Department of Radiology Zealand University Hospital, Roskilde, Denmark; Department of Radiology Aarhus University Hospital, Skejby, Denmark; Copenhagen University Hospital, Gentofte, Denmark.
Pulmonary Research Unit (PLUZ), Department of Internal Medicine, Zealand University Hospital, Naestved, Denmark; Institute for Regional Health Research, University of Southern Denmark, Odense, Denmark.
Eur J Radiol. 2021 May;138:109664. doi: 10.1016/j.ejrad.2021.109664. Epub 2021 Mar 18.
Distant metastases are found in the many of patients with lung cancer at time of diagnosis. Several diagnostic tools are available to distinguish between metastatic spread and benign lesions in the adrenal gland. However, all require additional diagnostic steps after the initial CT. The purpose of this study was to evaluate if texture analysis of CT-abnormal adrenal glands on the initial CT correctly differentiates between malignant and benign lesions in patients with confirmed lung cancer.
In this retrospective study 160 patients with endoscopic ultrasound-guided biopsy from the left adrenal gland and a contrast-enhanced CT in portal venous phase were assessed with texture analysis. A region of interest encircling the entire adrenal gland was used and from this dataset the slice with the largest cross section of the lesion was analyzed individually.
Several texture parameters showed statistically significantly difference between metastatic and benign lesions but with considerable between-groups overlaps in confidence intervals. Sensitivity and specificity were assessed using ROC-curves, and in univariate binary logistic regression the area under the curve ranged from 36 % (Kurtosis 0.5) to 69 % (Entropy 2.5) compared to 73 % in the best fitting model using multivariate binary logistic regression.
In lung cancer patients with abnormal adrenal gland at imaging, adrenal gland texture analyses appear not to have any role in discriminating benign from malignant lesions.
在诊断时,许多肺癌患者已经有远处转移。有几种诊断工具可用于区分肾上腺转移和良性病变。然而,所有这些工具都需要在初始 CT 后进行额外的诊断步骤。本研究旨在评估 CT 异常肾上腺的纹理分析是否能正确区分确诊肺癌患者的恶性和良性病变。
在这项回顾性研究中,对 160 例经内镜超声引导活检的左肾上腺患者和门静脉期增强 CT 进行了纹理分析。使用包含整个肾上腺的感兴趣区域,并对该数据集的最大病变横截面积的切片进行单独分析。
一些纹理参数在转移性和良性病变之间存在统计学显著差异,但置信区间内存在相当大的组间重叠。使用 ROC 曲线评估敏感性和特异性,在单变量二元逻辑回归中,曲线下面积范围为 36%(峰度 0.5)至 69%(熵 2.5),而在使用多元二元逻辑回归的最佳拟合模型中为 73%。
在影像学上有异常肾上腺的肺癌患者中,肾上腺纹理分析似乎不能区分良性和恶性病变。