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Commissioning and clinical implementation of an Autoencoder based Classification-Regression model for VMAT patient-specific QA in a multi-institution scenario

文献类型：期刊论文

作者Yang, Ruijie1; Yang, Xueying2; Wang, Le3,4,5; Li, Dingjie6; Guo, Yuexin7; Li, Ying8; Guan, Yumin9; Wu, Xiangyang10; Xu, Shouping11; Zhang, Shuming1,12
刊名RADIOTHERAPY AND ONCOLOGY
出版日期2021-08-01
卷号161页码:230-240
ISSN号0167-8140
关键词Machine learning VMAT patient-specific QA Multi-institution validation Commissioning Clinical implementation
DOI10.1016/j.radonc.2021.06.024
通讯作者Geng, Lisheng(lisheng.geng@buaa.edu.cn) ; Sui, Jing(jsui@bnu.edu.cn)
英文摘要Background and purpose: To commission and implement an Autoencoder based Classification-Regression (ACLR) model for VMAT patient-specific quality assurance (PSQA) in a multi-institution scenario. Materials and methods: 1835 VMAT plans from seven institutions were collected for the ACLR model com-missioning and multi-institutional validation. We established three scenarios to validate the gamma passing rates (GPRs) prediction and classification accuracy with the ACLR model for different delivery equipment, QA devices, and treatment planning systems (TPS). The prediction performance of the ACLR model was evaluated using mean absolute error (MAE) and root mean square error (RMSE). The classification performance was evaluated using sensitivity and specificity. An independent end-to-end test (E2E) and routine QA of the ACLR model were performed to validate the clinical use of the model. Results: For multi-institution validations, the MAEs were 1.30-2.80% and 2.42-4.60% at 3%/3 mm and 3%/2 mm, respectively, and RMSEs were 1.55-2.98% and 2.83-4.95% at 3%/3 mm and 3%/2 mm, respec-tively, with different delivery equipment, QA devices, and TPS, while the sensitivity was 90% and speci-ficity was 70.1% at 3%/2 mm. For the E2E, the deviations between the predicted and measured results were within 3%, and the model passed the consistency check for clinical implementation. The predicted results of the model were the same in daily QA, while the deviations between the repeated monthly mea-sured GPRs were all within 2%. Conclusions: The performance of the ACLR model in multi-institution scenarios was validated on a large scale. Routine QA of the ACLR model was established and the model could be used for VMAT PSQA clinically. (c) 2021 Elsevier B.V. All rights reserved. Radiotherapy and Oncology 161 (2021) 230-240
WOS关键词QUALITY-ASSURANCE ; RADIOMIC ANALYSIS ; ERROR-DETECTION ; IMRT ; RADIOTHERAPY
资助项目National Key Research and Development Program[2020YFE020088] ; National Natural Science Foundation of China[11735003] ; National Natural Science Foundation of China[11975041] ; National Natural Science Foundation of China[11961141004] ; National Natural Science Foundation of China[61773380] ; National Natural Science Foundation of China[82022035] ; National Natural Science Foundation of China[81071237] ; Beijing Municipal Commission of Science and Technology Collabo-rative Innovation Project[Z201100005620012] ; Beijing Municipal Commission of Science and Technology Collabo-rative Innovation Project[Z181100001518005] ; Beijing Natural Science Foundation[7202223] ; Capital's Funds for Health Improvement and Research[20202Z40919] ; fundamental Research Funds for the Central Universities ; Key project of Henan Provincial Department of Education[20B320035] ; NIH/NCI P30 Cancer Center Support Grant[CA008748] ; China International Medical Foundation[HDRS2020030206]
WOS研究方向Oncology ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
出版者ELSEVIER IRELAND LTD
WOS记录号WOS:000678802700032
资助机构National Key Research and Development Program ; National Natural Science Foundation of China ; Beijing Municipal Commission of Science and Technology Collabo-rative Innovation Project ; Beijing Natural Science Foundation ; Capital's Funds for Health Improvement and Research ; fundamental Research Funds for the Central Universities ; Key project of Henan Provincial Department of Education ; NIH/NCI P30 Cancer Center Support Grant ; China International Medical Foundation
源URL[http://ir.ia.ac.cn/handle/173211/45560]  
专题自动化研究所_脑网络组研究中心
通讯作者Geng, Lisheng; Sui, Jing
作者单位1.Peking Univ Third Hosp, Dept Radiat Oncol, Beijing, Peoples R China
2.Beihang Univ, Sch Phys, 9 Nansan St,Shahe Higher Educ Pk, Beijing 102206, Peoples R China
3.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing, Peoples R China
4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
5.Univ Chinese Acad Sci, Chinese Acad Sci, Sch Artificial Intelligence, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
6.Henan Canc Hosp, Dept Radiat Therapy, Zhengzhou, Peoples R China
7.Zhengzhou Univ, Dept Radiat Oncol, Affiliated Hosp 1, Zhengzhou, Peoples R China
8.Chongqing Med Univ, Dept Oncol, Affiliated Hosp 1, Chongqing, Peoples R China
9.Yantai Yuhuangding Hosp, Dept Radiat Therapy, Yantai, Peoples R China
10.Shanxi Prov Canc Hosp, Dept Radiotherapy, Xian, Peoples R China
推荐引用方式
GB/T 7714		 Yang, Ruijie,Yang, Xueying,Wang, Le,et al. Commissioning and clinical implementation of an Autoencoder based Classification-Regression model for VMAT patient-specific QA in a multi-institution scenario[J]. RADIOTHERAPY AND ONCOLOGY,2021,161:230-240.
APA Yang, Ruijie.,Yang, Xueying.,Wang, Le.,Li, Dingjie.,Guo, Yuexin.,...&Sui, Jing.(2021).Commissioning and clinical implementation of an Autoencoder based Classification-Regression model for VMAT patient-specific QA in a multi-institution scenario.RADIOTHERAPY AND ONCOLOGY,161,230-240.
MLA Yang, Ruijie,et al."Commissioning and clinical implementation of an Autoencoder based Classification-Regression model for VMAT patient-specific QA in a multi-institution scenario".RADIOTHERAPY AND ONCOLOGY 161(2021):230-240.

入库方式： OAI收割

来源：自动化研究所

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