2023 SFR 数据挑战赛的目的是邀请研究人员开发人工智能 (AI) 模型，以识别胰腺肿块的存在，并在腹部计算机断层扫描 (CT) 检查中区分良性和恶性胰腺肿块。获得的匿名腹部 CT 检查门静脉期的数据是从 18 个法国中心收集的。腹部CT检查分为三组：无病变CT检查、胰腺良性肿块CT检查、胰腺恶性肿块CT检查。每个团队至少包括一名放射科医生、一名数据科学家和一名工程师。胰腺病变由放射科专家注释。 CT 检查通过健康数据托管认证平台分批分发。数据分为四批，两批用于训练，一批用于内部评估，一批用于外部评估。培训使用了 14 个中心 83% 的数据，外部评估使用了其他 4 个中心的数据。用于对参与者进行排名的指标（即最终分数）是平均灵敏度、平均精度和平均曲线下面积的加权平均值。总共 1037 次腹部 CT 检查分为两个训练集（包括 500 次和 232 次 CT 检查） ）、内部评估集（包括139个CT检查）和外部评估集（包括166个CT检查）。训练集于2023年9月7日和10月13日分发，评估集于2023年10月15日分发。共有10支队伍共93名成员参加了数据挑战，最终最好成绩为0.72。本次SFR 2023数据挑战基于多中心 CT 数据的研究表明，在真实数据上使用 AI 进行胰腺病变检测是可能的，但良性和恶性胰腺病变的区分仍然具有挑战性。版权所有 © 2024 Société française de radiologie。由 Elsevier Masson SAS 出版。版权所有。

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.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.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.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.Copyright © 2024 Société française de radiologie. Published by Elsevier Masson SAS. All rights reserved.