| Wang Hanlin,Liu Jiacheng,Yao Kaining,et al.Automatic planning of IMRT for rectum cancer based on optimization parameters tree search algorithm[J].Chinese Journal of Radiological Medicine and Protection,2021,41(1):66-73 |
| Automatic planning of IMRT for rectum cancer based on optimization parameters tree search algorithm |
| Received:May 30, 2020 Revised:September 08, 2020 |
| DOI：10.3760/cma.j.issn.0254-5098.2021.01.014 |
| KeyWords:Automatic IMRT Rectum cancer ESAPI OPTSA |
| FundProject:国家重大研发计划（2019YFF01014405）；北京市医管局培育计划（PX2019042）；北京市自然科学基金（1202009）；首都卫生发展科研专项（首发2018-4-1027）；教育部科技发展中心产学研创新基金-"智融兴教"基金（2018A01019）；中央高校基本科研业务费/北京大学临床医学+X青年专项（PKU2020LCXQ019） |
| Author Name | Affiliation | E-mail | | Wang Hanlin | Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing), Department of Radiotherapy, Peking University Cancer Hospital&Institute, Beijing 100142, China | | | Liu Jiacheng | Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing), Department of Radiotherapy, Peking University Cancer Hospital&Institute, Beijing 100142, China | | | Yao Kaining | Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing), Department of Radiotherapy, Peking University Cancer Hospital&Institute, Beijing 100142, China | | | Wang Ruoxi | Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing), Department of Radiotherapy, Peking University Cancer Hospital&Institute, Beijing 100142, China | | | Zhang Jian | Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing), Department of Radiotherapy, Peking University Cancer Hospital&Institute, Beijing 100142, China | | | Yue Haizhen | Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing), Department of Radiotherapy, Peking University Cancer Hospital&Institute, Beijing 100142, China | | | Zhang Yibao | Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing), Department of Radiotherapy, Peking University Cancer Hospital&Institute, Beijing 100142, China | | | Wu Hao | Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing), Department of Radiotherapy, Peking University Cancer Hospital&Institute, Beijing 100142, China | hao.wu@bjcancer.org |
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| Abstract:: |
| Objective To solve the problems in intensity-modulated radiation therapy (IMRT) planning, such as large labor cost and high dependence on the experience of physicists and great inconsistency in the quality of plan, and to discuss an unsupervised automatic treatment planning procedure of IMRT. Methods The eclipse scripting application programming interface (ESAPI) within the Eclipse treatment planning system (TPS) 15.6 and optimization parameters tree search algorithm (OPTSA) were used to emulate and realize the whole planning process. Interacted with the TPS through ESAPI, relevant dosimetric parameters were input and output. The OPTSA evaluated the plan qualities based on dosimetric parameters of the targets and organs at risk (OARs) and iteratively adjusted the optimization objective parameters to achieve a progressively improving IMRT plan. In order to verify the effectiveness of the automatic planning, twenty historical rectum cancer cases were selected from the clinical database, and the dose distribution and specific dosimetric parameters were compared between the plans generated by the OPTSA and the manual plans under the same constraints. Results All the auto plans have met clinical requirements. Furthermore, 90% and 10% of the auto plans were deemed as clinically improved and equally compared with the manual plans, respectively. The average CI for the PTV was 0.88 and 0.80 for the auto and manual plans respectively. Compared with the manual plans, the mean doses of all the OARs in the auto plans were reduced by 11% in average. The average elapsed time of automatic planning and manual planning was (28.15±3.61) and (36.7±4.6) min, respectively. Conclusions The plans created by the proposed algorithm have been shown to be at least as good as the manual plans. In addition, this method can shorten the labor time in plan designing while ensuring the plan quality and consistency of the plan. |
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