Predicting passing rate for VMAT validation using machine learning based on plan complexity parameters
10.3760/cma.j.cn112271-20220812-00331
- VernacularTitle:基于计划复杂度参数利用机器学习预测容积弧形调强验证通过率研究
- Author:
Jinling YI
1
;
Jiming YANG
;
Xiyao LEI
;
Boda NING
;
Xiance JIN
;
Ji ZHANG
Author Information
1. 温州医科大学附属第一医院放疗中心，温州 325000
- Keywords:
VMAT;
Quality assurance;
Random forest algorithm;
Prediction model
- From:
Chinese Journal of Radiological Medicine and Protection
2022;42(12):966-972
- CountryChina
- Language:Chinese
-
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 1 st 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%/3 mm and 2%/2 mm 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.
