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

Fan Zhang,1,2,* Gen Chen,1,* Mengqi Huang,1 Yang Yang,1 Zixiong Wang,1 Yaqi Shen,1 Yan Luo,1,* Xuemei Hu,1,* Zhen Li1 1Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People’s Republic of China; 2Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xuemei Hu, Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, Hubei, 430030, People’s Republic of China, Tel +86-13647201714, Email [email protected] Yan Luo, Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Qiaokou District, Wuhan, Hubei, 430030, People’s Republic of China, Tel +86-13476867839, Email [email protected]: 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.Keywords: hepatocellular carcinoma, magnetic resonance imaging, Ki-67, prognosis, biomarkers, tumor microenvironment