Comparative evaluation of multimarker algorithms for early-stage HCC detection in multicenter prospective studies

To improve the detection of early-stage hepatocellular carcinoma (HCC) from benign chronic liver disease (CLD), algorithms combining demographic characteristics and serum biomarkers, such as GAAD and GALAD, have been developed. GAAD combines gender (biological sex), age, alpha-fetoprotein (AFP), des-gamma carboxy-prothrombin (DCP); GALAD combines the same characteristics and biomarkers as GAAD with the addition of <i>Lens culinaris</i> agglutinin-reactive AFP (AFP-L3). Changing disease etiologies and treatment paradigms have raised questions regarding the utility of AFP-L3 in HCC surveillance. Our work demonstrates that the GAAD (Cobas) algorithm demonstrated good clinical performance and was as sensitive and specific as the GALAD (Cobas) and GALAD (μTASWAKO) algorithms in differentiating HCC and CLD controls, across all disease stages, etiologies, and geographical regions; therefore, AFP-L3 may have a negligible role in HCC detection. Our study provides supporting evidence that in participants with CLD undergoing guideline-directed HCC surveillance, the GAAD (Cobas) algorithm may be used as an effective method for the detection of HCC, potentially resulting in improved patient outcomes.

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Publication Details

Journal
JHEP Reports
Published
2024-11-08
DOI
https://doi.org/10.1016/j.jhepr.2024.101263
Citations
14
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
5.66

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article

Comparative evaluation of multimarker algorithms for early-stage HCC detection in multicenter prospective studies

Kairat Madin, Henry Lik‐Yuen Chan, Masatoshi Kudo, Teerha Piratvisuth et al.
14 citations
JHEP Reports
Radiomics and Machine Learning in Medical Imaging
5.66
article

Comparative evaluation of multimarker algorithms for early-stage HCC detection in multicenter prospective studies

Kairat Madin, Henry Lik‐Yuen Chan, Masatoshi Kudo, Teerha Piratvisuth, Konstantin Kroeniger, Peter Findeisen, Johannes Kolja Hegel, Enrico Narciso De Toni, Wenzel Schöning, Jörg Trojan, Jinlin Hou, Katharina Malinowsky, Arndt Vogel, Thomas Berg, Ashish Sharma
article en
14 citations

Abstract

To improve the detection of early-stage hepatocellular carcinoma (HCC) from benign chronic liver disease (CLD), algorithms combining demographic characteristics and serum biomarkers, such as GAAD and GALAD, have been developed. GAAD combines gender (biological sex), age, alpha-fetoprotein (AFP), des-gamma carboxy-prothrombin (DCP); GALAD combines the same characteristics and biomarkers as GAAD with the addition of <i>Lens culinaris</i> agglutinin-reactive AFP (AFP-L3). Changing disease etiologies and treatment paradigms have raised questions regarding the utility of AFP-L3 in HCC surveillance. Our work demonstrates that the GAAD (Cobas) algorithm demonstrated good clinical performance and was as sensitive and specific as the GALAD (Cobas) and GALAD (μTASWAKO) algorithms in differentiating HCC and CLD controls, across all disease stages, etiologies, and geographical regions; therefore, AFP-L3 may have a negligible role in HCC detection. Our study provides supporting evidence that in participants with CLD undergoing guideline-directed HCC surveillance, the GAAD (Cobas) algorithm may be used as an effective method for the detection of HCC, potentially resulting in improved patient outcomes.

JHEP ReportsVol. 7(2)
Goethe University Frankfurt (DE), Roche (Switzerland) (CH), Prince of Songkla University (TH), Chinese University of Hong Kong (HK), Toronto General Hospital (CA), Princess Margaret Cancer Centre (CA), Medizinische Hochschule Hannover (DE), Institut für Medizinische Biometrie, Informatik und Epidemiologie (DE), Roche Pharma AG (Germany) (DE), Computer Algorithms for Medicine (AT), Vivantes Klinikum (DE), Southern Medical University (CN), Charité - Universitätsmedizin Berlin (DE), Ludwig-Maximilians-Universität München (DE), Kindai University (JP), Leipzig University (DE)
Amgen, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, AstraZeneca, Gilead Sciences, FibroGen, EA Pharma Co., Ltd., Bayer HealthCare, Eisai, Ipsen
Openalex Percentile: Top 3%
Radiomics and Machine Learning in Medical Imaging
5.66
DOIOpen Access PDFOpenAlex
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