Department of Radiology, Niigata Prefectural Shibata Hospital, Shibata City, Niigata, Japan.
Department of Radiological Technology, Niigata University Graduate School of Health Sciences, Niigata City, Niigata, Japan.
J Appl Clin Med Phys. 2024 Jan;25(1):e14215. doi: 10.1002/acm2.14215. Epub 2023 Nov 21.
We sought to develop machine learning models to predict the results of patient-specific quality assurance (QA) for volumetric modulated arc therapy (VMAT), which were represented by several dose-evaluation metrics-including the gamma passing rates (GPRs)-and criteria based on the radiomic features of 3D dose distribution in a phantom.
A total of 4,250 radiomic features of 3D dose distribution in a cylindrical dummy phantom for 140 arcs from 106 clinical VMAT plans were extracted. We obtained the following dose-evaluation metrics: GPRs with global and local normalization, the dose difference (DD) in 1% and 2% passing rates (DD1% and DD2%) for 10% and 50% dose threshold, and the distance-to-agreement in 1-mm and 2-mm passing rates (DTA1 mm and DTA2 mm) for 0.5%/mm and 1.0%.mm dose gradient threshold determined by measurement using a diode array in patient-specific QA. The machine learning regression models for predicting the values of the dose-evaluation metrics using the radiomic features were developed based on the elastic net (EN) and extra trees (ET) models. The feature selection and tuning of hyperparameters were performed with nested cross-validation in which four-fold cross-validation is used within the inner loop, and the performance of each model was evaluated in terms of the root mean square error (RMSE), the mean absolute error (MAE), and Spearman's rank correlation coefficient.
The RMSE and MAE for the developed machine learning models ranged from <1% to nearly <10% depending on the dose-evaluation metric, the criteria, and dose and dose gradient thresholds used for both machine learning models. It was advantageous to focus on high dose region for predicating global GPR, DDs, and DTAs. For certain metrics and criteria, it was possible to create models applicable for patients' heterogeneity by training only with dose distributions in phantom.
The developed machine learning models showed high performance for predicting dose-evaluation metrics especially for high dose region depending on the metric and criteria. Our results demonstrate that the radiomic features of dose distribution can be considered good indicators of the plan complexity and useful in predicting measured dose evaluation metrics.
我们旨在开发机器学习模型，以预测基于体部旋转调强放射治疗（VMAT）三维剂量分布的放射组学特征的若干剂量评估指标（包括伽马通过率（GPR））和标准的患者特异性质量保证（QA）结果。
从 106 例临床 VMAT 计划的 140 个弧中提取了 106 例临床 VMAT 计划的 140 个弧和 4250 个圆柱型模体的三维剂量分布的放射组学特征。我们获得了以下剂量评估指标：全局和局部归一化的 GPR，1%和 2%通过率（DD1%和 DD2%）的剂量差（DD），10%和 50%剂量阈值的剂量差，以及 0.5%/mm 和 1.0%/mm 剂量梯度阈值的 1mm 和 2mm 通过率（DTA1mm 和 DTA2mm）的剂量一致性距离（DTA）。使用模体中二极管阵列的测量值确定患者特异性 QA 中的剂量评估指标。使用弹性网络（EN）和 ExtraTrees（ET）模型，基于放射组学特征开发了用于预测剂量评估指标值的机器学习回归模型。使用嵌套交叉验证进行特征选择和超参数调优，其中四折交叉验证在内循环中使用，使用 Spearman 秩相关系数评估每个模型的性能，以均方根误差（RMSE）、平均绝对误差（MAE）。
对于所开发的机器学习模型，根据剂量评估指标、标准以及用于机器学习模型的剂量和剂量梯度阈值，剂量评估指标的 RMSE 和 MAE 范围为<1%至近<10%。对于预测全局 GPR、DD 和 DTA，关注高剂量区域是有利的。对于某些指标和标准，仅通过训练模体中的剂量分布，就可以创建适用于患者异质性的模型。
所开发的机器学习模型在预测剂量评估指标方面表现出了较高的性能，特别是对于基于指标和标准的高剂量区域。我们的结果表明，剂量分布的放射组学特征可以被认为是计划复杂性的良好指标，并可用于预测测量的剂量评估指标。
