Hepatocellular carcinoma risk after viral response in hepatitis C virus-advanced fibrosis: Who to screen and for how long?

Hepatitis C virus (HCV) chronic infection is associated with fibrosis progression, end-stage liver complications and HCC. Not surprisingly, HCV infection is a leading cause of liver-related morbidity and mortality worldwide. After sustained virological response (SVR), the risk of developing hepatocellular carcinoma is not completely eliminated in patients with established cirrhosis or with advanced fibrosis. Therefore, lifelong surveillance is currently recommended. This strategy is likely not universally cost-effective and harmless, considering that not all patients with advanced fibrosis have the same risk of developing HCC. Factors related to the severity of liver disease and its potential to improve after SVR, the molecular and epigenetic changes that occur during infection and other associated comorbidities might account for different risk levels and are likely essential for identifying patients who would benefit from screening programs after SVR. Efforts to develop predictive models and risk calculators, biomarkers and genetic panels and even deep learning models to estimate the individual risk of HCC have been made in the direct-acting antiviral agents era, when thousands of patients with advanced fibrosis and cirrhosis have reached SVR. These tools could help to identify patients with very low HCC risk in whom surveillance might not be justified. In this review, factors affecting the probability of HCC development after SVR, the benefits and risks of surveillance, suggested strategies to estimate individualized HCC risk and the current evidence to recommend lifelong surveillance are discussed.

Automated summary

Chronic hepatitis C virus (HCV) infection is closely related to the development of hepatocellular carcinoma (HCC), and lifelong surveillance is recommended even after sustained virological response (SVR), although not all patients have the same risk. Efforts have been made to develop predictive models and risk calculators to identify patients with very low HCC risk.