Cross-Geometry Generalisation of a Physics-Informed 3D Dose-Prediction Ensemble

OBJECTIVE
Deep-learning dose-prediction models are often tied to their training beam configurations, limiting re-usability. We tested whether an ensemble of three complementary 3D networks trained on one photon beam configuration can generalise to five unseen configurations on a held-out anatomy.
METHODS
Photon dose plans for five anatomies and six beam configurations were generated with matRad. A 13-channel input tensor (CT image, PTV/OAR masks, physics-informed per-beam ray-tracing maps) was built for every plan. Three networks (SOTA-UNet3D, Res-UNet3D, Trans-UNet3D) were trained on the training configuration (C0) of four anatomies and combined by voxel-wise mean as the Voltron ensemble. The held-out anatomy (TG-119) was evaluated under C0 and C1–C5 using the 3D gamma index (3%/3 mm) and mean absolute error (MAE).
RESULTS
Voltron achieved 51.42% gamma pass rate on C0 (MAE 8.91 Gy). Across the unseen configurations the mean was 52.49% (range 47.38%–56.94%; MAE 8.94–10.44 Gy). The largest decrease relative to C0 was 4.04 points (C1); three configurations exceeded it by 1.75–5.52 points. Performance on unseen geometries was comparable to the in-distribution baseline.
CONCLUSION
A physics-informed 3D dose-prediction ensemble trained on one beam configuration may retain its dosimetric quality on unseen co-planar geometries, suggesting an avenue for reusable models in data-scarce settings.

Keywords: Deep learning; dose distribution; generalization; neural networks; radiation dosimetry; radiotherapy.