Texture Analysis Based on Gd-EOB-DTPA-Enhanced MRI for Identifying Vessels Encapsulating Tumor Clusters (VETC)-Positive Hepatocellular Carcinoma

• Published in: Journal of hepatocellular carcinoma 2021-01, Vol.8, p.349-359
• Main Authors: Fan, Yanfen, Yu, Yixing, Wang, Ximing, Hu, Mengjie, Du, Mingzhan, Guo, Lingchuan, Sun, Shifang, Hu, Chunhong
• Format: Article
• Language: English
• Subjects: diagnosis; Gadolinium; Gene expression; Hepatectomy; Hepatocellular carcinoma; imaging features; Liver cancer; Magnetic resonance imaging; Medical prognosis; Metastasis; Multivariate analysis; Original Research; Patients; preoperative; quantitative; Reproducibility; Respiration; Statistical analysis; Tumors
• ISSN: 2253-5969; 2253-5969
• DOI: 10.2147/JHC.S293755

To determine the potential findings associated with vessels encapsulating tumor clusters (VETC)-positive hepatocellular carcinoma (HCC), with particular emphasis on texture analysis based on gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI. Eighty-one patients with VETC-negative HCC and 52 patients with VETC-positive HCC who underwent Gd-EOB-DTPA-enhanced MRI before curative partial hepatectomy were retrospectively evaluated in our institution. MRI texture analysis was performed on arterial phase (AP) and hepatobiliary phase (HBP) images. The least absolute shrinkage and selection operator (LASSO) logistic regression was used to select texture features most useful for identifying VETC-positive HCC. Univariate and multivariate analyses were used to determine significant variables for identifying the VETC-positive HCC in clinical factors and the texture features of MRI. Receiver operating characteristic (ROC) analysis and DeLong test were performed to compare the identified performances of significant variables for identifying VETC-positive HCC. LASSO logistic regression selected 3 features in AP and HBP images, respectively. In multivariate analysis, the Log-sigma-4.0-mm-3D first-order Kurtosis derived from AP images (odds ratio [OR] = 4.128, = 0.001) and the Wavelet-LHL-GLDM Dependence Non Uniformity Normalized derived from HBP images (OR = 2.280, = 0.004) were independent significant variables associated with VETC-positive HCC. The combination of the two texture features for identifying VETC-positive HCC achieved an AUC value of 0.844 (95% confidence interval CI, 0.777, 0.910) with a sensitivity of 80.8% (95% CI, 70.1%, 91.5%) and specificity of 74.1% (95% CI, 64.5%, 83.6%). Texture analysis based on Gd-EOB-DTPA-enhanced MRI can help identify VETC-positive HCC.