Deep Learning of Histopathology Predicts Outcomes After Surgery for Pancreatic Cancer.

Date publication

avril 2026

Journal

JCO clinical cancer informatics

Auteurs

Membres identifiés du Cancéropôle Est :
Pr CONROY Thierry

Tous les auteurs :
Wong A, Bourega TA, Nicolle R, Elqaderi A, Nowak KM, Light N, Wang X, Quan W, Ji Z, Abbas-Aghababazadeh F, Henault D, He JC, Chen Z, Hutchinson S, Dodd A, Wilson J, Jang GH, Biankin A, Chang D, O'Callaghan CJ, Biagi J, ,Conroy T, Hammel P, ,Notta F, Knox JJ, Gallinger S, Krishnan RG, Cros J, Grant RC

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/42054617

Résumé

Predicting recurrence of pancreatic cancer after surgery could inform clinical decision making, including adjuvant therapies and follow-up. This study aimed to develop and validate a deep learning model using digitized whole-slide images (WSI) of histopathology.

Mots clés

Humans, Deep Learning, Pancreatic Neoplasms, surgery, Prognosis, Male, Female, Middle Aged, Carcinoma, Pancreatic Ductal, surgery, Aged, Neoplasm Recurrence, Local, pathology, Pancreatectomy, Biomarkers, Tumor

Référence

JCO Clin Cancer Inform. 2026 04;10(2):e2500268