Identification and validation of an explainable machine learning model for hepatocellular carcinoma at high risk: a retrospective multicenter cohort study
The XGBoost model demonstrated superior predictive performance compared with other four ML models and all clinical staging systems in identifying ER, which may support individualized TACE management by guiding therapy selection.
Authors
- Shufang Hao
- Chao An (ORCID: https://orcid.org/0000-0003-4029-2709)
- Wendao Liu (ORCID: https://orcid.org/0000-0002-5124-9338)
- Xiaoyu Liang (ORCID: https://orcid.org/0000-0003-2915-214X)
- Chengzhi Li (ORCID: https://orcid.org/0000-0001-9193-3014)
- Xue Li (ORCID: https://orcid.org/0000-0003-0159-494X)
- Hongyu Wang (ORCID: https://orcid.org/0000-0002-4556-9546)
- Junwei Chen (ORCID: https://orcid.org/0000-0002-5785-0984)
- Jinwei Li (ORCID: https://orcid.org/0000-0002-4599-995X)
- Guolian Zhu
- Yushu Ouyang
- Mengxuan Zuo
- Yan Fu (ORCID: https://orcid.org/0000-0001-8300-4573)
Institutions
- Guangzhou University of Chinese Medicine (CN)
- Sun Yat-sen University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Shantou University (CN)
- National Cancer Center (US)
- Cancer Hospital of Shantou University Medical College (CN)
- Guangdong Academy of Medical Sciences (CN)
- Sun Yat-sen University Cancer Center (CN)
- Third Affiliated Hospital of Sun Yat-sen University (CN)
- First Affiliated Hospital of Jinan University (CN)
- State Key Laboratory of Oncology in South China
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-09-23
- DOI
- https://doi.org/10.1097/js9.0000000000003480
- Citations
- 1
- Primary Topic
- Hepatocellular Carcinoma Treatment and Prognosis
- Type
- article
- Field-Weighted Citation Impact
- 1.32