Dynamic contrast-enhanced MRI texture analysis predicts microvascular invasion in hepatocellular carcinoma before operation

Abstract

Objective To investigate the prognostic value of the texture analysis contrast-enhanced MR imaging (DCE-MRI) in predicting microvascular invasion in hepatocellular carcinoma (HCC) before operation. Methods Sixty patients with HCC confirmed by pathology in the Chinese Academy Medical Sciences from January 2014 to December 2016, were enrolled in our study retrospectively. According to the post-operative pathology, the patients were divided into positive microvascular invation[MVI(+)]group including 30 patients, and negative MVI[MVI(-)] group including 30 patients. All patients underwent normal MR and DCE-MRI before surgery. Sixty seven texture features were extracted from the original data of arterial phase (AP) and portal venous phase (PVP) of DCE-MRI. All data were calculated by using Omni-Kinetics (OK) software of the United States. The difference between MVI(+) group and MVI(-) group was statistically significant using the independent sample t test. The identified methods of the DCE-MR texture features in predicting MVI adopted the principal component analysis (PCA) and the establishing prediction model including dimensionality reduction, modeling, prediction and verification. The model was established by logistic regression method. According to the histopathology, 80% data of AP and PVP were used as training group [48 cases, MVI(+) and MVI(-) group 24 cases respectively], 20% as validation group[12 cases, MVI(+) and MVI(-) group 6 cases respectively]. The DCE-MRI images of AP and PVP were modeled and cross-referenced respectively, and the diagnostic efficiency of ROC evaluation model was adopted. Results There were 15 significant different texture features of the AP and three significant different texture features of the PVP between MVI(+) group and MVI(-) group respectively. The PCA method extracted the important DCE-MRI texture features and analyzed the 15 features of AP. The UPP and energy showed a good correlation (r>0.90), therefore the UPP were removed. Fourteen texture features were analyzed using the PCA method. There were four important texture features including the GLCM Correlation, Hara Variance, GLCM sum Variance and GLCM sum Entropy in the AP. Moreover, there were three important texture features including GLCM difference Entropy, Long Run Low Grey Level Emphasis and GLCM difference Variance in the PVP. Through the prediction model was established and crossly validated. There were three significant different texture features in the AP of DCE-MRI, including GLCM Correlation, GLCM Contrast and GLCM sum Entropy. And there were two significant different texture features in the PVP of DCE-MRI, including GLCM difference Variance and Long Run Low Grey Level Emphasis. In the training and validation group, the areas under the ROC of the AP model and PVP model were 0.774, 0.681, 0.889 and 0.611 respectively. The diagnosed accuracy rate of the AP model (83.30%, 10/12) was higher than that of the PVP model (42.00%, 5/12). Conclusion The DCE-MRI texture analysis technique could predict the MVI of HCC before operation, and the predictive accuracy of the AP texture feature was higher. Key words: Carcinoma,hepatocellular; Microvascular invasion; Texture analysis; Magnetic resonance imaging