CEUS ‐Based Microvascular Invasion Predictor in HCC : Improving Prognostic Stratification Following Thermal Ablation

BACKGROUND & AIMS: Microvascular invasion (MVI) critically impacts hepatocellular carcinoma (HCC) management. We aimed to develop and validate a deep learning model integrating contrast-enhanced ultrasound (CEUS) and clinical features for assessing MVI risk preoperatively, and to explore its prognostic associations in the thermal ablation (TA) cohort. METHODS: We enrolled 688 patients with solitary HCC ≤ 5 cm undergoing CEUS before surgical resection (SR) or TA. CEUS features were extracted via a vision transformer and integrated with clinical features to build an MVI classifier for SR patients. SHAP plots visualized key variables. Recurrence-free survival (RFS) was then compared between model-stratified risk groups in the TA cohort. Cox regression analysis identified risk factors for RFS. RESULTS: The model achieved AUCs of 0.89 (internal validation) and 0.81 (external validation) for MVI prediction. The decision curve analysis further confirmed the clinical utility. SHAP plots identified AFP-L3%, PIVKA-II, and tumour size as the most influential clinical features. Among the CEUS features, the most significant feature was the arterial-phase dynamic sequence. In the TA cohort, patients with model-predicted high-risk MVI had significantly lower 1-, 2- and 3-year RFS rates (75.5%, 54.9%, 39.4%) compared to those with low-risk (83.8%, 80.0%, 78.0%) (p < 0.001). High MVI risk predicted by the model (HR = 2.90; 95% CI: 1.69-4.96) and AFP-L3% (HR = 1.34; 95% CI: 1.06-1.69) were independently associated with RFS in TA patients. CONCLUSIONS: The model accurately predicted MVI in surgical candidates and showed exploratory prognostic value in TA patients. As a retrospective single-centre study, it warrants prospective validation.