To explore the clinical value of texture analysis of MR images (multiphase Gd-EOB-DTPA-enhanced MRI and T2 weighted imaging (T2WI) to identify the differentiated degree of hepatocellular carcinoma (HCC). One hundred four participants were enrolled in this retrospective study. Each participant performed preoperative Gd-EOB-DTPA-enhanced MR scanning. Texture features were analyzed by MaZda, and B11 program was used for data analysis and classification. The diagnosis efficiencies of texture features and conventional imaging features in identifying the differentiated degree of HCC were assessed by receiver operating characteristic analysis. The relationship between texture features and differentiated degree of HCC was evaluated by Spearman’s correlation coefficient. The grey-level co-occurrence matrix -based texture features were most frequently extracted and the nonlinear discriminant analysis was excellent with the misclassification rate ranging from 3.33 to 14.93%. The area under the curve (AUC) of the combined texture features between poorly- and well-differentiated HCC, poorly- and moderately-differentiated HCC, moderately- and well-differentiated HCC was 0.812, 0.879 and 0.808 respectively, while the AUC of tumor size was 0.649, 0.660 and 0.517 respectively. The tumor size was significantly different between poorly- and moderately-HCC (p = 0.014). The COMBINE AUC values were not increased with tumor size combined. Texture analysis of Gd-EOB-DTPA-enhanced MRI and T2WI was valuable and might be a promising method in identifying the differentiated degree of HCC. The poorly-differentiated HCC was more heterogeneous than well- and moderately-differentiated HCC.

中文翻译：

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MR图像的纹理分析，以鉴定肝细胞癌的分化程度：一项回顾性研究。

基于灰度共生矩阵的纹理特征最常被提取，​​并且非线性判别分析非常出色，误分类率在3.33％至14.93％之间。分化差的和高度分化的肝癌，分化差的和中等分化的肝癌，分化程度中等和好的分化的肝癌之间的组合纹理特征的曲线下面积（AUC）分别为0.812、0.879和0.808，而肿瘤的AUC大小分别为0.649、0.660和0.517。弱和中度肝癌的肿瘤大小显着不同（p = 0.014）。合并肿瘤大小后，COMBINE AUC值未增加。Gd-EOB-DTPA增强的MRI和T2WI的质构分析非常有价值，可能是鉴定HCC分化程度的有前途的方法。