Background and purpose: Microvascular invasion (MVI) is one of the most important risk factors for postoperative recurrence of hepatocellular carcinoma (HCC). Accurate prediction of MVI before operation affects the selection of treatment strategy. The purpose of this study was to explore the value of MRI T2-weighted imaging (T2WI) texture analysis in evaluating the MVI for HCC. Methods: T2WI findings and clinical data were retrospectively reviewed in 220 patients with HCC confirmed by pathology

and all patients underwent surgical operation. Then these patients were divided into the with MVI group and the without MVI group according to the results of pathology. The texture features of these lesions were statistically analyzed

including mean value

standard deviation

skewness

kurtosis

variance

energy

entropy

correlation

interia

inverse difference moment and contrast. Results: A total of 220 HCC patients were enrolled in the study

including 186 males and 34 females

aged from 27 to 84 (median age 54). There were 71 cases with MVI confirmed by pathology. On T2WI of these 220 HCC patients

93 cases showed with MVI while 127 patients did not have MVI; the sensitivity and specificity were 60.6% and 66.4%

respectively. The results of T2WI texture analysis for 220 HCC patients showed the values of kurtosis

inverse difference moment and correlation in the group with MVI were  higher compared to the group without MVI

while the values of entropy and interia in the group with MVI were lower compared to the group without MVI; there were significant differences in all these texture parameters between the two groups (P0.05). However

there was no significant difference in the rest of texture parameters between the two groups (P0.05). The values of area under curve (AUC) for texture parameters of kurtosis

entropy

interia

inverse difference moment and correlation were 0.621

0.318

0.355

0.698 and 0.677

respectively. For the diagnostic efficiency in the with MVI group

the values of AUC using T2WI

T2WI texture features

and the combination of T2WI and texture features were 0.635

0.719 and 0.782

respectively; the sensitivity and specificity of texture features and the combination of T2WI and texture features were 74.6% and 88.1%

70.5% and 74.5%

respectively. Conclusion: T2WI texture parameters are helpful for predicting MVI

and the diagnostic efficiency of the combination of T2WI and texture features is higher than that of T2WI or texture analysis.