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Detection and characterization of pancreatic lesion with artificial intelligence: The SFR 2023 artificial intelligence data challenge.
Aouad, Theodore; Laurent, Valerie; Levant, Paul; Rode, Agnes; Brillat-Savarin, Nina; Gaillot, Pénélope; Hoeffel, Christine; Frampas, Eric; Barat, Maxime; Russo, Roberta; Wagner, Mathilde; Zappa, Magaly; Ernst, Olivier; Delagnes, Anais; Fillias, Quentin; Dawi, Lama; Savoye-Collet, Céline; Copin, Pauline; Calame, Paul; Reizine, Edouard; Luciani, Alain; Bellin, Marie-France; Talbot, Hugues; Lassau, Nathalie.
Afiliação
  • Aouad T; CentraleSupelec, INRIA, CVN, Université Paris-Saclay, 91190 Gif-sur-Yvette, France. Electronic address: theodoreaouad@gmail.com.
  • Laurent V; Department of Radiology, University Hospital of Nancy, Laboratoire IADI INSERM U 1254, 54035 Nancy, France.
  • Levant P; Société Française de Radiologie, 75013 Paris, France.
  • Rode A; Department of Diagnostic and Interventional Radiology, Hospices Civils de Lyon, Hôpital de la Croix Rousse, 69317 Lyon, France.
  • Brillat-Savarin N; Department of Radiology, Hôpital Paris Saint Joseph, 75014 Paris, France.
  • Gaillot P; Department of Diagnostic and Interventional Radiology, Assistance Publique-Hopitaux de Paris, CHU de Bicêtre, 94270 Le Kremlin-Bicêtre, France.
  • Hoeffel C; Department of Radiology, HMB, CHU Reims, 51100 Reims, France; CReSTIC, Université de Reims-Champagne-Ardenne, UFR Sciences Exactes et Naturelles, 51100 Reims, France.
  • Frampas E; Department of Radiology, Hôtel Dieu, CHU Nantes, 44093 Nantes, France.
  • Barat M; Department of Radiology, Hôpital Cochin, Assistance Publique-Hopitaux de Paris, 75014 Paris, France; Génomique et Signalisation des Tumeurs Endocrines, Institut Cochin, INSERM U 1016, CNRS UMR8104, 75014 Paris, France; Faculté de Médecine, Université Paris Cité, 75006 Paris, France.
  • Russo R; Department of Radiology, Hôpital Paul Brousse, Assistance Publique-Hopitaux de Paris, 94800 Villejuif, France.
  • Wagner M; Department of Radiology, Assistance Publique-Hopitaux de Paris, Sorbonne Université, Hôpital Universitaire Pitié-Salpêtrière, 75013 Paris, France.
  • Zappa M; Department of Radiology, Centre Hospitalier de Cayenne, Cayenne 97306, France.
  • Ernst O; Medical Imaging Department, Lille University Hospital, 59000 Lille, France.
  • Delagnes A; Department of Radiology, CHU Angers, Angers University Hospital, 49933 Angers, France.
  • Fillias Q; Department of Radiology, Hospital Lapeyronie, CHU Montpellier, 34000 Montpellier, France.
  • Dawi L; Department of Radiology, Gustave Roussy, 94805 Villejuif, France.
  • Savoye-Collet C; Department of Radiology, Normandie Université, UNIROUEN, Quantif-LITIS EA 4108, Rouen University Hospital, 76031 Rouen, France.
  • Copin P; Department of Radiology, Hôpital Beaujon, AP-HP.Nord, 92110 Clichy, France.
  • Calame P; Department of Radiology, University of Bourgogne Franche-Comté, CHU Besançon, 25030 Besançon, France.
  • Reizine E; Department of Radiology, Hopital Henri Mondor, Assistance Publique-Hopitaux de Paris, University Paris Est Créteil, 94000 Créteil, France.
  • Luciani A; Société Française de Radiologie, 75013 Paris, France; Department of Radiology, Hopital Henri Mondor, Assistance Publique-Hopitaux de Paris, University Paris Est Créteil, 94000 Créteil, France; INSERM, U955, Team 18, 94000 Créteil, France.
  • Bellin MF; Société Française de Radiologie, 75013 Paris, France; Department of Diagnostic and Interventional Radiology, Assistance Publique-Hopitaux de Paris, CHU de Bicêtre, 94270 Le Kremlin-Bicêtre, France.
  • Talbot H; CentraleSupelec, INRIA, CVN, Université Paris-Saclay, 91190 Gif-sur-Yvette, France.
  • Lassau N; Department of Radiology, Gustave Roussy, 94805 Villejuif, France; Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay, Inserm, CNRS, CEA, BIOMAPS, UMR 1281, Université Paris-Saclay, Villejuif 94800, France.
Diagn Interv Imaging ; 2024 Jul 23.
Article em En | MEDLINE | ID: mdl-39048455
ABSTRACT

PURPOSE:

The purpose of the 2023 SFR data challenge was to invite researchers to develop artificial intelligence (AI) models to identify the presence of a pancreatic mass and distinguish between benign and malignant pancreatic masses on abdominal computed tomography (CT) examinations. MATERIALS AND

METHODS:

Anonymized abdominal CT examinations acquired during the portal venous phase were collected from 18 French centers. Abdominal CT examinations were divided into three groups including CT examinations with no lesion, CT examinations with benign pancreatic mass, or CT examinations with malignant pancreatic mass. Each team included at least one radiologist, one data scientist, and one engineer. Pancreatic lesions were annotated by expert radiologists. CT examinations were distributed in balanced batches via a Health Data Hosting certified platform. Data were distributed into four batches, two for training, one for internal evaluation, and one for the external evaluation. Training used 83 % of the data from 14 centers and external evaluation used data from the other four centers. The metric (i.e., final score) used to rank the participants was a weighted average of mean sensitivity, mean precision and mean area under the curve.

RESULTS:

A total of 1037 abdominal CT examinations were divided into two training sets (including 500 and 232 CT examinations), an internal evaluation set (including 139 CT examinations), and an external evaluation set (including 166 CT examinations). The training sets were distributed on September 7 and October 13, 2023, and evaluation sets on October 15, 2023. Ten teams with a total of 93 members participated to the data challenge, with the best final score being 0.72.

CONCLUSION:

This SFR 2023 data challenge based on multicenter CT data suggests that the use of AI for pancreatic lesions detection is possible on real data, but the distinction between benign and malignant pancreatic lesions remains challenging.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article