Deep learning-based dose prediction for stereotactic prostate cancer radiotherapy with CyberKnife.

BACKGROUND: Deep learning-based (DL) approaches have gained interest in predicting dose distributions in radiotherapy of prostate cancer treated with volumetric modulated arc therapy and intensity-modulated radiation therapy. Meanwhile, research on predicting dose distributions in high-precision stereotactic radiotherapy treatments has remained relatively underrepresented. PURPOSE: We aimed to expand the previous studies by developing a DL-based framework for predicting dose distributions for robotic, stereotactic prostate cancer radiotherapy. METHODS: We harnessed a U-Net-based convolutional neural network for predicting clinically achievable dose distributions based on CT images, delineated structures, and distance information from the planning target volume. A dataset of 462 patients treated with CyberKnife (Accuray Inc.) utilizing an Iris collimator was divided into training (70%, n = 323), validation (10%, n = 46), and test (20%, n = 93) sets. RESULTS: In the independent test set, the mean absolute error between the mean doses of predictions and clinical plans was 0.63 Gy for the rectum and 1.04 Gy for the bladder. CONCLUSIONS: The proposed U-Net-based model demonstrated the ability to learn and reproduce characteristic dose distributions in CyberKnife prostate cancer radiotherapy. The model may provide patient-specific dose estimates for setting initial planning objectives to assist in automating treatment planning and improving inter-planner consistency.