3D Swin Transformer for patient-specific proton dose prediction of brain cancer patients.

background and purposeAccurate dose plans in proton radiotherapy with consistent target in complex anatomical regions such as the brain are crucial. This study investigates a Swin Transformer-based deep learning model for voxel-wise dose prediction in brain cancer proton therapy, evaluating its spatial and dosimetric fidelity against clinically delivered plans. Patient/material and methods: A cohort of 206 patients with primary brain tumors were retrospectively analyzed. Dual-energy computed tomography (CT) scans, clinical contours, and corresponding proton dose plans were used to train and test a 3D Swin Transformer integrated within a UNet architecture. The model was evaluated on an independent test set (n = 20) using 3D gamma analysis (3%/3 mm), mean absolute error (MAE), and clinical target volume (CTV) coverage (V95%). Mean dose-volume histograms (DVHs) were compared across CTV.

resultsThe model achieved a median gamma pass rate of 99.8% within the CTV (range: 78.6-100%), 83.2% outside the CTV (range: 52.3-99.8%), and a whole-volume median pass rate of 90.0% (range: 53.7-99.8%). The median MAE was 0.72 Gy (range: 0.2816-1.8966 Gy). Predicted dose distributions preserved high-dose conformity, with a median of V95% of 97.9% (range: 78.8-100%). DVH curves closely matched the clinical reference plans across all evaluated structures.

interpretationThe proposed Swin Transformer-based model is a step toward accurate, anatomy-aware dose prediction for brain tumor proton therapy. Future work will address prospective validation and optimization for clinical deployment.