Noisy probing dose facilitated dose prediction for pencil beam scanning proton therapy: Physics enhances generalizability

作者：Lian Zhang, Jason Holmes, Xiao Zhang, Zhengliang Liu, Hongying Feng, Mingzhu Li, Terence T. Sio, Carlos Vargas, Sameer R. Keole, Kristin Stützer, Sheng Li, Tianming Liu, Jiajian Shen, William W. Wong, Sujay A. Vora, Wei Liu · 发表于：Medical Physics · 年份：2026 · DOI：10.1002/mp.70509 · 被引用次数：2 · 研究领域：Advanced Radiotherapy Techniques、Radiation Therapy and Dosimetry、Advanced X-ray and CT Imaging

BACKGROUND: Accurate and efficient dose calculation is essential for online adaptive planning in proton therapy. Deep learning (DL) has shown promising dose prediction results for pencil beam scanning proton therapy (PBSPT) in recent years, but existing DL-based dose prediction methods still suffer from limited generalizability and an inability to effectively handle outlier clinical cases. This may lead to inaccurate dose delivery to targets or excessive irradiation to organs at risk (OARs), thereby compromising the safety and efficacy of online adaptive proton therapy. PURPOSE: To design a physics-aware and generalizable AI-based PBSPT dose prediction method that incorporates underlying physics to enhance generalizability, particularly in handling outlier clinical cases. METHODS: This study analyzed PBSPT plans of 103 prostate (93 for training and 10 for testing) and 78 lung cancer patients (68 for training and 10 for testing) from our institution, with each case comprising CT images and structure sets. Using the doses generated by our Monte Carlo-based dose engine as the reference standard, we compared three methods: the region of interest (ROI)-based method, the beam mask and sliding window method, and the proposed noisy probing dose method, which rapidly generates a low-statistics dose via uniformly weighted spots on an expanded spot-placement target volume without optimization. To evaluate the generalizability of these methods to rare treatment planning scenarios, 12 cas...