Estimating Microvascular Invasion in Patients with Resectable Multinodular Hepatocellular Carcinoma by Using Preoperative Contrast-Enhanced MRI: Establishment and Validation of a Risk Score

Objective: To determine the preoperative clinicoradiological factors to predict microvascular invasion (MVI) in patients with resectable multinodular hepatocellular carcinoma (mHCC), and further to establish and validate a stratified risk scoring system. Methods: Two hundred and seventy-three patients with pathologically confirmed mHCC (≥ 2 lesions) without major vascular invasion and biliary tract tumor thrombosis, who underwent preoperative contrast-enhanced MRI and hepatectomy, were consecutively enrolled (training/validation cohort=193/80). Preoperative clinicoradiological variables were collected and analyzed. The multivariable logistic regression was performed to determine the independent predictors of MVI and create a risk score system. The C-index, calibration curve and decision curve were used to evaluate the performance of the risk score. A risk score-based prognostic stratification system was performed in mHCC patients. The risk score system was further verified in the validation cohort. Results: AFP > 400 ng/mL, presence of satellite nodule, mosaic architecture and increased total tumor diameter were independent predictors of MVI while fat in mass was an independent protective factor of MVI. The risk score yielded satisfactory C-index values (training/validation cohort: 0.777/0.758) and fitted well in calibration curves. Decision curve analysis further confirmed its clinical utility. Based on the risk score, mHCC patients were stratified into high-/low-MVI-risk subgroups with significantly different recurrence-free survival (both P < 0.001). Conclusion: The presented risk score incorporating clinicoradiological parameters could stratify mHCC patients into high-risk and low-risk subgroups and predict prognosis in patients with resectable mHCC. Keywords: hepatocellular carcinoma, prognosis, magnetic resonance imaging, statistical model