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Machine Learning for Patient-Specific Quality Assurance of VMAT: Prediction and Classification Accuracy
Li, Jiaqi1; Wang, Le2,3,4; Zhang, Xile1; Liu, Lu1; Li, Jun1; Chan, Maria F.5; Sui, Jing2,3,4; Yang, Ruijie1
Source PublicationINTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
ISSN0360-3016
2019-11-15
Volume105Issue:4Pages:893-902
Abstract

Purpose: To assess the accuracy of machine learning to predict and classify quality assurance (QA) results for volumetric modulated arc therapy (VMAT) plans. Methods and Materials: Three hundred three VMAT plans, including 176 gynecologic cancer and 127 head and neck cancer plans, were chosen in this study. Fifty-four complexity metrics were extracted from the QA plans and considered as inputs. Patient-specific QA was performed, and gamma passing rates (GPRs) were used as outputs. One Poisson lasso (PL) regression model was developed, aiming to predict individual GPR, and 1 random forest (RF) classification model was developed to classify QA results as "pass" or "fail." Both technical validation (TV) and clinical validation (CV) were used to evaluate the model reliability. GPR prediction accuracy of PL and classification performance of PL and RF were evaluated. Results: In TV, the mean prediction error of PL was 1.81%, 2.39%, and 4.18% at 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively. No significant differences in prediction errors between TV and CV were observed. In QA results classification, PL had a higher specificity (accurately identifying plans that can pass QA), whereas RF had a higher sensitivity (accurately identifying plans that may fail QA). By using 90% as the action limit at a 3%/2 mm criterion, the specificity of PL and RF was 97.5% and 87.7% in TV and 100% and 71.4% in CV, respectively. The sensitivity of PL and RF was 31.6% and 100% in TV and 33.3% and 100% in CV, respectively. With 100% sensitivity, the QA workload of 81.2% of plans in TV and 62.5% of plans in CV could be reduced by RF. Conclusions: The PL model could accurately predict GPR for most VMAT plans. The RF model with 100% sensitivity was preferred for QA results classification. Machine learning can be a useful tool to assist VMAT QA and reduce QA workload. (C) 2019 Elsevier Inc. All rights reserved.

KeywordVMAT Machine Learning Patient-Specific Quality Assurance
DOI10.1016/j.ijrobp.2019.07.049
WOS KeywordPER-BEAM ; IMRT ; QA ; MODULATION ; COMPLEXITY ; TOOL ; VERIFICATION ; RAPIDARC ; ROUTINE
Indexed BySCI
Language英语
Funding ProjectStrategic Priority Research Program of Chinese Academy of Science[XDB32040100] ; NIH/NCI P30 Cancer Center[CA008748] ; Interdisciplinary Medicine Seed Found of Peking University[BMU20160585] ; National natural Science Foundation of China[81071237] ; National natural Science Foundation of China[81071237] ; National natural Science Foundation of China[81071237] ; National natural Science Foundation of China[81071237] ; Interdisciplinary Medicine Seed Found of Peking University[BMU20160585] ; NIH/NCI P30 Cancer Center[CA008748] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32040100]
WOS Research AreaOncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectOncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000491350500029
PublisherELSEVIER SCIENCE INC
Sub direction classification人工智能+医疗
Citation statistics
Cited Times:70[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26060
Collection脑图谱与类脑智能实验室_脑网络组研究
Corresponding AuthorSui, Jing; Yang, Ruijie
Affiliation1.Peking Univ, Dept Radiat Oncol, Hosp 3, Beijing, Peoples R China
2.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
4.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Inst Automat, Beijing, Peoples R China
5.Mem Sloan Kettering Canc Ctr, Dept Med Phys, New York, NY 10021 USA
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li, Jiaqi,Wang, Le,Zhang, Xile,et al. Machine Learning for Patient-Specific Quality Assurance of VMAT: Prediction and Classification Accuracy[J]. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS,2019,105(4):893-902.
APA Li, Jiaqi.,Wang, Le.,Zhang, Xile.,Liu, Lu.,Li, Jun.,...&Yang, Ruijie.(2019).Machine Learning for Patient-Specific Quality Assurance of VMAT: Prediction and Classification Accuracy.INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS,105(4),893-902.
MLA Li, Jiaqi,et al."Machine Learning for Patient-Specific Quality Assurance of VMAT: Prediction and Classification Accuracy".INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS 105.4(2019):893-902.
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