ArticleJournal of hepatocellular carcinoma2026
Noninvasive Prediction of High Ki-67 Expression in Hepatocellular Carcinoma Using Multiparametric MRI and Clinical Biomarkers.
Article in Journal of hepatocellular carcinoma, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Classification of hepatocellular carcinoma Ki-67 expression status by a simple combination of magnetic resonance slow diffusion coefficient (SDC) and apparent diffusion coefficient (ADC): initial promising results.Quantitative imaging in medicine and surgery · 2026Article
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9 authors.
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Abstract
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<0.05). Interobserver agreement for imaging measurements was excellent (ICC > 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.
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