Cited by 212Liver Disease Diagnosis and TreatmentAdvanced MRI Techniques and ApplicationsLiver Disease and Transplantation

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Multiparametric magnetic resonance imaging predicts clinical outcomes in patients with chronic liver disease

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Pavlides, Michael, Banerjee, Rajarshi, Sellwood, Joanne, Kelly, Catherine J., Robson, Matthew D., Booth, Jonathan C., Collier, Jane, Neubauer, Stefan, Barnes, Eleanor·2016·Journal of Hepatology

DOI 10.1016/j.jhep.2015.10.009PMC PMC4751288

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Abstract

BACKGROUND & AIMS: Multiparametric magnetic resonance (MR) imaging has been demonstrated to quantify hepatic fibrosis, iron, and steatosis. The aim of this study was to determine if MR can be used to predict negative clinical outcomes in liver disease patients. METHODS: Patients with chronic liver disease (n=112) were recruited for MR imaging and data on the development of liver related clinical events were collected by medical records review. The median follow-up was 27months. MR data were analysed blinded for the Liver Inflammation and Fibrosis score (LIF; <1, 1-1.99, 2-2.99, and ⩾3 representing normal, mild, moderate, and severe liver disease, respectively), T2∗ for liver iron content and proportion of liver fat. Baseline liver biopsy was performed in 102 patients. RESULTS: Liver disease aetiologies included non-alcoholic fatty liver disease (35%) and chronic viral hepatitis (30%). Histologically, fibrosis was mild in 54 (48%), moderate in 17 (15%), and severe in 31 (28%) patients. Overall mortality was 5%. Ten patients (11%) developed at least one liver related clinical event. The negative predictive value of LIF<2 was 100%. Two patients with LIF 2-2.99 and eight with LIF⩾3 had a clinical event. Patients with LIF⩾3 had a higher cumulative risk for developing clinical events, compared to those with LIF<1 (p=0.02) and LIF 1-1.99 (p=0.03). Cox regression analysis including all 3 variables (fat, iron, LIF) resulted in an enhanced LIF predictive value. CONCLUSIONS: Non-invasive standardised multiparametric MR technology may be used to predict clinical outcomes in patients with chronic liver disease.

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Fig. 1 Study flow chart. The Liver Inflammation and Fibrosis (LIF) score is a standardised continuous score (0–4) derived from liver T1 and T2∗ values. T1 primarily reflects the amount of extracellular fluid and can change with inflammation and fibrosis and T2∗ primarily reflects the amount of iron deposition. Liver iron has a confounding effect on T1, and this is accounted for in the LIF score calculation. ∗Liver iron concentration from T2∗ maps and hence LIF calculation was not possible in 4 cases. MR, magnetic resonance; 1H-MRS: proton (1H) magnetic resonance spectroscopy; LIF, Liver Inflammation and Fibrosis score.CC BY · PubMed Central

Fig. 2 Examples of LiverMultiScan MR data. Representative images from patients in each Liver Inflammation and Fibrosis (LIF) severity category, produced by analysis of the raw data using LiverMultiScan. LIF was measured in operator chosen regions of interest in the right liver lobe, in the liver parenchyma, away from vascular and biliary structures. The LIF scores measured for each image in this figure are indicated under each image. The predefined colour scale used to generate these maps is also included. LIF, Liver Inflammation and Fibrosis score. (This figure appears in colour on the web.)CC BY · PubMed Central

Fig. 3 Kaplan–Meier curves for liver related event free survival with patients stratified according to Liver Inflammation and Fibrosis scores. In the entire cohort, (A) there were significant differences between those with Liver Inflammation and Fibrosis (LIF) ⩾3 vs. LIF <1 (p = 0.02) and vs. LIF 1–1.99 (p = 0.003). There was a strong trend towards significance between LIF 2–2.99 vs. LIF 1–1.99 (p = 0.054). Including only compensated patients at baseline, (B) there was a significant difference between LIF ⩾ 3 vs. LIF 1–1.99 (p = 0.023) and a strong trend towards significance between LIF 2–2.99 vs. LIF 1–1.99 (p = 0.058). (This figure appears in colour on the web.)CC BY · PubMed Central

Fig. 4 Kaplan–Meier curves for liver related event free survival with patients stratified according to severity of liver iron and fat. There were no significant differences between the curves for (A) iron or (B) fat. Liver fat was categorised according to the liver fat content measured by proton magnetic resonance spectroscopy as 0: <1.5%, 1: 1.5% <fat <7.5% and 2: ⩾7.5%. Liver iron was categorised according to T2∗ as low iron: T2∗ >12.5 ms or high iron: T2∗ ⩽12.5 ms. (This figure appears in colour on the web.)CC BY · PubMed Central

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Histological grading and staging of chronic hepatitis.
Ishak, K., Baptista, A., Bianchi, L., Callea, F., Groote, J. D., Gudat, F., Denk, H., Desmet, V., Korb, G., Macsween, R. N., et al. (1995) · Journal of Hepatology · doi:10.1016/0168-8278(95)80226-6 · Cited by 5,290
Global epidemiology of hepatitis C virus infection
Shepard, C. W., Finelli, L., & Alter, M. J. (2005) · The Lancet Infectious Diseases · doi:10.1016/s1473-3099(05)70216-4 · Cited by 2,758
Natural history and prognostic indicators of survival in cirrhosis: A systematic review of 118 studies
D'Amico, G., Garcia-Tsao, G., & Pagliaro, L. (2006) · Journal of Hepatology · doi:10.1016/j.jhep.2005.10.013 · Cited by 2,687
Prevalence of Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis Among a Largely Middle-Aged Population Utilizing Ultrasound and Liver Biopsy: A Prospective Study
Williams, C. D., Stengel, J. Z., Asike, M., Torres, D. M., Shaw, J. C., Contreras, M., Landt, C. L., & Harrison, S. A. (2010) · Gastroenterology · doi:10.1053/j.gastro.2010.09.038 · Cited by 2,182
Pitfalls of Liver Stiffness Measurement: A 5-Year Prospective Study of 13,369 Examinations
Castéra, L., Foucher, J., Bernard, P., Carvalho, F., Allaix, D., Merrouche, W., Couzigou, P., & de Lédinghen, V. (2009) · Hepatology · doi:10.1002/hep.23425 · Cited by 1,157
Serum markers detect the presence of liver fibrosis: A cohort study
Rosenberg, W. M., Voelker, M., Thiel, R. P., Becka, M., Burt, A. D., Schuppan, D., Hübscher, S. G., Roskams, T., Pinzani, M., & Arthur, M. J. P. (2004) · Gastroenterology · doi:10.1053/j.gastro.2004.08.052 · Cited by 1,103
Shortened Modified Look-Locker Inversion recovery (ShMOLLI) for clinical myocardial T1-mapping at 1.5 and 3 T within a 9 heartbeat breathhold
Piechnik, S. K., Ferreira, V. M., Dall’Armellina, E., Cochlin, L. E., Greiser, A., Neubauer, S., & Robson, M. D. (2010) · Journal of Cardiovascular Magnetic Resonance · doi:10.1186/1532-429x-12-69 · Cited by 670
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Williams, R. L., Aspinall, R., Bellis, M. A., Camps-Walsh, G., Cramp, M., Dhawan, A. K., Ferguson, J., Forton, D. M., Foster, G. R., Gilmore, S. I., et al. (2014) · The Lancet · doi:10.1016/s0140-6736(14)61838-9 · Cited by 601
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Singh, S., Venkatesh, S. K., Wang, Z., Miller, F. H., Motosugi, U., Low, R. N., Hassanein, T., Asbach, P., Godfrey, E. M., Yin, M., et al. (2014) · Clinical Gastroenterology and Hepatology · doi:10.1016/j.cgh.2014.09.046 · Cited by 549
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Pais, R., Charlotte, F., Fedchuk, L., Bédossa, P., Lebray, P., Poynard, T., & Ratziu, V. (2013) · Journal of Hepatology · doi:10.1016/j.jhep.2013.04.027 · Cited by 520

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Young, L. A. J., Ceresa, C. D. L., Mózes, F. E., Ellis, J., Valkovič, L., Colling, R. T., Coussios, C. C., Friend, P. J., & Rodgers, C. T. (2021) · Magnetic Resonance in Medicine · doi:10.1002/mrm.28930 · Cited by 4
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Verde, F., Romeo, V., & Maurea, S. (2020) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims.2020.02.03 · Cited by 4
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Ghavamian, A., Liu, C., Kang, B., Yuan, X., Wang, X., Gao, L., & Zhao, X. (2022) · British Journal of Radiology · doi:10.1259/bjr.20211008 · Cited by 3
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Cruz, M., Ferreira, A. A., Papanikolaou, N. K., Banerjee, R., & Caseiro‐Alves, F. (2020) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2020.06.004 · Cited by 3
Self-citation
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Ahanonu, E., Goerke, U., Johnson, K. M., Toner, B. P., Martín, D. R., Deshpande, V. S., Bilgin, A., & Altbach, M. I. (2024) · NMR in Biomedicine · doi:10.1002/nbm.5266 · Cited by 2
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Gandhi, D. B., Pednekar, A. S., Wang, H., Tkach, J. A., Dudley, J. A., Manhard, M. K., Trout, A. T., & Dillman, J. R. (2022) · Journal of Computer Assisted Tomography · doi:10.1097/rct.0000000000001409 · Cited by 2
Voxel-Wise Analysis of Paediatric Liver MRI
Ridgway, G. R., Janowski, K., Dennis, A., Bachtiar, V., McGonigle, J., Everitt, C., Darekar, A., Breen, D. J., Green, D., Neubauer, S., et al. (2018) · Communications in computer and information science · doi:10.1007/978-3-319-95921-4_7 · Cited by 2
Self-citation
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Mukherjee, S. (2020) · World Journal of Pharmacology · doi:10.5497/wjp.v9.i1.1 · Cited by 2
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Aitcheson, G. A., Cezar, C., John, I., & John, B. V. (2021) · PubMed Central · Cited by 2
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Wilman, H., Jacobs, J., Newbould, R. D., Halberstadt, M., Gyngell, M., Herlihy, A., Fernandes, C., Kelly, C., Maguire, M., Jayaratne, N., et al. (2018) · Journal of Hepatology · doi:10.1016/s0168-8278(18)31381-3 · Cited by 2
Self-citation
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Alzoubi, O., Arar, A., Singh, V., Ertürk, Ş. M., Mózes, F. E., & Pavlides, M. (2022) · Portal hypertension & cirrhosis · doi:10.1002/poh2.6 · Cited by 2
Self-citation
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McKiernan, P. J. (2020) · Journal of Pediatric Gastroenterology and Nutrition · doi:10.1097/mpg.0000000000002949 · Cited by 2
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Palaniyappan, N., Guha, I. N., & Aithal, G. P. (2018) · Springer eBooks · doi:10.1007/978-3-319-72628-1_14 · Cited by 1
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Kathale, S. V., & Vyas, J. (2020) · American Journal of PharmTech Research · doi:10.46624/ajptr.2020.v10.i1.015 · Cited by 1
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Self-citation
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Ronot, M., & Vilgrain, V. (2016) · Liver International · doi:10.1111/liv.13089
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Chan, H. L., & Wong, V. W. (2017) · Springer eBooks · doi:10.1007/978-981-10-4843-2_9
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Xiao, B., Huang, H., Wang, L., Qiu, S.-W., Guo, S., & Wáng, Y. X. J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/633024
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Pavlides, M. (2020) · Springer eBooks · doi:10.1007/978-3-030-37173-9_8
Self-citation
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Imajo, K., Honda, Y., Yoneda, M., Saito, S., & Nakajima, A. (2022) · Choonpa Igaku · doi:10.3179/jjmu.jjmu.r.203
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Parmar, K. L., O'Reilly, D. A., Valle, J. W., Braun, M., Malcomson, L., Jones, R. P., Balaa, F. K., Rees, M., Welsh, F. K. S., Filobbos, R., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2021-059369
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Yang, L., Shen, Q., Huang, H., Xie, J., Liu, Z., & Xu, C. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4850050
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Saviano, A., & Serfaty, L. (2025) · EMC - Hépatologie · doi:10.1016/s1155-1976(25)46793-x
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Yang, R., Shen, C., Jia, Y., Yao, Y., Zhou, Y., Cheng, Y., Cheng, Y., Zeng, R., Wan, Z., Zhao, Q., et al. (2025) · Clinical and Translational Gastroenterology · doi:10.14309/ctg.0000000000000949 · Cited by 2

Authors: Pavlides, M., Banerjee, R., Sellwood, J., Kelly, C. J., Robson, M. D., Booth, J. C., Collier, J., Neubauer, S., & Barnes, E.
Affiliations: University of Oxford (GB)
Perspectum Ltd. (United Kingdom) (GB)
Royal Berkshire Hospital (GB)
Corresponding author: Barnes, Eleanor
ORCID: Pavlides, Michael
Banerjee, Rajarshi
Robson, Matthew D.
Collier, Jane
Neubauer, Stefan
Barnes, Eleanor
Venue: Journal of Hepatology
Publisher: Elsevier BV
Published: 2016-02
Volume: 64
Issue: 2
Pages: 308-315
ISSN: 0168-8278 (ISSN-L)
DOI: 10.1016/j.jhep.2015.10.009
PMC: PMC4751288
OpenAlex: W2201457922
Cited by: 212
References: 37
Citations by year: 2026: 10 · 2025: 17 · 2024: 18 · 2023: 9 · 2022: 24 · 2021: 29 · 2020: 38 · 2019: 14 · 2018: 24 · 2017: 14 · 2016: 15
FWCI: 13.85
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: cc-by
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Journal of Hepatology (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Funders: Oxford NIHR Biomedical Research Centre
Topics: Health Sciences › Medicine › Hepatology › Liver Disease Diagnosis and Treatment
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
Health Sciences › Medicine › Hepatology › Liver Disease and Transplantation
Keywords: chronic liver disease, multiparametric MRI, liver fibrosis, liver iron concentration, hepatic steatosis, non-alcoholic fatty liver disease, chronic viral hepatitis, T2-weighted imaging
MeSH terms: Adult, Biopsy, Female, Fibrosis, United Kingdom, Hepatitis, Chronic, Humans, Inflammation, Liver, Magnetic Resonance Imaging, Male, Middle Aged, Predictive Value of Tests, Prognosis, Severity of Illness Index, Risk Assessment, Kaplan-Meier Estimate, Non-alcoholic Fatty Liver Disease

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Multiparametric magnetic resonance imaging predicts clinical outcomes in patients with chronic liver disease

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