Evaluation of Gd-EOB-DTPA MRI With Diffusion and Clinicopathologic Features for Predicting Microvascular Invasion in...

To develop and validate a prediction model combining Gd-EOB-DTPA-enhanced magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) parameters and clinicopathologic features for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC).

This study applied a retrospective method to collect preoperative MRI imaging data of patients with HCC from January 2021 to June 2025. All 279 patients (mean age of 58, M:F=203:76, 195 cases in training set and 84 cases in validation set) underwent MRI by Gd-EOB-DTPA and received DWI imaging scan before surgery. The MRI imaging features were observed, and the apparent diffusion coefficient (ADC) of the tumor solid region and tumor-to-liver parenchyma relative intensity ratio (RIR) were measured. LASSO logistic regression algorithm and multivariate analysis method were conducted to analyze the correlation between preoperative MRI imaging features and MVI. The prediction model was established, and the efficiency evaluation of the model was performed.

Among 279 patients, 66 cases (23.66%) were MVI-positive. The study subjects were assigned to a training set (195 cases) and a validation set (84 cases) at a 7:3 ratio. Twelve variables were selected by LASSO regression. Multivariate analysis identified RIR transitional phase (OR=0.21), tumor size (OR=4.52), and ADC (OR=0.63) as independent predictors of MVI (P=0.029, <0.001, 0.032). The model achieved an AUC of 0.85 in the training set and 0.83 in the validation set. The negative predictive value (NPV) was 0.94 and 0.90 in the training and validation sets, respectively, while the positive predictive value (PPV) was limited to 0.49 and 0.40. Calibration curve and decision curve analyses demonstrated good consistency and clinical utility.

On the basis of clinicopathologic features, MRI imaging features, and DWI parameters, this study preliminarily constructs a prediction model for positive MVI risk in HCC patients. The model exhibits good discrimination and a high NPV for effectively ruling out MVI, but its limited PPV warrants cautious interpretation of positive predictions due to a high false-positive rate.