Predictive Significance of Preoperative Fibrinogen-to-Albumin Ratio in Microvascular Invasion of Hepatocellular Carcinoma

Abstract

EP007 Purpose: Hepatocellular carcinoma (HCC) is a deadly disease with high postoperative recurrence and microvascular invasion (MVI) is a significant prognostic factor affecting overall survival in HCC patients after surgical resection. However, the diagnosis of MVI can only be determined by pathological examination of specimens. There is a lack of criterion in preoperative diagnosis of MVI. Accurate preoperative prediction of MVI is conducive to clinical decisions. In this study, we will develop a preoperative diagnostic model for MVI based on fibrinogen-to-albumin ratio (FAR). Methods: Data from 193 patients with HCC who underwent surgery at Beijing Hospital between January 2013 and October 2020 were retrospectively collected. Patients were grouped according to an optimal value of FAR. Logistic regression analysis was used to identify variables significantly associated with MVI that were then included in the nomogram. And the discrimination and calibration ability of the nomogram were evaluated by using R software. Results: MVI was confirmed in 88 (45.6%) patients by a pathological examination. Multivariate logistic regression analysis identified four risk factors independently associated with MVI: Tumor size [odds ratio (OR) = 3.263; 95% confidence interval (CI): 1.300–6.261; P = 0.012], serum α-fetoprotein (20–400 ng/mL, OR = 2.326; 95%CI: 1.026–5.271; P = 0.043; ≥400 ng/mL, OR = 2.818; 95%CI: 1.214–6.542; P = 0.016), total protein (OR = 1.107; 95%CI: 1.038–1.181; P = 0.002), and FAR (OR = 2.600; 95%CI: 1.079–6.261; P = 0.033). A nomogram incorporating statistically significant factors was developed and the AUROC was 0.755. Calibration curve with Unreliability test indicated favorable calibration (P = 0.956). Decision curve analysis revealed promising clinical application of the diagnostic nomogram. Conclusion: We have developed a preoperative prediction model for MVI in HCC patients based on FAR. The model could aid physicians in clinical treatment decision making.