Independent verification of brachytherapy treatment plan by using deep learning inference modeling

Abstract

This study aims to develop a deep learning-based strategy for treatment plan check and verification of high-dose rate (HDR) brachytherapy. A deep neural network was trained to verify the dwell positions and times for a given input brachytherapy isodose distribution. In our modeling, each dwell position is represented by a Gaussian heatmap located in the vicinity of the dwell positions. A deep inception network based architecture was established to learn the mapping between CT, dose distribution and the heatmap volume. The dwell position coordinates were obtained from the predicted heatmap volume by finding the location of the Gaussian peak using non-maximum suppression. An encoder network was employed to predict dwell time by using the same input. 110 HDR brachytherapy cervical patients were used to train the proposed network. Additional 10 patients were employed to evaluate the accuracy of the proposed method through comparing the dwell position coordinates and dwell times with the results from a treatment planning system. The proposed deep learning-based dwell positions and times verification method achieved excellent predictive performance. For the tested patients, the deviation of the deep learning predicted dwell position coordinates was around one pixel from the planned positions (on average, a pixel is ∼0.5 mm), and the relative deviations of the predicted dwell times were within 2%. A deep learning-based plan check and verification method was established for brachytherapy. Our study showed that the model is capable of predicting the dwell positions and times reliably and promises to provide an efficient and accurate tool for independent verification of HDR brachytherapy treatment plan.