Clinicoradiomics models provide superior and more consistent performance for predicting microvascular invasion in hepatocellular carcinomaRadiomics Models Help Predict Microvascular Invasion in Liver Cancer

This systematic review and meta-analysis evaluated the diagnostic performance of radiomics-based models for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). MVI is a key determinant of prognosis in HCC, strongly associated with early recurrence and reduced survival following curative treatment. Accurate preoperative prediction of MVI remains challenging using conventional clinical and imaging features. Radiomics has emerged as a promising approach by enabling quantitative analysis of tumor heterogeneity from medical imaging. This study aimed to systematically evaluate the diagnostic performance of radiomics-based models for predicting MVI in HCC. A systematic review and meta-analysis were conducted, including 26 studies evaluating CT- and MRI-based radiomics models. Data on diagnostic performance were extracted, and pooled analyses were performed using random-effects models, with subgroup analyses by imaging modality and model type. Overall, radiomics models demonstrated good diagnostic performance, with a pooled AUC of 0.85 (95% CI 0.82-0.88). Clinicoradiomics models achieved higher and more consistent performance than radiomics-only models (MRI: 0.87 vs 0.85; CT: 0.87 vs 0.81). Among 3D-based models, the pooled AUC was 0.85 (95% CI 0.82-0.88). In conclusion, radiomics-based models show promising performance for preoperative prediction of MVI in HCC, particularly when combined with clinical variables. However, considerable heterogeneity and limited external validation highlight the need for methodological standardization and prospective multicenter validation before clinical implementation.