Machine Learning Models for Disease-Free Survival Analysis after Liver Resection for Hepatocellular Carcinoma: A Multicentric French Collaborative Study.

Fiche publication

Date publication

janvier 2026

Journal

Liver cancer

Auteurs

Membres identifiés du Cancéropôle Est :
Dr BOULAGNON-ROMBI Camille

Tous les auteurs :
Rhaiem R, Abdo A, Calderaro J, Dokmak S, Herrero A, Toubert C, Zimmermann P, Heurgué A, Boulagnon-Rombi C, Amaddeo G, Luciani A, Regnault H, Alaux M, Lesurtel M, Paradis V, Sommacale D, Soubrane O, Caruso S, Merieux R, Kianmanesh R, Brustia R

Lien Pubmed

https://www.ncbi.nlm.nih.gov/pubmed/41868609

Résumé

Liver resection (LR) is a potentially curative treatment of hepatocellular carcinoma (HCC), but early recurrence rates remain high reaching as high as 70%. Accurate prediction of disease-free survival (DFS) after LR is crucial to optimize patients' selection for clinical trials evaluating adjuvant strategies. This study assessed machine learning (ML) models for predicting DFS after LR for HCC.