Detection and characterization of pancreatic lesions with 3D CT data using an automated deep learning-based solution.

Fiche publication

Date publication

juillet 2026

Journal

European journal of radiology

Auteurs

Membres identifiés du Cancéropôle Est :
Pr HOEFFEL Christine

Tous les auteurs :
Goubalan S, Della Corte A, Laurent V, De Craene M, Nempont O, Lobantsev A, Popoff A, Bouyrie M, Rode A, Aouad T, Levant P, Brillat-Savarin N, Gaillot P, Hoeffel C, Frampas E, Barat M, Russo R, Wagner M, Zappa M, Ernst O, Delagnes A, Fillias Q, Dawi L, Savoye-Collet C, Copin P, Calame P, Reizine E, Luciani A, Bellin MF, Talbot H, Lassau N, Boussel L

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/42476063

Résumé

Pancreatic ductal adenocarcinoma (PDA) is a leading cause of cancer-related deaths, with early diagnosis hampered by nonspecific symptoms and limitations of existing imaging techniques. This study aimed to develop a deep learning (DL) algorithm to automatically classify pancreas lesions on contrast-enhanced CT scans as normal, benign, or malignant, to assist radiologists in detecting early-stage pancreatic cancer.