Analysis of Complexity Metrics and Patient-Specific Quality Assurance Prediction Based on Domestically Made Accelerator URT-Linac 506c
Objective To study the relationship between the complexity metrics of volumetric modulated arc therapy(VMAT)plan and patient specific quality assurance(PSQA)based on the URT-Linac 506c accelerator.And to establish the machine learning models to predict and evaluate the PSQA results.Methods A total of 150 patients treated on URT-Linac 506c accelerator in the VMAT program were randomly selected as the research object,and all plans were verified by PSQA dose based on electronic portal imaging device detector on the accelerator.The gamma pass rate of dose verification results was analyzed with the threshold of 10%and the standard of 2%/2 mm.At the same time,based on multi-leaf collimator location and monitor unit,11 complexity parameters were extracted for each plan.The relationship between complexity metrics and the results from PSQA were studied,in the meantime,two tree-based machine learning models were established to predict PSQA results.Results The correlation analysis between plan complexity metrics and the PSQA results showed that they were not strictly linearly correlated,but the higher the complexity of the plan,the lower the PSQA pass rate.The gradient boosting decision tree(GBDT)model and random forest(RF)model had similar prediction level for PSQA with average prediction errors of were 0.55%of GBDT and 0.54%of RF respectively.Due to the imbalance in the distribution of PSQA results,the model with changed weights indeed could improve the prediction ability for plans with low pass rates.Conclusion For the domestically-made accelerator URT-Linac 506c,the two tree-based machine learning models showed in this study can provide certain assistance in predicting PSQA results.The establishment of a more accurate model needs to further improve the sample size of the collected patients.
URT-Linac 506c acceleratorplan complexity metricsmachine learningpatient specific quality assurance
国家科技期刊平台
NSTL
万方数据
维普
