Integrating lesser omentum adipose CT in dual-phase tumor imaging: A multi-label deep learning framework for preoperative microvascular invasion prediction and survival analysis in hepatocellular carcinoma
Our multi-label deep learning framework accurately predicts MVI in HCC patients and enables stratified analysis of OS, which could guide personalized treatment and improve outcomes through timely intervention.
Authors
- Shidi Miao (ORCID: https://orcid.org/0000-0001-5697-3408)
- Zengyao Liu (ORCID: https://orcid.org/0000-0002-2303-2684)
- Mingxuan Wang (ORCID: https://orcid.org/0000-0002-2858-6512)
- Meng Sun (ORCID: https://orcid.org/0000-0001-6060-2976)
- Xuemei Ding
- Lin Zhao (ORCID: https://orcid.org/0000-0003-0545-4194)
- Yuyang Jiang (ORCID: https://orcid.org/0000-0003-3904-4855)
- Ruitao Wang (ORCID: https://orcid.org/0000-0002-4703-7367)
- Qiujun Wang
Institutions
- Harbin University of Science and Technology (CN)
- University of Ulster (GB)
- Harbin Medical University (CN)
- Third Affiliated Hospital of Harbin Medical University (CN)
- Second Affiliated Hospital of Harbin Medical University (CN)
- First Affiliated Hospital of Harbin Medical University (CN)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-12-02
- DOI
- https://doi.org/10.1016/j.cmpb.2025.109197
- Primary Topic
- Cardiovascular Disease and Adiposity
- Type
- article
- Field-Weighted Citation Impact
- 0.00