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Multi-task autoencoder based classification-regression model for patient-specific VMAT QA

文献类型：期刊论文

作者Wang, Le2,3,5; Li, Jiaqi4,6; Zhang, Shuming4; Zhang, Xile4; Zhang, Qilin4; Chan, Maria F.1; Yang, Ruijie4; Sui, Jing2,3,5
刊名PHYSICS IN MEDICINE AND BIOLOGY
出版日期2020-12-07
卷号65期号:23页码:12
ISSN号0031-9155
关键词VMAT QA patient-specific QA deep learning radiotherapy
DOI10.1088/1361-6560/abb31c
通讯作者Yang, Ruijie(ruijyang@yahoo.com) ; Sui, Jing(kittysj@gmail.com)
英文摘要Patient-specific quality assurance (PSQA) of volumetric modulated arc therapy (VMAT) to assure accurate treatment delivery is resource-intensive and time-consuming. Recently, machine learning has been increasingly investigated in PSQA results prediction. However, the classification performance of models at different criteria needs further improvement and clinical validation (CV), especially for predicting plans with low gamma passing rates (GPRs). In this study, we developed and validated a novel multi-task model called autoencoder based classification-regression (ACLR) for VMAT PSQA. The classification and regression were integrated into one model, both parts were trained alternatively while minimizing a defined loss function. The classification was used as an intermediate result to improve the regression accuracy. Different tasks of GPRs prediction and classification based on different criteria were trained simultaneously. Balanced sampling techniques were used to improve the prediction accuracy and classification sensitivity for the unbalanced VMAT plans. Fifty-four metrics were selected as inputs to describe the plan modulation-complexity and delivery-characteristics, while the outputs were PSQA GPRs. A total of 426 clinically delivered VMAT plans were used for technical validation (TV), and another 150 VMAT plans were used for CV to evaluate the generalization performance of the model. The ACLR performance was compared with the Poisson Lasso (PL) model and found significant improvement in prediction accuracy. In TV, the absolute prediction error (APE) of ACLR was 1.76%, 2.60%, and 4.66% at 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively; whereas the APE of PL was 2.10%, 3.04%, and 5.29% at 3%/3 mm, 3%/2 mm, and 2%/2 mm, respectively. No significant difference was found between CV and TV in prediction accuracy. ACLR model set with 3%/3 mm can achieve 100% sensitivity and 83% specificity. The ACLR model could classify the unbalanced VMAT QA results accurately, and it can be readily applied in clinical practice for virtual VMAT QA.
WOS关键词QUALITY-ASSURANCE ; RADIOMIC ANALYSIS ; IMRT ; MODULATION ; COMPLEXITY ; RADIOTHERAPY ; RAPIDARC ; BEAMS
资助项目Strategic Priority Research Program of Chinese Academy of Science Capital's Funds for Health Improvement and Research[XDB32040100] ; National Natural Science Foundation of China[81071237] ; National Natural Science Foundation of China[61773380] ; Beijing Municipal Commission of science and technology collaborative innovation project[Z201100005620012] ; Capital's Funds for Health Improvement and Research[2020-2Z-40919] ; Natural Science Foundation of Beijing[7202223] ; Interdisciplinary Medicine Seed Found of Peking University[BMU20160585] ; NIH/NCI P30 Cancer Center Support Grant[CA008748]
WOS研究方向Engineering ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
出版者IOP PUBLISHING LTD
WOS记录号WOS:000592683300001
资助机构Strategic Priority Research Program of Chinese Academy of Science Capital's Funds for Health Improvement and Research ; National Natural Science Foundation of China ; Beijing Municipal Commission of science and technology collaborative innovation project ; Capital's Funds for Health Improvement and Research ; Natural Science Foundation of Beijing ; Interdisciplinary Medicine Seed Found of Peking University ; NIH/NCI P30 Cancer Center Support Grant
源URL[http://ir.ia.ac.cn/handle/173211/41665]  
专题自动化研究所_脑网络组研究中心
通讯作者Yang, Ruijie; Sui, Jing
作者单位1.Mem Sloan Kettering Canc Ctr, Dept Med Phys, New York, NY 10021 USA
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
3.Chinese Acad Sci, Brainnetome Ctr, Beijing, Peoples R China
4.Peking Univ Third Hosp, Dept Radiat Oncol, Beijing, Peoples R China
5.Chinese Acad Sci, Univ Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Inst Automat, Beijing, Peoples R China
6.Capital Med Univ, Beijing Childrens Hosp, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Le,Li, Jiaqi,Zhang, Shuming,et al. Multi-task autoencoder based classification-regression model for patient-specific VMAT QA[J]. PHYSICS IN MEDICINE AND BIOLOGY,2020,65(23):12.
APA Wang, Le.,Li, Jiaqi.,Zhang, Shuming.,Zhang, Xile.,Zhang, Qilin.,...&Sui, Jing.(2020).Multi-task autoencoder based classification-regression model for patient-specific VMAT QA.PHYSICS IN MEDICINE AND BIOLOGY,65(23),12.
MLA Wang, Le,et al."Multi-task autoencoder based classification-regression model for patient-specific VMAT QA".PHYSICS IN MEDICINE AND BIOLOGY 65.23(2020):12.

入库方式： OAI收割

来源：自动化研究所

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