The best predictive model for hepatocellular carcinoma in patients with chronic hepatitis B infection.

Chronic hepatitis B (CHB) is a serious threat to human health, with about 820,000 deaths annually from related complications such as cirrhosis and hepatocellular carcinoma (HCC). Recently, the use of oral antiviral agents has improved the prognosis of patients with CHB infections and reduced the risk of HCC significantly; however, the hepatitis B virus remains a major factor in the development of HCC and this has raised many concerns[1-3]. Therefore, numerous studies have been conducted to assess the risk of HCC in patients with CHB infections, and different models have been proposed to predict risk of developing HCC[4-7]. However, since each study developed different models, depending on the use of antiviral agents or the type of antiviral agents, it is necessary to understand the characteristics of each model when using the models for the evaluation of HCC in patients with CHB infections[4, 6]. In addition, because different variables such as host factor, viral activity, and cirrhosis are used to evaluate the risk of HCC development, it is also necessary to assess the risk by validating the variables. Recently, studies that have also evaluated the risk of HCC using transient elastography and artificial intelligence (AI) systems have indicated that these HCC risk prediction models are also noteworthy. In this review, we aimed to compare HCC risk prediction models in patients with CHB infections reported to date to confirm the variables used, specificity of each model, and to consider an appropriate HCC risk prediction method.