1. Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease
- Author
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Jérémy Dana, Aïna Venkatasamy, Antonio Saviano, Joachim Lupberger, Yujin Hoshida, Valérie Vilgrain, Pierre Nahon, Caroline Reinhold, Benoit Gallix, Thomas F. Baumert, Institut de Recherche sur les Maladies Virales et Hépatiques (IVH), Université de Strasbourg (UNISTRA)-Institut National de la Santé et de la Recherche Médicale (INSERM), L'Institut hospitalo-universitaire de Strasbourg (IHU Strasbourg), Institut National de Recherche en Informatique et en Automatique (Inria)-l'Institut de Recherche contre les Cancers de l'Appareil Digestif (IRCAD)-Les Hôpitaux Universitaires de Strasbourg (HUS)-La Fédération des Crédits Mutuels Centre Est (FCMCE)-L'Association pour la Recherche contre le Cancer (ARC)-La société Karl STORZ, McGill University = Université McGill [Montréal, Canada], Pôle Hépato-digestif [Strasbourg], Nouvel Hôpital Civil [Strasbourg], CHU Strasbourg-CHU Strasbourg, Harold C. Simmons Comprehensive Cancer Center [Dallas, TX, États-Unis], University of Texas Southwestern Medical Center [Dallas], Laboratory of Imaging Biomarkers, UMR1149, INSERM-University Paris-Diderot, Paris, AP-HP - Hôpitaux Universitaires Paris Seine-Saint-Denis (GHU 93), Augmented Intelligence and Precision Health Laboratory (AIPHL), Department of Radiology, McGill University, Montreal, QC H3G 1A4, Canada, ANR-10-LABX-0028,HepSys,Functional genomics of viral hepatitis and liver disease(2010), ANR-10-IAHU-0002,MIX-Surg,Institut de Chirurgie Mini-Invasive guidée par l'Image(2010), European Project: 671231,H2020,ERC-2014-ADG,HEPCIR(2016), and European Project: 667273,H2020,H2020-PHC-2015-two-stage,HEP-CAR(2016)
- Subjects
Liver Cirrhosis ,Chronic liver disease Histo-pathological features Pejorative evolution Quantitative biomarkers Elastography Machine learning Radiomics Deep learning ,Sciences du Vivant [q-bio]/Médecine humaine et pathologie ,Article ,methods ,Artificial Intelligence ,Hypertension, Portal ,Machine learning ,Humans ,Quantitative biomarkers ,Histo-pathological features ,diagnostic imaging ,pathology ,Radiomics ,Hepatology ,Liver Neoplasms ,Chronic liver disease ,Deep learning ,[SDV.MHEP.HEG]Life Sciences [q-bio]/Human health and pathology/Hépatology and Gastroenterology ,Magnetic Resonance Imaging ,Fatty Liver ,Liver ,Pejorative evolution ,Disease Progression ,Elasticity Imaging Techniques ,Elastography ,Biomarkers - Abstract
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. journal article review 2022 Jun 2022 02 09 imported
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- 2022