A Male-ABCD Algorithm for Hepatocellular Carcinoma Risk Prediction in HBsAg Carriers

Abstract BACKGROUND: Hepatocellular carcinoma (HCC) development among hepatitis B surface antigen (HBsAg) carriers shows gender disparity, influenced by underlying liver diseases that display variations in laboratory tests. We aimed to construct a risk-stratified HCC prediction model for HBsAg-positive male adults.METHODS: HBsAg-positive, 35-69 years males (N=6 153) were recruited from a multi-center population-based liver cancer screening study. Randomly, three centers were set as training, the other three centers as validation. Within 2 years since initiation, we administrated at least two rounds of HCC screening using B-ultrasonography and α-fetoprotein (AFP). We used logistic regression models to determine potential risk factors, built and examined the operating characteristics of a point-based algorithm for HCC risk prediction.RESULTS: With 2 years of follow-up, 302 HCC cases were diagnosed. A male-ABCD algorithm was constructed including participant’s Age, Blood levels of GGT (γ-glutamyl-transpeptidase), Counts of platelets, white cells, Concentration of DCP (des-γ-carboxy-prothrombin) and AFP, with scores ranging from 0 to 18.3. The area under receiver operating characteristic was 0.91(0.89-0.93), larger than existing models. At 1.5 points of risk-score, 26.10% of the participants in training, 14.94% in validation were recognized at-low-risk, with sensitivity of identifying HCC remained 100%. At 2.5 points, 46.51% of the participants in training, 33.68% in validation were recognized at-low-risk with 99.06% and 97.78% of sensitivity. At 4.5 points, only 20.86% of training, 23.73% of validation were recognized at-high-risk, with positive prediction value of 22.85% and 12.35% respectively.DISCUSSION: Male-ABCD algorithm identified individual’s risk for HCC occurrence within short-term for their HCC precision surveillance.