Texture Analysis of Adenomatous and Metastatic Adrenal Lesions on Native and Contrast-enhanced Computed Tomography

Abstract

Differentiating lipid-poor adrenal adenomas from adrenal metastases is not possible without performing a dedicated contrast-enhanced CT (CECT) protocol. Our purpose is to evaluate the possible role of texture analysis in classifying adrenal lesions. This is a retrospective study. We evaluated 33 patients with 47 metastases and 43 patients with 47 adenomas. Seven of the adenomas were lipid-poor, and the rest – lipid-rich adenomas. We used commercially available software for lesion segmentation and texture analysis on native and arterial phase CT images with a slice thickness of 2 mm. The segmentation was semi-automated, and features were computed on the resulting regions of interest (ROI) for each lesion on both native and arterial phases. Two conventional (HU standard deviation, Histogram Entropy) and six second-order texture features (GLCM – Homogeneity, Energy, Entropy log 10, Contrast, Dissimilarity, NGDLM–Busyness) were calculated. For statistical analysis the IBM SPSS 19 package was used to compare the following groups in both phases: 1. Adenomas--Metastasis; 2. Lipid-rich adenomas (LRA)--metastases; 3. Lipid-rich--lipid-poor adenomas (LPA); 4. Lipid-poor adenomas--metastases. There was a statistically significant difference in the distribution of some conventional (Histogram Entropy) and second-order features (GLCM – Homogeneity, Energy, Entropy log 10, Dissimilarity) in the first group on the native as well as on the enhanced images. The results were similar when comparing lipid-rich adenomas to metastases. Only native phase derived features were discriminative between lipid-rich and lipid-poor adenomas, with no difference between parameters on CECT. No difference was found between any of the texture features in lipid-poor adenomas and metastases for both phases. First- and second-order texture features were graded based on their potential for serving as classifier tools. Unenhanced features ranked higher. Further research and validation are needed to discover the most robust set of features for differentiating between lipid-poor adenomas and metastases.