Graduate School of Medicine, Hokkaido University, North 15, West 7, Kita-ku, Sapporo, Hokkaido 060-8638, Japan.
Graduate School of Engineering, Hokkaido University, North 13, West 8, Kita-ku, Sapporo, Hokkaido 060-8628, Japan.
J Radiat Res. 2021 Sep 13;62(5):926-933. doi: 10.1093/jrr/rrab054.
The purpose of this work is to show the usefulness of a prediction method of tumor location based on partial least squares regression (PLSR) using multiple fiducial markers. The trajectory data of respiratory motion of four internal fiducial markers inserted in lungs were used for the analysis. The position of one of the four markers was assumed to be the tumor position and was predicted by other three fiducial markers. Regression coefficients for prediction of the position of the tumor-assumed marker from the fiducial markers' positions is derived by PLSR. The tracking error and the gating error were evaluated assuming two possible variations. First, the variation of the position definition of the tumor and the markers on treatment planning computed tomograhy (CT) images. Second, the intra-fractional anatomical variation which leads the distance change between the tumor and markers during the course of treatment. For comparison, rigid predictions and ordinally multiple linear regression (MLR) predictions were also evaluated. The tracking and gating errors of PLSR prediction were smaller than those of other prediction methods. Ninety-fifth percentile of tracking/gating error in all trials were 3.7/4.1 mm, respectively in PLSR prediction for superior-inferior direction. The results suggested that PLSR prediction was robust to variations, and clinically applicable accuracy could be achievable for targeting tumors.
本研究旨在展示一种基于多元基准标记物的偏最小二乘回归（PLSR）预测肿瘤位置的方法的有效性。分析使用了四个插入肺部的内部基准标记物的呼吸运动轨迹数据。假设四个标记物中的一个位置为肿瘤位置，并由其他三个基准标记物预测。通过 PLSR 得出预测肿瘤假定标记物位置的回归系数。分别假设两种可能的变化来评估跟踪误差和门控误差。首先，肿瘤和标记物在治疗计划计算机断层扫描（CT）图像上的位置定义变化。其次，分次内解剖变化导致治疗过程中肿瘤和标记物之间的距离变化。为了进行比较，还评估了刚性预测和有序多元线性回归（MLR）预测。PLSR 预测的跟踪和门控误差小于其他预测方法。在所有试验中，PLSR 预测在上下方向上的跟踪/门控误差的第 95 百分位数分别为 3.7/4.1mm。结果表明，PLSR 预测对变化具有鲁棒性，并且可以实现针对肿瘤的临床应用精度。
