Department of Radiology, University of Cambridge, Cambridge, CB2 0QQ, UK.
Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK.
Sci Rep. 2021 Apr 15;11(1):8262. doi: 10.1038/s41598-021-87598-w.
Radiomic image features are becoming a promising non-invasive method to obtain quantitative measurements for tumour classification and therapy response assessment in oncological research. However, despite its increasingly established application, there is a need for standardisation criteria and further validation of feature robustness with respect to imaging acquisition parameters. In this paper, the robustness of radiomic features extracted from computed tomography (CT) images is evaluated for liver tumour and muscle, comparing the values of the features in images reconstructed with two different slice thicknesses of 2.0 mm and 5.0 mm. Novel approaches are presented to address the intrinsic dependencies of texture radiomic features, choosing the optimal number of grey levels and correcting for the dependency on volume. With the optimal values and corrections, feature values are compared across thicknesses to identify reproducible features. Normalisation using muscle regions is also described as an alternative approach. With either method, a large fraction of features (75-90%) was found to be highly robust (< 25% difference). The analyses were performed on a homogeneous CT dataset of 43 patients with hepatocellular carcinoma, and consistent results were obtained for both tumour and muscle tissue. Finally, recommended guidelines are included for radiomic studies using variable slice thickness.
影像组学特征正成为一种很有前途的非侵入性方法，可以在肿瘤学研究中对肿瘤进行分类，并对治疗反应进行定量评估。然而，尽管其应用越来越广泛，但仍需要制定标准化标准，并进一步验证特征对成像采集参数的稳健性。在本文中，我们评估了从 CT 图像中提取的影像组学特征的稳健性，比较了两种不同层厚（2.0mm 和 5.0mm）重建图像中特征值的差异。本文提出了一些新方法来解决纹理影像组学特征的内在依赖性问题，选择了最佳的灰度级数量，并对体积依赖性进行了校正。使用最优值和校正值，在不同层厚之间比较特征值，以确定可重现的特征。还描述了使用肌肉区域进行归一化的另一种方法。使用这两种方法，发现很大一部分特征（75%至 90%）具有高度稳健性（<25%的差异）。分析是在一个包含 43 例肝细胞癌患者的同质 CT 数据集上进行的，肿瘤和肌肉组织均得到了一致的结果。最后，本文还包括了使用可变层厚进行影像组学研究的推荐指南。
