Machine learning–based decision-tree model for patients with single-large hepatocellular carcinoma
The clinically interpretable ML-based decision-tree model effectively stratifies patients with SLHCC according to prognosis using routine clinical and laboratory data. This model complements conventional staging systems and could support personalized treatment planning and patient counseling in real-world clinical practice.
Authors
- Chien‐Wei Su (ORCID: https://orcid.org/0000-0003-3889-7004)
- Chien‐An Liu (ORCID: https://orcid.org/0000-0002-3594-3629)
- Pei‐Chang Lee (ORCID: https://orcid.org/0000-0002-7492-2484)
- Jiing‐Chyuan Luo (ORCID: https://orcid.org/0000-0003-4306-5464)
- Yi‐Hsiang Huang (ORCID: https://orcid.org/0000-0001-5241-5425)
- Yi-Chen Lin (ORCID: https://orcid.org/0009-0001-5663-7364)
- Shu-Cheng Chou
- Chun-Ting Ho
- Jaw‐Ching Wu
- Ming‐Chih Hou
Institutions
- National Yang Ming Chiao Tung University (TW)
- Taipei Veterans General Hospital (TW)
Publication Details
- Journal
- Journal of the Chinese Medical Association
- Published
- 2025-12-08
- DOI
- https://doi.org/10.1097/jcma.0000000000001324
- Citations
- 1
- Primary Topic
- Hepatocellular Carcinoma Treatment and Prognosis
- Type
- article
- Field-Weighted Citation Impact
- 1.32