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Non-Invasive Characterization of Liver Disease: By Multimodal Quantitative Magnetic Resonance
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).ORCID iD: 0000-0002-9876-8274
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

There is a large and unmet need for diagnostic tool that can be used to characterize chronic liver diseases (CLD). In the earlier stages of CLD, much of the diagnostics involves performing biopsies, which are evaluated by a histopathologist for the presence of e.g. fat, iron, inflammation, and fibrosis. Performing biopsies, however, have two downsides: i) biopsies are invasive and carries a small but non-negligible risk for serious complications, ii) biopsies only represents a tiny portion of the liver and are thus prone to sampling error. Moreover, in the later stages of CLD, when the disease has progressed far enough, the ability of the liver to perform its basic function will be compromised. In this stage, there is a need for better methods for accurately measuring liver function. Additionally, measures of liver function can also be used when developing new drugs, as biomarkers for drug-induced liver injury (DILI), which is a serious drug-safety issue.

Magnetic resonance imaging (MRI) is a non-invasive medical imaging modality, which have shown much promise with regards to characterizing liver disease in all of the abovementioned aspects. The aim of this PhD project was to develop and validate MR-based methods that can be used to non-invasively characterize liver disease.

Paper I investigated if R2* mapping, a MR-method for measuring liver iron content, can be confounded by liver fat. The results show fat does affect R2*. The conclusion was therefore that fat must be taken into account when measuring small amounts of liver iron, as a small increase in R2* could be due to either small amounts of iron or large amounts of fat.

Paper II examined whether T1 mapping, which is another MR-method, can be used for staging liver fibrosis. The results of previous research have been mixed; some studies have been very promising, whereas other studies have been less promising. Unfortunately, the results in Paper II belongs to the less promising studies.

Paper III focused on measuring liver function by dynamic contrast-enhanced MRI (DCEMRI) using a liver specific contrast agent, which is taken up the hepatocytes and excreted to the bile. The purpose of the paper was to extend and validate a method for estimating uptake and efflux rates of the contrast agent. The method had previously only been applied in health volunteers. Paper II showed that the method can be applied to CLD patients and that the uptake of the contrast agent is lower in patients with advanced fibrosis.

Paper IV also used studied liver function with DCE-MRI in patients with primary sclerosing cholangitis (PSC). PSC is a CLD where the bile ducts are attacked by the immune system. When diagnosing PSC patients, it is common to use magnetic resonance cholangiopancreatography (MRCP), which is a method for imaging the bile ducts. Paper IV examined if there was any correlation between number and severity of the morphological changes, seen on MRCP, and measures of liver function derived using DCE-MRI. However, the results showed no such correlation. The conclusion was that the results indicates that MRCP should not be used to predict parenchymal function.

Paper V developed a method for translating DCE-MRI liver function parameters from rats to humans. This translation could be of value when developing new drugs, as a tool for predicting which drugs might cause drug-induced liver injury.

In summary, this thesis has shown that multimodal quantitative MR has a bright future for characterizing liver disease from a range of different aspects.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. , p. 77
Series
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1722
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
URN: urn:nbn:se:liu:diva-162653DOI: 10.3384/diss.diva-162653ISBN: 9789179299422 (print)OAI: oai:DiVA.org:liu-162653DiVA, id: diva2:1377773
Public defence
2020-01-30, Granitsalen, Campus US, Linköping, 09:15 (English)
Opponent
Supervisors
Available from: 2019-12-13 Created: 2019-12-12 Last updated: 2020-02-04Bibliographically approved
List of papers
1. Liver R2*is affected by both iron and fat: A dual biopsy-validated study of chronic liver disease
Open this publication in new window or tab >>Liver R2*is affected by both iron and fat: A dual biopsy-validated study of chronic liver disease
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2019 (English)In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 50, no 1, p. 325-333Article in journal (Refereed) Published
Abstract [en]

Background Liver iron content (LIC) in chronic liver disease (CLD) is currently determined by performing an invasive liver biopsy. MRI using R2* relaxometry is a noninvasive alternative for estimating LIC. Fat accumulation in the liver, or proton density fat fraction (PDFF), may be a possible confounder of R2* measurements. Previous studies of the effect of PDFF on R2* have not used quantitative LIC measurement. Purpose To assess the associations between R2*, LIC, PDFF, and liver histology in patients with suspected CLD. Study Type Prospective. Population Eighty-one patients with suspected CLD. Field Strength/Sequence 1.5 T. Multiecho turbo field echo to quantify R2*. PRESS MRS to quantify PDFF. Assessment Each patient underwent an MR examination, followed by two needle biopsies immediately following the MR examination. The first biopsy was used for conventional histological assessment of LIC, whereas the second biopsy was used to quantitatively measure LIC using mass spectrometry. R2* was correlated with both LIC and PDFF. A correction for the influence of fat on R2* was calculated. Statistical Tests Pearson correlation, linear regression, and area under the receiver operating curve. Results There was a positive linear correlation between R2* and PDFF (R = 0.69), after removing data from patients with elevated iron levels, as defined by LIC. R2*, corrected for PDFF, was the best method for identifying patients with elevated iron levels, with a correlation of R = 0.87 and a sensitivity and specificity of 87.5% and 98.6%, respectively. Data Conclusion PDFF increases R2*. Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:325-333.

Place, publisher, year, edition, pages
WILEY, 2019
Keywords
liver iron content; liver fat; R2*; PDFF; iron overload
National Category
Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-158842 (URN)10.1002/jmri.26601 (DOI)000471831600033 ()30637926 (PubMedID)
Note

Funding Agencies|Region Ostergotland; Medical Research council of Southeast Sweden [FORSS #12621]; Swedish Research Council [VR/NT #2014-6157, VR/MH, #2007-2884]; Forskningsradet i Sydostra Sverige; Linkoping University; Linkoping University Hospital Research Foundations; Vinnova [#2013-01314]; Vetenskapsradet

Available from: 2019-07-15 Created: 2019-07-15 Last updated: 2019-12-12
2. Model-inferred mechanisms of liver function from magnetic resonance imaging data: Validation and variation across a clinically relevant cohort
Open this publication in new window or tab >>Model-inferred mechanisms of liver function from magnetic resonance imaging data: Validation and variation across a clinically relevant cohort
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2019 (English)In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, 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
National Category
Pharmaceutical Sciences
Identifiers
urn:nbn:se:liu:diva-159165 (URN)10.1371/journal.pcbi.1007157 (DOI)000474703000068 ()31237870 (PubMedID)2-s2.0-85069296906 (Scopus ID)
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: 2019-12-12Bibliographically approved

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