RapidBrachyDL: Rapid Radiation Dose Calculations in Brachytherapy Via Deep Learning

Detailed and accurate absorbed dose calculations from radiation interactions with the human body can be obtained with the Monte Carlo (MC) method. However, the MC method can be slow for use in the time-sensitive clinical workflow. The aim of this study was to provide a solution to the accuracy-time trade-off for ¹⁹²Ir-based high-dose-rate brachytherapy by using deep learning.

RapidBrachyDL, a 3-dimensional deep convolutional neural network (CNN) model, is proposed to predict dose distributions calculated with the MC method given a patient's computed tomography images, contours of clinical target volume (CTV) and organs at risk, and treatment plan. Sixty-one patients with prostate cancer and 10 patients with cervical cancer were included in this study, with data from 47 patients with prostate cancer being used to train the model.

Compared with ground truth MC simulations, the predicted dose distributions by RapidBrachyDL showed a consistent shape in the dose-volume histograms (DVHs); comparable DVH dosimetric indices including 0.73% difference for prostate CTV D₉₀, 1.1% for rectum D_2cc, 1.45% for urethra D_0.1cc, and 1.05% for bladder D_2cc; and substantially smaller prediction time, acceleration by a factor of 300. RapidBrachyDL also demonstrated good generalization to cervical data with 1.73%, 2.46%, 1.68%, and 1.74% difference for CTV D₉₀, rectum D_2cc, sigmoid D_2cc, and bladder D_2cc, respectively, which was unseen during the training.

Deep CNN-based dose estimation is a promising method for patient-specific brachytherapy dosimetry. Desired radiation quantities can be obtained with accuracies arbitrarily close to those of the source MC algorithm, but with much faster computation times. The idea behind deep CNN-based dose estimation can be safely extended to other radiation sources and tumor sites by following a similar training process.