Dose Prediction for Prostate Radiation Treatment: Feasibility of a Distance-Based Deep Learning Model
This study aims to demonstrate the feasibility of using a novel distance-based representation of 3D CT-scan images to train a deep learning model for dose predictions in radiation treatment planning. The distance representation is inspired by previous knowledge of the domain to increase the generalizability of the deep learning models for radiation treatment planning. Conventional knowledge-based planning methods extract engineered features from 3D CT-scan images, as well as other patients' features, to predict the best achievable dose in a cancerous area and other organs at risk. Recent studies have shown higher accuracy in voxel-level dose prediction using deep learning models compared to the conventional machine learning approaches. Since the data resources for training these models are limited, most of the studies use 2D contour information to represent the patient anatomy. This representation loses volumetric information, and it is sensitive to small changes in patient orientation and translation. The distance-based representation introduced in this paper is inspired by the domain knowledge and is able to maintain the volumetric distance information despite the 2D slicing of 3D CT-image. According to prior studies in the radiation treatment planning domain, there is a strong association between the organs-at-risk distance from the cancerous volume and the patient's vulnerability to receive excessive dose. Therefore, the contour value in prior representation was replaced by voxel distance from cancerous volume. This modification in representation makes it transition and orientation invariant and adds potential robustness to patient positioning differences during the imaging/planning process. We evaluated the distance-based deep learning models through experiments for prediction of prostate cancer patients' vulnerability and voxel-level dose distribution using convolutional neural network and U-net models, respectively. The results were compared with contour-based U-net model as well as conventional machine learning with engineered representations. We found that the performance was comparable or higher than the prior state-of-the-art results for prostate-cancer dose distribution prediction.
