  Purpose: To develop a DL-based PBSPT dose prediction workflow with high
accuracy and balanced complexity to support on-line adaptive proton therapy
clinical decision and subsequent replanning.
  Methods: PBSPT plans of 103 prostate cancer patients and 83 lung cancer
patients previously treated at our institution were included in the study, each
with CTs, structure sets, and plan doses calculated by the in-house developed
Monte-Carlo dose engine. For the ablation study, we designed three experiments
corresponding to the following three methods: 1) Experiment 1, the conventional
region of interest (ROI) method. 2) Experiment 2, the beam mask (generated by
raytracing of proton beams) method to improve proton dose prediction. 3)
Experiment 3, the sliding window method for the model to focus on local details
to further improve proton dose prediction. A fully connected 3D-Unet was
adopted as the backbone. Dose volume histogram (DVH) indices, 3D Gamma passing
rates, and dice coefficients for the structures enclosed by the iso-dose lines
between the predicted and the ground truth doses were used as the evaluation
metrics. The calculation time for each proton dose prediction was recorded to
evaluate the method's efficiency.
  Results: Compared to the conventional ROI method, the beam mask method
improved the agreement of DVH indices for both targets and OARs and the sliding
window method further improved the agreement of the DVH indices. For the 3D
Gamma 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 the
sliding window method further improved them. A similar trend was also observed
for the dice coefficients. In fact, this trend was especially remarkable for
relatively low prescription isodose lines. The dose predictions for all the
testing cases were completed within 0.25s.
