Intelligent inverse treatment planning via deep reinforcement learning, a proof-of-principle study in high dose-rate brachytherapy for cervical cancer.
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Abstract
Inverse treatment planning in radiation therapy is formulated as optimization problems. The objective function and constraints consist of multiple terms designed for different clinical and practical considerations. Weighting factors of these terms are needed to define the optimization problem. While a treatment planning system can solve the optimization problem with given weights, adjusting the weights for high-quality plans is typically performed by human planners. Such weight-tuning task is labor intensive, time consuming, and it critically affects the final plan quality. An automatic weight-tuning approach is strongly desired. The weight-tuning procedure to improve plan quality is essentially a decision-making problem. Motivated by the tremendous success in deep learning for decision making with human-level intelligence, we propose a novel framework to adjust the weights in a human-like manner. Using treatment planning in high-dose-rate brachytherapy for cervical cancer as an example, we develop a weight-tuning policy network (WTPN) that observes dose-volume histograms and outputs an action to adjust weights, similar to the behaviors of human planners. We train the WTPN via end-to-end deep reinforcement learning. Experience replay is performed with the epsilon greedy algorithm. After training is completed, we apply the trained WTPN to guide treatment planning of five testing patient cases. The trained WTPN successfully learns the treatment planning goals to guide the weight-tuning process. On average, quality score of plans generated under the WTPN’s guidance is improved by ~8.5% compared to the initial plan with arbitrarily set weights, and by 10.7% compared to the plans generated by human planners. To our knowledge, this is the first tool to adjust organ weights for treatment planning problem in a human-like fashion based on intelligence learnt from training process. This is different from existing strategies based on pre-defined rules. The study demonstrates potential feasibility to develop intelligent treatment planning approaches via deep reinforcement learning.
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