MRI-based habitat radiomics and deep learning for predicting vessels encapsulating tumor clusters and survival in hepatocellular carcinoma
Vessels encapsulating tumor clusters (VETC) is a critical predictor of aggressive hepatocellular carcinoma. The deep learning radiomics (DLR) nomogram model helps predict VETC, and the DLR score serves as an independent prognostic factor for recurrence-free survival. The model demonstrated favorable interpretability through the SHAP method.
Authors
- L Wang
- Yongliang Liu (ORCID: https://orcid.org/0000-0002-1492-8374)
- Hongdi Du (ORCID: https://orcid.org/0000-0003-0562-3832)
- Yanfen Fan (ORCID: https://orcid.org/0009-0005-8067-7939)
- Q Wu
- Jinjing Wang (ORCID: https://orcid.org/0000-0001-6668-0713)
- Yixing Yu (ORCID: https://orcid.org/0000-0001-6895-962X)
- Mingzhan Du
- Lixiu Cao
- Changhao Cao
- Chunyan Gu
- Tao Zhang
Institutions
- Nantong University (CN)
- Soochow University (CN)
- Affiliated Hospital of Nantong University (CN)
- Suzhou Kowloon Hospital (CN)
- Tangshan People's Hospital (CN)
- First Affiliated Hospital of Soochow University (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-12-22
- DOI
- https://doi.org/10.1186/s13244-025-02167-3
- Primary Topic
- Hepatocellular Carcinoma Treatment and Prognosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- China Postdoctoral Science Foundation