Preoperative prediction of microvascular invasion in small hepatocellular carcinoma (≤ 3 cm) by leveraging cross-phase variations of MRI contrast phases

Noninvasive preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) remains a significant clinical challenge worldwide. This study encoded feature variations across multi-phase MRI sequences for the preoperative prediction of MVI. Data were retrospectively examined for 115 consecutive patients (mean age: 57.2 ± 10.7 years; 95 men) who had undergone surgical resection for HCC (tumor diameter less than 3 cm). All of them underwent preoperative MR examination and were randomly partitioned into training and testing cohorts at an 8:2 ratio. Peritumoral expansions (3 mm, 5 mm, and 10 mm) were generated from manually segmented intratumoral regions of interest (ROIs) across the arterial, venous, and delayed phases. Radiomics features were subsequently extracted from both the original and expanded ROIs. We then evaluated the predictive performance of five encoding methods combined with four machine learning classifiers using both clinical characteristics and radiomics features. Multi-phase models incorporating temporal encoding methods consistently outperformed single-phase models and those relying solely on clinical characteristics. Among all evaluated combinations, the Fourier-inspired temporal encoding method combined with an ExtraTrees classifier, applied to the original tumoral ROI with a 5-mm peritumoral expansion, achieved the best diagnostic performance, with an AUC of 0.850 (95% CI: 0.675–0.978). While radiologists routinely monitor multi-phase contrast dynamics qualitatively, our findings demonstrate that these cross-phase variations provide quantifiable, highly predictive information for MVI. This framework represents a promising, noninvasive quantitative tool for preoperative MVI risk stratification.