Development and validation of MRI-derived deep learning score for non-invasive prediction of PD-L1 expression and prognostic stratification in head and neck squamous cell carcinoma
The proposed DLS offered a non-invasive and accurate approach for assessing PD-L1 expression in patients with HNSCC and effectively stratified HNSCC patients to benefit from immunotherapy based on PFS.
Authors
- Fan Bai (ORCID: https://orcid.org/0009-0005-8966-6451)
- Cong Ding (ORCID: https://orcid.org/0000-0002-6808-9975)
- Genji Bai (ORCID: https://orcid.org/0000-0002-6891-9721)
- Yue Jai Kang (ORCID: https://orcid.org/0000-0001-5560-3333)
- Junfang Xian
Institutions
- Beijing Tongren Hospital (CN)
- Capital Medical University (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2025-02-16
- DOI
- https://doi.org/10.1186/s40644-025-00837-5
- 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
- Beijing Municipal Administration of Hospitals
- National Key Research and Development Program of China