Deep learning-assisted tumor radiomic dynamics on MRI predict pathological complete response in HCC undergoing immune-based therapy followed by hepatectomy
Dynamic radiomic changes effectively predict pCR in uHCC after conversion therapy. Combining delta radiomics with AFP response significantly improves predictive performance, offering a non-invasive method for assessing pCR and potentially guiding personalized treatment decisions.
Authors
- Qi Gao (ORCID: https://orcid.org/0000-0001-6230-9116)
- He Tian (ORCID: https://orcid.org/0000-0002-5388-0297)
- Lu-Na Wang
- Liyu Sun
- Wu Lw
- Fei Li (ORCID: https://orcid.org/0009-0004-3413-5330)
- Shiqi Zhou (ORCID: https://orcid.org/0000-0003-1480-2349)
- HUI-CHUAN SUN
- Yuehua Chen (ORCID: https://orcid.org/0009-0006-1343-7449)
- Ying-Hao Shen
- C. Huang
- B. Xu
- Zi-Yue Yang
- Fan Jia
- Xiao-Dong Zhu
- Jian Zhou
- Tian-Qiang Song
- Zi-Yi Wang
- Hui Li
- Ling-Li Chen
- Yuan Ji
Institutions
- Shanghai Medical College of Fudan University (CN)
- Sun Yat-sen University (CN)
- Fudan University (CN)
- Ruijin Hospital (CN)
- Tianjin Medical University Cancer Institute and Hospital (CN)
- Zhongshan Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- United Imaging Healthcare (China) (CN)
Publication Details
- Journal
- Hepatology
- Published
- 2026-02-26
- DOI
- https://doi.org/10.1097/hep.0000000000001724
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00