Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington.
Department of Radiation Oncology, University of Washington School of Medicine, Seattle, Washington; Department of Radiology, University of Washington School of Medicine, Seattle, Washington.
Int J Radiat Oncol Biol Phys. 2018 Sep 1;102(1):219-228. doi: 10.1016/j.ijrobp.2018.05.033. Epub 2018 May 19.
To 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).
One 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.
The 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.
The 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.
利用一种新方法，通过分析在患者特定质量保证（QA）期间生成的伽马分布来提高强度调制放射治疗（IMRT）中的误差检测。
将 23 个患者治疗中的 186 个 IMRT 射束传输到一个体模中，并使用电子门户成像设备剂量测定法进行测量。这些治疗涵盖了一系列解剖部位；一半是头颈部治疗，另一半来自肺癌、直肠癌、肉瘤和胶质母细胞瘤的治疗。在各种情况下，使用测量剂量和三维治疗计划系统计算的剂量为每个射束计算平面伽马分布或伽马图像：没有误差的计划以及具有模拟随机或系统多叶准直器错位误差的计划。伽马图像随机分为 2 组：用于模型开发的训练集和用于验证的测试集。为每个伽马图像计算了放射组学特征。通过在这些放射组学特征上训练逻辑回归模型来开发误差检测模型。将模型应用于测试集以量化其预测能力，通过计算接收器操作特性曲线的曲线下面积（AUC）来确定，并且与传统基于阈值的伽马分析进行比较。
在测试集上，随机多叶准直器错位模型的 AUC 为 0.761，而基于阈值的伽马分析的 AUC 为 0.512。系统错位模型的 AUC 为 0.717，而基于阈值的伽马分析的 AUC 为 0.660。此外，当应用于未设计用于检测的误差时，这些模型可以区分这里模拟的两种误差，其 AUC 约为 0.5（相当于随机猜测）。
使用 QA 图像的放射组学分析来检测患者特定的 IMRT QA 中的误差的可行性已经得到证明。这种方法代表了 IMRT QA 的重大进展，与当前 QA 方法相比具有更高的灵敏度和特异性，并有可能区分不同类型的误差。
