Establishment and Validation of Diagnostic Model of Microvascular Invasion in Solitary Hepatocellular Carcinoma.

[BACKGROUND] The microvascular invasion (MVI) score evaluates the presence of MVI in patients with hepatocellular carcinoma (HCC) by integrating multiple factors associated with MVI. We aimed to establish a MVI scoring system for HCC based on the clinical characteristics and serum biomarkers of patients with HCC.

[METHODS] A total of 1027 patients with HCC hospitalized at Shandong Provincial Hospital from January 2016 to August 2021 were included and randomly divided into the development group and validation group at a ratio of 3:1. Univariable and multivariable logistic regression analyses were conducted to identify independent risk factors for MVI in HCC patients. Based on these independent risk factors, the preoperative MVI scoring system (diagnostic model) for HCC was established and verified. The receiver operating characteristic (ROC) curves, calibration curves and decision curve analyses (DCA) were employed to evaluate the discrimination and clinical application of the diagnostic model.

[RESULTS] Independent risk factors for MVI of HCC involved Hepatitis B virus infection (HBV), large tumor diameter, higher logarithm of Alpha-fetoprotein (Log AFP), higher logarithm of AFP-L3% (Log AFP-L3%), higher logarithm of protein induced by vitamin K absence or antagonist-II (Log PIVKA-II) and higher logarithm of Carbohydrate antigen 125 (Log CA125). The diagnostic model incorporating these six independent risk factors was finally established. The areas under the ROC curve (AUC) assessed by the nomogram in the development cohort and validation cohort were 0.806 (95% CI, 0.773-0.839) and 0.818 (95% CI, 0.763-0.874) respectively. The calibration curve revealed that the results predicted by our diagnostic model for MVI in HCC were highly consistent with the postoperative pathological outcomes. The DCA further indicated promising clinical application of the diagnostic model.

[CONCLUSION] An effective preoperative diagnostic model for MVI of HCC based on readily available tumor markers and clinical characteristics has been established, which is both clinically significant and easy to implement for diagnosing MVI.