Deep learning based on intratumoral heterogeneity predicts histopathologic grade of hepatocellular carcinoma
The ITH-based feature-fusion model provided a non-invasive method for classifying tumor differentiation in HCC, which may serve as a promising strategy for stratification management.
Authors
- Shaoming Song (ORCID: https://orcid.org/0000-0001-8085-4476)
- Tianchen Zhang (ORCID: https://orcid.org/0000-0003-4023-9932)
- Zizheng Wang (ORCID: https://orcid.org/0000-0001-5983-5326)
- Ruiqiu Chen (ORCID: https://orcid.org/0000-0003-1374-3180)
- Zhiyuan Yao (ORCID: https://orcid.org/0009-0009-1920-236X)
- Rong Liu (ORCID: https://orcid.org/0000-0001-5170-6474)
- Gong Zhang (ORCID: https://orcid.org/0000-0002-9100-4810)
- Kai Liu (ORCID: https://orcid.org/0000-0001-6039-2681)
- Guineng Zeng
Institutions
- Nankai University (CN)
- Chinese PLA General Hospital (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-03-18
- DOI
- https://doi.org/10.1186/s12885-025-13781-1
- Citations
- 6
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 8.16