TY - JOUR
T1 - Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease
AU - Dana, Jérémy
AU - Venkatasamy, Aïna
AU - Saviano, Antonio
AU - Lupberger, Joachim
AU - Hoshida, Yujin
AU - Vilgrain, Valérie
AU - Nahon, Pierre
AU - Reinhold, Caroline
AU - Gallix, Benoit
AU - Baumert, Thomas F.
N1 - Funding Information:
This work was supported by French state funds managed within the “Plan Investissements d’Avenir” and by the ANR (ANR-10-IAHU-02 to B.G), by the ARC, Paris and Institut Hospitalo-Universitaire, Strasbourg (TheraHCC1.0 and 2.0 IHUARC IHU201301187 and IHUARC2019 to T.F.B.), the European Union (ERC-AdG-2014-671231-HEPCIR to T.F.B. and Y.H., ERC-AdG-2020-667273-FIBCAN to T.F.B. and Y. H.), ANRS, Paris (ECTZ171594 to J.L., ECTZ131760 to J.L. and P.N., ECTZ160436 and ECTZ103701 to T.F.B), NIH (DK099558 and CA233794 to Y.H., CA209940 and R03AI131066 to T.F.B.), Cancer Prevention and Research Institute of Texas (RR180016 to Y.H), US Department of Defense (W81XWH-16-1-0363 to T.F.B. and Y.H.), the Irma T. Hirschl/Monique Weill-Caulier Trust (Y.H.) and the Foundation of the University of Strasbourg (HEPKIN to T.F.B.) and the Institut Universitaire de France (IUF; T.F.B.). This work has been published under the framework of the LABEX ANR-10-LABX-0028_HEPSYS and Inserm Plan Cancer and benefits from funding from the state managed by the French National Research Agency as part of the Investments for the future program.
Publisher Copyright:
© 2022, Asian Pacific Association for the Study of the Liver.
PY - 2022
Y1 - 2022
N2 - Chronic liver diseases, resulting from chronic injuries of various causes, lead to cirrhosis with life-threatening complications including liver failure, portal hypertension, hepatocellular carcinoma. A key unmet medical need is robust non-invasive biomarkers to predict patient outcome, stratify patients for risk of disease progression and monitor response to emerging therapies. Quantitative imaging biomarkers have already been developed, for instance, liver elastography for staging fibrosis or proton density fat fraction on magnetic resonance imaging for liver steatosis. Yet, major improvements, in the field of image acquisition and analysis, are still required to be able to accurately characterize the liver parenchyma, monitor its changes and predict any pejorative evolution across disease progression. Artificial intelligence has the potential to augment the exploitation of massive multi-parametric data to extract valuable information and achieve precision medicine. Machine learning algorithms have been developed to assess non-invasively certain histological characteristics of chronic liver diseases, including fibrosis and steatosis. Although still at an early stage of development, artificial intelligence-based imaging biomarkers provide novel opportunities to predict the risk of progression from early-stage chronic liver diseases toward cirrhosis-related complications, with the ultimate perspective of precision medicine. This review provides an overview of emerging quantitative imaging techniques and the application of artificial intelligence for biomarker discovery in chronic liver disease.
AB - Chronic liver diseases, resulting from chronic injuries of various causes, lead to cirrhosis with life-threatening complications including liver failure, portal hypertension, hepatocellular carcinoma. A key unmet medical need is robust non-invasive biomarkers to predict patient outcome, stratify patients for risk of disease progression and monitor response to emerging therapies. Quantitative imaging biomarkers have already been developed, for instance, liver elastography for staging fibrosis or proton density fat fraction on magnetic resonance imaging for liver steatosis. Yet, major improvements, in the field of image acquisition and analysis, are still required to be able to accurately characterize the liver parenchyma, monitor its changes and predict any pejorative evolution across disease progression. Artificial intelligence has the potential to augment the exploitation of massive multi-parametric data to extract valuable information and achieve precision medicine. Machine learning algorithms have been developed to assess non-invasively certain histological characteristics of chronic liver diseases, including fibrosis and steatosis. Although still at an early stage of development, artificial intelligence-based imaging biomarkers provide novel opportunities to predict the risk of progression from early-stage chronic liver diseases toward cirrhosis-related complications, with the ultimate perspective of precision medicine. This review provides an overview of emerging quantitative imaging techniques and the application of artificial intelligence for biomarker discovery in chronic liver disease.
KW - Chronic liver disease
KW - Deep learning
KW - Elastography
KW - Histo-pathological features
KW - Machine learning
KW - Pejorative evolution
KW - Quantitative biomarkers
KW - Radiomics
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U2 - 10.1007/s12072-022-10303-0
DO - 10.1007/s12072-022-10303-0
M3 - Review article
C2 - 35138551
AN - SCOPUS:85124566661
JO - Hepatology International
JF - Hepatology International
SN - 1936-0533
ER -