  Purpose: Prior AI-based dose prediction studies in photon and proton therapy
often neglect underlying physics, limiting their generalizability to handle
outlier clinical cases, especially for pencil beam scanning proton therapy
(PBSPT). Our aim is to design a physics-aware and generalizable AI-based PBSPT
dose prediction method that has the underlying physics considered to achieve
high generalizability to properly handle the outlier clinical cases. Methods
and Materials: This study analyzed PBSPT plans of 103 prostate and 78 lung
cancer patients from our institution,with each case comprising CT images,
structure sets, and plan doses from our Monte-Carlo dose engine (serving as the
ground truth). Three methods were evaluated in the ablation study: the
ROI-based method, the beam mask and sliding window method, and the noisy
probing dose method. Twelve cases with uncommon beam angles or prescription
doses tested the methods' generalizability to rare treatment planning
scenarios. Performance evaluation used DVH indices, 3D Gamma passing rates
(3%/2mm/10%), and dice coefficients for dose agreement. Results: The noisy
probing dose method showed improved agreement of DVH indices, 3D Gamma passing
rates, and dice coefficients compared to the conventional methods for the
testing cases. The noisy probing dose method showed better generalizability in
the 6 outlier cases than the ROI-based and beam mask-based methods with 3D
Gamma passing rates (for prostate cancer, targets: 89.32%$\pm$1.45% vs.
93.48%$\pm$1.51% vs. 96.79%$\pm$0.83%, OARs: 85.87%$\pm$1.73% vs.
91.15%$\pm$1.13% vs. 94.29%$\pm$1.01%). The dose predictions were completed
within 0.3 seconds. Conclusions: We've devised a novel noisy probing dose
method for PBSPT dose prediction in prostate and lung cancer patients. With
more physics included, it enhances the generalizability of dose prediction in
handling outlier clinical cases.
