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Published on: September 30, 2021
Development and clinical validation of a novel algorithmic score (GAAD) for detecting HCC in prospective cohort
Teerha Piratvisuth1, Jinlin Hou2, Tawesak Tanwandee3
1NKC Institute of Gastroenterology and Hepatology, Songklanagarind Hospital, Prince of Songkla University, Hat Yai, Thailand.
Insights
The GAAD algorithm, combining PIVKA-II, AFP, age, and gender, significantly improves early-stage hepatocellular carcinoma (HCC) detection in patients with chronic liver disease (CLD). This novel diagnostic tool offers higher accuracy than individual biomarkers for HCC surveillance.
Area of Science:
- Hepatology and oncology diagnostics
- Biomarker-based disease detection
- In vitro diagnostics development
Background:
- Alpha-fetoprotein (AFP) and des-gamma carboxyprothrombin (PIVKA-II) are biomarkers for hepatocellular carcinoma (HCC) but have limited diagnostic value alone.
- The GAAD algorithm is a novel in vitro diagnostic tool that combines PIVKA-II and AFP measurements with age and gender to improve diagnostic accuracy.
Purpose of the Study:
- To develop, implement, and clinically validate the GAAD algorithm for differentiating early-stage and all-stage HCC from benign chronic liver disease (CLD).
- To assess the diagnostic performance of the GAAD algorithm compared to individual biomarkers (AFP and PIVKA-II).
Main Methods:
- Prospective enrollment of patients aged ≥18 years with HCC or CLD into algorithm development (n=1084) and clinical validation (n=877) studies.
- Serum samples analyzed using a cobas® e 601 analyzer, with performance evaluated via receiver operating characteristic curve analyses (AUC).
Main Results:
- The GAAD algorithm demonstrated superior performance in differentiating early-stage HCC from CLD, with an AUC of 90.7% in development and 91.4% in validation studies.
- GAAD achieved 71.8% sensitivity and 90.0% specificity for early-stage HCC detection in the development study, and 70.1% sensitivity with 93.7% specificity in the validation study.
- The algorithm showed strong specificity across various disease stages, etiologies, and regions, reducing false positives.
Conclusions:
- The GAAD algorithm significantly enhances the detection of early-stage HCC in patients with CLD undergoing surveillance.
- Further phase III and IV studies are recommended to evaluate the integration of the GAAD algorithm into routine clinical practice.
Background:
Alpha-fetoprotein (AFP) and des-gamma carboxyprothrombin (DCP), also known as protein induced by vitamin K absence-II (PIVKA-II [DCP]) are biomarkers for HCC with limited diagnostic value when used in isolation. The novel GAAD algorithm is an in vitro diagnostic combining PIVKA-II (DCP) and AFP measurements, age, and gender (biological sex) to generate a semi-quantitative result. We conducted prospective studies to develop, implement, and clinically validate the GAAD algorithm for differentiating HCC (early and all-stage) and benign chronic liver disease (CLD), across disease stages and etiologies.
Methods:
Patients aged ≥18 years with HCC or CLD were prospectively enrolled internationally into algorithm development [n = 1084; 309 HCC cases (40.7% early-stage) and 736 controls] and clinical validation studies [n = 877; 366 HCC cases (47.6% early-stage) and 303 controls]. Serum samples were analyzed on a cobas® e 601 analyzer. Performance was assessed using receiver operating characteristic curve analyses to calculate AUC.
Results:
For algorithm development, AUC for differentiation between early-stage HCC and CLD was 90.7%, 84.4%, and 77.2% for GAAD, AFP, and PIVKA-II, respectively. The sensitivity of GAAD for the detection of early-stage HCC was 71.8% with 90.0% specificity. Similar results were shown in the clinical validation study; AUC for differentiation between early-stage HCC and CLD was 91.4% with 70.1% sensitivity and 93.7% specificity. GAAD also showed strong specificity, with a lower rate of false positives regardless of disease stage, etiology, or region.
Conclusions:
The GAAD algorithm significantly improves early-stage HCC detection for patients with CLD undergoing HCC surveillance. Further phase III and IV studies are warranted to assess the utility of incorporating the algorithm into clinical practice.
