Development of a predictive model for distant metastasis in HCC patients post-TACE using clinical data, radiomics, and deep learning
The combined model can accurately predict distant metastases in HCC patients after TACE. This nomogram model improves personalized clinical decision-making by stratifying TACE-treated HCC patients into distinct risk cohorts, enabling tailored surveillance protocols and adjuvant therapy allocation for high-risk metastasis subgroups.
Authors
- Weifu Zhang
- Han Long (ORCID: https://orcid.org/0000-0003-2947-5758)
- Xiang Ding
- Peng Yang (ORCID: https://orcid.org/0000-0001-7543-7264)
- Chenglong Liu (ORCID: https://orcid.org/0000-0002-8421-7017)
- Jianjun Hao (ORCID: https://orcid.org/0000-0002-8352-4031)
- Zhe Yang (ORCID: https://orcid.org/0000-0002-6192-3963)
- Ying Wang (ORCID: https://orcid.org/0000-0002-2571-9367)
Institutions
- Affiliated Hospital of Taishan Medical University (CN)
- Heze Municipal Hospital (CN)
- First Affiliated Hospital of Bengbu Medical College (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- Journal of Cancer Research and Clinical Oncology
- Published
- 2025-09-16
- DOI
- https://doi.org/10.1007/s00432-025-06308-5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Shandong First Medical University