Radiomic signatures to estimate survival in patients with advanced hepatocellular carcinoma treated with sorafenib: Cancer and Leukemia Group B 80802 (Alliance)
OS can be accurately predicted in patients with HCC receiving sorafenib by combining certain radiomics features with clinical metadata, centered primarily on baseline characteristics.
Authors
- Laurent Dercle (ORCID: https://orcid.org/0000-0002-1322-0710)
- Fatima‐Zohra Mokrane (ORCID: https://orcid.org/0000-0003-1610-1474)
- Susan M. Geyer (ORCID: https://orcid.org/0009-0002-5649-6909)
- Hao Yang (ORCID: https://orcid.org/0000-0003-4172-012X)
- Anthony B. El-Khoueiry (ORCID: https://orcid.org/0000-0002-9053-3841)
- Jeffrey A. Meyerhardt (ORCID: https://orcid.org/0000-0002-1120-0898)
- Lyndon Luk (ORCID: https://orcid.org/0000-0002-4468-3128)
- A. Liu
- Alan P. Venook (ORCID: https://orcid.org/0000-0001-9749-6548)
- Andrew B. Nixon (ORCID: https://orcid.org/0000-0003-3971-2964)
- Federico Innocenti (ORCID: https://orcid.org/0000-0001-9247-979X)
- Qian Shi (ORCID: https://orcid.org/0000-0002-4640-8832)
- Steven Jacobson
- Lawrence H. Schwartz
- Yujia Wen (ORCID: https://orcid.org/0009-0000-5398-4494)
- Thomas Eche (ORCID: https://orcid.org/0000-0002-7464-4330)
- Binsheng Zhao (ORCID: https://orcid.org/0000-0003-1835-7916)
- Eileen Mary O'Reilly (ORCID: https://orcid.org/0000-0002-8076-9199)
- Ghassan K. Abou‐Alfa (ORCID: https://orcid.org/0000-0002-1522-8054)
- Man Yang (ORCID: https://orcid.org/0000-0002-1473-6575)
Institutions
- University of North Carolina at Chapel Hill (US)
- Mayo Clinic (US)
- Memorial Sloan Kettering Cancer Center (US)
- Duke University (US)
- Cornell University (US)
- Columbia University Irving Medical Center (US)
- University of Chicago (US)
- USC Norris Comprehensive Cancer Center (US)
- Dana-Farber Cancer Institute (US)
- Hôpital Rangueil (FR)
- The University of Texas Health Science Center (US)
- UCSF Helen Diller Family Comprehensive Cancer Center (US)
Publication Details
- Journal
- ESMO Open
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1016/j.esmoop.2025.105848
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Bristol-Myers Squibb
- Eli Lilly and Company
- AstraZeneca
- Sanofi
- Radiological Society of North America
- Alnylam Pharmaceuticals
- Celgene
- Varian Medical Systems
- Helsinn
- Exelixis
- BeiGene
- Incyte
- Puma Biotechnology
- Flatiron Health
- Canon Medical Systems USA
- Eisai
- Servier
- Swedish Orphan Biovitrum
- Ipsen
- National Institutes of Health
- Genentech
- National Cancer Institute