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Purpose: To develop a DL-based PBSPT dose prediction workflow with highaccuracy and balanced complexity to support on-line adaptive proton therapyclinical decision and subsequent replanning.  Methods: PBSPT plans of 103 prostate cancer patients and 83 lung cancerpatients previously treated at our institution were included in the study, eachwith CTs, structure sets, and plan doses calculated by the in-house developedMonte-Carlo dose engine. For the ablation study, we designed three experimentscorresponding to the following three methods: 1) Experiment 1, the conventionalregion of interest (ROI) method. 2) Experiment 2, the beam mask (generated byraytracing of proton beams) method to improve proton dose prediction. 3)Experiment 3, the sliding window method for the model to focus on local detailsto further improve proton dose prediction. A fully connected 3D-Unet wasadopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passingrates, and dice coefficients for the structures enclosed by the iso-dose linesbetween the predicted and the ground truth doses were used as the evaluationmetrics. The calculation time for each proton dose prediction was recorded toevaluate the method's efficiency.  Results: Compared to the conventional ROI method, the beam mask methodimproved the agreement of DVH indices for both targets and OARs and the slidingwindow method further improved the agreement of the DVH indices. For the 3DGamma passing rates in the target, OARs, and BODY (outside target and OARs),the beam mask method can improve the passing rates in these regions and thesliding window method further improved them. A similar trend was also observedfor the dice coefficients. In fact, this trend was especially remarkable forrelatively low prescription isodose lines. The dose predictions for all thetesting cases were completed within 0.25s.

Beam mask and sliding window-facilitated deep learning-based accurate and efficient dose prediction for pencil beam scanning proton therapy