Development of a machine learning model to predict overall survival for large hepatocellular carcinoma at BCLC stage A or B after curative hepatectomy
The GBM-based model demonstrated the potential to predict prognosis for patients with LHCC after curative hepatectomy. This interpretable model may assist in personalized risk assessment and tailoring postoperative management strategies.
Authors
- Minjun Li (ORCID: https://orcid.org/0000-0002-4382-5585)
- Jia‐Yong Su (ORCID: https://orcid.org/0000-0002-0960-5394)
- Mingjian Huang (ORCID: https://orcid.org/0000-0002-1921-4397)
- Tse-Yen Yang (ORCID: https://orcid.org/0000-0002-3165-132X)
- Yuliang Ran (ORCID: https://orcid.org/0000-0003-2555-1311)
- Jie Zhang (ORCID: https://orcid.org/0000-0002-8541-6616)
- Yu Wang (ORCID: https://orcid.org/0000-0002-8009-9076)
- Wenfeng Gong (ORCID: https://orcid.org/0000-0002-8929-2008)
- Jinyan Huang (ORCID: https://orcid.org/0000-0002-8053-0209)
- Shuang Shen (ORCID: https://orcid.org/0000-0002-2856-1833)
- Bang-De Xiang (ORCID: https://orcid.org/0000-0001-5186-9564)
- Holger Wei� (ORCID: https://orcid.org/0009-0004-0991-4324)
- Yaoting Huang
- Qing-Man Qin
Institutions
- Guangxi Medical University (CN)
- Guangxi Center for Disease Prevention and Control (CN)
- Tumor Hospital of Guangxi Medical University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-10-21
- DOI
- https://doi.org/10.3389/fimmu.2025.1640075
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science and Technology Major Project
- Guangxi Key Research and Development Program