Modeling physician's preference in treatment plan approval of stereotactic body radiation therapy of prostate cancer

Objective. Treatment planning of radiation therapy is a time-consuming task. It is desirable to develop automatic planning approaches to generate plans favorable to physicians. The purpose of this study is to develop a deep learning based virtual physician network (VPN) that models physician's preference on plan approval for prostate cancer stereotactic body radiation therapy (SBRT). Approach. VPN takes one planning target volume (PTV) and eight organs at risk structure images, as well as a dose distribution of a plan seeking approval as input. It outputs a probability of approving the plan, and a dose distribution indicating improvements to the input dose. Due to the lack of unapproved plans in our database, VPN is trained using an adversarial framework. 68 prostate cancer patients who received 45 Gy in 5-fraction SBRT were selected in this study, with 60 patients for training and cross validation, and 8 patients for independent testing. Main results. The trained VPN was able to differentiate approved and unapproved plans with Area under the curve 0.97 for testing data. For unapproved plans, after applying VPN's suggested dose improvement, the improved dose agreed with ground truth with relative differences 2.03±2.17% for PTV D98%, 0.49±0.29% for PTV V95%, 3.08±2.24% for penile bulb Dmean, 3.73±2.20% for rectum V50%, and 2.06±1.73% for bladder V50%. Significance. VPN was developed to accurately model a physician's preference on plan approval and to provide suggestions on how to improve the dose distribution.