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

Abstract 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.