Prediction of gamma pass rate for thoracic intensity-modulated radiotherapy plan dose verification using a machine learning model based on planomics
10.3760/cma.j.cn113030-20230725-00021
- VernacularTitle:基于计划文件的机器学习模型预测胸部IMRT计划的γ通过率
- Author:
Tiantian CUI
1
;
Xiangyue LIU
1
;
Nan MENG
1
;
Yongqiang WANG
1
;
Hong GE
1
;
Zhaoyang LOU
1
;
Bing LI
1
Author Information
1. 郑州大学附属肿瘤医院 河南省肿瘤医院放疗科，郑州 450008
- Publication Type:Journal Article
- Keywords:
Machine learning;
Planomics;
Intensity-modulated radiotherapy plan;
Radiotherapy
- From:
Chinese Journal of Radiation Oncology
2025;34(1):81-87
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a machine learning classification prediction model using planning-omics (planomics) features to predict the γ pass rate of intensity-modulated radiotherapy (IMRT) plan dose verification in fixed-field thoracic tumors, and evaluate the application of planomics in radiotherapy quality assurance.Methods:The fixed-field IMRT plans of 240 patients with chest tumors admitted to Department of Radiotherapy, Henan Cancer Hospital from August 2022 to March 2023 were retrospectively analyzed. All plans underwent dose verification using the electronic portal imaging system detector on the Varian accelerator to collect field dose data. The dose verification results were analyzed through Portal Dosimetry in the treatment planning system of Eclipse. The γ pass rate standard was set at 2%/2 mm with a 10% dose threshold. From the planning documents, 48 conventional planning features, 2476 planomics features, and the combination of the previous two feature sets were extracted. Subsequently, an auto-encoder classification model was constructed. To evaluate the classification efficacy of various feature sets, 20 random train-test divisions were conducted by calculating the area under the receiver operating characteristic curve (AUC) values along with the accuracy rates.Results:After the feature selection, 2 conventional features and 16 planomics features were finally selected. In the testing set, the AUC values for the model using combined features, planomics features, and conventional planned features were 0.802±0.030, 0.740±0.069, and 0.673±0.083, respectively. In contrast, in the training set, these AUC values were 0.844±0.074, 0.816±0.047, and 0.687±0.036, respectively. The accuracy rates were 0.752±0.083, 0.703±0.110, and 0.648±0.081 in the testing set, and 0.753±0.098, 0.751±0.075, and 0.624±0.054 in the training set for the combined, planomics, and conventional planning feature sets, respectively.Conclusions:For thoracic fixed-field adjusted radiotherapy planning, the machine learning method based on planomics features can be utilized to build a classification model for predicting the γ pass rate. Combining planomics features with conventional planned features can enhance the predictive performance of the classification models.
