GAFAD: An LC-MS/MS-Based Model for Early Hepatocellular Carcinoma Detection Beyond GALAD's Limitation.

Area of Science:

• Hepatology and Cancer Diagnostics
• Biomarker Discovery and Validation
• Tumorigenesis and Disease Progression

Background:

• The GALAD score, a common tool for hepatocellular carcinoma (HCC) detection, may overestimate accuracy in early-stage disease due to its derivation from advanced tumor cohorts.
• Limitations of the AFP-L3 assay, including poor sensitivity at low concentrations, hinder the detection of small or AFP-negative HCC tumors.

Purpose of the Study:

• To develop and validate the GAFAD score, a novel multivariable model for enhanced early HCC detection.
• To replace the AFP-L3 assay with a more sensitive fucosylated AFP percentage (AFP-Fuc%) measurement using LC-MS/MS.

Main Methods:

• Developed the GAFAD model by substituting AFP-L3 with AFP-Fuc% in a hepatitis B virus (HBV)-related cohort.
• Trained and tested the GAFAD model in a diagnostically challenging cohort with overlapping biomarker levels.
• Validated the GAFAD model in two independent cohorts encompassing diverse etiologies (HBV, HCV, non-viral).

Main Results:

• GAFAD demonstrated superior diagnostic performance compared to GALAD in both development and validation cohorts, evidenced by higher AUC, sensitivity, and accuracy at 90% specificity.
• GAFAD achieved significantly better performance metrics (AUC: 0.938 vs. 0.887; sensitivity: 82% vs. 66%) in the development cohort.
• In external validation, GAFAD also outperformed GALAD (AUC: 0.874 vs. 0.841; sensitivity: 72% vs. 57%), including for early-stage and AFP-negative HCC.

Conclusions:

• The GAFAD score offers a reliable and generalizable tool for early HCC detection.
• GAFAD shows improved clinical applicability in surveillance and diagnosis of HCC across diverse patient populations and etiologies.
• GAFAD's enhanced performance, particularly in challenging cases, supports its integration into clinical practice for improved HCC management.