Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Department Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Clin Radiol. 2020 Jun;75(6):479.e17-479.e22. doi: 10.1016/j.crad.2020.01.012. Epub 2020 Feb 20.
To determine the value of contrast-enhanced computed tomography (CT)-derived radiomic features in the preoperative prediction of Ki-67 expression in adrenocortical carcinoma (ACC) and to detect significant associations between radiomic features and Ki-67 expression in ACC.
For this retrospective analysis, patients with histopathologically proven ACC were reviewed. Radiomic features were extracted for all patients from the preoperative contrast-enhanced abdominal CT images. Statistical analysis identified the radiomic features predicting the Ki-67 index in ACC and analysed the correlation with the Ki-67 index.
Fifty-three cases of ACC that met eligibility criteria were identified and analysed. Of the radiomic features analysed, 10 showed statistically significant differences between the high and low Ki-67 expression subgroups. Multivariate linear regression analysis yielded a predictive model showing a significant association between radiomic signature and Ki-67 expression status in ACC (R=0.67, adjusted R=0.462, p=0.002). Further analysis of the independent predictors showed statistically significant correlation between Ki-67 expression and shape flatness, elongation, and grey-level long run emphasis (p=0.002, 0.01, and 0.04, respectively). The area under the curve for identification of high Ki-67 expression status was 0.78 for shape flatness and 0.7 for shape elongation.
Radiomic features derived from preoperative contrast-enhanced CT images show encouraging results in the prediction of the Ki-67 index in patients with ACC. Morphological features, such as shape flatness and elongation, were superior to other radiomic features in the detection of high Ki-67 expression.
确定对比增强计算机断层扫描（CT）衍生的放射组学特征在术前预测肾上腺皮质癌（ACC）中 Ki-67 表达的价值，并检测 ACC 中放射组学特征与 Ki-67 表达之间的显著相关性。
这项回顾性分析回顾了经组织病理学证实的 ACC 患者。从所有患者的术前增强腹部 CT 图像中提取放射组学特征。统计分析确定了预测 ACC 中 Ki-67 指数的放射组学特征，并分析了与 Ki-67 指数的相关性。
确定并分析了符合入选标准的 53 例 ACC 病例。在分析的放射组学特征中，有 10 个特征在 Ki-67 高表达亚组和低表达亚组之间存在统计学差异。多变量线性回归分析得出了一个预测模型，显示放射组学特征与 ACC 中 Ki-67 表达状态之间存在显著相关性（R=0.67，调整 R=0.462，p=0.002）。对独立预测因子的进一步分析显示，Ki-67 表达与形状平坦度、伸长率和灰度长运行强调之间存在统计学显著相关性（p=0.002、0.01 和 0.04）。用于识别高 Ki-67 表达状态的曲线下面积为形状平坦度的 0.78 和形状伸长率的 0.7。
术前增强 CT 图像衍生的放射组学特征在预测 ACC 患者的 Ki-67 指数方面取得了令人鼓舞的结果。形态特征，如形状平坦度和伸长率，在检测高 Ki-67 表达方面优于其他放射组学特征。
