Prediction of prognosis of immune checkpoint inhibitors combined with anti-angiogenic agents for unresectable hepatocellular carcinoma by machine learning-based radiomics
The effective clinical-radiomics nomogram based on MRI imaging is a promising tool in predicting the prognosis in uHCC patients receiving ICIs combined with anti-angiogenic agents, potentially leading to more effective clinical outcomes.
Authors
- Xiaoye Yuan
- Xuni Xu
- Yehao Wang (ORCID: https://orcid.org/0009-0001-5464-136X)
- Gang Chen (ORCID: https://orcid.org/0000-0001-6067-6959)
- Xue Jiang (ORCID: https://orcid.org/0000-0002-1422-9258)
- Haoran Jiang (ORCID: https://orcid.org/0009-0006-9575-3575)
- Mengjing Zhao
- Yuxia Duan (ORCID: https://orcid.org/0000-0003-4608-5823)
- Gang Li (ORCID: https://orcid.org/0000-0002-5852-0856)
Institutions
- Wenzhou Medical University (CN)
- First Affiliated Hospital of Wenzhou Medical University (CN)
- Xian Central Hospital (CN)
- Shaoxing People's Hospital (CN)
- Jinhua Central Hospital (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-05-19
- DOI
- https://doi.org/10.1186/s12885-025-14247-0
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Natural Science Foundation of China
- Chinese Society of Clinical Oncology
- Natural Science Foundation of Zhejiang Province