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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
Yang, Ruijie1; Yang, Xueying2; Wang, Le3,4,5; Li, Dingjie6; Guo, Yuexin7; Li, Ying8; Guan, Yumin9; Wu, Xiangyang10; Xu, Shouping11; Zhang, Shuming1,12; Chan, Maria F.13; Geng, Lisheng2,14; Sui, Jing5,15
Source PublicationRADIOTHERAPY AND ONCOLOGY
ISSN0167-8140
2021-08-01
Volume161Issue:10.1016/j.radonc.2021.06.024Pages:230-240
Abstract

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

KeywordMachine learning VMAT patient-specific QA Multi-institution validation Commissioning Clinical implementation
DOI10.1016/j.radonc.2021.06.024
WOS KeywordQUALITY-ASSURANCE ; RADIOMIC ANALYSIS ; ERROR-DETECTION ; IMRT ; RADIOTHERAPY
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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
WOS Research AreaOncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectOncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000678802700032
PublisherELSEVIER IRELAND LTD
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/45561
Collection脑网络组研究
Corresponding AuthorGeng, Lisheng; Sui, Jing
Affiliation1.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
11.Gen Hosp Peoples Liberat Army, Dept Radiat Oncol, Beijing, Peoples R China
12.Beijing Hosp, Dept Ultrasound, Beijing, Peoples R China
13.Mem Sloan Kettering Canc Ctr, Dept Med Phys, New York, NY 10021 USA
14.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing, Peoples R China
15.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China
Recommended Citation
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(10.1016/j.radonc.2021.06.024):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(10.1016/j.radonc.2021.06.024),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 161.10.1016/j.radonc.2021.06.024(2021):230-240.
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