Dual-arc VMAT machine parameter optimization for localized prostate cancer using deep reinforcement learning

Objective.To develop and evaluate a deep reinforcement learning (RL) framework for rapid and automatic machine parameter optimization of volumetric modulated arc therapy (VMAT) treatment plans for localized prostate cancer.Approach.A multi-task policy network combining convolution and long short-term memory was trained to sequentially predict the set of actions on the dose rate and multi-leaf collimator positions over the range of two arcs. The network uses as input the cumulative dose grid at the current gantry angle, contours of the planning target volume (PTV) and organs at risk, and the set of machine parameters at all preceding gantry angles. The method was evaluated on a set of 15 localized prostate cancer patients for a prescription dose of 60 Gy in 20 fractions. For each case, the final state dose distribution was compared against clinical plans. For seamless integration with the clinical workflow, the proposed model was integrated into a clinical treatment planning system (TPS), enabling dosimetric review and final plan adjustments.Main results.The RL framework produced deliverable dual-arc VMAT plans in an average of 20.7 ± 5.0 s over the test set. Dosimetric comparison to clinical plans showed no statistically significant differences for the mean rectum dose as well as for the bladder V6160 Gy, indicating that the RL model was as efficient in sparing these structures as human planners. While the approach showed limitations in terms of PTV coverage and maximum body dose, our proposed integration to TPS showed the RL plans could be automatically refined to clinical quality in an additional 83.8 ± 7.2 s.Significance.The accuracy and fast run time of the approach show the potential of the framework to significantly streamline VMAT treatment planning and enable adaptive radiation therapy.