Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois; Quantitative Imaging Core Lab, Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, Illinois.
Acad Radiol. 2021 Apr;28(4):e93-e100. doi: 10.1016/j.acra.2020.03.004. Epub 2020 Apr 14.
To evaluate the effect of the anatomic size on 3D radiomic imaging features of the breast cancer hepatic metastases.
CT scans of 81 liver metastases from 54 patients with breast cancer were evaluated. Ten most common 3D radiomic features from the histogram and gray level co-occurrence matrix (GLCM) categories were calculated for the hepatic metastases (HM) and compared to normal liver (NL). The effect of size was evaluated by using linear mixed-effects regression models. The effect of size on different radiomic features was analyzed for both liver lesions and background liver.
Three-dimensional radiomic features from GLCM demonstrate an important size dependence. The texture-feature size dependence was found to be different among feature categories and between the HM and NL, thus demonstrating a discriminatory power for the tissue type. Significant difference in the slope was found for GLCM homogeneity (NL slope = 0.004, slope difference 95% confidence interval [CI] 0.06-0.1, p <0.001), contrast (NL slope = 45, slope difference 95% CI 205-305, p <0.001), correlation (NL slope = 0.04, slope difference 95% CI 0.11-0.21, p <0.001), and dissimilarity (NL slope = 0.7, slope difference 95% CI 3.6-5.4, p <0.001). The GLCM energy (NL slope = 0.002, slope difference 95% CI -0.0005 to -0.0003, p <0.007), and entropy (NL slope = 1.49, slope difference 95% CI 0.07-0.52, p <0.009) exhibited size-dependence for both NL and HM, although demonstrating a difference in the slope between themselves.
Radiomic features of breast cancer hepatic metastasis exhibited significant correlation with tumor size. This finding demonstrates the complex behavior of imaging features and the need to include feature-specific properties into radiomic models.
评估乳腺癌肝转移瘤的解剖学大小对 3D 放射组学成像特征的影响。
对 54 例乳腺癌患者的 81 个肝转移瘤的 CT 扫描进行评估。计算了直方图和灰度共生矩阵（GLCM）类别中最常见的 10 个 3D 放射组学特征，用于比较肝转移瘤（HM）和正常肝（NL）。采用线性混合效应回归模型评估大小的影响。分析了大小对不同肝病变和背景肝的放射组学特征的影响。
GLCM 的三维放射组学特征显示出重要的大小依赖性。发现纹理特征的大小依赖性在特征类别之间以及 HM 和 NL 之间存在差异，因此具有对组织类型的区分能力。GLCM 同质性（NL 斜率=0.004，斜率差异 95%置信区间[CI]0.06-0.1，p<0.001）、对比度（NL 斜率=45，斜率差异 95%CI205-305，p<0.001）、相关性（NL 斜率=0.04，斜率差异 95%CI0.11-0.21，p<0.001）和不相似性（NL 斜率=0.7，斜率差异 95%CI3.6-5.4，p<0.001）的差异具有统计学意义。GLCM 能量（NL 斜率=0.002，斜率差异 95%CI-0.0005 至-0.0003，p<0.007）和熵（NL 斜率=1.49，斜率差异 95%CI0.07-0.52，p<0.009）在 NL 和 HM 中均表现出大小依赖性，但斜率存在差异。
乳腺癌肝转移瘤的放射组学特征与肿瘤大小呈显著相关。这一发现表明成像特征的复杂行为，并需要将特征特异性属性纳入放射组学模型。
