Predictive Efficacy of a Combined Triphasic CT Radiomics and Clinical Feature Model for Ki-67 Expression in Hepatocellular Carcinoma.

Objective: Ki-67 is a well-established biomarker for tumor aggressiveness and poor prognosis in hepatocellular carcinoma (HCC). A reliable non-invasive method for preoperative Ki-67 assessment is clinically needed for risk stratification and individualized treatment. This study aimed to develop and validate a prediction model integrating triphasic contrast-enhanced CT radiomics with clinical features for preoperative Ki-67 expression status in HCC. Materials and Methods: This retrospective dual-center study enrolled 200 patients with 213 pathologically confirmed HCC lesions, Ki-67 expression was dichotomized as high (Ki-67 >20%) and low (≤20%) based on established clinical criteria. Radiomic features were extracted from arterial, portal venous, and delayed phases. After rigorous feature selection, logistic regression was used to construct single-phase models, a multi-phase radiomics fusion model, a clinical model, and a combined clinical-radiomics fusion model. Performance was assessed by area under the curve, net reclassification index, integrated discrimination improvement, and decision curve analysis. Results: The combined fusion model showed robust discrimination, with AUCs of 0.866 and 0.824 in the training and internal test sets, respectively. In independent external validation (n=64), it achieved an AUC of 0.829 (95% CI: 0.709-0.948), significantly outperforming the arterial phase model (AUC=0.713, Conclusion: The fusion model integrating multi-phase CECT radiomic features with clinical indicators provides an effective, non-invasive tool for preoperative prediction of Ki-67 expression in HCC. It may facilitate risk stratification and inform individualized treatment planning in clinical practice.