Predicting 3D dose distribution with scale attention network for prostate cancer radiotherapy.

The growing demand for radiation therapy to treat cancer has been directed to focus on improving treatment planning flow for patients. Accurate dose prediction, therefore, plays a prominent role in this regard. In this study, we propose a framework based on our newly developed scale attention networks (SA-Net) to attain voxel-wise dose prediction. Our network 's dynamic scale attention model incorporates low-level details with high-level semantics from feature maps at different scales. To achieve more accurate results, we used distance data between each local voxel and the organ surfaces instead of binary masks of organs at risk as well as CT image as input of the network. The proposed method is tested on prostate cancer treated with Volumetric Modulated Arc Therapy (VMAT), where the model was training with 120 cases and tested on 20 cases. The average dose difference between the predicted dose and the clinical planned dose was 0.94 Gy, which is equivalent to 2.1% as compared to the prescription dose of 45 Gy. We also compared the performance of SA-Net dose prediction framework with different input format, the signed distance map vs. binary mask and showed the signed distance map was a better format as input to the model training. These findings show that our deep learning-based strategy of dose prediction is effectively feasible for automating the treatment planning in prostate cancer radiography.