Deep learning enhanced MRI radiomics in predicting pathological response of head and neck squamous carcinoma to neoadjuvant chemoimmunotherapy: a retrospective analysis
In conclusion, deep-learning features from MRI can augment traditional imaging analysis to uncover hidden predictive patterns reflecting responsiveness to NACI in HNSCC patients, and integration of different data types provides a more robust prediction strategy.
Authors
- Han-Jin Shi (ORCID: https://orcid.org/0000-0002-6983-6303)
- Xiaohui Duan
- Qunxing Li
- Hua Chai (ORCID: https://orcid.org/0000-0002-5236-7926)
- Jingkang Liu
- Huijun Hu (ORCID: https://orcid.org/0000-0003-1086-4788)
- Zhaoyu Lin (ORCID: https://orcid.org/0000-0003-2095-7814)
- Shijia Kuang
- Yongmei Tan
- Jinsong Li (ORCID: https://orcid.org/0000-0001-6833-5390)
- Zhen Li (ORCID: https://orcid.org/0000-0002-6418-9485)
- Dong Zeng (ORCID: https://orcid.org/0000-0001-6015-5010)
- Jin Li (ORCID: https://orcid.org/0000-0001-6046-6935)
- Yani Zhang (ORCID: https://orcid.org/0000-0002-5888-3390)
- Haotian Cao
- Lingjie Yang
- Tianjun Lan (ORCID: https://orcid.org/0000-0002-5680-6164)
- Youyuan Wang (ORCID: https://orcid.org/0000-0001-7176-0961)
- Chunyan Chen (ORCID: https://orcid.org/0000-0002-5919-7624)
- Liansheng Wang (ORCID: https://orcid.org/0000-0001-8538-6560)
Institutions
- Foshan University (CN)
- Sun Yat-sen University (CN)
- Sun Yat-sen Memorial Hospital (CN)
- First People's Hospital of Foshan (CN)
- Sun Yat-sen University Cancer Center (CN)
- Southern Medical University (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-10-28
- DOI
- https://doi.org/10.1097/js9.0000000000003710
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72