Radiomic Features Demonstrate High Diagnostic Accuracy for Predicting Ki-67 Expression in Hepatocellular CarcinomaRadiomic Features Help Predict Ki-67 Levels in Liver Cancer

This study aimed to evaluate the diagnostic value of radiomic features in predicting Ki-67 expression levels in hepatocellular carcinoma (HCC) through a meta-analysis. Electronic databases, including PubMed, Web of Science, the Cochrane Library, and Embase, were systematically searched for relevant clinical studies published through 20 August 2025. Studies using radiomic features to predict Ki-67 expression levels in patients with HCC were included. Sensitivity, specificity, and summary receiver operating characteristic curves were evaluated, and the area under the curve (AUC) was calculated. A total of 17 studies involving 1,708 patients with HCC were included. The pooled sensitivity was 0.87 (95% confidence interval [CI], 0.81-0.91), the pooled specificity was 0.79 (95% CI, 0.71-0.85), and the overall AUC was 0.90 (95% CI, 0.87-0.93). The pooled AUC values for Ki-67 cutoff values of 10% and >10% were 0.89 (95% CI, 0.86-0.92) and 0.90 (95% CI, 0.87-0.92), respectively. The AUC values for magnetic resonance imaging- and ultrasound-derived radiomic features were 0.88 (95% CI, 0.85-0.91) and 0.92 (95% CI, 0.89-0.94), respectively. The AUC for prediction models based on logistic regression was 0.89 (95% CI, 0.86-0.92). Radiomic features showed promising pooled diagnostic performance for predicting high Ki-67 expression in HCC. However, substantial heterogeneity was present among the included studies. Further standardized research is needed to validate these findings.