中国医疗设备2024，Vol.39Issue(1)：29-34,6.DOI:10.3969/j.issn.1674-1633.2024.01.006
基于国产加速器URT-Linac 506c的计划复杂度分析和患者计划质量保证预测
Analysis of Complexity Metrics and Patient-Specific Quality Assurance Prediction Based on Domestically Made Accelerator URT-Linac 506c
摘要
Abstract
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.关键词
联影URT-Linac 506c加速器/计划复杂度/机器学习/患者计划质量保证Key words
URT-Linac 506c accelerator/plan complexity metrics/machine learning/patient specific quality assurance分类
医药卫生引用本文复制引用
祝鹤龄,杨波,祝起禛,梁永广,杨景茹,王贝,王嘉欣,邱杰..基于国产加速器URT-Linac 506c的计划复杂度分析和患者计划质量保证预测[J].中国医疗设备,2024,39(1):29-34,6.基金项目
中央高水平医院临床科研业务费资助(2022-PUMCH-B-116). （2022-PUMCH-B-116）
