Automatic IMRT planning for gastric cancer based on deep reinforcement learning
10.3760/cma.j.cn113030-20230606-00166
- VernacularTitle:基于深度强化学习的胃癌IMRT自动计划设计
- Author:
Hanlin WANG
1
;
Xue BAI
;
Binbing WANG
;
Guoping SHAN
Author Information
1. 浙江省肿瘤医院放射物理科，中国科学院杭州医学研究所，杭州 310022
- Keywords:
Stomach neoplasms;
Radiotherapy, intensity-modulated;
Automatic planning;
Deep reinforcement learning;
Multi-agent optimization policy network
- From:
Chinese Journal of Radiation Oncology
2024;33(7):642-649
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop and evaluate an unsupervised intensity-modulated radiation therapy (IMRT) automated planning scheme for the Eclipse commercial treatment planning system (TPS), aiming to simulate the manual operation during the whole optimization process.Methods:A retrospective analysis was performed on 25 gastric cancer patients aged 40-60 years who had completed radiotherapy in Zhejiang Cancer Hospital from March 2022 to March 2023. All patients were divided into the training ( n=7) and test sets ( n=18). All patients were treated with the same clinically prescribed dose standard: 45 Gy/25 times. Abdominal CT scan was performed using Philips simulator with a thickness of 5 mm. Based on the deep reinforcement learning (DRL) framework, a multi-agent optimization policy network (MOPN) was proposed to simulate the process of clinical manual planning design and obtain high quality automatic planning according to adjusting multiple optimization objectives. The automatic plan for all cases was generated by code programming using the eclipse scripting application program interface (ESAPI). Wilcoxon signed rank test was used to investigate the significance of the difference between automatic planning and clinical manual planning. Results:After the initial optimization objectives were adjusted by MOPN, the average plan score of all automatic plans was increased from 576.1±221.2 to 1852.8±294.9. Compared with clinical manual plans, the average D max of the spinal cord, the average D mean and V 5 Gy of the liver in the MOPN plans were reduced by 21.4%, 9.8% and 11.5%, respectively. Conclusions:With the help of ESAPI tool, MOPN can realize data interaction with TPS and the automation of IMRT treatment plan for gastric cancer. The trained MOPN can mimic the manual operation of the planner to adjust multiple optimization objectives and gradually improve the plan quality.
