Scoring models for predicting hepatocellular carcinoma risk in hcv patients after antiviral therapy

Hepatitis C virus (HCV) infection is a major global health concern, with a significant risk of developing hepatocellular carcinoma (HCC) in infected individuals. The advent of oral antiviral therapy has revolutionized the management of HCV, achieving sustained virologic response (SVR) rates and reducing the risk of HCC. However, some patients still remain at risk for HCC even after successful antiviral treatment. Scoring systems have emerged as valuable tools to predict HCC risk and assist in post-treatment surveillance. This review aims to summarize and evaluate the existing scoring systems developed to assess HCC risk in HCV patients after oral antiviral therapy. We systematically searched relevant databases for published articles. We included studies that focused on the development, validation, and clinical application of scoring models for HCC risk prediction. We identified scoring systems, utilizing different variables such as demographic data, liver function tests, imaging findings, and genetic markers. We discuss the strengths and limitations of each scoring system and compare their predictive accuracy. Furthermore, we explore the potential for combining multiple scoring models to enhance risk stratification. The findings of this review highlight the utility of scoring systems in identifying patients at higher risk of developing HCC despite achieving SVR with oral antiviral therapy. Additionally, we discuss the implications of these scoring systems for clinical practice, risk stratification, and long-term surveillance of HCV patients. In conclusion, scoring systems offer a valuable approach to estimate the risk of HCC in HCV patients post oral antiviral treatment. A better understanding of these scoring models will help clinicians in tailored followup strategies and early detection of HCC, ultimately improving patient outcomes. Further research is needed to refine and validate these scoring systems in different populations and to explore their potential inclusion into clinical guidelines.