-4) subjective score (SS) in reading session 1. This was repeated 5 weeks later in reading session 2. The CAD results were then disclosed, and in reading session 3, the readers could amend the scores assigned during reading session 2. Diagnostic accuracy was assessed by using a receiver operating characteristic (ROC) regression model and was quantified with the area under the ROC curve (AUC). Results Mean AUCs were significantly lower for less experienced (

Published
2014
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13. Differentiation of transitional zone prostate cancer from benign hyperplasia nodules: evaluation of discriminant criteria at multiparametric MRI

Author

A. L. Chesnais, Sylvain Roche, Emilie Niaf, Marc Colombel, Flavie Bratan, Olivier Rouvière, Florence Mège-Lechevallier, Muriel Rabilloud, Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Application des ultrasons à la thérapie (LabTAU), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre Léon Bérard [Lyon]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut de génétique humaine (IGH), Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS), Biostatistiques santé, Département biostatistiques et modélisation pour la santé et l'environnement [LBBE], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Service d'urologie [Centre Hospitalier Lyon Sud - HCL], Centre Hospitalier Lyon Sud [CHU - HCL] (CHLS), Hospices Civils de Lyon (HCL)-Hospices Civils de Lyon (HCL), Université de Lyon-Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Hospices Civils de Lyon (HCL)-Université Jean Monnet [Saint-Étienne] (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université Jean Monnet - Saint-Étienne (UJM)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), Centre Léon Bérard [Lyon]-Université Claude Bernard Lyon 1 (UCBL), and Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)

Subjects
Male, medicine.medical_specialty, medicine.medical_treatment, [SDV]Life Sciences [q-bio], Prostatic Hyperplasia, Malignancy, 030218 nuclear medicine & medical imaging, 03 medical and health sciences, Prostate cancer, 0302 clinical medicine, Prostate, Image Processing, Computer-Assisted, medicine, Humans, Effective diffusion coefficient, Radiology, Nuclear Medicine and imaging, Prospective Studies, ComputingMilieux_MISCELLANEOUS, Aged, Prostatectomy, medicine.diagnostic_test, business.industry, Prostatic Neoplasms, Cancer, Magnetic resonance imaging, General Medicine, Middle Aged, medicine.disease, Magnetic Resonance Imaging, Sagittal plane, 3. Good health, Diffusion Magnetic Resonance Imaging, medicine.anatomical_structure, 030220 oncology & carcinogenesis, Radiology, business
Abstract

Aim To assess topographical and magnetic resonance imaging (MRI) features in characterizing prostate transitional zone (TZ) nodules. Materials and methods Two radiologists evaluated all TZ nodules visible at multiparametric MRI in 52 consecutive patients who underwent radical prostatectomy. The radiologists assessed topographical (anteroposterior and superior–inferior location, crossing of the sagittal midline) and T2-weighted (shape, presence and distinctness of capsule, distinctness of contours, presence of cysts) features, the apparent diffusion coefficient (ADC), and eight semi-quantitative and quantitative enhancement parameters derived from dynamic contrast-enhanced (DCE) imaging. The nature of the nodules was assessed using prostatectomy specimens. Five statistical methods taking into account multiple testing were used. Results One hundred and thirty-seven nodules (117 benign, 20 malignant) were evaluated. Mean ADC, all topographical, and all T2-weighted features were significant predictors of malignancy according to at least four out of the five statistical methods. Particularly, 20/20 and 18/20 cancers involved the anterior and apical third of the TZ, respectively. None of the enhancement parameters was significantly different between cancers and benign nodules. By assessing the presence of cysts, the nodules' capsule, and their anteroposterior and superior–inferior location, 111/117 benign nodules were correctly diagnosed, without misclassifying any cancer. Conclusion Topographical, T2-weighted, and diffusion-weighted features can be used to characterize TZ nodules. DCE imaging does not seem to provide additional information.

Published
2013
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14. Influence of imaging and histological factors on prostate cancer detection and localisation on multiparametric MRI: a prospective study

Author

Marc Colombel, Olivier Rouvière, Christelle Melodelima, Emilie Niaf, Rémi Souchon, Anne Laure Chesnais, Flavie Bratan, and Florence Mège-Lechevallier

Subjects
Male, medicine.medical_specialty, Databases, Factual, medicine.medical_treatment, Biopsy, Contrast Media, Malignancy, Prostate cancer, medicine, Image Processing, Computer-Assisted, Humans, Radiology, Nuclear Medicine and imaging, False Positive Reactions, Prospective Studies, Prospective cohort study, Neuroradiology, Aged, Observer Variation, Prostatectomy, medicine.diagnostic_test, business.industry, Prostate, Prostatic Neoplasms, Reproducibility of Results, Magnetic resonance imaging, General Medicine, Middle Aged, Prostate-Specific Antigen, medicine.disease, Diffusion Magnetic Resonance Imaging, Prostate surgery, Radiology, business
Abstract

To assess factors influencing prostate cancer detection on multiparametric (T2-weighted, diffusion-weighted, and dynamic contrast-enhanced) MRI. One hundred and seventy-five patients who underwent radical prostatectomy were included. Pre-operative MRI performed at 1.5 T (n = 71) or 3 T (n = 104), with (n = 58) or without (n = 117) an endorectal coil were independently interpreted by two radiologists. A five-point subjective suspicion score (SSS) was assigned to all focal abnormalities (FAs). MR findings were then compared with whole-mount sections. Readers identified 192–214/362 cancers, with 130–155 false positives. Detection rates for tumours of 2 cc were 33–45/155 (21–29 %), 15–19/35 (43–54 %) and 8–9/12 (67–75 %) for Gleason ≤6, 17/27 (63 %), 42–45/51 (82–88 %) and 34/35 (97 %) for Gleason 7 and 4/5 (80 %), 13/14 (93 %) and 28/28 (100 %) for Gleason ≥8 cancers respectively. At multivariate analysis, detection rates were influenced by tumour Gleason score, histological volume, histological architecture and location (P

Published
2012

15. Characterization of prostate cancer using T2 mapping at 3T: A multi-scanner study

Author

A. Hoang Dinh, Christelle Melodelima, Rémi Souchon, Olivier Rouvière, Flavie Bratan, Marc Colombel, and Florence Mège-Lechevallier

Subjects
Male, Scanner, medicine.medical_specialty, T2 mapping, medicine.medical_treatment, Normal tissue, Malignancy, Prostate cancer, Predictive Value of Tests, Prostate, medicine, Humans, Radiology, Nuclear Medicine and imaging, Prostate multiparametric MRI, Aged, Retrospective Studies, T2, Radiological and Ultrasound Technology, Prostatectomy, business.industry, Prostatic Neoplasms, General Medicine, Middle Aged, medicine.disease, Magnetic Resonance Imaging, T2 value, medicine.anatomical_structure, Prostate cancer aggressiveness, Radiology, business
Abstract

Rationale and objectives To assess the prostate T2 value as a predictor of malignancy on two different 3 T scanners. Patients and methods Eighty-three pre-prostatectomy multiparametric MRIs were retrospectively evaluated [67 obtained on a General Electric MRI (scanner 1) and 16 on a Philips MRI (scanner 2)]. After correlation with prostatectomy specimens, readers measured the T2 value of regions-of-interest categorized as “cancers”, “false positive lesions”, or “normal tissue”. Results On scanner 1, in PZ, cancers had significantly lower T2 values than false positive lesions ( P = 0.02) and normal tissue ( P = 2 × 10 −9 ). Gleason ≥ 6 cancers had similar T2 values than false positive lesions and significantly higher T2 values than Gleason ≥ 7 cancers ( P = 0.009). T2 values corresponding to a 25% and 75% risk of Gleason ≥ 7 malignancy were respectively 132 ms (95% CI: 129–135 ms) and 77 ms (95% CI: 74–81 ms). In TZ, cancers had significantly lower T2 values than normal tissue ( P = 0.008), but not than false positive findings. Mean T2 values measured on scanner 2 were not significantly different than those measured on scanner 1 for all tissue classes. Conclusion All tested tissue classes had similar mean T2 values on both scanners. In PZ, the T2 value was a significant predictor of Gleason ≥ 7 cancers.

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16. Focal treatment of prostate cancer using Focal One device: pilot study results

Author

Olivier Rouvière, Albert Gelet, Flavie Bratan, Jean-Yves Chapelon, and Sebastien Crouzet

Subjects
Prostate cancer, medicine.medical_specialty, Text mining, genetic structures, Focal treatment, business.industry, Ultrasound, medicine, Oral Presentation, Radiology, Nuclear Medicine and imaging, Radiology, medicine.disease, business
Abstract

Objectives: To evaluated the efficacy of Focal One device for focal treatment of localized prostate cancer.

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