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

Author: Paul Schuster, Amandine Crombé, Hubert Nivet, Alice Berger, Laurent Pourriol, Nicolas Favard, Alban Chazot, Florian Alonzo-Lacroix, Emile Youssof, Alexandre Ben Cheikh, Julien Balique, Basile Porta, François Petitpierre, Grégoire Bouquet, Charles Mastier, Flavie Bratan, Jean-François Bergerot, Vivien Thomson, Nathan Banaste, and Guillaume Gorincour
Subjects: Medicine, Science
Abstract: 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: 2021
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2. Teleradiology as a relevant indicator of the impact of COVID-19 pandemic management on emergency room activities: a nationwide worrisome survey

Author: Jordan Vatele, Stéphanie Gentile, Vivien Thomson, Bénédicte Devictor, Marine Cloux, Nicolas Girouin, Flavie Bratan, Jean-François Bergerot, Mylène Seux, Nathan Banaste, Karim Tazarourte, and Guillaume Gorincour
Subjects: Teleradiology, COVID-19, Emergency service, Hospital, Health services accessibility, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract: Abstract Objectives To evaluate the impact of COVID-19’s lockdown on radiological examinations in emergency services. Methods Retrospective, multicentre analysis of radiological examinations requested, via our teleradiology network, from 2017 to 2020 during two timeframes (calendar weeks 5–8 and then 12–15). We included CT scans or MRIs performed for strokes, multiple traumas (Body-CT), cranial traumas (CTr) and acute non-traumatic abdominal pain (ANTAP). We evaluated the number and percentages of examinations performed, of those with a pathological conclusion, and of examinations involving the chest. Results Our study included 25 centres in 2017, 29 in 2018, 43 in 2019 and 59 in 2020. From 2017 to 2019, the percentages of examinations were constant, which was also true for chest CTs. Each centre’s number of examinations, gender distribution and patient ages were unchanged. In 2020, examinations significantly decreased: suspected strokes decreased by 36% (1052 vs 675, p
Published: 2021
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3. Diagnostic value and relative weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers.

Author: Olivier Rouvière, Tristan Dagonneau, Fanny Cros, Flavie Bratan, Laurent Roche, Florence Mège-Lechevallier, Alain Ruffion, Sébastien Crouzet, Marc Colombel, and Muriel Rabilloud
Subjects: Medicine, Science
Abstract: To assess the diagnostic weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers (csPCa).We used a prospective database of 262 patients who underwent T2-weighted, diffusion-weighted, and dynamic contrast-enhanced (DCE) imaging before prostatectomy. For each lesion, two independent readers (R1, R2) prospectively defined nine features: shape, volume (V_Max), signal abnormality on each pulse sequence, number of pulse sequences with a marked (S_Max) and non-visible (S_Min) abnormality, likelihood of extracapsular extension (ECE) and PSA density (dPSA). Overall likelihood of malignancy was assessed using a 5-level Likert score. Features were evaluated using the area under the receiver operating characteristic curve (AUC). csPCa was defined as Gleason ≥7 cancer (csPCa-A), Gleason ≥7(4+3) cancer (csPCa-B) or Gleason ≥7 cancer with histological extraprostatic extension (csPCa-C).For csPCa-A, the Signal1 model (S_Max+S_Min) provided the best combination of signal-related variables, for both readers. The performance was improved by adding V_Max, ECE and/or dPSA, but not shape. All models performed better with DCE findings than without. When moving from csPCa-A to csPCa-B and csPCa-C definitions, the added value of V_Max, dPSA and ECE increased as compared to signal-related variables, and the added value of DCE decreased. For R1, the best models were Signal1+ECE+dPSA (AUC = 0,805 [95%CI:0,757-0,866]), Signal1+V_Max+dPSA (AUC = 0.823 [95%CI:0.760-0.893]) and Signal1+ECE+dPSA [AUC = 0.840 (95%CI:0.774-0.907)] for csPCa-A, csPCA-B and csPCA-C respectively. The AUCs of the corresponding Likert scores were 0.844 [95%CI:0.806-0.877, p = 0.11], 0.841 [95%CI:0.799-0.876, p = 0.52]) and 0.849 [95%CI:0.811-0.884, p = 0.49], respectively. For R2, the best models were Signal1+V_Max+dPSA (AUC = 0,790 [95%CI:0,731-0,857]), Signal1+V_Max (AUC = 0.813 [95%CI:0.746-0.882]) and Signal1+ECE+V_Max (AUC = 0.843 [95%CI: 0.781-0.907]) for csPCa-A, csPCA-B and csPCA-C respectively. The AUCs of the corresponding Likert scores were 0. 829 [95%CI:0.791-0.868, p = 0.13], 0.790 [95%CI:0.742-0.841, p = 0.12]) and 0.808 [95%CI:0.764-0.845, p = 0.006]), respectively.Combination of simple variables can match the Likert score's results. The optimal combination depends on the definition of csPCa.
Published: 2017
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4. Accuracy of Elastic Fusion of Prostate Magnetic Resonance and Transrectal Ultrasound Images under Routine Conditions: A Prospective Multi-Operator Study.

Author: Paul Moldovan, Corina Udrescu, Emmanuel Ravier, Rémi Souchon, Muriel Rabilloud, Flavie Bratan, Thomas Sanzalone, Fanny Cros, Sébastien Crouzet, Albert Gelet, Olivier Chapet, and Olivier Rouvière
Subjects: Medicine, Science
Abstract: To evaluate in unselected patients imaged under routine conditions the co-registration accuracy of elastic fusion between magnetic resonance (MR) and ultrasound (US) images obtained by the Koelis Urostation™.We prospectively included 15 consecutive patients referred for placement of intraprostatic fiducials before radiotherapy and who gave written informed consent by signing the Institutional Review Board-approved forms. Three fiducials were placed in the prostate under US guidance in standardized positions (right apex, left mid-gland, right base) using the Koelis Urostation™. Patients then underwent prostate MR imaging. Four operators outlined the prostate on MR and US images and an elastic fusion was retrospectively performed. Fiducials were used to measure the overall target registration error (TRE3D), the error along the antero-posterior (TREAP), right-left (TRERL) and head-feet (TREHF) directions, and within the plane orthogonal to the virtual biopsy track (TRE2D).Median TRE3D and TRE2D were 3.8-5.6 mm, and 2.5-3.6 mm, respectively. TRE3D was significantly influenced by the operator (p = 0.013), fiducial location (p = 0.001) and 3D axis orientation (p
Published: 2016
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5. 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-Francois Berger, Nathan Banaste, Vivien Thomson, Jean-Christophe Lecomte, and Guillaume Gorincour
Published: 2022
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6. Characterization of high-grade prostate cancer at multiparametric MRI using a radiomic-based computer-aided diagnosis system as standalone and second reader

Author: Tristan Jaouen, Rémi Souchon, Paul C Moldovan, Flavie Bratan, Audrey Duran, Au Hoang-Dinh, Florian Di Franco, Sabine Debeer, Marine Dubreuil-Chambardel, Nicolas Arfi, Alain Ruffion, Marc Colombel, Sébastien Crouzet, Christelle Gonindard-Melodelima, and Olivier Rouvière
Subjects: Radiological and Ultrasound Technology, Radiology, Nuclear Medicine and imaging, General Medicine
Published: 2023
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7. Teleradiology as a relevant indicator of the impact of COVID-19 pandemic management on emergency room activities: a nationwide worrisome survey

Author: Mylène Seux, Karim Tazarourte, Marine Cloux, Jean-François Bergerot, Stéphanie Gentile, Jordan Vatele, Bénédicte Devictor, Vivien Thomson, Nicolas Girouin, Nathan Banaste, G. Gorincour, and Flavie Bratan
Subjects: lcsh:Medical physics. Medical radiology. Nuclear medicine, medicine.medical_specialty, Abdominal pain, Teleradiology, lcsh:R895-920, Population, 030204 cardiovascular system & hematology, 03 medical and health sciences, Hospital, 0302 clinical medicine, Internal medicine, medicine, Radiology, Nuclear Medicine and imaging, 030212 general & internal medicine, Renal colic, education, Neuroradiology, education.field_of_study, medicine.diagnostic_test, business.industry, Health services accessibility, COVID-19, Interventional radiology, medicine.disease, Appendicitis, Radiological weapon, Original Article, medicine.symptom, business, Emergency service
Abstract: Objectives To evaluate the impact of COVID-19’s lockdown on radiological examinations in emergency services. Methods Retrospective, multicentre analysis of radiological examinations requested, via our teleradiology network, from 2017 to 2020 during two timeframes (calendar weeks 5–8 and then 12–15). We included CT scans or MRIs performed for strokes, multiple traumas (Body-CT), cranial traumas (CTr) and acute non-traumatic abdominal pain (ANTAP). We evaluated the number and percentages of examinations performed, of those with a pathological conclusion, and of examinations involving the chest. Results Our study included 25 centres in 2017, 29 in 2018, 43 in 2019 and 59 in 2020. From 2017 to 2019, the percentages of examinations were constant, which was also true for chest CTs. Each centre’s number of examinations, gender distribution and patient ages were unchanged. In 2020, examinations significantly decreased: suspected strokes decreased by 36% (1052 vs 675, p p p p p Conclusion Teleradiology enabled us to monitor the impact of the COVID-19 pandemic management on emergency activities, showing a global decrease in the population's use of care.
Published: 2021

8. 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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9. 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

10. 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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11. 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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12. 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

13. Whole-Body CT in Patients with Multiple Traumas: Factors Leading to Missed Injury

Author: Jean-François Bergerot, Ingrid Millet, Vivien Thomson, Nathan Banaste, Bérénice Caurier, and Flavie Bratan
Subjects: Adult, Male, Pediatrics, medicine.medical_specialty, Adolescent, Logistic regression, 030218 nuclear medicine & medical imaging, Young Adult, 03 medical and health sciences, Injury Severity Score, 0302 clinical medicine, medicine, Humans, Whole Body Imaging, Radiology, Nuclear Medicine and imaging, Diagnostic Errors, Young adult, Child, Aged, Retrospective Studies, Aged, 80 and over, Multiple Trauma, business.industry, Infant, 030208 emergency & critical care medicine, Retrospective cohort study, Odds ratio, Middle Aged, Stepwise regression, Confidence interval, Child, Preschool, Female, Observational study, Tomography, X-Ray Computed, business
Abstract: Purpose To determine radiologic and clinical markers predictive of missed injuries at early whole-body CT image interpretation. Materials and Methods For this retrospective study, 2354 consecutive whole-body CT examinations were performed in patients with multiple traumas from 26 hospitals interpreted at a teleradiology center study during on-call period from February 2011 to September 2016. All whole-body CT images were interpreted by the on-call radiologist and reviewed within 12-48 hours by another radiologist to detect missed injury as the standard of reference. The first and review reports of all examinations were retrospectively reviewed. Univariable and multivariable logistic regression with a stepwise selection method were performed to identify clinical and radiologic predictors of missed injury. Results This study included 639 women (27.1%) and 1715 men (72.8%). The median age of men, women, and the entire population was 34 years (age range, 1-96 years). On a per-scan basis, there were 304 (12.9%) missed injuries and 59 (2.5%) were clinically significant. On a per-injury basis, the missed injury rate was 530 of 5979 (8.8%). More than two injured body parts (odds ratio, 1.4 [95% confidence interval: 1.1, 1.8]; P = .01), patient age older than 30 years (odds ratio, 2.8 [95% confidence interval: 2.1, 3.8]; P < .001), and an initial clinical severity class of 1 (odds ratio, 1.9 [95% confidence interval: 1.3, 2.8]; P < .001) were independent predictive factors of missed injury. Conclusion Multiple traumas with more than two injured body parts, age older than 30 years, or an initial clinical severity class of 1 were associated with missed injury at whole-body CT. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Novelline in this issue.
Published: 2018
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14. 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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15. 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

16. Variability induced by the MR imager in dynamic contrast-enhanced imaging of the prostate

Author: A. Sarran, Alain Ruffion, Florence Mège-Lechevallier, S. Brunelle, Sebastien Crouzet, Flavie Bratan, Olivier Rouvière, Marc Colombel, C. Zemmour, Département de médecine nucléaire, Hospices Civils de Lyon (HCL), Institut de Génomique Fonctionnelle de Lyon (IGFL), École normale supérieure de Lyon (ENS de Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-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), Département de radiologie [Institut Paoli Calmettes], Institut Paoli-Calmettes, Fédération nationale des Centres de lutte contre le Cancer (FNCLCC)-Fédération nationale des Centres de lutte contre le Cancer (FNCLCC), 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), École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), 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), and Dupuis, Christine
Subjects: Male, medicine.medical_treatment, Lumen (anatomy), Contrast Media, Perfusion scanning, [SDV.CAN]Life Sciences [q-bio]/Cancer, 030218 nuclear medicine & medical imaging, Lesion, 03 medical and health sciences, 0302 clinical medicine, [SDV.CAN] Life Sciences [q-bio]/Cancer, Prostate, medicine, Humans, Radiology, Nuclear Medicine and imaging, Arterial input function, Aged, Retrospective Studies, Prostate cancer, Radiological and Ultrasound Technology, business.industry, Prostatectomy, Multiparametric MRI, Prostatic Neoplasms, Mean age, General Medicine, Middle Aged, Perfusion imaging, Magnetic Resonance Imaging, 3. Good health, Dynamic contrast, medicine.anatomical_structure, Dynamic contrast-enhanced imaging, 030220 oncology & carcinogenesis, medicine.symptom, Nuclear medicine, business
Abstract: International audience; PURPOSE:To evaluate the variability induced by the imager in discriminating high-grade (Gleason≥7) prostate cancers (HGC) using dynamic contrast-enhanced MRI.MATERIAL AND METHODS:We retrospectively selected 3T MRIs with temporal resolution
Published: 2017
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17. International Society for Therapeutic Ultrasound Conference 2016

Author: Brian Fowlkes, Pejman Ghanouni, Narendra Sanghvi, Constantin Coussios, Paul C. Lyon, Michael Gray, Christophoros Mannaris, Marie de Saint Victor, Eleanor Stride, Robin Cleveland, Robert Carlisle, Feng Wu, Mark Middleton, Fergus Gleeson, Jean-Franҫois Aubry, Kim Butts Pauly, Chrit Moonen, Jacob Vortman, Shirley Sharabi, Dianne Daniels, David Last, David Guez, Yoav Levy, Alexander Volovick, Javier Grinfeld, Itay Rachmilevich, Talia Amar, Zion Zibly, Yael Mardor, Sagi Harnof, Michael Plaksin, Yoni Weissler, Shy Shoham, Eitan Kimmel, Omer Naor, Nairouz Farah, Dong-Guk Paeng, Zhiyuan Xu, John Snell, Anders H. Quigg, Matthew Eames, Changzhu Jin, Ashli C. Everstine, Jason P. Sheehan, Beatriz S. Lopes, Neal Kassell, Thomas Looi, Vera Khokhlova, Charles Mougenot, Kullervo Hynynen, James Drake, Michael Slayton, Richard C. Amodei, Keegan Compton, Ashley McNelly, Daniel Latt, John Kearney, David Melodelima, Aurelien Dupre, Yao Chen, David Perol, Jeremy Vincenot, Jean-Yves Chapelon, Michel Rivoire, Wei Guo, Guoxin Ren, Guofeng Shen, Michael Neidrauer, Leonid Zubkov, Michael S. Weingarten, David J. Margolis, Peter A. Lewin, Nathan McDannold, Jonathan Sutton, Natalia Vykhodtseva, Margaret Livingstone, Thiele Kobus, Yong-Zhi Zhang, Michael Schwartz, Yuexi Huang, Nir Lipsman, Jennifer Jain, Martin Chapman, Tejas Sankar, Andres Lozano, Robert Yeung, Christakis Damianou, Nikolaos Papadopoulos, Omer Brokman, Eyal Zadicario, Ori Brenner, David Castel, Shih-Ying Wu, Julien Grondin, Wenlan Zheng, Marc Heidmann, Maria Eleni Karakatsani, Carlos J. Sierra Sánchez, Vincent Ferrera, Elisa E. Konofagou, Marinos Yiannakou, HongSeok Cho, Hwayoun Lee, Mun Han, Jong-Ryul Choi, Taekwan Lee, Sanghyun Ahn, Yongmin Chang, Juyoung Park, Nicholas Ellens, Ari Partanen, Keyvan Farahani, Raag Airan, Alexandre Carpentier, Michael Canney, Alexandre Vignot, Cyril Lafon, Jean-yves Delattre, Ahmed Idbaih, Henrik Odéen, Bradley Bolster, Eun Kee Jeong, Dennis L. Parker, Pooja Gaur, Xue Feng, Samuel Fielden, Craig Meyer, Beat Werner, William Grissom, Michael Marx, Hans Weber, Valentina Taviani, Brian Hargreaves, Jun Tanaka, Kentaro Kikuchi, Ayumu Ishijima, Takashi Azuma, Kosuke Minamihata, Satoshi Yamaguchi, Teruyuki Nagamune, Ichiro Sakuma, Shu Takagi, Mathieu D. Santin, Laurent Marsac, Guillaume Maimbourg, Morgane Monfort, Benoit Larrat, Chantal François, Stéphane Lehéricy, Mickael Tanter, Gesthimani Samiotaki, Shutao Wang, Camilo Acosta, Eliza R. Feinberg, Zsofia I. Kovacs, Tsang-Wei Tu, Georgios Z. Papadakis, William C. Reid, Dima A. Hammoud, Joseph A. Frank, Zsofia i. Kovacs, Saejeong Kim, Neekita Jikaria, Michele Bresler, Farhan Qureshi, Jingjing Xia, Po-Shiang Tsui, Hao-Li Liu, Juan C. Plata, Bragi Sveinsson, Vasant A. Salgaonkar, Matthew Adams, Chris Diederich, Eugene Ozhinsky, Matthew D. Bucknor, Viola Rieke, Andrew Mikhail, Lauren Severance, Ayele H. Negussie, Bradford Wood, Martijn de Greef, Gerald Schubert, Mario Ries, Megan E. Poorman, Mary Dockery, Vandiver Chaplin, Stephanie O. Dudzinski, Ryan Spears, Charles Caskey, Todd Giorgio, Marcia M. Costa, Efthymia Papaevangelou, Anant Shah, Ian Rivens, Carol Box, Jeff Bamber, Gail ter Haar, Scott R. Burks, Matthew Nagle, Ben Nguyen, Blerta Milo, Nhan M. Le, Shaozhen Song, Kanheng Zhou, Ghulam Nabi, Zhihong Huang, Shmuel Ben-Ezra, Shani Rosen, Senay Mihcin, Jan Strehlow, Ioannis Karakitsios, Nhan Le, Michael Schwenke, Daniel Demedts, Paul Prentice, Sabrina Haase, Tobias Preusser, Andreas Melzer, Jean-Louis Mestas, Kamel Chettab, Gustavo Stadthagen Gomez, Charles Dumontet, Bettina Werle, Fabrice Marquet, Pierre Bour, Fanny Vaillant, Sana Amraoui, Rémi Dubois, Philippe Ritter, Michel Haïssaguerre, Mélèze Hocini, Olivier Bernus, Bruno Quesson, Amit Livneh, Dan Adam, Justine Robin, Bastien Arnal, Mathias Fink, Mathieu Pernot, Tatiana D. Khokhlova, George R. Schade, Yak-Nam Wang, Wayne Kreider, Julianna Simon, Frank Starr, Maria Karzova, Adam Maxwell, Michael R. Bailey, Jonathan E. Lundt, Steven P. Allen, Jonathan R. Sukovich, Timothy Hall, Zhen Xu, Philip May, Daniel W. Lin, Charlotte Constans, Thomas Deffieux, Jean-Francois Aubry, Eun-Joo Park, Yun Deok Ahn, Soo Yeon Kang, Dong-Hyuk Park, Jae Young Lee, J. Vidal-Jove, E. Perich, A. Ruiz, A. Jaen, N. Eres, M. Alvarez del Castillo, Rachel Myers, James Kwan, Christian Coviello, Cliff Rowe, Calum Crake, Sean Finn, Edward Jackson, Antonios Pouliopoulos, Caiqin Li, Marc Tinguely, Meng-Xing Tang, Valeria Garbin, James J. Choi, Lisa Folkes, Michael Stratford, Sandra Nwokeoha, Tong Li, Navid Farr, Samantha D’Andrea, Kayla Gravelle, Hong Chen, Donghoon Lee, Joo Ha Hwang, Sophie Tardoski, Jacqueline Ngo, Evelyne Gineyts, Jean-Pau Roux, Philippe Clézardin, Allegra Conti, Rémi Magnin, Matthieu Gerstenmayer, François Lux, Olivier Tillement, Sébastien Mériaux, Stefania Della Penna, Gian Luca Romani, Erik Dumont, Tao Sun, Chanikarn Power, Eric Miller, Oleg Sapozhnikov, Sergey Tsysar, Petr V. Yuldashev, Victor Svet, Dongli Li, Antonio Pellegrino, Nik Petrinic, Clive Siviour, Antoine Jerusalem, Peter V. Yuldashev, Bryan W. Cunitz, Barbrina Dunmire, Claude Inserra, Matthieu Guedra, Cyril Mauger, Bruno Gilles, Maxim Solovchuk, Tony W. H. Sheu, Marc Thiriet, Yufeng Zhou, Esra Neufeld, Christian Baumgartner, Davnah Payne, Adamos Kyriakou, Niels Kuster, Xu Xiao, Helen McLeod, Christopher Dillon, Allison Payne, Vera A. Khokhova, Ilya Sinilshchikov, Yulia Andriyakhina, Andrey Rybyanets, Natalia Shvetsova, Alex Berkovich, Igor Shvetsov, Caroline J. Shaw, John Civale, Dino Giussani, Christoph Lees, Valery Ozenne, Solenn Toupin, Vasant Salgaonkar, Elena Kaye, Sebastien Monette, Majid Maybody, Govindarajan Srimathveeravalli, Stephen Solomon, Amitabh Gulati, Mario Bezzi, Jürgen W. Jenne, Thomas Lango, Michael Müller, Giora Sat, Christine Tanner, Stephan Zangos, Matthias Günther, Au Hoang Dinh, Emilie Niaf, Flavie Bratan, Nicolas Guillen, Rémi Souchon, Carole Lartizien, Sebastien Crouzet, Olivier Rouviere, Yang Han, Thomas Payen, Carmine Palermo, Steve Sastra, Kenneth Olive, Johanna M. van Breugel, Maurice A. van den Bosch, Benjamin Fellah, Denis Le Bihan, Luis Hernandez-Garcia, Charles A. Cain, Erasmia Lyka, Delphine Elbes, Chunhui Li, Satoshi Tamano, Hayato Jimbo, Shin Yoshizawa, Keisuke Fujiwara, Kazunori Itani, Shin-ichiro Umemura, Dan Stoianovici, Zulfadhli Zaini, Ryo Takagi, Shenyan Zong, Ron Watkins, Aurea Pascal-Tenorio, Peter Jones, Kim Butts-Pauly, Donna Bouley, Yazhu Chen, Chung-Yin Lin, Han-Yi Hsieh, Kuo-Chen Wei, Camille Garnier, Gilles Renault, Reza Seifabadi, Emmanuel Wilson, Avinash Eranki, Peter Kim, Dennis Lübke, Peter Huber, Joachim Georgii, Caroline V. Dresky, Julian Haller, Pavel Yarmolenko, Karun Sharma, Haydar Celik, Guofeng Li, Weibao Qiu, Hairong Zheng, Meng-Yen Tsai, Po-Chun Chu, Taylor Webb, Urvi Vyas, Matthew Walker, Jidan Zhong, Adam C. Waspe, Mojgan Hodaie, Feng-Yi Yang, Sin-Luo Huang, Yuval Zur, Benny Assif, Christian Aurup, Hermes Kamimura, Antonio A. Carneiro, Sven Rothlübbers, Julia Schwaab, Graeme Houston, Haim Azhari, Noam Weiss, Jacob Sosna, S. Nahum Goldberg, Victor Barrere, Kee W. Jang, Bobbi Lewis, Xiaotong Wang, Visa Suomi, David Edwards, Zahary Larrabee, Arik Hananel, Boaz Rafaely, Rasha Elaimy Debbiny, Carmel Zeltser Dekel, Michael Assa, George Menikou, Petros Mouratidis, José A. Pineda-Pardo, Marta Del Álamo de Pedro, Raul Martinez, Frida Hernandez, Silvia Casas, Carlos Oliver, Patricia Pastor, Lidia Vela, Jose Obeso, Paul Greillier, Ali Zorgani, Stefan Catheline, Vyacheslav Solovov, Michael O. Vozdvizhenskiy, Andrew E. Orlov, Chueh-Hung Wu, Ming-Kuan Sun, Tiffany T. Shih, Wen-Shiang Chen, Fabrice Prieur, Arnaud Pillon, Valerie Cartron, Patrick Cebe, Nathalie Chansard, Maxime Lafond, Pauline Muleki Seya, Jean-Christophe Bera, Tanguy Boissenot, Elias Fattal, Alexandre Bordat, Helene Chacun, Claire Guetin, Nicolas Tsapis, Kazuo Maruyama, Johan Unga, Ryo Suzuki, Cécile Fant, Bernadette Rogez, Mercy Afadzi, Ola Finneng Myhre, Siri Vea, Astrid Bjørkøy, Petros Tesfamichael Yemane, Annemieke van Wamel, Sigrid Berg, Rune Hansen, Bjørn Angelsen, and Catharina Davies
Subjects: 0301 basic medicine, medicine.medical_specialty, Therapeutic ultrasound, business.industry, Tel aviv, medicine.medical_treatment, 02 engineering and technology, 021001 nanoscience & nanotechnology, 03 medical and health sciences, 030104 developmental biology, Ophthalmology, medicine, Radiology, Nuclear Medicine and imaging, Medical physics, 0210 nano-technology, business
Published: 2017
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18. 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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19. Quantitative Analysis of Prostate Multiparametric MR Images for Detection of Aggressive Prostate Cancer in the Peripheral Zone: A Multiple Imager Study

Author: Sebastien Crouzet, Marc Colombel, Au Hoang Dinh, Alain Ruffion, Olivier Rouvière, Christelle Melodelima, Jérôme Lehaire, Flavie Bratan, Florence Mège-Lechevallier, Rémi Souchon, Laboratoire d'Ecologie Alpine (LECA), Centre National de la Recherche Scientifique (CNRS)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Université Joseph Fourier - Grenoble 1 (UJF)-Université Grenoble Alpes (UGA), 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, 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), Département de médecine nucléaire, Hospices Civils de Lyon (HCL), Department of Urinary and Vascular Radiology, Institut de Génomique Fonctionnelle de Lyon (IGFL), École normale supérieure - Lyon (ENS Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS), Centre de Recherche en Cancérologie de Lyon (CRCL), Université de Lyon-Université de Lyon-Institut National de la Santé et de la Recherche Médicale (INSERM)-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 Claude Bernard Lyon 1 (UCBL), Université de Lyon, Laboratoire d'Ecologie Alpine (LECA ), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes [2016-2019] (UGA [2016-2019]), 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)-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), École normale supérieure de Lyon (ENS de Lyon)-Institut National de la Recherche Agronomique (INRA)-Université Claude Bernard Lyon 1 (UCBL), Centre de Recherche en Cancérologie de Lyon (UNICANCER/CRCL), Université de Lyon-Université de Lyon-Centre Léon Bérard [Lyon]-Institut National de la Santé et de la Recherche Médicale (INSERM), Université de Lyon-Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), and Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL)
Subjects: Male, Percentile, Patients, analysis, medicine.medical_treatment, Contrast Media, [SDV.CAN]Life Sciences [q-bio]/Cancer, Sensitivity and Specificity, 030218 nuclear medicine & medical imaging, methods, Time, Diffusion, 03 medical and health sciences, 0302 clinical medicine, Image Interpretation, Computer-Assisted, medicine, Effective diffusion coefficient, Humans, Radiology, Nuclear Medicine and imaging, Prospective Studies, Aged, Prostatectomy, Receiver operating characteristic, medicine.diagnostic_test, business.industry, Prostate, Prostatic Neoplasms, Reproducibility of Results, Magnetic resonance imaging, Middle Aged, Image Enhancement, Magnetic Resonance Imaging, Confidence interval, 3. Good health, Data set, Evaluation Studies as Topic, 030220 oncology & carcinogenesis, Kurtosis, pathology, France, Neoplasm Grading, business, Nuclear medicine
Abstract: International audience; Purpose To assess the intermanufacturer variability of quantitative models in discriminating cancers with a Gleason score of at least 7 among peripheral zone (PZ) lesions seen at 3-T multiparametric magnetic resonance (MR) imaging. Materials and Methods An institutional review board-approved prospective database of 257 patients who gave written consent and underwent T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced imaging before prostatectomy was retrospectively reviewed. It contained outlined lesions found to be suspicious for malignancy by two independent radiologists and classified as malignant or benign after correlation with prostatectomy whole-mount specimens. One hundred six patients who underwent imaging with 3-T MR systems from two manufacturers were selected (data set A, n = 72; data set B, n = 34). Eleven parameters were calculated in PZ lesions: normalized T2-weighted signal intensity, skewness and kurtosis of T2-weighted signal intensity, T2 value, wash-in rate, washout rate, time to peak (TTP), mean apparent diffusion coefficient (ADC), 10th percentile of the ADC, and skewness and kurtosis of the histogram of the ADC values. Parameters were selected on the basis of their specificity for a sensitivity of 0.95 in diagnosing cancers with a Gleason score of at least 7, and the area under the receiver operating characteristic curve (AUC) for the models was calculated. Results The model of the 10th percentile of the ADC with TTP yielded the highest AUC in both data sets. In data set A, the AUC was 0.90 (95% confidence interval [CI]: 0.85, 0.95) or 0.89 (95% CI: 0.82, 0.94) when it was trained in data set A or B, respectively. In data set B, the AUC was 0.84 (95% CI: 0.74, 0.94) or 0.86 (95% CI: 0.76, 0.95) when it was trained in data set A or B, respectively. No third variable added significantly independent information in any data set. Conclusion The model of the 10th percentile of the ADC with TTP yielded accurate results in discriminating cancers with a Gleason score of at least 7 among PZ lesions at 3 T in data from two manufacturers. ((c)) RSNA, 2016 Online supplemental material is available for this article
Published: 2016
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20. 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

21. Valeur ajoutée de l’IRM de prostate avant première série de biopsies : étude MRI-FIRST

Author: Laurent Lemaitre, Pierre Colin, R. Renard Penna, A. Schott Pethelaz, Philippe Camparo, Jochen Walz, G. Fiard, Paul C. Moldovan, A. Villers, Pascal Eschwege, Catherine Roy, Nicolas Mottet, Ahmed Khairoune, B. Malavaud, J.-L. Descotes, Pierre Mozer, M. Dubreuil Chambardel, M.O. Timsit, Y. Lebras, Sébastien Crouzet, Daniel Portalez, Nicolas Grenier, P. Puech, Jean Michel Correas, Herve Lang, Jean-Philippe Fendler, A. Ruffion, François Cornelis, Muriel Rabilloud, M. Decaussin Petrucci, Pierre Nevoux, E. Potiron, Romain Boutier, Olivier Rouvière, F. Mege Lechevallier, Marc Colombel, Fanny Cros, François Cornud, Gilles Pasticier, Michel Claudon, G. Pagnoux, S. Brunelle, Hocine Habchi, Laurent Magaud, N. Barry Delongchamps, P. Hallouin, and Flavie Bratan
Subjects: Gynecology, medicine.medical_specialty, business.industry, Urology, Medicine, business
Abstract: Objectifs Evaluer l’apport de l’IRM de prostate avant premiere serie de biopsies. Methodes Au total, 225 patients adresses pour une premiere biopsie de prostate (âge ≤ 75 ans, PSA ≤ 20 ng/mL, TR ≤ T2c) ont ete inclus dans cette etude prospective multicentrique (16 centres). Apres IRM multi-parametrique, un premier operateur sans connaissance de l’IRM a preleve 12 biopsies systematiques (plus deux biopsies optionnelles guidees par echographie). Un second operateur a obtenu des biopsies sur une ou deux cibles suspectes (Likert ≥ 3/5, 3 biopsies par cible). Les biopsies ciblees ont ete realisees sous guidage cognitif (4 centres), fusion echo/IRM (11 centres) ou echographie de contraste (1 centre). Le cancer significatif (CS) etait defini selon 3 definitions : CS-A, ISUP grade groupe (GG) ≥ 2 ou ISUP GG 1 avec invasion maximale ≥ 6 mm ; CS-B, ISUP GG ≥ 2 ; CS-C, ISUP GG ≥ 3. Resultats Le taux de biopsies positives etait de 128/225 (56,9 %) pour tous cancers, 96/225 (42,7 %) pour les CS-A, de 87/225 (38,7 %) pour les CS-B et de 47/225 (20,9 %) pour CS-C. L’IRM etait negative (Likert ≤ 2/5) chez 45 patients (20 %). Suivant la definition du CS, la valeur predictive negative de l’IRM etait de 88,9–95,6 %. Le nombre de patients detectes par les biopsies systematiques seules, les biopsies ciblees seules et les deux types de biopsies etaient de 15, 19 et 62, respectivement pour les CS-A. Il etait respectivement de 12, 18 et 57 pour les CS-B et de 3, 15 et 29 patients pour les CS-C. Conclusion Suivant la definition du CS, les biopsies ciblees ont detecte un CS qui aurait ete rate par les biopsies systematiques chez 6,7–8,4 % des 225 patients. Omettre les biopsies systematiques aurait fait rater un CS chez 1,3-6,7 % des 225 patients.
Published: 2017
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22. 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

23. Traitement focal par HIFU des récidives locales des cancers de prostate initialement traités par radiothérapie

Author: Albert Gelet, Lionel Badet, Marc Colombel, Olivier Rouvière, Sébastien Crouzet, Flavie Bratan, and H. Tonoli-Catez
Subjects: Gynecology, medicine.medical_specialty, business.industry, Urology, medicine, business
Abstract: Objectifs Evaluer les resultats carcinologiques et fonctionnels du traitement HIFU focal chez les patients presentant une recidive locale d’un cancer de prostate initialement traite par radiotherapie. Methodes De mars 2009 a decembre 2014, tous les patients avec recidive locale unilaterale (IRM et biopsies dirigees) sans metastase decelable d’un cancer de prostate initialement traite par radiotherapie ont ete inclus dans une etude de traitement focal par HIFU (hemi-ablation). Traitements realises avec l’Ablatherm a focalisation dynamique jusqu’en juin 2013 puis FocalOne ensuite. Soixante patients, suivi moyen : 20 mois (3–34 mois), âge moyen : 70 ± 7 ans (mediane 72). Le risque de D’Amico avant radiotherapie : faible : 9 patients intermediaire : 26, haut : 13 et non connu : 26. La dose moyenne recue etait de 65 Gy. Delai moyen entre radiotherapie et HIFU : 6 ans. PSA moyen avant HIFU : 5,18 ± 5 ng/mL (mediane : 3,5 ng/mL). Resultats Le score de Gleason a la recidive etait de 6 chez 4 patients 7 chez 28, ≥ 8 chez 18 et non determine chez 11. PSA nadir moyen : 0,82 ± 2 ng/mL (mediane 0,18 ng/mL). Nombre session HIFU : une = 88,3 % et 2 = 11,7 % (n = 2). A 48 mois : survie specifique = 100 %, sans metastases = 93 % [IC : 74–98] et sans progression = 75 % [IC : 54–87]. Le taux de survie sans progression n’a pas ete significativement influence par le taux de PSA et le score de Gleason de la recidive (p = 0,7), mais il a ete influence par le status initial du patient avant radiotherapie. Complications : incontinence severe (grade 2/3) : n = 5 (8 %) (avec pose de 2 sphincter), sclerose uretre prostatique : n = 2, osteite pubienne (regressive apres antibiotherapie prolongee) n = 2. Deux lithiases vesicales ont ete traitees par endoscopie. Conclusion Le traitement par hemi-ablation HIFU des cancers de prostate en recidives limitee a un seul lobe prostatique apres radiotherapie permet d’obtenir un resultat carcinologique satisfaisant dans 75 % des cas avec un taux d’effets secondaire inferieur a celui habituellement observe apres traitement HIFU total.
Published: 2015
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24. 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

25. 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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26. 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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27. 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

28. Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI

Author: Olivier Rouvière, Emilie Niaf, Florence Mège-Lechevallier, Flavie Bratan, Carole Lartizien, 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, 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)-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), Department of Urinary and Vascular Radiology, Hospices Civils de Lyon (HCL), Université de Lyon, 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 ), 2 - Images et Modèles, Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé ( CREATIS ), 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-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 ), and Hospices Civils de Lyon ( HCL )
Subjects: Male, Computer science, medicine.medical_treatment, Feature selection, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, Naive Bayes classifier, Prostate cancer, Prostate, Image Interpretation, Computer-Assisted, medicine, Humans, Radiology, Nuclear Medicine and imaging, Diagnosis, Computer-Assisted, ComputingMilieux_MISCELLANEOUS, Aged, Radiological and Ultrasound Technology, medicine.diagnostic_test, Receiver operating characteristic, Prostatectomy, business.industry, Multiparametric MRI, Cancer, Discriminant Analysis, Prostatic Neoplasms, Pattern recognition, Magnetic resonance imaging, Mutual information, Middle Aged, Linear discriminant analysis, medicine.disease, Magnetic Resonance Imaging, Support vector machine, Radiography, medicine.anatomical_structure, ROC Curve, Computer-aided diagnosis, Artificial intelligence, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Abstract: This study evaluated a computer-assisted diagnosis (CADx) system for determining a likelihood measure of prostate cancer presence in the peripheral zone (PZ) based on multiparametric magnetic resonance (MR) imaging, including T2-weighted, diffusion-weighted and dynamic contrast-enhanced MRI at 1.5 T. Based on a feature set derived from grey-level images, including first-order statistics, Haralick features, gradient features, semi-quantitative and quantitative (pharmacokinetic modelling) dynamic parameters, four kinds of classifiers were trained and compared: nonlinear support vector machine (SVM), linear discriminant analysis, k-nearest neighbours and naive Bayes classifiers. A set of feature selection methods based on t-test, mutual information and minimum-redundancy-maximum-relevancy criteria were also compared. The aim was to discriminate between the relevant features as well as to create an efficient classifier using these features. The diagnostic performances of these different CADx schemes were evaluated based on a receiver operating characteristic (ROC) curve analysis. The evaluation database consisted of 30 sets of multiparametric MR images acquired from radical prostatectomy patients. Using histologic sections as the gold standard, both cancer and nonmalignant (but suspicious) tissues were annotated in consensus on all MR images by two radiologists, a histopathologist and a researcher. Benign tissue regions of interest (ROIs) were also delineated in the remaining prostate PZ. This resulted in a series of 42 cancer ROIs, 49 benign but suspicious ROIs and 124 nonsuspicious benign ROIs. From the outputs of all evaluated feature selection methods on the test bench, a restrictive set of about 15 highly informative features coming from all MR sequences was discriminated, thus confirming the validity of the multiparametric approach. Quantitative evaluation of the diagnostic performance yielded a maximal area under the ROC curve (AUC) of 0.89 (0.81-0.94) for the discrimination of the malignant versus nonmalignant tissues and 0.82 (0.73-0.90) for the discrimination of the malignant versus suspicious tissues when combining the t-test feature selection approach with a SVM classifier. A preliminary comparison showed that the optimal CADx scheme mimicked, in terms of AUC, the human experts in differentiating malignant from suspicious tissues, thus demonstrating its potential for assisting cancer identification in the PZ.
Published: 2012
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29. Hydatidose cardiaque

Author: Flavie Bratan and Vivien Thomson
Published: 2009
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30. Métastase intracavitaire

Author: Flavie Bratan and Vivien Thomson
Published: 2009
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31. Métastase péricardique

Author: Flavie Bratan and Vivien Thomson
Published: 2009
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32. Traitement HIFU de sauvetage pour récidive locale après curiethérapie : résultats carcinologiques et fonctionnels

Author: Lionel Badet, Flavie Bratan, H. Tonoli-Catez, Sébastien Crouzet, Jean-Yves Chapelon, Marc Colombel, and Olivier Rouvière
Subjects: business.industry, Urology, Medicine, business, Nuclear medicine
Abstract: Objectifs Evaluer les resultats carcinologiques et fonctionnels du traitement par HIFU en rattrapage chez les patients presentant une recidive locale d’un cancer de prostate initialement traite par curietherapie interstitielle. Methodes Trente-sept patients (2003–2014) pour recidive locale histologiquement demontree et sans metastases. De 2003 a 2007, les 12 premiers patients traites avec parametres HIFU post-radiotherapie externe. A partir de 2008, parametres specifiques post-curie (reduction de la dose thermique du fait de la fibrose intense post-curietherapie) (35 patients avril 2008/decembre 2014). Delai median entre curietherapie et HIFU de sauvetage : 5 ans. L’âge moyen (traitement HIFU) : 67 ± 4 ans (mediane 67), le PSA moyen pre-HIFU : 4,97 ± 2,9 ng/mL et score de Gleason pre-HIFU ≤ 6 : 21 patients, 7 : 16 patients et ≥ 8 : 10 patients. Le volume prostatique moyen : 13,3 ± 10 cm3. Resultats Nombre sessions HIFU : une chez 38 patients et deux chez 9. Suivi moyen HIFU : 28 mois (median 17). Le PSA nadir moyen : 0,78 ± 1,1 ng/mL (mediane 0,35) avec PSA nadir Conclusion Le traitement de sauvetage par HIFU apres curietherapie donne des resultats carcinologiques comparables a ceux obtenus apres radiotherapie externe. Malgre l’utilisation de parametres de traitement specifiques, la morbidite post-HIFU bien qu’ameliore, reste elevee (incontinence et stenose) et doit etre expliquee au patient.
Published: 2015
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33. 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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34. 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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34 results on '"Flavie Bratan"'

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

2. Teleradiology as a relevant indicator of the impact of COVID-19 pandemic management on emergency room activities: a nationwide worrisome survey

3. Diagnostic value and relative weight of sequence-specific magnetic resonance features in characterizing clinically significant prostate cancers.

4. Accuracy of Elastic Fusion of Prostate Magnetic Resonance and Transrectal Ultrasound Images under Routine Conditions: A Prospective Multi-Operator Study.

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

6. Characterization of high-grade prostate cancer at multiparametric MRI using a radiomic-based computer-aided diagnosis system as standalone and second reader

7. Teleradiology as a relevant indicator of the impact of COVID-19 pandemic management on emergency room activities: a nationwide worrisome survey

8. 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

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

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

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

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

13. Whole-Body CT in Patients with Multiple Traumas: Factors Leading to Missed Injury

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

15. 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

16. Variability induced by the MR imager in dynamic contrast-enhanced imaging of the prostate

17. International Society for Therapeutic Ultrasound Conference 2016

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

19. Quantitative Analysis of Prostate Multiparametric MR Images for Detection of Aggressive Prostate Cancer in the Peripheral Zone: A Multiple Imager Study

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

21. Valeur ajoutée de l’IRM de prostate avant première série de biopsies : étude MRI-FIRST

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

23. Traitement focal par HIFU des récidives locales des cancers de prostate initialement traités par radiothérapie

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

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

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

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

28. Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI

29. Hydatidose cardiaque

30. Métastase intracavitaire

31. Métastase péricardique

32. Traitement HIFU de sauvetage pour récidive locale après curiethérapie : résultats carcinologiques et fonctionnels

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

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

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34 results on '"Flavie Bratan"'

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