| Yi Jinling,Yang Jiming,Lei Xiyao,et al.Predicting passing rate for VMAT validation using machine learning based on plan complexity parameters[J].Chinese Journal of Radiological Medicine and Protection,2022,42(12):966-972 |
| Predicting passing rate for VMAT validation using machine learning based on plan complexity parameters |
| Received:August 12, 2022 |
| DOI：10.3760/cma.j.cn112271-20220812-00331 |
| KeyWords:VMAT|Quality assurance|Random forest algorithm|Prediction model |
| FundProject:温州市科技局(Y2020917,ZY2022016) |
| Author Name | Affiliation | E-mail | | Yi Jinling | Radiotherapy Center, the 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China | | | Yang Jiming | Radiotherapy and Chemotherapy Center, Ningbo First Hospital, Ningbo 315000, China | | | Lei Xiyao | Radiotherapy Center, the 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China | | | Ning Boda | Radiotherapy Center, the 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China | | | Jin Xiance | Radiotherapy Center, the 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China | | | Zhang Ji | Radiotherapy Center, the 1st Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China | jizhang1996@126.com |
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| Abstract:: |
| Objective To establish a prediction model using the random forest (RF) and support vector machine (SVM) algorithms to achieve the numerical and classification predictions of the gamma passing rate (GPR) for volumetric arc intensity modulation (VMAT) validation.Methods A total of 258 patients who received VMAT radiotherapy in the 1st Affiliated Hospital of Wenzhou Medical University from April 2019 to August 2020 were retrospectively selected for patient-specific QA measurements, including 38 patients who received VMAT radiotherapy for head and neck, and 220 patients who received VMAT radiotherapy for chest and abdomen. Thirteen complexity parameters were extracted from the patient's VMAT plans and the GPRs for VMAT validation under the analysis criteria of 3%/3mm and 2%/2mm were collected. The patients were randomly divided into a training cohort (70%) and a validation cohort (30%), and the complexity parameters for the numerical and classification predictions were screened using the RF and minimum redundancy maximum correlation (mRMR) method, respectively. Complexity models and mixed models were established using PTV volume, subfield width, and smoothness factors based on the RF and SVM algorithms individually. The prediction performance of the established models was analyzed and compared.Results For the validation cohort, the GPR numerical prediction errors of the complexity models based on RF and SVM under the two analysis criteria are as follows. The root-mean-square errors (RMSEs) under the analysis criterion of 3%/3 mm were 1.788% and 1.753%, respectively; the RMSEs under the analysis criterion of 2%/2 mm were 5.895% and 5.444%, respectively; the mean absolute errors (MAEs) under the analysis criterion of 3%/3 mm were 1.415% and 1.334%, respectively, and the MAEs under the analysis criteria of 2%/2 mm were 4.644% and 4.255%, respectively. For the validation cohort, the GPR numerical prediction errors of the mixed models based on RF and SVM under the two analysis criteria were as follows. The RMSEs under the analysis criterion of 3%/3 mm were 1.760% and 1.815%, respectively; the RMSEs under the analysis criterion of 2%/2 mm were 5.693% and 5.590%, respectively; the MAEs under the analysis criterion of 3%/3 mm were 1.386% and 1.319%, respectively, and the MAEs under the analysis criteria of 2%/2 mm were 4.523% and 4.310, respectively. For the validation cohort, the AUC result of the GPR classification prediction of the complexity models based on RF and SVM were 0.790 and 0.793, respectively under the analysis criterion of 3%/3 mm and were 0.763 and 0.754, respectively under the analysis criterion of 2%/2 mm. For the validation cohort, the AUC result of the GPR classification prediction of the mixed models based on RF and SVM were 0.806 and 0.859, respectively under the analysis criterion of 3%/3 mm and were 0.796 and 0.796, respectively under the analysis criterion of 2%/2 mm cohort.Conclusions Complexity models and mixed models were developed based on the RF and SVM method. Both types of models allow for the numerical and classification predictions of the GPRs of VMAT radiotherapy plans under analysis criteria of 3%/3 mm and 2%/2 mm. The mixed models have higher prediction accuracy than the complexity models. |
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