Multi-task autoencoder based classification-regression model for patient-specific VMAT QA | |
Wang, Le1,2,4; Li, Jiaqi3,5; Zhang, Shuming3; Zhang, Xile3; Zhang, Qilin3; Chan, Maria F.6; Yang, Ruijie3; Sui, Jing1,2,4 | |
发表期刊 | PHYSICS IN MEDICINE AND BIOLOGY |
ISSN | 0031-9155 |
2020-12-07 | |
卷号 | 65期号:23页码:12 |
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. |
关键词 | VMAT QA patient-specific QA deep learning radiotherapy |
DOI | 10.1088/1361-6560/abb31c |
关键词[WOS] | QUALITY-ASSURANCE ; RADIOMIC ANALYSIS ; IMRT ; MODULATION ; COMPLEXITY ; RADIOTHERAPY ; RAPIDARC ; BEAMS |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | 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 |
WOS研究方向 | Engineering ; Radiology, Nuclear Medicine & Medical Imaging |
WOS类目 | Engineering, Biomedical ; Radiology, Nuclear Medicine & Medical Imaging |
WOS记录号 | WOS:000592683300001 |
出版者 | IOP PUBLISHING LTD |
七大方向——子方向分类 | 人工智能+医疗 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/41666 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
通讯作者 | Yang, Ruijie; Sui, Jing |
作者单位 | 1.Chinese Acad Sci, Brainnetome Ctr, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.Peking Univ Third Hosp, Dept Radiat Oncol, Beijing, Peoples R China 4.Chinese Acad Sci, Univ Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Inst Automat, Beijing, Peoples R China 5.Capital Med Univ, Beijing Childrens Hosp, Beijing, Peoples R China 6.Mem Sloan Kettering Canc Ctr, Dept Med Phys, New York, NY 10021 USA |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
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. |
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