Stratification of Hepatocellular Carcinoma Risk Following HCV Eradication or HBV Control

Abstract

Hepatocellular carcinoma (HCC) incidence has dramatically decreased in patients infected with HCV and HBV due to the widespread use of highly effective antiviral agents. Nevertheless, a substantial proportion of patients with advanced fibrosis or cirrhosis following HCV clearance of in case of HBV control whatever the stage of fibrosis remains at risk of liver cancer development. Cancer predictors in these virus-free patients include routine parameters estimating coexisting comorbidities, persisting liver inflammation or function impairment, and results of non-invasive tests which can be easily combined into HCC risk scoring systems. The latter enables stratification according to various liver cancer incidences and allocation of patients into low, intermediate or high HCC risk probability groups. All international guidelines endorse lifelong surveillance of these patients using semi-annual ultrasound, with known sensibility issues. Refining HCC prediction in this growing population ultimately will trigger personalized management using more effective surveillance tools such as contrast-enhanced imaging techniques or circulating biomarkers while taking into account cost-effectiveness parameters.

Keywords: antivirals; hepatocellular carcinoma; risk scores; surveillance; viral hepatitis.

Figure 1

Potential application of hepatocellular carcinoma (HCC) risk stratification using scoring systems: reinforcement of surveillance programs in patients with the highest incidences to optimize screening uptake and efficacy. The optimal thresholds will depend upon the proportion of patients allocated to each class and on cost-effectiveness estimates.

Figure 2

Example of the use of machine learning approach using decision tree analysis in patients infected with HCV with compensated cirrhosis as a function of SVR status in the ANRS CO12 CirVir cohort (adapted from Audureau et al. [44] with permission from the Authors). Five main predictors were identified by the algorithm, yielding eight groups (five before SVR [Panel A] and three following SVR [Panel B]) from various combinations of these predictors and demon-strating markedly contrasting risks of HCC, as shown by the corresponding curves at each end node.