Texture analysis of contrast-enhanced magnetic resonance imaging predicts microvascular invasion in hepatocellular carcinoma

Background: Microvascular invasion is one of the important risk factors of postoperative recurrence of hepatocellular carcinoma. Texture analysis uses mathematical methods to analyze the gray's quantitative value and distribution of images, for quantifying the heterogeneity of tissues. Purpose: To investigate the feasibility of predicting MVI in HCC by analyzing the texture features of hepatic MRenhanced images. Methods: 110 patients with HCC who underwent MR-enhanced examinations were included in this study, were divided into MVI-positive group (n = 52) and MVI-negative group (n = 58) according to postoperative pathology. Clinical, pathological data and MR imaging features were collected. 11 texture parameters were selected from the gray histogram and gray level co-occurrence matrix (GLCM). Texture parameters of MR-enhanced images were calculated for statistical analysis. Results: There were statistically significant differences in tumor size, location, degree of differentiation, AFP level, signal, pseudocapsule, margin, peritumoral enhancement and intratumoral artery between MVI-positive group and MVI-negative group (P < 0.05). The AUC value of combining MR image features in prediction of MVI was 0.693(sensitivity and specificity: 53.8 %, 82.8 %, respectively). There were statistically significant differences in the texture parameters of GLCM between two groups (P < 0.05). The AUC value of combining texture parameters in prediction of MVI was 0.797 (sensitivity and specificity: 88.2 %, 62.7 %, respectively). Conclusion: MR image features and texture analysis have certain predictive effect on MVI, which are mutually verified and complementary. The texture parameters of GLCM could reflect tumor heterogeneity, which have great potential to help with preoperative decision. The combination of MR image features and texture analysis may improve the efficiency in prediction of MVI.

Automated summary

This study aimed to investigate the feasibility of predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) by analyzing the texture features of hepatic MR-enhanced images. The results showed that MR image features and texture analysis had a certain predictive effect on MVI, which were mutually verified and complementary. The texture parameters of the gray level co-occurrence matrix (GLCM) could reflect tumor heterogeneity and had the potential to assist with preoperative decision-making. The combination of MR image features and texture analysis may improve the efficiency in the prediction of MVI.