Prediction of radiomics-based machine learning in dose verification of intensity-modulated pelvic radiotherapy
10.3760/cma.j.cn112271-20221021-00416
- VernacularTitle:基于放射组学的机器学习预测盆腔调强放疗剂量验证的γ通过率
- Author:
Luqiao CHEN
1
;
Qianxi NI
;
Xiaozhou LI
;
Jinjia CAO
Author Information
1. 南华大学核科学技术学院，衡阳 421001
- Keywords:
Machine learning;
Intensity-modulated radiotherapy;
Radiomics;
Plevic;
Gamma pass rate
- From:
Chinese Journal of Radiological Medicine and Protection
2023;43(2):101-105
- CountryChina
- Language:Chinese
-
Abstract:
Objective:Based on radiomics characteristics, different machine learning classification models are constructed to predict the gamma pass rate of dose verification in intensity-modulated radiotherapy for pelvic tumors, and to explore the best prediction model.Methods:The results of three-dimensional dose verification based on phantom measurement were retrospectively analyzed in 196 patients with pelvic tumor intensity-modulated radiotherapy plans. The gamma pass rate standard was 3%/2 mm and 10% dose threshold. Prediction models were constructed by extracting radiomic features based on dose documentation. Four machine learning algorithms, random forest, support vector machine, adaptive boosting, and gradient boosting decision tree were used to calculate the AUC value, sensitivity, and specificity respectively. The classification performance of the four prediction models was evaluated.Results:The sensitivity and specificity of the random forest, support vector machine, adaptive boosting, and gradient boosting decision tree models were 0.93, 0.85, 0.93, 0.96, 0.38, 0.69, 0.46, and 0.46, respectively. The AUC values were 0.81 and 0.82 for the random forest and adaptive boosting models, respectively, and 0.87 for the support vector machine and gradient boosting decision tree models.Conclusions:Machine learning method based on radiomics can be used to construct a prediction model of gamma pass rate for specific dosimetric verification of pelvic intensity-modulated radiotherapy. The classification performance of the support vector machine model and gradient boosting decision tree model is better than that of the random forest model and adaptive boosting model.
