Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Department of Oncology, Chengdu Second People's Hospital, Chengdu, Sichuan, China.
Technol Cancer Res Treat. 2021 Jan-Dec;20:15330338211036325. doi: 10.1177/15330338211036325.
In radiotherapy, geometric indices are often used to evaluate the accuracy of contouring. However, the ability of geometric indices to identify the error of contouring results is limited primarily because they do not consider the clinical background. The purpose of this study is to investigate the relationship between geometric and clinical dosimetric indices.
Four different types of targets were selected (C-shaped target, oropharyngeal cancer, metastatic spine cancer, and prostate cancer), and the translation, scaling, rotation, and sine function transformation were performed with the software Python to introduce systematic and random errors. The transformed contours were regarded as reference contours. Dosimetric indices were obtained from the original dose distribution of the radiotherapy plan. The correlations between geometric and dosimetric indices were quantified by linear regression.
The correlations between the geometric and dosimetric indices were inconsistent. For systematic errors, and with the exception of the sine function transformation (R: 0.023-0.04, 0.05), the geometric transformations of the C-shaped target were correlated with the D98% and D (R: 0.689-0.988), 80% of which were < 0.001. For the random errors, the correlations obtained by the all targets were R > 0.384, < 0.05. The Wilcoxon signed-rank test was used to compare the spatial direction resolution capability of geometric indices in different directions of the C-shaped target (with systematic errors), and the results showed only the volumetric geometric indices with < 0.05.
Clinically, an assessment of the contour accuracy of the region-of-interest is not feasible based on geometric indices alone. Dosimetric indices should be added to the evaluations of the accuracy of the delineation results, which can be helpful for explaining the clinical dose response relationship of delineation more comprehensively and accurately.
在放射治疗中，几何指标常用于评估勾画的准确性。然而，由于这些指标没有考虑临床背景，因此其识别勾画结果误差的能力有限。本研究旨在探讨几何和临床剂量学指标之间的关系。
选择了 4 种不同类型的靶区（C 形靶区、口咽癌、转移性脊柱癌和前列腺癌），使用 Python 软件进行平移、缩放、旋转和正弦函数变换，以引入系统和随机误差。将变换后的轮廓视为参考轮廓。从放射治疗计划的原始剂量分布中获得剂量学指标。通过线性回归量化几何和剂量学指标之间的相关性。
几何和剂量学指标之间的相关性不一致。对于系统误差，除了正弦函数变换（R：0.023-0.04， 0.05）外，C 形靶区的几何变换与 D98%和 D（R：0.689-0.988）相关，其中 80%为 < 0.001。对于随机误差，所有靶区的相关性均为 R > 0.384， < 0.05。采用 Wilcoxon 符号秩检验比较 C 形靶区（存在系统误差）不同方向上几何指标的空间方向分辨率能力，结果仅显示体积几何指标的 < 0.05。
在临床实践中，仅基于几何指标评估感兴趣区域的勾画准确性是不可行的。应在勾画结果准确性的评估中加入剂量学指标，这有助于更全面、准确地解释勾画的临床剂量反应关系。
