3D Swin Transformer for patient-specific proton dose prediction of brain cancer patients
DOI:
https://doi.org/10.2340/1651-226X.2025.43969Keywords:
artificial intelligence, Proton radiotherapy, brain cancer, Deep learning, Dose predictionAbstract
Background and purpose: Accurate 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.
Results: The 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.
Interpretation: The 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.
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Copyright (c) 2025 Anne Haahr Andresen, Yasmin Lassen-Ramshad, Slávka Lukacova, Christian Rønn Hansen, Jesper Folsted Kallehauge
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