Learning-Based Prostate Needle Position Prediction for HDR Brachytherapy.

Abstract

PURPOSE/OBJECTIVE(S)\nDuring prostate high-dose-rate (HDR) brachytherapy, physicians select needle positions in a peripheral loading technique based on experience but without knowledge of the achievable dose distribution. Sub-optimal needle placement may lead to unfavorable dose distributions even after plan optimization. We propose a new deep learning-based method to predict HDR needle position for prostate HDR brachytherapy.\n\n\nMATERIALS/METHODS\nThe proposed framework consists of three major steps: (1) deformable registration via registration network (Reg-Net), (2) multi-atlas ranking and (3) needle regression. To model the global spatial relationship among multiple organs, binary masks of the target and organs at risk are transformed into distance maps which describe the distance of each local voxel to the organ surfaces. Reg-Net is utilized to deformably register the distance maps from multi-atlas set to match a patient s distance map and then bring needle maps from multi-atlas to this patient via spatial transformation. Several criteria are used for multi-atlas ranking including prostate volume similarity, multi-organ semantic image similarity and needle position criteria. Finally, needle regression is used to refine the final needle positions. A retrospective study of consecutively treated patients was used to evaluate the feasibility of the proposed method. We performed a five-fold cross validation to evaluate the proposed method. We generated a new dose plan for each patient based on predicted needle location. Target and organ dose volume histogram (DVH) metrics were used to quantify the difference between the clinical and predicted needle plans. All plans were normalized to prostate V100 of 95%.\n\n\nRESULTS\nNinety predicted needle plans were compared to clinical plans. In each case, the needle prediction algorithm completed within 1 minute. See table for comparison of target and organ DVH metrics (mean and standard deviation). Predicted needle plans had slightly greater target dose heterogeneity. The RTOG constraints of bladder V75% < 1 cc, rectum V75% < 1cc, and urethra V125% < 1 cc were met in all predicted plans.\n\n\nCONCLUSION\nIt is feasible to use this novel deep-learning-based method to predict needle locations for HDR prostate brachytherapy a priori. This strategy merits further study to improve utilization and quality of prostate brachytherapy.