Enhancing Hepatocellular Carcinoma Surveillance: Comparative Evaluation of AFP, AFP-L3, DCP and Composite Models in a...

Area of Science:

• Hepatology and Oncology
• Biomarker Discovery
• Diagnostic Accuracy

Background:

• Hepatocellular carcinoma (HCC) detection can be improved using biomarkers beyond alpha-fetoprotein (AFP).
• Composite biomarker models like GALAD, GAAP, and ASAP show promise, but comparative data across diverse populations are limited.
• Existing models may enhance early detection and patient stratification in HCC surveillance.

Purpose of the Study:

• To compare the diagnostic performance of various composite biomarker models for hepatocellular carcinoma (HCC).
• To evaluate biomarker performance across different etiologies and AFP statuses in a diverse cohort.
• To assess the utility of GALAD, GAAP, and ASAP scores in early and any-stage HCC detection.

Main Methods:

• A biobank-based case-control study included 562 adults: 120 controls, 277 with chronic liver disease, and 165 with HCC.
• Diagnostic performance was assessed using receiver operating characteristic analysis at optimal, established, and 90% specificity cut-offs.
• Subgroup analyses were performed based on HCC etiology (viral vs. non-viral) and AFP status.

Main Results:

• The GALAD score demonstrated the highest sensitivity for any-stage (90.3%) and early-stage (89.1%) HCC at optimal cut-offs.
• At established cut-offs, GALAD maintained high sensitivity (75.8% any-stage, 57.8% early-stage) with 93.5% specificity.
• GALAD performed best in non-viral HCC (AUC 0.872), while GAAP and ASAP showed high performance in viral HCC (AUC 0.955-0.960).

Conclusions:

• The GALAD score exhibits consistent and superior performance across diverse populations for HCC detection.
• Composite models like GAAP and ASAP, using fewer biomarkers, show comparable performance, especially in viral HCC.
• These findings support integrating composite biomarker models into personalized HCC surveillance strategies.