Efficient proton–photon patient selection via dose and NTCP prediction for head and neck cancer patients
This study highlights the potential of a novel AI-assisted framework with joint-modality prediction to enhance efficiency and precision for proton-photon patient selection in the heterogeneous dataset, demonstrating the generalizability and robustness of the proposed approach.
Authors
- Wangyao Li (ORCID: https://orcid.org/0000-0001-9312-4931)
- Fazal Hameed Khan (ORCID: https://orcid.org/0000-0003-4627-0535)
- Qiang Li (ORCID: https://orcid.org/0000-0003-0096-7679)
- Gregory N. Gan (ORCID: https://orcid.org/0000-0001-5090-0064)
- C.E. Lominska (ORCID: https://orcid.org/0000-0003-1829-1371)
- Lyu Li (ORCID: https://orcid.org/0009-0000-8609-1123)
- Hao Gao (ORCID: https://orcid.org/0000-0002-4253-7418)
- Jiaxin Li (ORCID: https://orcid.org/0000-0001-5360-0219)
- Zhong Chen (ORCID: https://orcid.org/0000-0003-4755-9357)
- Wei‐Qiang Chen (ORCID: https://orcid.org/0000-0002-7686-2331)
- Yanan Zhu
- Yuting Lin (ORCID: https://orcid.org/0009-0007-7771-0050)
- Chao Wang (ORCID: https://orcid.org/0000-0002-3243-3244)
Institutions
- Southern Illinois University Carbondale (US)
- Chinese Academy of Sciences (CN)
- Harbin Institute of Technology (CN)
- University of Kansas Medical Center (US)
- Institute of Modern Physics (CN)
- Houston Methodist Sugar Land Hospital (US)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1002/mp.70183
- Citations
- 1
- Primary Topic
- Advanced Radiotherapy Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.88