Efficient proton–photon patient selection via dose and NTCP prediction for head and neck cancer patients

作者：Jiaxin Li, Ya‐Nan Zhu, Lyu Li, Zhong Chen, Fazal Hameed Khan, Wangyao Li, Chao Wang, Gregory N. Gan, C.E. Lominska, Qiang Li, Wei‐Qiang Chen, Hao Gao, Yuting Lin · 发表于：Medical Physics · 年份：2025 · DOI：10.1002/mp.70183 · 被引用次数：1 · 研究领域：Advanced Radiotherapy Techniques、Radiation Therapy and Dosimetry、Prostate Cancer Diagnosis and Treatment

BACKGROUND: Compared to photon therapy (XT), proton therapy (PT) can often reduce normal tissue toxicity for head and neck (HN) cancer patients, despite being a limited resource. On the other hand, clinical decision-making process to select between PT and XT (e.g., treatment planning and then plan evaluation for comparing normal tissue complication probabilities (NTCP) between XT and PT) is time-consuming and resource demanding. PURPOSE: This study aims to develop and validate the feasibility of an artificial intelligence (AI)-based automated method for efficient patient selection between PT and XT. METHODS: A heterogeneous cohort of 104 bilateral HN patients with auto-planned PT and XT plans was analyzed, covering diverse tumor subsites and prescription dose levels. To ensure accurate dose and NTCP prediction, a joint-modality prediction framework was developed, incorporating a 3D attention-gated U-net with a multi-constrained loss function. A stratified 10-fold cross-validation strategy was employed to evaluate and compare model performance. The NTCP differences between XT and PT for grade II/III xerostomia/dysphagia exceeding certain thresholds are used to select patients for PT according to the Landelijk Indicatie Protocol Protonentherapie (versie 2.2) (LIPPv2.2). RESULTS: AI-assisted patient selection process took about 10.1 s per patient. Our method achieved an accuracy of 85.58% and a weighted accuracy of 81.11% in patient selection. For dysphagia grades ≥ 2 and ≥ 3, t...