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

Understand

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.