Computed tomography-based artificial intelligence for predicting preoperative microvascular invasion in hepatocellular carcinoma: a systematic review and meta-analysis

This meta-analysis evaluates the diagnostic performance of computed tomography (CT)-based artificial intelligence (AI) models versus radiologists for preoperative microvascular invasion (MVI) detection in hepatocellular carcinoma (HCC).

A systematic literature search was conducted in PubMed, Embase, and Web of Science to identify studies published up to February 2025 focusing on the diagnostic accuracy of CT-based AI models for the preoperative detection of MVI in HCC, compared with the diagnostic performance of radiologists. A bivariate random-effects model was employed to calculate the pooled sensitivity, specificity, and area under the curve (AUC), all presented with 95% confidence intervals (CIs). Heterogeneity among studies was assessed using the I² statistic. The methodological quality of included studies was evaluated using a modified version of the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool.

Of 918 identified studies, 32 studies with 3,709 cases were included. For the internal validation set, the pooled sensitivity, specificity, and AUC for detecting MVI in HCC were 0.83 (95% CI 0.79–0.87), 0.81 (95% CI 0.76–0.86), and 0.89 (95% CI 0.86–0.92), respectively. Radiologists achieved a sensitivity of 0.82 (95% CI 0.63–0.93), specificity of 0.65 (95% CI 0.45–0.81), and AUC of 0.80 (95% CI 0.77–0.84).

CT-based AI may have the potential to outperform radiologists in predicting MVI in HCC. However, existing evidence is limited by study heterogeneity and limited number of the direct comparison between AI and radiologists. Prospective multicenter studies are needed to validate its clinical utility.