Comparative Analysis of Multi-Biomarker Diagnostic Models for Early Detection of Hepatocellular Carcinoma and Their...

Hepatocellular carcinoma (HCC) remains a significant global health concern, often diagnosed at advanced stages, limiting the efficacy of surgical interventions. Early and accurate diagnosis is critical for improving surgical outcomes and reducing mortality. Traditional biomarkers, such as alpha-fetoprotein (AFP), des-gamma-carboxyprothrombin (DCP), and the lectin-bound fraction of AFP (AFP-L3), show limited sensitivity and specificity. Advanced diagnostic models, including GALAD, TAGALAD, and GAP_TALAD, offer a promising multi-biomarker approach but lack extensive evaluation in surgical contexts.

This retrospective study included a cohort of 267 untreated hepatocellular carcinoma patients and 231 control patients (with hepatitis or cirrhosis). We applied the predefined formulas for the TAGALAD, GAP_TALAD, and other models to the cohort data. The diagnostic performance of each model and individual biomarker for detecting HCC was assessed using receiver operating characteristic (ROC) curve analysis to determine the area under the curve (AUC), sensitivity, and specificity at optimal cut-offs. Additionally, key clinical subgroups, including pathologically confirmed HCC, clinically diagnosed HCC, early-stage HCC (TNM I+II), patients with complete data (no imputation), and hepatitis B virus (HBV)-related disease, were also analyzed.

TAGALAD and GAP_TALAD demonstrated superior performance compared to the GALAD model and traditional biomarkers across all patient subgroups. Notably, TAGALAD achieved the highest diagnostic accuracy, with an AUC of 0.880, sensitivity of 0.760, and specificity of 0.861, followed closely by GAP_TALAD (AUC = 0.874). Both models demonstrated excellent performance in early-stage HCC detection (TAGALAD AUC = 0.860, GAP_TALAD AUC = 0.867), highlighting their potential in identifying candidates for surgical resection or transplant at an early curative stage. In HBV-related HCC, TAGALAD (AUC = 0.874) and GAP_TALAD (AUC = 0.857) showed superior diagnostic accuracy compared to GALAD (AUC = 0.731) and single biomarkers (AUC = 0.598-0.799).

The TAGALAD and GAP_TALAD models offer a robust and reliable framework that supports early diagnosis of HCC. Their superior accuracy indicates a more reliable foundation for identifying candidates for curative surgical interventions, suggesting the potential to refine patient selection. Future research should focus on multi-center validation and the integration of novel biomarkers to further optimize these models for surgical decision-making and personalized treatment strategies.