Preoperative prediction of microvascular invasion in hepatocellular carcinoma using gadoxetic acid-enhanced golden-angle radial sparse parallel dynamic MRI.

[PURPOSE] To investigate the predictive value of preoperative gadoxetic acid-enhanced quantitative golden-angle radial sparse parallel (GRASP) dynamic MRI for microvascular invasion (MVI) status in hepatocellular carcinoma (HCC).

[METHODS] This single-institution prospective study included patients with suspected HCC who underwent gadoxetic acid-enhanced GRASP dynamic MRI. Quantitative parameters derived from dynamic MRI of tumor and peritumoral regions, along with clinical and conventional radiological features, were collected. Univariate analysis, LASSO regression and multivariate logistic regression were used to identify independent MVI predictors. Three models were developed: dynamic contrast-enhanced MRI (DCE-MRI), clinicoradiographic, and integrated, and validated using 5-fold stratified cross-validation. The models were evaluated using performance metrics, clinical decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

[RESULTS] 96 patients (9 females; mean age 57.41 ± 11.07 years) with pathologically confirmed HCC were enrolled, including 35 MVI-positive cases. Univariate analysis revealed significant associations between MVI and DCE-MRI parameters, including extracellular volume, uptake fraction, uptake rate, and arterial delay of tumor. Extracellular volume and uptake rate of tumor, apparent diffusion coefficient (ADC), and α-fetoprotein were identified as independent MVI predictors. Incorporating DCE-MRI into clinicoradiographic model significantly improved the AUC from 0.669 to 0.817 (p = 0.02), with increased NRI (0.336, p = 0.04) and IDI (0.215, p = 0.002). The DCA shows that the integrated model had the highest net profit rate.

[CONCLUSIONS] Gadoxetic acid-enhanced quantitative GRASP dynamic MRI reflects the hemodynamic changes associated with MVI. The integration of these quantitative parameters with clinicoradiographic features improved preoperative MVI prediction, demonstrating their potential clinical value.