Variability of noninvasive MRI and biological markers in compensated cirrhosis: insights for assessing disease progression

Christopher R Bradley, Eleanor F Cox, Naaventhan Palaniyappan, Guruprasad P Aithal, Susan T Francis, Indra Neil Guha, Christopher R Bradley, Eleanor F Cox, Naaventhan Palaniyappan, Guruprasad P Aithal, Susan T Francis, Indra Neil Guha

Abstract

Background: We annually monitored stable compensated cirrhosis (CC) patients to evaluate serial variation in blood serum, liver stiffness, and multiparametric magnetic resonance imaging (mpMRI) measures to provide reference change values (RCV) and sample size measures for future studies.

Methods: Patients were recruited from a prospectively followed CC cohort, with assessments at baseline and annually over three years. We report on blood markers, transient elastography liver stiffness measures (LSM) and noninvasive mpMRI (volume, T1 mapping, blood flow, perfusion) of the liver, spleen, kidneys, and heart in a stable CC group and a healthy volunteer (HV) group. Coefficient of variation over time (CoVT) and RCV are reported, along with hazard ratio to assess disease progression. Sample size estimates to power future trials of cirrhosis regression on mpMRI are presented.

Results: Of 60 CC patients enrolled, 28 with stable CC were followed longitudinally and compared to 10 HVs. CoVT in mpMRI measures was comparable between CC and HV groups. CoVT of Enhanced Liver Fibrosis score was low (< 5%) compared to Fibrosis-4 index (17.9%) and Aspartate Aminotransferase-to-Platelet-Ratio Index (19.4%). A large CoVT (20.7%) and RCV (48.3%) were observed for LSM. CoVT and RCV were low for liver, spleen, and renal T1 values (CoVT < 5%, RCV < 8%) and volume (CoVT < 10%, RCV < 16%); haemodynamic measures were high (CoVT 12-25%, RCV 16-47%).

Conclusions: Evidence of low CoVT and RCV in multiorgan T1 values. RCV and sample size estimates are provided for future longitudinal multiorgan monitoring in CC patients.

Trial registration: ClinicalTrials.gov identifier: NCT02037867 , Registered: 05/01/2013.

Keywords: Biomarkers; Disease progression; Liver cirrhosis; Multiparametric magnetic resonance imaging; Sample size.

Conflict of interest statement

The authors declare that they have no competing interests.

© 2022. The Author(s) under exclusive licence to European Society of Radiology.

Figures

Fig. 1
Fig. 1
Schematic of the study and consort diagram. Schematic of blood markers (MELD, UKELD, APRI, FIB4, ELF), Fibroscan® LSM, and multiparametric magnetic resonance imaging (volume, T1-mapping, blood flow, perfusion) of the liver, spleen and kidneys, and cardiac index. Illustration of healthy volunteers (HV) and compensated cirrhosis (CC) patients studied longitudinally as indicated by consort diagram. MELD Model for end-stage liver disease, UKELD United Kingdom model for end-stage liver disease, APRI Aspartate aminotransferase to platelet ratio index, FIB4 Fibrosis-4, ELF Enhanced liver fibrosis, LSM Liver stiffness measure, TE Transient elastography
Fig. 2
Fig. 2
Baseline magnetic resonance imaging parameters for 40 healthy volunteers (HV), 60 compensated cirrhosis (CC) patients, and 7 decompensated cirrhosis (DC) patients. Baseline measures (mean and standard error of the mean) of the liver (volume, portal vein area, total hepatic blood flow, liver perfusion, liver T1), spleen (volume, splenic artery flow and superior mesenteric artery flow, spleen perfusion, spleen T1), kidney (renal cortex T1), and heart (cardiac index and left ventricle [LV] wall mass index) are shown, with the percentage change between the HV and CC groups, and CC and DC groups shown by arrows. Asterisk indicates measures which are significantly different (p < 0.05, independent samples t-test) between the CC and HV group [10]
Fig. 3
Fig. 3
a Year-to-year percentage change in clinical measures in the stable compensated cirrhosis control group. b Year-to-year coefficient of variation (CoVT) in clinical measures in the stable compensated cirrhosis control group. Clinical measures of MELD, UKELD, APRI, FIB4, ELF scores, and Fibroscan® LSM are shown. Bars indicate the interquartile range and the horizontal bold line shows the median, dots represent outliers. MELD Model for end-stage liver disease, UKELD United Kingdom model for end-stage liver disease, APRI Aspartate aminotransferase to platelet ratio index, FIB4 Fibrosis-4, ELF Enhanced liver fibrosis, LSM Liver stiffness measure
Fig. 4
Fig. 4
Year-to-year percentage change in magnetic resonance imaging measures in the stable compensated cirrhosis control group. Measures liver (volume, portal vein area, total hepatic blood flow, liver perfusion, liver T1), spleen (volume, splenic and superior mesenteric artery flow, spleen perfusion, spleen T1), kidney (renal cortex T1), and heart (cardiac index and left ventricle [LV] wall mass index) are shown. Bars indicate interquartile range and horizontal bold line shows the median percentage change, dots represent outliers
Fig. 5
Fig. 5
Year-to-year coefficient of variation (CoVT) in magnetic resonance imaging measures in the stable compensated cirrhosis control group. Bars indicate the interquartile range and the horizontal bold line shows the median CoVT at each time point, dots represent outliers. The technical variation termed the analytical CoV (CoVA) measured in healthy volunteers from triplicate scans collected 1 week apart [10] is shown by the grey dashed line
Fig. 6
Fig. 6
ELF score, Fibroscan® LSM and liver T1 for the stable compensated cirrhosis control patients who completed all three annual follow-up scans: a individual subject percentage change from baseline values at year 1, year 2, and year 3; b group percentage change from baseline values at year 1, year 2, and year 3. Bars indicate the interquartile range and bold line shows the median percentage change. There was no significant difference from baseline (p > 0.05 Bonferroni-corrected); c Year-to-year coefficient of variation (CoVT) in magnetic resonance imaging measures. Bars indicate the interquartile range and the horizontal bold line shows the median CoV, dots represent outliers. ELF Enhanced liver fibrosis, LSM Liver stiffness measure
Fig. 7
Fig. 7
A schematic of the hazard ratio (HR) for disease progression. Panel a (i) shows the progression from healthy volunteer (HV) to compensated cirrhosis (CC), whilst panel a (ii) shows that the hazard ratio for progression from CC to decompensated cirrhosis (DC). Positive values indicate an increase in measure and negative values a decrease in measures. If the absolute value of the HR is >1 this indicates the reference change value (RCV) is less than the clinical change. Panel b shows sample size estimation for the number of CC patients required in clinical trial to detect a change from stage F4 (compensated cirrhosis, CC) to F3 (advanced cirrhosis) liver disease which has clinical significance; data points are shown for a 25%, 50%, 75%, and 100% regression from F4 to F3. BSA Body surface area

References

    1. Moreno C, Mueller S, Szabo G. Non-invasive diagnosis and biomarkers in alcohol-related liver disease. J Hepatol. 2019;70:273–283. doi: 10.1016/J.JHEP.2018.11.025.
    1. Vilar-Gomez E, Chalasani N. Non-invasive assessment of non-alcoholic fatty liver disease: Clinical prediction rules and blood-based biomarkers. J Hepatol. 2018;68:305–315. doi: 10.1016/J.JHEP.2017.11.013.
    1. Castera L, Friedrich-Rust M, Loomba R. Noninvasive assessment of liver disease in patients with nonalcoholic fatty liver disease. Gastroenterology. 2019;156:1264–1281. doi: 10.1053/J.GASTRO.2018.12.036.
    1. Li Y, Huang YS, Wang ZZ, et al. Systematic review with meta-analysis: the diagnostic accuracy of transient elastography for the staging of liver fibrosis in patients with chronic hepatitis B. Aliment Pharmacol Ther. 2016;43:458–469. doi: 10.1111/APT.13488.
    1. Jayaswal ANA, Levick C, Selvaraj EA, et al. Prognostic value of multiparametric magnetic resonance imaging, transient elastography and blood-based fibrosis markers in patients with chronic liver disease. Liver Int. 2020;40:3071–3082. doi: 10.1111/LIV.14625.
    1. Vergniol J, Foucher J, Terrebonne E, et al. Noninvasive tests for fibrosis and liver stiffness predict 5-year outcomes of patients with chronic hepatitis C. Gastroenterology. 2011;140:1970–1979. doi: 10.1053/J.GASTRO.2011.02.058.
    1. Angulo P, Bugianesi E, Bjornsson ES, et al. Simple noninvasive systems predict long-term outcomes of patients with nonalcoholic fatty liver disease. Gastroenterology. 2013;145:782–789. doi: 10.1053/J.GASTRO.2013.06.057.
    1. Parkes J, Roderick P, Harris S, et al. Enhanced liver fibrosis test can predict clinical outcomes in patients with chronic liver disease. Gut. 2010;59:1245–1251. doi: 10.1136/GUT.2009.203166.
    1. Bloom S, Kemp W, Nicoll A, et al. Liver stiffness measurement in the primary care setting detects high rates of advanced fibrosis and predicts liver-related events in hepatitis C. J Hepatol. 2018;69:575–583. doi: 10.1016/J.JHEP.2018.04.013.
    1. Bradley CR, Cox EF, Scott RA, et al. Multi-organ assessment of compensated cirrhosis patients using quantitative magnetic resonance imaging. J Hepatol. 2018;69:1015–1024. doi: 10.1016/J.JHEP.2018.05.037.
    1. Hoad CL, Palaniyappan N, Kaye P, et al. A study of T1 relaxation time as a measure of liver fibrosis and the influence of confounding histological factors. NMR Biomed. 2015;28:706–714. doi: 10.1002/NBM.3299.
    1. Iwakiri Y, Groszmann RJ. The hyperdynamic circulation of chronic liver diseases: From the patient to the molecule. Hepatology. 2006;43:S121–S131. doi: 10.1002/hep.20993.
    1. Møller S, Henriksen JH. Cardiovascular complications of cirrhosis. Postgrad Med J. 2009;85:44–54. doi: 10.1136/gut.2006.112177.
    1. Schrier RW, Arroyo V, Bernardi M, et al. Peripheral arterial vasodilation hypothesis: a proposal for the initiation of renal sodium and water retention in cirrhosis. Hepatology. 1988;8:1151–1157. doi: 10.1002/HEP.1840080532.
    1. Istaces N, Gulbis B. Study of FibroTest and hyaluronic acid biological variation in healthy volunteers and comparison of serum hyaluronic acid biological variation between chronic liver diseases of different etiology and fibrotic stage using confidence intervals. Clin Biochem. 2015;48:652–657. doi: 10.1016/J.CLINBIOCHEM.2015.03.020.
    1. Rossi E, Adams LA, Ching HL, et al. High biological variation of serum hyaluronic acid and hepascore, a biochemical marker model for the prediction of liver fibrosis. Clin Chem Lab Med. 2013;51:1107–1114. doi: 10.1515/CCLM-2012-0584/MACHINEREADABLECITATION/RIS.
    1. Jabor A, Kubíček Z, Fraňková S, et al. Enhanced liver fibrosis (ELF) score: reference ranges, biological variation in healthy subjects, and analytical considerations. Clin Chim Acta. 2018;483:291–295. doi: 10.1016/J.CCA.2018.05.027.
    1. Trivedi PJ, Muir AJ, Levy C, et al. Inter- and intra-individual variation, and limited prognostic utility, of serum alkaline phosphatase in a trial of patients with primary sclerosing cholangitis. Clin Gastroenterol Hepatol. 2021;19:1248–1257. doi: 10.1016/J.CGH.2020.07.032.
    1. Nascimbeni F, Lebray P, Fedchuk L, et al. Significant variations in elastometry measurements made within short-term in patients with chronic liver diseases. Clin Gastroenterol Hepatol. 2015;13:763–771.e6. doi: 10.1016/J.CGH.2014.07.037.
    1. Harrison SA, Dennis A, Fiore MM, et al. Utility and variability of three non-invasive liver fibrosis imaging modalities to evaluate efficacy of GR-MD-02 in subjects with NASH and bridging fibrosis during a phase-2 randomized clinical trial. PLoS One. 2018;13:e0203054. doi: 10.1371/JOURNAL.PONE.0203054.
    1. Cirrhosis in over 16s: assessment and management. NICE guideline NG50. .
    1. Palaniyappan N, Cox E, Bradley C, et al. Non-invasive assessment of portal hypertension using quantitative magnetic resonance imaging. J Hepatol. 2016;65:1131–1139. doi: 10.1016/J.JHEP.2016.07.021.
    1. Gardener AG, Francis ST. Multislice perfusion of the kidneys using parallel imaging: image acquisition and analysis strategies. Magn Reson Med. 2010;63:1627–1636. doi: 10.1002/MRM.22387.
    1. Cox EF, Buchanan CE, Bradley CR, et al. Multiparametric renal magnetic resonance imaging: validation, interventions, and alterations in chronic kidney disease. Front Physiol. 2017;8:696. doi: 10.3389/FPHYS.2017.00696/BIBTEX.
    1. Natori S, Lai S, Finn JP et al (2006) Cardiovascular function in multi-ethnic study of atherosclerosis: normal values by age, sex, and ethnicity. AJR Am J Roentgenol 186. 10.2214/AJR.04.1868
    1. Buxton RB, Frank LR, Wong EC, et al. A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magn Reson Med. 1998;40:383–396. doi: 10.1002/MRM.1910400308.
    1. Fahim MA, Hayen AD, Horvath AR, et al. Biological variation of high sensitivity cardiac troponin-T in stable dialysis patients: Implications for clinical practice. Clin Chem Lab Med. 2015;53:715–722. doi: 10.1515/CCLM-2014-0838/MACHINEREADABLECITATION/RIS.
    1. Selvaraj EA, Mózes FE, Jayaswal ANA, et al. Diagnostic accuracy of elastography and magnetic resonance imaging in patients with NAFLD: a systematic review and meta-analysis. J Hepatol. 2021;75:770–785. doi: 10.1016/J.JHEP.2021.04.044/ATTACHMENT/57673770-93F5-4735-AE87-FED4DA98ED9C/MMC3.PDF.
    1. Vali Y, Lee J, Boursier J, et al. Enhanced liver fibrosis test for the non-invasive diagnosis of fibrosis in patients with NAFLD: A systematic review and meta-analysis. J Hepatol. 2020;73:252–262. doi: 10.1016/J.JHEP.2020.03.036/ATTACHMENT/CEF13473-AF0E-4523-9A59-1913BBBDB1FB/MMC3.PDF.
    1. Bachtiar V, Kelly MD, Wilman HR et al (2019) Repeatability and reproducibility of multiparametric magnetic resonance imaging of the liver. PLoS One 14. 10.1371/JOURNAL.PONE.0214921
    1. Jayakumar S, Middleton MS, Lawitz EJ, et al. Longitudinal correlations between MRE, MRI-PDFF, and liver histology in patients with non-alcoholic steatohepatitis: analysis of data from a phase II trial of selonsertib. J Hepatol. 2019;70:133–141. doi: 10.1016/J.JHEP.2018.09.024.
    1. Kamath PS, Mookerjee RP. Expanding consensus in portal hypertension: Report of the Baveno VI Consensus Workshop: Stratifying risk and individualizing care for portal hypertension. J Hepatol. 2015;63:743–752. doi: 10.1016/J.JHEP.2015.05.022.
    1. Siddiqui MS, Yamada G, Vuppalanchi R, et al. Diagnostic accuracy of noninvasive fibrosis models to detect change in fibrosis stage. Clin Gastroenterol Hepatol. 2019;17:1877–1885. doi: 10.1016/J.CGH.2018.12.031.
    1. Hartl J. Liver elastography: clinical use and interpretation. 2020. Liver stiffness in autoimmune hepatitis; pp. 181–186.
    1. Mozes FE, Tunnicliffe EM, Moolla A, et al. Mapping tissue water T1 in the liver using the MOLLI T1 method in the presence of fat, iron and B0 inhomogeneity. NMR Biomed. 2019;32:e4030. doi: 10.1002/NBM.4030.
    1. Choi JY, Kim H, Sun M, Sirlin CB. Histogram analysis of hepatobiliary phase MR imaging as a quantitative value for liver cirrhosis: preliminary observations. Yonsei Med J. 2014;55:651. doi: 10.3349/YMJ.2014.55.3.651.
    1. Galbraith SM, Lodge MA, Taylor NJ, et al. Reproducibility of dynamic contrast-enhanced MRI in human muscle and tumours: comparison of quantitative and semi-quantitative analysis. NMR Biomed. 2002;15:132–142. doi: 10.1002/NBM.731.
    1. Sanyal AJ, Anstee QM, Trauner M, et al. Cirrhosis regression is associated with improved clinical outcomes in patients with nonalcoholic steatohepatitis. Hepatology. 2022;75:1235–1246. doi: 10.1002/HEP.32204.

Source: PubMed

3
S'abonner