Machine learning-driven prediction of intratumoral tertiary lymphoid structures in hepatocellular carcinoma using contrast-enhanced CT imaging and integrated clinical data
Our combined machine learning model, which integrates preoperative CT imaging and clinical data, provides an accurate, noninvasive method for assessing intratumoral TLSs expression in HCC. This tool has the potential to enhance clinical decision-making, guide therapeutic planning, and facilitate personalized treatment strategies for HCC patients.
Authors
- Weng Kung Peng (ORCID: https://orcid.org/0000-0002-7984-9319)
- Zhifan Zuo
- Qing Ren (ORCID: https://orcid.org/0000-0002-8802-4810)
- Ziwei Zhu (ORCID: https://orcid.org/0000-0002-3990-4774)
- Jun Wu (ORCID: https://orcid.org/0000-0001-5178-0906)
- Wei Zhang (ORCID: https://orcid.org/0000-0001-6911-0900)
- Lei Han (ORCID: https://orcid.org/0000-0001-6622-4662)
- Na Lin (ORCID: https://orcid.org/0000-0003-4515-4540)
- Yang Guo (ORCID: https://orcid.org/0009-0000-7581-7865)
Institutions
- Dalian Medical University (CN)
- General Hospital of Shenyang Military Region (CN)
- First Hospital of China Medical University (CN)
- Liaoning Cancer Hospital & Institute (CN)
- China Medical University (CN)
- The University of Osaka (JP)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-11-10
- DOI
- https://doi.org/10.3389/fonc.2025.1652509
- Citations
- 1
- Primary Topic
- Hepatocellular Carcinoma Treatment and Prognosis
- Type
- article
- Field-Weighted Citation Impact
- 1.32