The DOI matches its Crossref record; 106 of the 107 references it cites were found in a registry or an index.
Diagnostic accuracy of elastography and magnetic resonance imaging in patients with NAFLD: A systematic review and meta-analysis
Open accessAbstract
BACKGROUND AND AIMS: Vibration-controlled transient elastography (VCTE), point shear wave elastography (pSWE), 2-dimensional shear wave elastography (2DSWE), magnetic resonance elastography (MRE), and magnetic resonance imaging (MRI) have been proposed as non-invasive tests for patients with non-alcoholic fatty liver disease (NAFLD). This study evaluated their diagnostic accuracy for liver fibrosis and non-alcoholic steatohepatitis (NASH). METHODS: PubMED/MEDLINE, EMBASE and the Cochrane Library were searched for studies examining the diagnostic accuracy of these index tests, against histology as the reference standard, in adult patients with NAFLD. Two authors independently screened and assessed methodological quality of studies and extracted data. Summary estimates of sensitivity, specificity and area under the curve (sAUC) were calculated for fibrosis stages and NASH, using a random effects bivariate logit-normal model. RESULTS: We included 82 studies (14,609 patients). Meta-analysis for diagnosing fibrosis stages was possible in 53 VCTE, 11 MRE, 12 pSWE and 4 2DSWE studies, and for diagnosing NASH in 4 MRE studies. sAUC for diagnosis of significant fibrosis were: 0.83 for VCTE, 0.91 for MRE, 0.86 for pSWE and 0.75 for 2DSWE. sAUC for diagnosis of advanced fibrosis were: 0.85 for VCTE, 0.92 for MRE, 0.89 for pSWE and 0.72 for 2DSWE. sAUC for diagnosis of cirrhosis were: 0.89 for VCTE, 0.90 for MRE, 0.90 for pSWE and 0.88 for 2DSWE. MRE had sAUC of 0.83 for diagnosis of NASH. Three (4%) studies reported intention-to-diagnose analyses and 15 (18%) studies reported diagnostic accuracy against pre-specified cut-offs. CONCLUSIONS: When elastography index tests are acquired successfully, they have acceptable diagnostic accuracy for advanced fibrosis and cirrhosis. The potential clinical impact of these index tests cannot be assessed fully as intention-to-diagnose analyses and validation of pre-specified thresholds are lacking. LAY SUMMARY: Non-invasive tests that measure liver stiffness or use magnetic resonance imaging (MRI) have been suggested as alternatives to liver biopsy for assessing the severity of liver scarring (fibrosis) and fatty inflammation (steatohepatitis) in patients with non-alcoholic fatty liver disease (NAFLD). In this study, we summarise the results of previously published studies on how accurately these non-invasive tests can diagnose liver fibrosis and inflammation, using liver biopsy as the reference. We found that some techniques that measure liver stiffness had a good performance for the diagnosis of severe liver scarring.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“Several substantial comparative studies have shown that MRE provides at least equal quality in fi brosis staging as ultrasound-based elastography techniques.”
From EASL-EASD-EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD). (2024) · cites this paper as [157]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies
Whiting, P. F., Rutjes, A. W. S., Westwood, M. E., Mallett, S., Deeks, J. J., Reitsma, J. B., Leeflang, M. M. G., Sterne, J. A. C., Bossuyt, P. M. M., & the QUADAS-2 Group* (2011) · Annals of Internal Medicine · doi:10.7326/0003-4819-155-8-201110180-00009 · Cited by 14,199
Self-citationDesign and validation of a histological scoring system for nonalcoholic fatty liver disease†
Kleiner, D. E., Brunt, E. M., Van Natta, M., Behling, C., Contos, M. J., Cummings, O. W., Ferrell, L. D., Liu, Y., Torbenson, M. S., Unalp‐Arida, A., et al. (2005) · Hepatology · doi:10.1002/hep.20701 · Cited by 10,791
Younossi, Z. M., Koenig, A. B., Abdelatif, D., Fazel, Y., Henry, L., & Wymer, M. (2016) · Hepatology · doi:10.1002/hep.28431 · Cited by 10,781
EASL–EASD–EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease
(2016) · Journal of Hepatology · doi:10.1016/j.jhep.2015.11.004 · Cited by 4,836
Angulo, P., Kleiner, D. E., Dam-Larsen, S., Adams, L. A., Bjornsson, E. S., Charatcharoenwitthaya, P., Mills, P. R., Keach, J. C., Lafferty, H. D., Stahler, A., et al. (2015) · Gastroenterology · doi:10.1053/j.gastro.2015.04.043 · Cited by 2,987
Dulai, P. S., Singh, S., Patel, J., Soni, M., Prokop, L. J., Younossi, Z., Sebastiani, G., Ekstedt, M., Hagstrom, H., Nasr, P., et al. (2017) · Hepatology · doi:10.1002/hep.29085 · Cited by 1,965
Self-citationProgression of NAFLD to diabetes mellitus, cardiovascular disease or cirrhosis
Anstee, Q. M., Targher, G., & Day, C. P. (2013) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/nrgastro.2013.41 · Cited by 1,747
Noninvasive assessment of liver disease in patients with nonalcoholic fatty liver disease
Castera, L., Friedrich-Rust, M., & Loomba, R. (2019) · Gastroenterology · doi:10.1053/j.gastro.2018.12.036 · Cited by 1,494
Eddowes, P. J., Sasso, M., Allison, M., Tsochatzis, E. A., Anstee, Q. M., Sheridan, D. A., Guha, I. N., Cobbold, J., Deeks, J. J., Paradis, V., et al. (2019) · Gastroenterology · doi:10.1053/j.gastro.2019.01.042 · Cited by 1,425
Wong, V. W.-S., Vergniol, J., Wong, G. L.-H., Foucher, J., Chan, H. L.-Y., Le Bail, B., Choi, P. C.-L., Kowo, M., Chan, A. W.-H., Merrouche, W., et al. (2010) · Hepatology · doi:10.1002/hep.23312 · Cited by 1,322
Research works printed but not linked: 4
- Al Juboori, 2018, The utility of liver elastography in evaluation of liver fibrosis in obese patients undergoing bariatric surgery, Hepatology, 68
- Clet, 2018, Expanding the Use of the VCTE XL probe in morbid obese patients: validation of a new automated adaptive measurement depths algorithm in a large UK multicenter cohort, Hepatology, 68
- Hee, 2014, Comparison of NAFLD Fibrosis Score (NFS) and transient elastography (Fibroscan), alone and in combination, as non-invasive methods for the evaluation of fibrosis in non-alcoholic fatty liver disease (NAFLD), J Gastroenterol Hepatol, 29, 76
- Pais, 2011, Validation of a simple algorithm combining serum markers and elastometry for the diagnosis of fibrosis in NAFLD, Hepatology, 54
Cited by
Sorted by citations
Tacke, F., Horn, P., Wai-Sun Wong, V., Ratziu, V., Bugianesi, E., Francque, S., Zelber-Sagi, S., Valenti, L., Roden, M., Schick, F., et al. (2024) · Journal of Hepatology · doi:10.1016/j.jhep.2024.04.031 · Cited by 2,210
Cusi, K., Isaacs, S., Barb, D., Basu, R., Caprio, S., Garvey, W. T., Kashyap, S., Mechanick, J. I., Mouzaki, M., Nadolsky, K., et al. (2022) · Endocrine Practice · doi:10.1016/j.eprac.2022.03.010 · Cited by 1,021
EASL Clinical Practice Guidelines on the management of hepatocellular carcinoma
Sangro, B., Argemi, J., Ronot, M., Paradis, V., Meyer, T., Mazzaferro, V., Jepsen, P., Golfieri, R., Galle, P., Dawson, L., et al. (2025) · Journal of Hepatology · doi:10.1016/j.jhep.2024.08.028 · Cited by 985
Kaplan, D. E., Ripoll, C., Thiele, M. S., Fortune, B. E., Simonetto, D. A., García–Tsao, G., & Bosch, J. (2023) · Hepatology · doi:10.1097/hep.0000000000000647 · Cited by 549
(2024) · Obesity Facts · doi:10.1159/000539371 · Cited by 545
Metadata
- Authors
- Selvaraj, E. A., Mózes, F. E., Jayaswal, A. N. A., Zafarmand, M. H., Vali, Y., Lee, J. A., Levick, C. K., Young, L. A. J., Palaniyappan, N., Liu, C.-H., Aithal, G. P., Romero-Gómez, M., Brosnan, M. J., Tuthill, T. A., Anstee, Q. M., Neubauer, S., Harrison, S. A., Bossuyt, P. M., Pavlides, M., Anstee, Q., Daly, A., Johnson, K., Govaere, O., Cockell, S., Tiniakos, D., Bedossa, P., Oakley, F., Cordell, H., Day, C., Wonders, K., Bossuyt, P., Zafarmand, H., Vali, Y., Lee, J., Ratziu, V., Clement, K., Pais, R., Schuppan, D., Schattenberg, J., Vidal-Puig, T., Vacca, M., Rodrigues-Cuenca, S., Allison, M., Kamzolas, I., Petsalaki, E., Oresic, M., Hyötyläinen, T., McGlinchey, A., Mato, J. M., Millet, O., Dufour, J.-F., Berzigotti, A., Pavlides, M., Harrison, S., Neubauer, S., Cobbold, J., Mozes, F., Akhtar, S., Banerjee, R., Kelly, M., Shumbayawonda, E., Dennis, A., Erpicum, C., Romero-Gómez, M., Gómez-González, E., Ampuero, J., Castell, J., Gallego-Durán, R., Fernández, I., Montero-Vallejo, R., Karsdal, M., Erhardtsen, E., Rasmussen, D., Leeming, D. J., Fisker, M. J., Sinisi, A., Musa, K., Betsou, F., Sandt, E., Tonini, M., Bugianesi, E., Rosso, C., Armandi, A., Marra, F., Gastaldelli, A., Svegliati, G., Boursier, J., Francque, S., Vonghia, L., Ekstedt, M., Kechagias, S., Yki-Jarvinen, H., Luukkonen, P., van Mil, S., Papatheodoridis, G., Cortez-Pinto, H., Valenti, L., Petta, S., Miele, L., Geier, A., Trautwein, C., Aithal, G., Hockings, P., Newsome, P., Wenn, D., Pereira Rodrigues, C. M., Chaumat, P., Hanf, R., Trylesinski, A., Ortiz, P., Duffin, K., Brosnan, J., Tuthill, T., McLeod, E., Ertle, J., Younes, R., Ostroff, R., Alexander, L., Kjær, M. S., Mikkelsen, L. F., Balp, M.-M., Brass, C., Jennings, L., Martic, M., Loeffler, J., Hanauer, G., Shankar, S., Fournier, C., Pepin, K., Ehman, R., Myers, J., Ho, G., Torstenson, R., Myers, R., & Doward, L.
- Affiliations
- University of Oxford (GB)
- Nottingham University Hospitals NHS Trust (GB)
- Pfizer (United States) (US)
- Amsterdam University Medical Centers (NL)
- Instituto de Biomedicina de Sevilla (ES)
- ORCID
- Selvaraj, Emmanuel Anandraj
- Jayaswal, Arjun Narayan Ajmer
- Lee, Jenny A.
- Levick, Christina Kim
- Young, Liam Arnold Joseph
- Palaniyappan, Naaventhan
- Liu, Chang-Hai
- Aithal, Guruprasad Padur
- Tuthill, Theresa A.
- Anstee, Quentin M.
- Daly, Ann
- Johnson, Katherine
- Govaere, Olivier
- Cockell, Simon
- Tiniakos, Dina
- Oakley, Fiona
- Cordell, Heather
- Day, Chris
- Wonders, Kristy
- Ratziu, Vlad
- Pais, Raluca
- Schuppan, Detlef
- Schattenberg, Jörn
- Kamzolas, Ioannis
- Petsalaki, Evangelia
- Hyötyläinen, Tuulia
- Dufour, Jean-François
- Berzigotti, Annalisa
- Cobbold, Jeremy
- Banerjee, Rajarshi
- Kelly, Matt
- Shumbayawonda, Elizabeth
- Dennis, Andrea
- Gómez-González, Emilio
- Ampuero, Javier
- Karsdal, Morten
- Rasmussen, Daniel
- Betsou, Fay
- Bugianesi, Elisabetta
- Rosso, Chiara
- Armandi, Angelo
- Marra, Fabio
- Gastaldelli, Amalia
- Boursier, Jérôme
- Francque, Sven
- Vonghia, Luisa
- Ekstedt, Mattias
- Kechagias, Stergios
- Luukkonen, Panu
- Papatheodoridis, George
- Valenti, Luca
- Petta, Salvatore
- Miele, Luca
- Geier, Andreas
- Trautwein, Christian
- Hockings, Paul
- Newsome, Philip
- Ortiz, Pablo
- Duffin, Kevin
- Ertle, Judith
- Younes, Ramy
- Ostroff, Rachel
- Alexander, Leigh
- Kjær, Mette Skalshøi
- Mikkelsen, Lars Friis
- Balp, Maria-Magdalena
- Jennings, Lori
- Loeffler, Juergen
- Shankar, Sudha
- Fournier, Céline
- Pepin, Kay
- Ehman, Richard
- Ho, Gideon
- Doward, Lynda
- Venue
- Journal of Hepatology
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 75
- Issue
- 4
- Pages
- 770-785
- ISSN
- 0168-8278 (ISSN-L)
- OpenAlex
- W3161701468
- Cited by
- 376
- References
- 107
- Citations by year
- 2026: 67 · 2025: 84 · 2024: 77 · 2023: 81 · 2022: 52 · 2021: 14
- FWCI
- 8.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of Hepatology (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Deposito de Investigacion Universidad de Sevilla (University of Seville) (publishedVersion, cc-by-nc-nd)
- Pure Amsterdam UMC (publishedVersion, cc-by)
- University of Birmingham Research Portal (University of Birmingham) (publishedVersion, cc-by-nc-nd)
- Repository@Nottingham (University of Nottingham) (acceptedVersion, other-oa)
- Florence Research (University of Florence) (submittedVersion, cc-by-nc-nd)
- Repository@Nottingham (University of Nottingham) (submittedVersion, cc-by-nc-nd)
- Lirias (KU Leuven) (submittedVersion, cc-by-nc-nd)
- Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
- HAL (submittedVersion)
- Archivio Istituzionale della Ricerca (Universita Degli Studi Di Milano) (submittedVersion, other-oa)
- DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, cc-by-nc-nd)
- Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
- Nova Science Publishers (Nova Science Publishers, Inc.) (submittedVersion, other-oa)
- PubliCatt (Università Cattolica del Sacro Cuore) (submittedVersion, cc-by-nc-nd)
- CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (submittedVersion, cc-by-nc-nd)
- Newcastle University ePrints (Newcastle Univesity) (cc-by-nc-nd)
- Funders
- European Federation of Pharmaceutical Industries and Associations
- Innovative Medicines Initiative (777377)
- Topics
- Health Sciences › Medicine › Hepatology › Liver Disease Diagnosis and Treatment
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Ultrasound Imaging and Elastography
- Health Sciences › Medicine › Hepatology › Liver Disease and Transplantation
- Keywords
- non-alcoholic fatty liver disease, liver fibrosis, nonalcoholic steatohepatitis, vibration-controlled transient elastography, point shear wave elastography, two-dimensional shear wave elastography, magnetic resonance elastography, magnetic resonance imaging, noninvasive fibrosis assessment, liver cirrhosis, diagnostic accuracy
- MeSH terms
- Adult, Humans, Magnetic Resonance Imaging, ROC Curve, Area Under Curve, Elasticity Imaging Techniques, Non-alcoholic Fatty Liver Disease
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review