A machine learning approach to identify optimal candidates for transarterial chemoembolization in unresectable HBV-related hepatocellular carcinoma complicated by first-branch portal vein tumor thrombus: a multicenter study
The integrated application of RSF and PSM can effectively identify low-risk candidates for TACE among patients with unresectable HCC and PVTT1. Our findings provide strong evidence that for this carefully selected patient subgroup, TACE offers survival outcomes comparable to those for patients without PVTT, highlighting the clinical utility of machine learning in guiding treatment decisions for this challenging disease.
Authors
- Chunzhi Yuan
- Tenghui Han
- Suo Zhao
- Jun Zhu (ORCID: https://orcid.org/0000-0003-1824-7610)
- Jiawei Song (ORCID: https://orcid.org/0000-0002-4565-4759)
- Huadong Zhao
- Yichong Wang
- Xiuqin Li
- Lin Zhao
- Yuheng Zhang
- Qi Zhang
- Yumeng Tang
Institutions
- Sichuan University (CN)
- West China Second University Hospital of Sichuan University (CN)
- United States Air Force (US)
- People's Liberation Army 401 Hospital (CN)
- Air Force General Hospital PLA (CN)
- Airborne International (Netherlands) (NL)
- Tang Du Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2026-03-04
- DOI
- https://doi.org/10.3389/fonc.2026.1766607
- Primary Topic
- Hepatocellular Carcinoma Treatment and Prognosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Natural Sciences