Radiomics-based prediction of gamma pass rates for different intensity-modulated radiation therapy techniques for pelvic tumors
10.3760/cma.j.cn112271-20230314-00073
- VernacularTitle:基于放射组学的盆腔肿瘤不同调强放疗技术γ通过率的预测研究
- Author:
Qianxi NI
1
;
Yangfeng DU
;
Zhaozhong ZHU
;
Jinmeng PANG
;
Jianfeng TAN
;
Zhili WU
;
Jinjia CAO
;
Luqiao CHEN
Author Information
1. 湖南省肿瘤医院 中南大学湘雅医学院附属肿瘤医院放疗科，长沙 410013
- Keywords:
Pelvic tumor;
Intensity-modulated radiation therapy technique;
Radiomics;
Gamma pass rate;
Dose validation
- From:
Chinese Journal of Radiological Medicine and Protection
2023;43(8):595-600
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the feasibility of a classification prediction model for gamma pass rates (GPRs) under different intensity-modulated radiation therapy techniques for pelvic tumors using a radiomics-based machine learning approach, and compare the classification performance of four integrated tree models.Methods:With a retrospective collection of 409 plans using different IMRT techniques, the three-dimensional dose validation results were adopted based on modality measurements, with a GPR criterion of 3%/2 mm and 10% dose threshold. Then prediction were built models by extracting radiomics features based on dose documentation. Four machine learning algorithms were used, namely random forest (RF), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). Their classification performance was evaluated by calculating sensitivity, specificity, F1 score, and AUC value. Results:The RF, AdaBoost, XGBoost, and LightGBM models had sensitivities of 0.96, 0.82, 0.93, and 0.89, specificities of 0.38, 0.54, 0.62, and 0.62, F1 scores of 0.86, 0.81, 0.88, and 0.86, and AUC values of 0.81, 0.77, 0.85, and 0.83, respectively. XGBoost model showed the highest sensitivity, specificity, F1 score, and AUC value, outperforming the other three models. Conclusions:To build a GPR classification prediction model using a radiomics-based machine learning approach is feasible for plans using different intensity-modulated radiotherapy techniques for pelvic tumors, providing a basis for future multi-institutional collaborative research on GPR prediction.
