Statistical strategies for HCC risk prediction models in patients with chronic hepatitis B

Abstract

Risk prediction modelling for hepatocellular carcinoma (HCC) has been the focus of research in the last decade. The prediction models would help HCC risk stratification, so that patients at high risk of HCC would be able to receive more appropriate management and HCC surveillance. These models were mostly developed in treatment-naïve chronic hepatitis B patients in the early days. In recent years, more prediction models were derived and validated in patients who have received antiviral treatment, which account for the majority of patients who are at increased risk of HCC. Various statistical tests are adopted in developing and validating a risk prediction model - commonly Cox proportional hazards regression, time-dependent receiver operating characteristic (ROC) curve and area under the ROC curve. Even in well-validated models, there may be some pitfalls, e.g., generalizability and clinical applicability. The future direction of prediction model development should be directed towards a more personalised approach. Continuous optimisation of the predictive accuracy of the models would be achieved by involving more serial and dynamic parameters.

Keywords

Cirrhosis / hepatitis B virus / hepatocellular carcinoma / liver stiffness measurement / prediction models

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