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Physiologically Realistic and Validated Mathematical Liver Model Revels Hepatobiliary Transfer Rates for Gd-EOB-DTPA Using Human DCE-MRI Data
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, 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 Health Sciences. Östergötlands Läns Landsting, 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 Health Sciences. Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-4111-1693
Linköping University, Department of Clinical and Experimental Medicine, Division of Cell Biology. Linköping University, Faculty of Health Sciences.
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2014 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 4, 0095700- p.Article in journal (Refereed) Published
Abstract [en]

Objectives: Diffuse liver disease (DLD), such as non-alcoholic fatty liver disease (NASH) and cirrhosis, is a rapidly growing problem throughout the Westernized world. Magnetic resonance imaging (MRI), based on uptake of the hepatocyte-specific contrast agent (CA) Gd-EOB-DTPA, is a promising non-invasive approach for diagnosing DLD. However, to fully utilize the potential of such dynamic measurements for clinical or research purposes, more advanced methods for data analysis are required. Methods: A mathematical model that can be used for such data-analysis was developed. Data was obtained from healthy human subjects using a clinical protocol with high spatial resolution. The model is based on ordinary differential equations and goes beyond local diffusion modeling, taking into account the complete system accessible to the CA. Results: The presented model can describe the data accurately, which was confirmed using chi-square statistics. Furthermore, the model is minimal and identifiable, meaning that all parameters were determined with small degree of uncertainty. The model was also validated using independent data. Conclusions: We have developed a novel approach for determining previously undescribed physiological hepatic parameters in humans, associated with CA transport across the liver. The method has a potential for assessing regional liver function in clinical examinations of patients that are suffering of DLD and compromised hepatic function.

Place, publisher, year, edition, pages
Public Library of Science , 2014. Vol. 9, no 4, 0095700- p.
National Category
Medical and Health Sciences
Identifiers
URN: urn:nbn:se:liu:diva-106962DOI: 10.1371/journal.pone.0095700ISI: 000335226500139OAI: oai:DiVA.org:liu-106962DiVA: diva2:721393
Available from: 2014-06-04 Created: 2014-06-02 Last updated: 2017-12-05
In thesis
1. The Non-Invasive Liver Biopsy: Determining Hepatic Function in Diffuse and Focal LiverDisease
Open this publication in new window or tab >>The Non-Invasive Liver Biopsy: Determining Hepatic Function in Diffuse and Focal LiverDisease
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The liver is one of the largest organs within the human body and it handles many vital tasks such as nutrient processing, toxin removal, and synthesis of important proteins. The number of people suffering from chronic liver disease is on the rise, likely due to the present ‘western’ lifestyle. As disease develops in the liver there are pathophysiological manifestations within the liver parenchyma that are both common and important to monitor. These manifestations include inflammation, fatty infiltration (steatosis), excessive scar tissue formation (fibrosis and cirrhosis), and iron loading. Importantly, as the disease progresses there is concurrent loss of liver function. Furthermore, postoperative liver function insufficiency is an important concern when planning surgical treatment of the liver, because it is associated with both morbidity and mortality. Liver function can also be hampered due to drug-induced injuries, an important aspect to consider in drug-development.

Currently, an invasive liver needle biopsy is required to determine the aetiology and to stage or grade the pathophysiological manifestations. There are important limitations with the biopsy, which include, risk of serious complications, mortality, morbidity, inter- and intra-observer variability, sampling error, and sampling variability. Cleary, it would be beneficial to be able investigate the pathophysiological manifestations accurately, non-invasively, and on regional level.

Current available laboratory liver function blood panels are typically insufficient and often only indicate damage at a late stage. Thus, it would be beneficial to have access to biomarkers that are both sensitive and responds to early changes in liver function in both clinical settings and for the pharmaceutical industry and regulatory agencies.

The main aim of this thesis was to develop and evaluate methods that can be used for a ‘non-invasive liver biopsy’ using magnetic resonance (MR). We also aimed to develop sensitive methods for measure liver function based on gadoxetate-enhanced MR imaging (MRI).

The presented work is primarily based on a prospective study on c. 100 patients suffering from chronic liver disease of varying aetiologies recruited due to elevated liver enzyme levels, without clear signs of decompensated cirrhosis. Our results show that the commonly used liver fat cut-off for diagnosing steatosis should be lowered from 5% to 3% when using MR proton-density fat fraction (PDFF). We also show that MR elastography (MRE) is superior in staging fibrosis.

Finally we presented a framework for quantifying liver function based on gadoxetate-enhanced MRI. The method is based on clinical images and a clinical approved contrast agent (gadoxetate). The framework consists of; state-of the-art image reconstruction and correction methods, a mathematical model, and a precise model parametrization method. The model was developed and validated on healthy subjects. Thereafter the model was found applicable on the chronic liver disease cohort as well as validated using gadoxetate levels in biopsy samples and blood samples. The liver function parameters correlated with clinical markers for liver function and liver fibrosis (used as a surrogate marker for liver function).

In summary, it should be possible to perform a non-invasive liver biopsy using: MRI-PDFF for liver fat and iron loading, MRE for liver fibrosis and possibly also inflammation, and measure liver function using the presented framework for analysing gadoxetate-enhanced MRI. With the exception of an MREtransducer no additional hardware is required on the MR scanner. The liver function method is likely to be useful both in a clinical setting and in pharmaceutical trials.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2017. 126 p.
Series
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1564
National Category
Radiology, Nuclear Medicine and Medical Imaging Gastroenterology and Hepatology Biomedical Laboratory Science/Technology Neurology Medical Laboratory and Measurements Technologies
Identifiers
urn:nbn:se:liu:diva-136545 (URN)10.3384/diss.diva-136545 (DOI)9789176855720 (ISBN)
Public defence
2017-05-23, Eken, Campus US, Linköping, 13:15 (English)
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Available from: 2017-04-19 Created: 2017-04-19 Last updated: 2017-04-30Bibliographically approved

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Forsgren, MikaelDahlqvist Leinhard, OlofDahlström, NilsCedersund, GunnarLundberg, Peter

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Forsgren, MikaelDahlqvist Leinhard, OlofDahlström, NilsCedersund, GunnarLundberg, Peter
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Division of Radiological SciencesFaculty of Health SciencesDepartment of Radiation PhysicsCenter for Medical Image Science and Visualization (CMIV)Department of Radiology in LinköpingDivision of Cell Biology
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