| Chen Luqiao,Ni Qianxi,Li Xiaozhou,Cao Jinjia.Prediction of radiomics-based machine learning in dose verification of intensity-modulated pelvic radiotherapy[J].Chinese Journal of Radiological Medicine and Protection,2023,43(2):101-105 |
| Prediction of radiomics-based machine learning in dose verification of intensity-modulated pelvic radiotherapy |
| Received:October 21, 2022 |
| DOI：10.3760/cma.j.cn112271-20221021-00416 |
| KeyWords:Machine learning Intensity-modulated radiotherapy Radiomics Plevic Gamma pass rate |
| FundProject:湖南省科技创新计划资助项目（2021SK51116）；湖南省卫生健康委科研计划项目（202109031926）；南华大学研究生教改项目（213YXJ032） |
| Author Name | Affiliation | E-mail | | Chen Luqiao | School of Nuclear Science and Technology, University of South China, Hengyang 421001, China | | | Ni Qianxi | Department of Radiation Oncology, Hunan Cancer Hospital, Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, Changsha 410013, China | | | Li Xiaozhou | Department of Emergency Medicine, Second Xiangya Hospital, Central South University, Changsha 410001, China | | | Cao Jinjia | School of Nuclear Science and Technology, University of South China, Hengyang 421001, China | caojinjia@usc.edu.cn |
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| 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%/2mm 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. |
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