Preoperative prediction of hepatocellular carcinoma histological grade using MRI-based artificial intelligence models: A systematic review and meta-analysis.

OBJECTIVE: To evaluate the diagnostic performance of magnetic resonance imaging (MRI)-based artificial intelligence (AI) models in preoperatively predicting hepatocellular carcinoma (HCC) histological grade. METHODS: We conducted this systematic review and meta-analysis in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA) guidelines. PubMed, Embase, and Web of Science were searched from inception to 10 February 2026. Bivariate random-effects models were used to pool diagnostic metrics. Quality and evidence certainty were assessed using revised QUADAS-AI and GRADE. RESULTS: Eighteen studies were included. In internal validation (n = 16), pooled sensitivity, specificity, diagnostic odds ratio (DOR), and area under the curve (AUC) for predicting high-grade HCC were 0.78 (95% CI: 0.71-0.84), 0.80 (95% CI: 0.75-0.85), 15.98 (95% CI: 9.86-25.91), and 0.85 (95% CI: 0.81-0.90), respectively. Sensitivity analysis using median-performing models yielded a lower AUC of 0.80 (95% CI: 0.76-0.85), indicating the primary estimates are an optimistic upper bound. In external validation (n = 6), pooled sensitivity and specificity declined to 0.70 (95% CI: 0.62-0.77) and 0.74 (95% CI: 0.69-0.79), respectively; the DOR was 6.91 (95% CI: 4.51-10.59) and the AUC was 0.75 (95% CI: 0.70-0.79). Deep learning models showed significantly higher sensitivity than machine learning models (0.88 vs. 0.72, P = 0.018). At a 20% pre-test probability, the positive post-test probabilities were 48% (internal) and 40% (external). CONCLUSIONS: MRI-based AI models show potential utility in preoperatively predicting HCC grade; however, given the very low evidence certainty and significant heterogeneity, their current clinical application warrants caution.