Machine learning based radiomic models outperform clinical biomarkers in predicting outcomes after immunotherapy for hepatocellular carcinoma
Prognostic markers predicting survival and response to immunotherapy in hepatocellular carcinoma are lacking. This study used deep learning and machine learning to develop and validate an integrated radiomic-clinical model which can predict survival and response to atezolizumab plus bevacizumab from pre-treatment imaging. Radiomic-based machine learning models can risk-stratify patients with advanced HCC receiving atezolizumab plus bevacizumab.
Authors
- Olivier Sutter (ORCID: https://orcid.org/0000-0002-2802-9652)
- Nathalie Ganne Carrié
- Claudia Campani (ORCID: https://orcid.org/0000-0003-3842-782X)
- Jean-Charles Nault
- Mathew Vithayathil (ORCID: https://orcid.org/0000-0002-1457-4385)
- Eric O. Aboagye (ORCID: https://orcid.org/0000-0003-2276-6771)
- Rohini Sharma
- Deniz Koku (ORCID: https://orcid.org/0009-0007-5161-3797)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Université de Bordeaux (FR)
- Inserm (FR)
- Sorbonne Université (FR)
- Université Sorbonne Paris Nord (FR)
- Centre de Recherche des Cordeliers (FR)
- Assistance Publique – Hôpitaux de Paris (FR)
- Institut de Mathématiques de Bordeaux (FR)
- Imperial College London (GB)
Publication Details
- Journal
- Journal of Hepatology
- Published
- 2025-04-17
- DOI
- https://doi.org/10.1016/j.jhep.2025.04.017
- Citations
- 30
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 40.80