Deep learning for high-resolution dose prediction in high dose rate brachytherapy for breast cancer treatment

This study aimed to enable dose predictions as accurate as MC simulations in 1mm × 1mm × 1mm voxels within a clinically acceptable timeframe.
Approach: Computed tomography scans of 98 breast cancer patients treated with Iridium-192-based HDR BT were used: 70 for training, 14 for validation, and 14 for testing. A new cropping strategy based on the distance to the seed was devised to reduce the volume size, enabling efficient training of 3D DL models using 1 mm × 1 mm × 1 mm dose grids. Additionally, novel DL architecture with layer-level fusion were proposed to predict MC simulated dose to medium-in-medium (Dm,m). These architectures fuse information from TG-43 dose to water-in-water (Dw,w) with patient tissue composition at the layer-level. Different inputs describing patient body composition were investigated.
Main results: The proposed approach demonstrated state-of-the-art performance, on par with the MC Dm,m maps, but 300 times faster. The mean absolute percent error for dosimetric indices between the MC and DL-predicted complete treatment plans was 0.17%±0.15% for the planning target volume V100, 0.30%±0.32% for the skin D2cc, 0.82%±0.79% for the lung D2cc, 0.34%±0.29% for the chest wall D2cc and 1.08%±0.98% for the heart D2cc.
Significance: Unlike the time-consuming MC simulations, the proposed novel strategy efficiently converts TG-43 Dw,w maps into precise Dm,m maps at high resolution, enabling clinical integration.PMID:38604185 | DOI:...
Source: Physics in Medicine and Biology - Category: Physics Authors: Source Type: research