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Objective: To investigate the non-invasive prediction of hepatocellular carcinoma (HCC) with vessels encapsulating tumor clusters (VETC) based on qualitative and quantitative imaging features of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI. Methods: 109 patients with pathologically confirmed HCC who underwent Gd-EOB-DTPA enhanced MRI and immunochemical staining for CD34 were retrospectively evaluated in our institution (the first affiliated hospital of Soochow university). Pre-operative imaging features of Gd-EOB-DTPA-enhanced MRI were qualitatively and quantitatively reviewed by radiologists. Significant variables for differentiating the VETC-positive and VETC-negative HCCs were identified in univariate and multivariate analyses. Receiver operating characteristic (ROC) analysis was performed to determine the optimal cut-off values for quantitative variables. The nomogram based on the coefficient of multivariate analysis was constructed to evaluate the probability of VETC-positive HCCs. Results: The multivariate analysis showed that the serum AST level >40 U  l−1 (p = 0.007), non-rim diffuse and heterogeneous arterial phase hyperenhancement (p = 0.035), tumor-to-liver SI ratio of 1.135 or more on AP images (p = 0.001), and tumor-to-liver SI ratio of 0.585 or less on HBP images (p = 0.002) were significant predictors for predicting VETC-positive HCCs. Combing all four significant variables provided a diagnostic accuracy of 82.6%, sensitivity of 83.9%, specificity of 80.9% for identifying VETC status. The area under the receiver operating characteristics curve value of the logistical regression coefficient-based nomogram was 0.885 (95% confidence intervals, 0.824–0.946). Conclusion: Qualitative and quantitative imaging features of Gd-EOB-DTPA-enhanced MRI integrating laboratory examination can provide good diagnostic performance. Advances in knowledge: VETC is a novel identified microvascular pattern; associations between imaging features and VETC status have not been investigated. Pre-operative diagnosis of VETC status in HCC is essential to help predict the outcome of patients and make a decision for the therapeutic schedule.

Background Nuclear protein Ki-67 indicates the status of cell proliferation and has been regarded as an attractive biomarker for the prognosis of HCC. The aim of this study is to investigate which radiomics model derived from different sequences and phases of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI was superior to predict Ki-67 expression in hepatocellular carcinoma (HCC), then further to validate the optimal model for preoperative prediction of Ki-67 expression in HCC. Methods This retrospective study included 151 (training cohort: n = 103; validation cohort: n = 48) pathologically confirmed HCC patients. Radiomics features were extracted from the artery phase (AP), portal venous phase (PVP), hepatobiliary phase (HBP), and T2-weighted (T2W) images. A logistic regression with the least absolute shrinkage and selection operator (LASSO) regularization was used to select features to build a radiomics score (Rad-score). A final combined model including the optimal Rad-score and clinical risk factors was established. Receiver operating characteristic (ROC) curve analysis, Delong test and calibration curve were used to assess the predictive performance of the combined model. Decision cure analysis (DCA) was used to evaluate the clinical utility. Results The AP radiomics model with higher decision curve indicating added more net benefit, gave a better predictive performance than the HBP and T2W radiomic models. The combined model (AUC = 0.922 vs. 0.863) including AP Rad-score and serum AFP levels improved the predictive performance more than the AP radiomics model (AUC = 0.873 vs. 0.813) in the training and validation cohort. Calibration curve of the combined model showed a good agreement between the predicted and the actual probability. DCA of the validation cohort revealed that at a range threshold probability of 30–60%, the combined model added more net benefit compared with the AP radiomics model. Conclusions A combined model including AP Rad-score and serum AFP levels based on enhanced MRI can preoperatively predict Ki-67 expression in HCC.

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Background: Dual-phenotype hepatocellular carcinoma (DPHCC) is highly aggressive and difficult to distinguish from hepatocellular carcinoma (HCC). Purpose: To develop and validate clinical and radiomics models based on contrast-enhanced MRI for the preoperative diagnosis of DPHCC. Study type: Retrospective. Population: A total of 87 patients with DPHCC and 92 patients with non-DPHCC randomly divided into a training cohort (n = 125: 64 non-DPHCC; 61 DPHCC) and a validation cohort (n = 54: 28 non-DPHCC; 26 DPHCC). Field Strength/Sequence: A 3.0 T; dynamic contrast-enhanced MRI with time-resolved T1-weighted imaging sequence. Assessment: In the clinical model, the maximum tumor diameter and hepatitis B virus (HBV) were independent risk factors of DPHCC. In the radiomics model, a total of 1781 radiomics features were extracted from tumor volumes of interest (VOIs) in the arterial phase (AP) and portal venous phase (PP) images. For feature reduction and selection, Pearson correlation coefficient (PCC) and recursive feature elimination (RFE) were used. Clinical, AP, PP, and combined radiomics models were established using machine learning algorithms (support vector machine [SVM], logistic regression [LR], and logistic regression-least absolute shrinkage and selection operator [LR-LASSO]) and their discriminatory efficacy assessed and compared.Statistical Tests: The independent sample t test, Mann-Whitney U test, Chi-square test, regression analysis, receiver operating characteristic curve (ROC) analysis, Pearson correlation analysis, the Delong test. A P value < 0.05 was considered statistically significant. Results: In the validation cohort, the combined radiomics model (area under the curve [AUC] = 0.908, 95% confidence interval [CI]: 0.831-0.985) showed the highest diagnostic performance. The AUCs of the PP (AUC = 0.879, 95% CI: 0.779-0.979) and combined radiomics models were significantly higher than that of clinical model (AUC = 0.685, 95% CI: 0.526-0.844). There were no significant differences in AUC between AP or PP radiomics model and combined radiomics model (P = 0.286, 0.180 and 0.543). Conclusion: MRI radiomics models may be useful for discriminating DPHCC from non-DPHCC before surgery.