To investigate the value of texture analysis based on contrast-enhanced T1-weighted images(CE-T1WI)in grading cerebral gliomas.

From October 2014 to October 2019, 29 patients with low grade gliomas(LGG)and 63 patients with high grade gliomas(HGG), which were confirmed by postoperative pathology in Affiliated Hospital of Yangzhou University, were retrospectively reviewed.MaZda software was used to extract texture features of CE-T1WI of gliomas, and then 9 histogram parameters(mean, variance, skewness, kurtosis, Pere.1%, Pere.10%, Pere.50%, Pere.90%, Pere.99%)between the two groups were compared.Multivariate logistic regression model was established with the parameters which showed statistical differences between the two groups, and then receiver operating characteristic curve(ROC)was drew to evaluate the efficiency of the model.

Three parameters(kurtosis, Pere.1%, Pere.10%)were higher in LGG than HGG(all P<0.05). Four parameters(mean, variance, Pere.90, Pere.99%)were lower in LGG than HGG(all P<0.05). There were no significant differences of two parameters(skewness, Pere.50%)between the two groups(both P>0.05). Among the parameters which showed statistically significant differences between the two groups, variance had the highest efficiency with sensitivity, specificity and area under the curve(AUC)of 79.4%, 86.2% and 0.878.The efficiency of the multivariate logistic regression model established with the seven parameters was better than that established with all the parameters, and its sensitivity, specificity and AUC were 87.3%, 79.3% and 0.882, respectively.

Histogram parameters based on CE-T1WI can effectively distinguish LGG from HGG, and the multivariate logistic regression model with histogram parameters which showed statistically significant difference between the two groups is more effective.