Two Nomograms to Select Hepatocellular Carcinoma Patients with Macroscopic Vascular Invasion for Hepatic Resection

J Cancer. 2018 Sep 7;9(18):3287-3294. doi: 10.7150/jca.25899. eCollection 2018.

Abstract

Background: Hepatocellular carcinoma (HCC) patients with macroscopic vascular invasion (MaVI) have limited lifespans. According to recent studies, surgical treatment may be the most promising option. However, the current staging system does not select patients who will benefit most from hepatic resection. Study design: A total of 123 patients undergoing hepatic resection for HCC with macroscopic vascular invasion (MaVI) between 2010 and 2014 at The Third Affiliated Hospital of Sun Yat-sen University were selected. We developed nomograms for overall survival (OS) and recurrence-free survival (RFS) using a Cox proportional hazards model. We assessed nomogram model performance based on the concordance index (C-index) and a calibration plot. Results: The 1- and 3-year overall survival (OS) rates for all patients were 84% and 71%, respectively. Correspondingly, the 1- and 3-year recurrence-free survival (RFS) rates were 55% and 35%, respectively. In the multivariate Cox model, the extent of vascular invasion, tumour count, fibrinogen, HBV DNA load and serum potassium significantly affected prognosis. The C-index of the two nomograms were 0.80 and 0.69 for OS and RFS respectively. Based on our nomogram, patients predicted to have 1-year and 3-year RFS rates of more than 80% and 56% had actual 1-year and 3-year RFS rates of 81.8% and 57.1%, respectively, including 9.0% and 17.1% of the HCC patients with MaVI in our database. Conclusion: Surgical treatments are a therapeutic option that can provide more survival benefit for HCC patients with MaVI. With the help of our nomograms, selected HCC patients with MaVI can benefit from hepatic resection and have the same survival rate as that for early-stage HCC patients.

Keywords: Hepatic resection; Hepatocellular carcinoma; Macroscopic vascular invasion; Nomogram; Prognostic predictive model.