Comparative Evaluation of the ASAP and GAAD Algorithms for Hepatocellular Carcinoma Detection in a Chronic Liver Disease Cohort in Korea

Background: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide. To enhance early detection, the ASAP (age, sex, alpha-fetoprotein [AFP], protein induced by vitamin K absence or antagonist-II [PIVKA-II]) and GAAD (gender, age, AFP, des-gamma-carboxyprothrombin [DCP]/PIVKA-II) models were developed by integrating demographic data with serum biomarkers. We compared their performance in a Korean chronic liver disease cohort. Methods: We retrospectively analyzed data from 524 patients, including 132 with and 392 without HCC. AFP and PIVKA-II levels were measured using Abbott (ASAP) and Roche (GAAD) analyzers. Performance was assessed based on area under the ROC curve (AUROC) and optimal cutoff values for the overall cohort, etiologic subgroups (hepatitis B virus [HBV], hepatitis C virus [HCV], alcohol-related), and early-stage HCC (modified Union for International Cancer Control stage I or II). Results: =0.702), with AUROCs remaining high (0.911 for ASAP and 0.916 for GAAD). Conclusions: The ASAP and GAAD algorithms provide excellent and comparable diagnostic performance for detecting HCC, including in early-stage cases, regardless of etiology. Given Korea's high HBV prevalence and platform variability, these models serve as robust, non-invasive complementary tools for surveillance. This validation supports their clinical utility in the Korean population.