Hepatocellular Carcinoma Risk Scores from Modeling to Real Clinical Practice in Areas Highly Endemic for Hepatitis B Infection

Abstract

Hepatocellular carcinoma (HCC) accounts for the major-ity of primary liver cancers and represents a global health challenge. Liver cancer ranks third in cancer-related mortality with 830,000 deaths and sixth in incidence with 906,000 new cases annually worldwide. HCC most com-monly occurs in patients with underlying liver disease, es-pecially chronic hepatitis B virus (HBV) infection in highly endemic areas. Predicting HCC risk based on scoring models for patients with chronic liver disease is a simple, effective strategy for identifying and stratifying patients to improve the early diagnosis rate and prognosis of HCC. We examined 23 HCC risk scores published worldwide in CHB patients with (n=10) or without (n=13) antiviral treatment. We also described the characteristics of the risk score’s predictive performance and application status. In the future, higher predictive accuracy could be achieved by combining novel technologies and machine learning algo-rithms to develop and update HCC risk score models and integrated early warning and diagnosis systems for HCC in hospitals and communities.