Related Experiment Video
Updated: Sep 14, 2025
An Intestine/Liver Microphysiological System for Drug Pharmacokinetic and Toxicological Assessment
Published on: December 3, 2020
Comparison of the GALAD, GAAP, and ASAP Scores for Hepatocellular Carcinoma Detection in Patients With Chronic Liver
Kessarin Thanapirom1,2,3, Sirinporn Suksawatamnuay1,2,3, Panarat Thaimai1,2,3
1Division of Gastroenterology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand.
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.
