Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, South Korea.
Department of Radiation Oncology, Gachon University Gil Medical Center, Incheon, South Korea.
Sci Rep. 2023 Jul 7;13(1):11027. doi: 10.1038/s41598-023-35570-1.
This study aims to evaluate the specific characteristics of various multileaf collimator (MLC) position errors that are correlated with the indices using dose distribution. The dose distribution was investigated using the gamma, structural similarity, and dosiomics indices. Cases from the American Association of Physicists in Medicine Task Group 119 were planned, and systematic and random MLC position errors were simulated. The indices were obtained from distribution maps and statistically significant indices were selected. The final model was determined when all values of the area under the curve, accuracy, precision, sensitivity, and specificity were higher than 0.8 (p < 0.05). The dose-volume histogram (DVH) relative percentage difference between the error-free and error datasets was examined to investigate clinical relations. Seven multivariate predictive models were finalized. The common significant dosiomics indices (GLCM Energy and GLRLM_LRHGE) can characterize the MLC position error. In addition, the finalized logistic regression model for MLC position error prediction showed excellent performance with AUC > 0.9. Furthermore, the results of the DVH were related to dosiomics analysis in that it reflects the characteristics of the MLC position error. It was also shown that dosiomics analysis could provide important information on localized dose-distribution differences in addition to DVH information.
本研究旨在评估与剂量分布相关的各型多叶准直器（MLC）位置误差的具体特征。使用伽玛、结构相似性和 dosiomics 指数对剂量分布进行了研究。对美国医学物理学家协会任务组 119 的病例进行了计划，并对系统和随机 MLC 位置误差进行了模拟。从分布图谱中获取指数，并选择具有统计学意义的指数。当曲线下面积、准确性、精密度、灵敏度和特异性的所有值均高于 0.8（p < 0.05）时，确定最终模型。检查误差数据集之间无误差和误差的剂量-体积直方图（DVH）相对百分比差异，以研究临床关系。最终确定了七个多变量预测模型。常见的显著 dosiomics 指数（GLCM Energy 和 GLRLM_LRHGE）可以描述 MLC 位置误差。此外，MLC 位置误差预测的最终逻辑回归模型表现出优异的性能，AUC > 0.9。此外，DVH 的结果与 dosiomics 分析有关，因为它反映了 MLC 位置误差的特征。还表明，除了 DVH 信息外，dosiomics 分析还可以提供关于局部剂量分布差异的重要信息。
