Machine Learning Models for Disease-Free Survival Analysis after Liver Resection for Hepatocellular Carcinoma: A Multicentric French Collaborative Study
RSF significantly improved DFS prediction compared with Cox regression and yielded the best discriminatory performance in both training and external validation cohorts. These findings highlight the value of ML-based survival models, particularly RSF, for enhancing individualized postoperative prognostication and refining patient selection for clinical trials evaluating adjuvant treatment to surveillance after LR for HCC.
Authors
- Julien Caldéraro (ORCID: https://orcid.org/0000-0002-5370-0807)
- Cyprien Toubert
- Perrine Zimmermann (ORCID: https://orcid.org/0000-0002-0598-1579)
- R. Kianmanesh
- Paradis
- Astrid Herrero
- Rudy Merieux (ORCID: https://orcid.org/0009-0004-6643-8555)
- Marie Alaux
- Hélène Regnault
- Camille Boulagnon-Rombi
- Mickael Lesurtel
- Safi Dokmak
- Alexandra Heurgué
- Rami Rhaiem
- Ammar Abdo
- Olivier Soubrane
- Raffaele Brustia
- Stefano Caruso
- Daniele Sommacale
- Alain Luciani
- Giuliana Amaddeo
Institutions
- Inserm (FR)
- Université Paris-Est Créteil (FR)
- Université de Montpellier (FR)
- Université Paris Cité (FR)
- Hôpital Beaujon (FR)
- Institute Mutualiste Montsouris (FR)
- Centre Hospitalier Universitaire de Montpellier (FR)
- Assistance Publique – Hôpitaux de Paris (FR)
- Centre Hospitalier Universitaire de Reims (FR)
- Centre de Recherche sur l'Inflammation (FR)
- Hôpitaux Universitaires Henri-Mondor (FR)
- Institut Mondor de Recherche Biomédicale (FR)
- Université de Reims Champagne-Ardenne (FR)
Publication Details
- Journal
- Liver Cancer
- Published
- 2026-01-30
- DOI
- https://doi.org/10.1159/000550554
- Primary Topic
- Hepatocellular Carcinoma Treatment and Prognosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00