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Model-inferred mechanisms of liver function from magnetic resonance imaging data: Validation and variation across a clinically relevant cohort
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0003-4630-6550
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Surgery, Orthopaedics and Cancer Treatment, Department of Radiation Physics. Linköping University, Center for Medical Image Science and Visualization (CMIV).
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-4111-1693
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2019 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, PLOS COMPUTATIONAL BIOLOGY, Vol. 15, no 6, article id e1007157Article in journal (Refereed) Published
Abstract [en]

Estimation of liver function is important to monitor progression of chronic liver disease (CLD). A promising method is magnetic resonance imaging (MRI) combined with gadoxetate, a liver-specific contrast agent. For this method, we have previously developed a model for an average healthy human. Herein, we extended this model, by combining it with a patient-specific non-linear mixed-effects modeling framework. We validated the model by recruiting 100 patients with CLD of varying severity and etiologies. The model explained all MRI data and adequately predicted both timepoints saved for validation and gadoxetate concentrations in both plasma and biopsies. The validated model provides a new and deeper look into how the mechanisms of liver function vary across a wide variety of liver diseases. The basic mechanisms remain the same, but increasing fibrosis reduces uptake and increases excretion of gadoxetate. These mechanisms are shared across many liver functions and can now be estimated from standard clinical images.

Author summary

Being able to accurately and reliably estimate liver function is important when monitoring the progression of patients with liver disease, as well as when identifying drug-induced liver injury during drug development. A promising method for quantifying liver function is to use magnetic resonance imaging combined with gadoxetate. Gadoxetate is a liver-specific contrast agent, which is taken up by the hepatocytes and excreted into the bile. We have previously developed a mechanistic model for gadoxetate dynamics using averaged data from healthy volunteers. In this work, we extended our model with a non-linear mixed-effects modeling framework to give patient-specific estimates of the gadoxetate transport-rates. We validated the model by recruiting 100 patients with liver disease, covering a range of severity and etiologies. All patients underwent an MRI-examination and provided both blood and liver biopsies. Our validated model provides a new and deeper look into how the mechanisms of liver function varies across a wide variety of liver diseases. The basic mechanisms remain the same, but increasing fibrosis reduces uptake and increases excretion of gadoxetate.

Place, publisher, year, edition, pages
San Francisco, CA, United States: Public Library of Science , 2019. Vol. 15, no 6, article id e1007157
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:liu:diva-159165DOI: 10.1371/journal.pcbi.1007157ISI: 000474703000068PubMedID: 31237870Scopus ID: 2-s2.0-85069296906OAI: oai:DiVA.org:liu-159165DiVA, id: diva2:1339540
Note

Funding Agencies|Swedish Research Council [2014-6157, 2007-2884]; Medical Research council of Southeast Sweden [12621]; Vinnova [2013-01314]; Linkoping University, CENIIT [15.09]; Swedish fund for research without animal experiments [Nytank2015]

Available from: 2019-07-30 Created: 2019-07-30 Last updated: 2019-08-23Bibliographically approved

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Forsgren, MikaelKarlsson, MarkusDahlqvist Leinhard, OlofDahlström, NilsNorén, BengtRomu, ThobiasIgnatova, SimoneEkstedt, MattiasKechagias, StergiosLundberg, PeterCedersund, Gunnar
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Division of Radiological SciencesFaculty of Medicine and Health SciencesDepartment of Radiation PhysicsCenter for Medical Image Science and Visualization (CMIV)Department of Radiology in LinköpingDepartment of Biomedical EngineeringFaculty of Science & EngineeringDivison of NeurobiologyClinical pathologyDivision of Cardiovascular MedicineDepartment of GastroentorologyMedical radiation physicsDivision of Biomedical EngineeringDepartment of Clinical and Experimental Medicine
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