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Hepatocellular carcinoma (HCC) risk prediction is important to developing individualized surveillance approaches. We designed a novel HCC prediction model using liver stiffness on transient elastography for patients receiving antiviral therapy against hepatitis B virus (HBV) infection. We recruited 2037 patients receiving entecavir or tenofovir as first-line antivirals and used the Cox regression analysis to determine key variables for model construction. Within 58.1 months (median), HCC developed in 182 (8.9%) patients. Patients with HCC showed a higher prevalence of cirrhosis (90.7% vs. 45.9%) and higher liver stiffness values (median 13.9 vs. 7.2 kPa) than those without. A novel nomogram (score 0–304) was established using age, platelet count, cirrhosis development, and liver stiffness values, which were independently associated with increased HCC risk, along with hepatitis B e antigen positivity and serum albumin and total bilirubin levels. Cumulative HCC probabilities were 0.7%, 5.0%, and 22.7% in the low- (score ≤87), intermediate- (88–222), and high-risk (≥223) groups, respectively. The c-index value was 0.799 (internal validity: 0.805), higher than that of the PAGE-B (0.726), modified PAGE-B (0.756), and modified REACH-B (0.761) models (all p &lt; 0.05). Our nomogram showed acceptable performance in predicting HCC in Asian HBV-infected patients receiving potent antiviral therapy.

cancers

Novel Liver Stiffness-Based Nomogram for Predicting Hepatocellular Carcinoma Risk in Patients with Chronic Hepatitis B Virus Infection Initiating Antiviral Therapy