Comparison between models for detecting hepatocellular carcinoma in patients with chronic liver diseases of various etiologies: ASAP score versus GALAD score.

Background: Diagnostic panels based on multiple biomarkers and clinical characteristics are considered more favorable than individual biomarker to diagnose hepatocellular carcinoma (HCC). Based on age, sex, alpha-fetoprotein (AFP), and protein induced by vitamin K absence II (PIVKA-II) with/without AFP-L3, ASAP and GALAD models are potential diagnostic panels. The diagnostic performances of these two panels were compared relative to HCC detection among patients with various etiologies of chronic liver diseases (CLDs).

Methods: A multicenter case-control study recruited CLDs patients with and without HCC from 14 Chinese hospitals. The etiologies of CLDs included hepatitis B virus (HBV), hepatitis C virus (HCV), alcoholic liver disease (ALD), and nonalcoholic fatty liver disease (NAFLD). Using area under the receiver operating characteristic curve (AUC) values, the diagnostic performances of ASAP and GALAD models were compared to detect HCC among patients with various etiologies of CLDs.

Results: Among 248 HCC patients and 722 CLD controls, the ASAP model demonstrated the highest AUC (0.886) to detect HCC at any stage, outperforming the GALAD model (0.853, P = 0.001), as well as any individual biomarker (0.687-0.799, all P < 0.001). In the subgroup analysis of various CLDs etiologies, the ASAP model outperformed the GALAD model to HCC independent of CLDs etiology. In addition, the ASAP model performed better in detecting early-stage (BCLC stage 0/A) HCC versus the GALAD model.

Conclusions: Despite using one less laboratory variable (AFP-L3), the ASAP model demonstrated better diagnostic performance than the GALAD model to detect all-stage HCC among patients with various etiologies of CLDs-related HCC.