Development and clinical validation of a novel algorithmic score (GAAD) for detecting HCC in prospective cohort studies
Issued Date
2023-11-01
Resource Type
eISSN
2471254X
Scopus ID
2-s2.0-85180967926
Pubmed ID
37938100
Journal Title
Hepatology Communications
Volume
7
Issue
11
Rights Holder(s)
SCOPUS
Bibliographic Citation
Hepatology Communications Vol.7 No.11 (2023) , e0317
Suggested Citation
Piratvisuth T., Hou J., Tanwandee T., Berg T., Vogel A., Trojan J., De Toni E.N., Kudo M., Eiblmaier A., Klein H.G., Hegel J.K., Madin K., Kroeniger K., Sharma A., Chan H.L.Y. Development and clinical validation of a novel algorithmic score (GAAD) for detecting HCC in prospective cohort studies. Hepatology Communications Vol.7 No.11 (2023) , e0317. doi:10.1097/HC9.0000000000000317 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/97770
Title
Development and clinical validation of a novel algorithmic score (GAAD) for detecting HCC in prospective cohort studies
Author's Affiliation
Siriraj Hospital
MicroCoat Biotechnologie GmbH
Kindai University
Chinese University of Hong Kong, Faculty of Medicine
Roche Diagnostics GmbH
Charité – Universitätsmedizin Berlin
Universitätsklinikum Frankfurt
Prince of Songkla University
Gottfried Wilhelm Leibniz Universität Hannover
Universitätsklinikum Leipzig und Medizinische Fakultät
Southern Medical University
Klinikum der Universität München
Roche Diagnostics International AG
Center for Human Genetics and Laboratory Medicine
MicroCoat Biotechnologie GmbH
Kindai University
Chinese University of Hong Kong, Faculty of Medicine
Roche Diagnostics GmbH
Charité – Universitätsmedizin Berlin
Universitätsklinikum Frankfurt
Prince of Songkla University
Gottfried Wilhelm Leibniz Universität Hannover
Universitätsklinikum Leipzig und Medizinische Fakultät
Southern Medical University
Klinikum der Universität München
Roche Diagnostics International AG
Center for Human Genetics and Laboratory Medicine
Corresponding Author(s)
Other Contributor(s)
Abstract
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.