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 - Publisher Copyright:
© 2022, Asian Pacific Association for the Study of the Liver.
PY - 2022/6
Y1 - 2022/6
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
SN - 1936-0533
VL - 16
SP - 509
EP - 522
JO - Hepatology International
JF - Hepatology International
IS - 3
ER -