A deep learning-based clinical-radiomics model predicting the treatment response of immune checkpoint inhibitors (ICIs)-based conversion therapy in potentially convertible hepatocelluar carcinoma patients: a tumor marker prognostic study
The deep learning-based clinical-radiomics model exhibited satisfactory predictive capability in forecasting the DCB derived from ICIs-based conversion therapy in potentially convertible HCC, and was associated with a diverse range of immune-related mechanisms.
Authors
- Zhiyu Xiao (ORCID: https://orcid.org/0000-0003-0746-7116)
- Yongcong Yan (ORCID: https://orcid.org/0000-0002-7269-764X)
- Weidong Wang
- Zi‐Feng Ma (ORCID: https://orcid.org/0000-0001-5002-9766)
- Zijian Lin
- Kai Mao
Institutions
- Sun Yat-sen University (CN)
- Sun Yat-sen Memorial Hospital (CN)
- Shanghai Public Health Clinical Center (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-03-14
- DOI
- https://doi.org/10.1097/js9.0000000000002322
- Citations
- 15
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 20.40