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A reinforcement learning application of guided Monte Carlo Tree Search algorithm for beam orientation selection in radiation therapy by Sadeghnejad-Barkousaraie, Azar; Bohara, Gyanendra; Jiang, Steve; Nguyen, Dan is a scholarly article available to read on EtoBox.

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Due to the large combinatorial problem, current beam orientation optimization algorithms for radiotherapy, such as column generation (CG), are typically heuristic or greedy in nature, leading to suboptimal solutions. We propose a reinforcement learning strategy using Monte Carlo Tree Search capable of finding a superior beam orientation set and in less time than CG.We utilized a reinforcement learning structure involving a supervised learning network to guide Monte Carlo tree search (GTS) to explore the decision space of beam orientation selection problem. We have previously trained a deep neural network (DNN) that takes in the patient anatomy, organ weights, and current beams, and then approximates beam fitness values, indicating the next best beam to add. This DNN is used to probabilistically guide the traversal of the branches of the Monte Carlo decision tree to add a new beam to the plan. To test the feasibility of the algorithm, we solved for 5-beam plans, using 13 test prostate cancer patients, different from the 57 training and validation patients originally trained the DNN. To show the strength of GTS to other search methods, performances of three other search methods inclu

Author

Sadeghnejad-Barkousaraie, Azar; Bohara, Gyanendra; Jiang, Steve; Nguyen, Dan

Published

2020

Language

EN