Abstract

There are currently several prediction models for hepatocellular carcinoma (HCC) in chronic hepatitis B (CHB) receiving oral antiviral therapy. However, most models are based on pre-treatment clinical parameters. The current study aimed to develop a novel and practical prediction model for HCC by using both pre- and post-treatment parameters in this population.We included two treatment-naïve CHB cohorts who were initiated on oral antiviral therapies: the derivation cohort (n=1,480, Korea prospective SAINT cohort) and the validation cohort (n=426, the US retrospective Stanford Bay cohort). We employed logistic regression, decision tree, lasso regression, support vector machine, and random forest algorithms to develop the HCC prediction model and selected the most optimal method.We evaluated both pre-treatment and the 12-month clinical parameters on-treatment and found the 12-month on-treatment values to have superior HCC prediction performance. The lasso logistic regression algorithm using the presence of cirrhosis at baseline and alpha-fetoprotein and platelet at 12 months showed the best performance (AUROC=0.843 in the derivation cohort. The model performed well in the external validation cohort (AUROC=0.844) and better than other existing prediction models including the APA, PAGE-B, and GAG models (AUROC=0.769 to 0.818).We provided a simple-to-use HCC prediction model based on presence of cirrhosis at baseline and two objective laboratory markers (AFP and platelets) measured 12 months after antiviral initiation. The model is highly accurate with excellent validation in an external cohort from a different country (AUROC 0.844). (Clinical trial number: KCT0003487).

View details for DOI 10.1111/liv.14820

View details for PubMedID 33550661