ArticleJournal of clinical and experimental hepatology
Comparison of the GALAD, GAAP, and ASAP Scores for Hepatocellular Carcinoma Detection in Patients With Chronic Liver Diseases.
Article in Journal of clinical and experimental hepatology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Molecular mechanisms of tumor-associated macrophages in hepatocellular carcinoma development and therapy.World journal of gastroenterology · 2026Review
- Diagnostic Performance Comparison of AFP, PIVKA-II, GALAD Model, and ASAP Model Across Two Chemiluminescence Immunoassay Platforms for Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
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Authors and funding
7 authors.
Funding
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Abstract
Background: Developing biomarker panels for early hepatocellular carcinoma (HCC) detection is crucial to overcome the limitations of current imaging-based surveillance strategies. The GALAD, GAAP, and ASAP scores are well-established algorithms for estimating the risk of HCC based on gender, age, alpha-fetoprotein (AFP), protein induced by vitamin K absence or antagonist-II, and AFP-L3. This study aimed to evaluate the diagnostic performance of these biomarkers and models in detecting HCC in patients with chronic liver diseases (CLDs). Methods: The study enrolled 529 patients, comprising 193 with HCC, 223 with chronic hepatitis, and 113 with cirrhosis. HCC was diagnosed based on the standard imaging criteria. The diagnostic performance of the GALAD, GAAP, and ASAP models, along with individual biomarkers, was assessed using the area under the receiver operating characteristic curve (AUC) to identify HCC in patients with various etiologies of CLDs. Results: The GALAD, GAAP, and ASAP models showed better AUCs (0.876-0.889) in detecting any stage of HCC in patients with CLD than individual biomarkers (0.741-0.842). These models also exhibited improved accuracy for early HCC detection (0.825-0.889) compared with individual biomarkers (0.654-0.710). The GAAP score achieved the best accuracy in detecting early HCC in patients with CLD. Furthermore, the GAAP and ASAP models performed best in identifying all-stage HCC in patients with viral hepatitis, while GAAP and GALAD scores were most effective in those with nonviral etiologies. The optimal cutoff values for detecting HCC were GALAD >0.13, GAAP > -0.64, and ASAP > -0.71, all with sensitivities and specificities above 80%. Conclusions: The GAAP model demonstrated excellent discriminatory ability between HCC and CLD in both viral and nonviral subgroups and outperformed other models in detecting early-stage HCC.
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