CT Image-Based Texture Analysis to Predict Microvascular Invasion in Primary Hepatocellular Carcinoma.

Abstract

The objective of this study was to determine the clinical value of computed tomography (CT) image-based texture analysis in predicting microvascular invasion of primary hepatocellular carcinoma (HCC). CT images of patients with HCC from May 2017 to May 2019 confirmed by surgery and histopathology were retrospectively analyzed. Image features including tumor margin, tumor capsule, peritumoral enhancement, hypoattenuating halo, intratumoral arteries, and tumor-liver differences were assessed. All patients were divided into microvascular invasion (MVI)-negative group (n = 34) and MVI-positive group (n = 68). Preoperative CT images were further imported into MaZda software, where the regions of interest of the lesions were manually delineated. Texture features of lesions based on pre-contrast, arterial, portal, and equilibrium phase CT images were extracted. Thirty optimal texture parameters were selected from each phase by Fisher's coefficient (Fisher), classification error probability combined with average correlation coefficient (POE+ACC), and mutual information (MI). Finally, receiver operating characteristic curve analysis was performed. The results showed that the Edmonson-Steiner grades, tumor size, tumor margin, and intratumoral artery characteristics were significantly different between the two groups (P = 0.012, < 0.001, < 0.001, = 0.003, respectively). There were 58 parameters with significant differences between the MVI-negative and MVI-positive groups (P < 0.001 for all). Among them, 12, 14, 17, and 15 parameters were derived from the pre-contrast phase, arterial phase, portal phase, and equilibrium phase respectively. According to the ROC analysis, optimal texture parameters based on the pre-contrast, arterial, portal, and equilibrium phases were 135dr_GLevNonU (AUC, 0.766; the cutoff value, 1055.00), Vertl_RLNonUni (AUC, 0.764; the cutoff value, 5974.38), 45dgr_GLevNonU (AUC, 0.762; the cutoff value, 924.34), and Vertl_RLNonUni (AUC, 0.754; the cutoff value, 4868.80), respectively. Texture analysis of preoperative CT images may be used as a non-invasive method to predict microvascular invasion in patients with primary hepatocellular carcinomas, and further to guide the treatment and evaluate prognosis. The most valuable parameters were derived from the gray-level run-length matrix.