| Liu Jiacheng,Wang Hanlin,Wang Qingying,Yao Kaining,Wang Meijiao,Yue Haizhen,Wang Ruoxi,Du Yi,Wu Hao.Fully automatic volumetric modulated arc therapy planning based on dose prediction combined with an iterative optimization algorithm[J].Chinese Journal of Radiological Medicine and Protection,2021,41(11):830-835 |
| Fully automatic volumetric modulated arc therapy planning based on dose prediction combined with an iterative optimization algorithm |
| Received:June 08, 2021 |
| DOI：10.3760/cma.j.issn.0254-5098.2021.11.006 |
| KeyWords:Automatic planning Iterative optimization algorithm Dose prediction Deep Learning Rectal cancer |
| FundProject:国家重点研发计划（2019YFF01014405）；国家自然科学基金（12005007）；北京市自然科学基金（1202009，1212011） |
| Author Name | Affiliation | E-mail | | Liu Jiacheng | Institute of Medical Technology, Peking University Health Science Center 100191, China | | | Wang Hanlin | Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing), Department of Radiotherapy, Peking University Cancer Hospital & Institute, Beijing 100142, China | | | Wang Qingying | Institute of Medical Technology, Peking University Health Science Center 100191, 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 Meijiao | 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 | | | Wang Ruoxi | Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing), Department of Radiotherapy, Peking University Cancer Hospital & Institute, Beijing 100142, China | | | Du Yi | Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing), Department of Radiotherapy, Peking University Cancer Hospital & Institute, Beijing 100142, China | | | Wu Hao | Institute of Medical Technology, Peking University Health Science Center 100191, China | hao.wu@bjcancer.org |
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| Abstract:: |
| Objective To develope an automatic volumetric modulated arc therapy (VMAT) planning for rectal cancer based on a dose-prediction model for organs at risk(OARs) and an iterative optimization algorithm for objective parameter optimization. Methods Totally 165 VMAT plans of rectal cancer patients treated in Peking University Cancer Hospital & Institute from June 2018 to January 2021 were selected to establish automatic VMAT planning. Among them, 145 cases were used for training the deep-learning model and 20 for evaluating the feasibility of the model by comparing the automatic planning with manual plans. The deep learning model was used to predict the essential dose-volume histogram (DVH) index as initial objective parameters(IOPs) and the iterative optimization algorithm can automatically modify the objective parameters according to the result of protocol-based automatic iterative optimization(PBAIO). With the predicted IOPs, the automatic planning model based on the iterative optimization algorithm was achieved using a program mable interface. Results The IOPs of OARs of 20 cases were effectively predicted using the deep learning model, with no significantly statistical difference in the conformity index(CI) for planning target volume(PTV)and planning gross tumor volume(PGTV)between automatic and manual plans(P>0.05). The homogeneity index (HI) of PGTV in automatic and manual plans was 0.06 and 0.05, respectively(t=-6.92, P<0.05). Compared with manual plans, the automatic plans significantly decreased the V30 for urinary bladder by 2.7% and decreased the V20 for femoral head sand auxiliary structure(avoidance)by 8.37% and 15.95%, respectively (t=5.65, 11.24, P<0.05). Meanwhile, the average doses to bladder, femoral heads, and avoidance decreased by 1.91, 4.01, and 3.88 Gy, respectively(t=9.29, 2.80, 10.23, P<0.05) using the automatic plans. The time of automatic VMAT planning was (71.49±25.48)min in 20 cases. Conclusions The proposed automatic planning based on dose prediction and an iterative optimization algorithm is feasible and has great potential for sparing OARs and improving the utilization rate of clinical resources. |
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