Noninvasive Prediction of High Ki-67 Expression in Hepatocellular Carcinoma Using Multiparametric MRI and Clinical Biomarkers

Purpose: This study aimed to develop and validate a noninvasive multiparametric magnetic resonance imaging (MRI) model integrating hepatobiliary-phase T1 mapping (T1HBP), tumor-to-liver R2-star ratio (TLRR2*), and clinical biomarkers to predict high Ki-67 expression (>30%) in patients with hepatocellular carcinoma (HCC). Patients and Methods: In this retrospective study, 60 patients with histopathologically confirmed HCC who underwent preoperative multiparametric MRI-including T1 mapping, proton density fat fraction (PDFF), and R2-star sequences-were enrolled. Based on immunohistochemical analysis, patients were classified into high (n=22) and low (n=38) Ki-67 expression groups. Clinical data and quantitative MRI parameters were compared between groups. Univariate and multivariate logistic regression analyses were conducted to identify independent predictors of high Ki-67 expression. The diagnostic performance of each parameter and the combined model was evaluated using receiver operating characteristic (ROC) curve analysis. Results: Multivariate analysis identified serum total bilirubin (TBil; OR=1.109, p=0.032), T1HBP (OR=1.004, p=0.026), and TLRR2* (OR=5.428, p=0.034) as independent predictors of high Ki-67 expression. The multiparametric model incorporating TBil, T1HBP, and TLRR2* achieved superior predictive performance, with an area under the ROC curve (AUC) of 0.813 (95% CI: 0.704-0.923), significantly outperforming individual parameters (T1HBP AUC=0.682, TLRR2* AUC=0.671, TBil AUC=0.664; all p 0.80). Conclusion: The combined multiparametric MRI model incorporating T1HBP, TLRR2*and TBil provides a noninvasive approach for predicting high proliferative activity in HCC, representing a promising tool for preoperative risk stratification and personalized treatment planning.