Preoperative ternary classification using DCE-MRI radiomics and machine learning for HCC, ICC, and HIPT
A machine learning model integrates DCE-MRI radiomics and clinical data for liver lesion differentiation. The fusion model outperforms single-modality models with 0.933 AUC and 84.5% accuracy. This model provides a noninvasive, reliable tool for personalized liver disease diagnosis and treatment planning.
Authors
- Huizhen Lv (ORCID: https://orcid.org/0000-0003-2554-4293)
- Lu Xie (ORCID: https://orcid.org/0000-0001-7541-2243)
- Xin Wen (ORCID: https://orcid.org/0000-0003-4864-8255)
- Xiaodong Zhang (ORCID: https://orcid.org/0000-0002-7940-802X)
- Junyuan Zhong (ORCID: https://orcid.org/0009-0003-8845-1251)
- Tianxian Chen
- Peng Xie
- Ling Zhang (ORCID: https://orcid.org/0000-0002-2942-8022)
- Xiaochun Wang (ORCID: https://orcid.org/0000-0002-9390-1697)
- Zhong-Jian Liao
- Wei Yuan (ORCID: https://orcid.org/0009-0008-9623-0228)
- Yujin Yin
Institutions
- Nanchang University (CN)
- Ganzhou People's Hospital (CN)
- Gannan Medical University (CN)
- Southern Medical University (CN)
- Fuzhou University (CN)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-08-14
- DOI
- https://doi.org/10.1186/s13244-025-02062-x
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08