Department of Radiology, University of Southern California, Los Angeles, CA, USA.
The Phantom Laboratory, Greenwich, NY, USA.
J Appl Clin Med Phys. 2021 Feb;22(2):98-107. doi: 10.1002/acm2.13162. Epub 2021 Jan 12.
The objective of this study was to evaluate the robustness and reproducibility of computed tomography-based texture analysis (CTTA) metrics extracted from CT images of a customized texture phantom built for assessing the association of texture metrics to three-dimensional (3D) printed progressively increasing textural heterogeneity.
A custom-built 3D-printed texture phantom comprising of six texture patterns was used to evaluate the robustness and reproducibility of a radiomics panel under a variety of routine abdominal imaging protocols. The phantom was scanned on four CT scanners (Philips, Canon, GE, and Siemens) to assess reproducibility. The robustness assessment was conducted by imaging the texture phantom across different CT imaging parameters such as slice thickness, field of view (FOV), tube voltage, and tube current for each scanner. The texture panel comprised of 387 features belonging to 15 subgroups of texture extraction methods (e.g., Gray-level Co-occurrence Matrix: GLCM). Twelve unique image settings were tested on all the four scanners (e.g., FOV125). Interclass correlation two-way mixed with absolute agreement (ICC3) was used to assess the robustness and reproducibility of radiomic features. Linear regression was used to test the association between change in radiomic features and increased texture heterogeneity. Results were summarized in heat maps.
A total of 5612 (23.2%) of 24 090 features showed excellent robustness and reproducibility (ICC ≥ 0.9). Intensity, GLCM 3D, and gray-level run length matrix (GLRLM) 3D features showed best performance. Among imaging variables, changes in slice thickness affected all metrics more intensely compared to other imaging variables in reducing the ICC3. From the analysis of linear trend effect of the CTTA metrics, the top three metrics with high linear correlations across all scanners and scanning settings were from the GLRLM 2D/3D and discrete cosine transform (DCT) texture family.
The choice of scanner and imaging protocols affect texture metrics. Furthermore, not all CTTA metrics have a linear association with linearly varying texture patterns.
本研究旨在评估从为评估纹理指标与三维（3D）打印渐进增加的纹理异质性之间的相关性而构建的定制纹理体模的 CT 图像中提取的基于 CT 的纹理分析（CTTA）指标的稳健性和可重复性。
使用定制的 3D 打印纹理体模，该体模由六个纹理图案组成，以评估在各种常规腹部成像方案下，放射组学面板的可重复性和稳健性。该体模在四台 CT 扫描仪（飞利浦、佳能、GE 和西门子）上进行扫描，以评估可重复性。稳健性评估是通过在不同的 CT 成像参数下对纹理体模进行成像来进行的，例如切片厚度、视野（FOV）、管电压和管电流。纹理面板由属于 15 个纹理提取方法亚组（例如灰度共生矩阵：GLCM）的 387 个特征组成。在所有四台扫描仪上测试了 12 个独特的图像设置（例如，FOV125）。使用双向混合绝对一致性的组内相关系数（ICC3）来评估放射组学特征的稳健性和可重复性。线性回归用于测试放射组学特征的变化与纹理异质性增加之间的关联。结果总结在热图中。
在 24090 个特征中，共有 5612 个（23.2%）特征表现出优异的稳健性和可重复性（ICC≥0.9）。强度、GLCM 3D 和灰度游程长度矩阵（GLRLM）3D 特征表现出最佳性能。在成像变量中，与其他成像变量相比，切片厚度的变化更强烈地影响所有指标，从而降低了 ICC3。从 CTTA 指标的线性趋势效应分析中，可以看出在所有扫描仪和扫描设置中具有高度线性相关性的前三个指标来自 GLRLM 2D/3D 和离散余弦变换（DCT）纹理族。
扫描仪和成像协议的选择会影响纹理指标。此外，并非所有 CTTA 指标都与线性变化的纹理模式具有线性关联。
