MRI Radiomics for Preoperative Microvascular Invasion Stratification in Hepatocellular Carcinoma: Comparative...

Microvascular invasion (MVI) represents a critical prognostic determinant in hepatocellular carcinoma (HCC), yet preoperative prediction remains challenging. MRI-based radiomics offers promise for noninvasive MVI stratification through quantitative feature extraction from intratumoral and peritumoral regions, with growing interest in integrating both compartments to capture tumor-host interactions. A comprehensive systematic literature search of PubMed, Embase, Scopus, and Web of Science through October 29, 2025, identified studies developing MRI-based radiomics or deep learning models for MVI prediction in HCC with histopathological reference standards. Random-effects meta-analysis synthesized diagnostic accuracy metrics. Risk of bias and methodological quality were assessed using QUADAS-2 and RQS-2. Thirteen retrospective studies (N = 3173 patients) met inclusion criteria. Combined intratumoral-peritumoral models demonstrated the highest diagnostic accuracy with pooled area under the receiver operating characteristic curve (AUC) of 0.83 (95% CI: 0.80-0.87). Peritumoral-only models (7 studies, n = 473) yielded AUC = 0.78, comparable to intratumoral-only approaches (8 studies, n = 633; AUC = 0.78). The incremental diagnostic benefit of combined models over peritumoral-only (delta AUC = 0.05, p = 0.110) and intratumoral-only models (delta AUC = 0.05, p = 0.097) was modest and not statistically significant. Peritumoral ring width did not significantly modify performance (p = 0.828). Subgroup analysis revealed that methodologically rigorous studies employing external validation achieved lower pooled AUC compared to internally validated investigations, suggesting optimism bias in reported metrics. MRI radiomics enables promising noninvasive MVI prediction in HCC, though combined intratumoral-peritumoral approaches provide limited incremental benefit over single-compartment models. Prospective multicenter external validation with standardized preprocessing and nested cross-validation protocols remain essential for clinical translation and evidence credibility.