Detection and characterization of pancreatic lesion with artificial intelligence: The SFR 2023 artificial intelligence data challenge.

Journal: Diagnostic and interventional imaging
Published Date:

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.

Authors

  • Theodore Aouad
    Université Paris-Saclay, CentraleSupélec, Inria, Centre for Visual Computing, 91190, Gif-sur-Yvette, France.
  • Valérie Laurent
    Department of Adult Radiology, CHRU de Nancy, 5 Rue du Morvan, 54500, Vandoeuvre-lès-Nancy, France.
  • Paul Levant
    Société Française de Radiologie, 75013 Paris, France.
  • Agnes Rode
    Department of Diagnostic and Interventional Radiology, Hospices Civils de Lyon, Hôpital de la Croix Rousse, 69317 Lyon, France.
  • Nina Brillat-Savarin
    Department of Radiology, Hôpital Paris Saint Joseph, 75014 Paris, France.
  • Pénélope Gaillot
    Department of Diagnostic and Interventional Radiology, Assistance Publique-Hopitaux de Paris, CHU de Bicêtre, 94270 Le Kremlin-Bicêtre, France.
  • Christine Hoeffel
    Department of Radiology, Robert Debré Hospital, 51092, Reims, France.
  • Eric Frampas
    Department of Radiology, Hôtel Dieu, CHU Nantes, 44093 Nantes, France.
  • Maxime Barat
    Radiology Department, Hopital Cochin - AP-HP. Centre Université de Paris, 27 Rue du Faubourg Saint-Jacques, Paris 75014, France; Université de Paris, 85 boulevard Saint-Germain, Paris 75006, France.
  • Roberta Russo
    Department of Radiology, Hôpital Paul Brousse, Assistance Publique-Hopitaux de Paris, 94800 Villejuif, France.
  • Mathilde Wagner
    UMR 7371, Université Sorbonne, CNRS, Inserm U114615, rue de l'École de Médecine, 75006, Paris, France.
  • Magaly Zappa
    Department of Radiology, Centre Hospitalier de Cayenne, Cayenne 97306, France.
  • Olivier Ernst
    Medical Imaging Department, Lille University Hospital, 59000 Lille, France.
  • Anais Delagnes
    Department of Radiology, CHU Angers, Angers University Hospital, 49933 Angers, France.
  • Quentin Fillias
    Department of Radiology, Hospital Lapeyronie, CHU Montpellier, 34000 Montpellier, France.
  • Lama Dawi
    Department of Radiology, Gustave Roussy, 94805 Villejuif, France.
  • Céline Savoye-Collet
    Department of Radiology, Normandie Université, UNIROUEN, Quantif-LITIS EA 4108, Rouen University Hospital, 76031 Rouen, France.
  • Pauline Copin
    Department of Radiology, Hôpital Beaujon, AP-HP.Nord, 92110 Clichy, France.
  • Paul Calame
    Department of Radiology, CHU de Besancon, Besançon 25030, France.
  • Edouard Reizine
    Service d'Imagerie Médicale, AP-HP, Hôpitaux Universitaires Henri Mondor, Créteil 94000, France; Faculté de Santé, Université Paris Est Créteil, Créteil 94000, France; INSERM IMRB, U 955, Equipe 18, Créteil 94000, France.
  • Alain Luciani
    Medical Imaging Department, Henri Mondor University Hospital, AP-HP, Créteil, France, Inserm, U955, Team 18, 94000 Créteil, France.
  • Marie-France Bellin
    Radiology Department, Hôpital de Bicêtre - AP-HP, Université Paris-Saclay, Le Kremlin-Bicêtre, France.
  • Hugues Talbot
    OPIS - Optimisation Imagerie et Santé, Université Paris-Saclay, Inria, CentraleSupélec, CVN - Centre de vision numérique, 91190 Gif-Sur-Yvette, France.
  • Nathalie Lassau
    Laboratoire d'Imagerie Biomédicale Multimodale Paris-Saclay, BIOMAPS, UMR 1281, Université Paris-Saclay, Inserm, CNRS, CEA, 94800 Villejuif, France; Department of Imaging, Institut Gustave Roussy, Université Paris-Saclay. 94800 Villejuif, France.