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首页> 外文期刊>International Journal of Radiation Oncology, Biology, Physics >Error Detection in Intensity-Modulated Radiation Therapy Quality Assurance Using Radiomic Analysis of Gamma Distributions
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Error Detection in Intensity-Modulated Radiation Therapy Quality Assurance Using Radiomic Analysis of Gamma Distributions

机译：使用伽马分布的射线分析，强度调制放射治疗质量保证的误差检测

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PurposeTo improve the detection of errors in intensity-modulated radiation therapy (IMRT) with a novel method that uses quantitative image features from radiomics to analyze gamma distributions generated during patient specific quality assurance (QA). Methods and MaterialsOne hundred eighty-six IMRT beams from 23 patient treatments were delivered to a phantom and measured with electronic portal imaging device dosimetry. The treatments spanned a range of anatomic sites; half were head and neck treatments, and the other half were drawn from treatments for lung and rectal cancers, sarcoma, and glioblastoma. Planar gamma distributions, or gamma images, were calculated for each beam using the measured dose and calculated doses from the 3-dimensional treatment planning system under various scenarios: a plan without errors and plans with either simulated random or systematic multileaf collimator mispositioning errors. The gamma images were randomly divided into 2 sets: a training set for model development and testing set for validation. Radiomic features were calculated for each gamma image. Error detection models were developed by training logistic regression models on these radiomic features. The models were applied to the testing set to quantify their predictive utility, determined by calculating the area under the curve (AUC) of the receiver operator characteristic curve, and were compared with traditional threshold-based gamma analysis. ResultsThe AUC of the random multileaf collimator mispositioning model on the testing set was 0.761 compared with 0.512 for threshold-based gamma analysis. The AUC for the systematic mispositioning model was 0.717 versus 0.660 for threshold-based gamma analysis. Furthermore, the models could discriminate between the 2 types of errors simulated here, exhibiting AUCs of approximately 0.5 (equivalent to random guessing) when applied to the error they were not designed to detect. ConclusionsThe feasibility of error detection in patient-specific IMRT QA using radiomic analysis of QA images has been demonstrated. This methodology represents a substantial step forward for IMRT QA with improved sensitivity and specificity over current QA methods and the potential to distinguish between different types of errors.
机译：Purposeto改善了强度调制放射治疗（IMRT）中的误差的检测，该方法采用了一种新的方法，该方法使用来自射线学的定量图像特征来分析患者特定质量保证（QA）产生的伽马分布。方法和材料从23例患者治疗中的数百八十六个IMRT梁​​被输送到幻像，并用电子门户成像装置剂量测定。该治疗涉及一系列解剖部位;一半是头部和颈部治疗，另一半从肺癌和直肠癌，肉瘤和胶质母细胞瘤的治疗中抽出。使用测量剂量的每条梁计算平面伽马分布，或伽马图像，并在各种场景下从三维治疗计划系统计算的剂量计算：一个没有错误的计划，并且具有模拟随机或系统的多叶准直器失置错误的计划。将伽玛图像随机分为2组：用于验证的模型开发和测试集的训练集。针对每个伽马图像计算辐射瘤特征。通过在这些射出功能上训练逻辑回归模型开发出错误检测模型。将模型应用于测试集以量化其预测实用程序，通过计算接收器操作员特征曲线的曲线（AUC）下的区域来确定，并与传统的基于阈值的伽马分析进行比较。试验组的随机多叶准直流器失误模型的结果为0.761，与0.512用于基于阈值的伽马分析。系统失值模型的AUC为0.717，对于基于阈值的伽马分析。此外，模型可以在这里模拟的2种类型的错误之间区分，当应用于它们不被设计成检测时，展示约0.5（相当于随机猜测）的AUC。结论已经证实了使用QA图像的辐射学分析的患者特异性IMRT QA出差检测的可行性。该方法代表了IMRT QA的正常步骤，其具有改善的灵敏度和特异性对电流QA方法的特异性以及区分不同类型的误差的可能性。

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