The DOI matches its Crossref record; 94 of the 94 references it cites were found in a registry or an index.
Non-Invasive markers for hepatic fibrosis
Open accessAbstract
With great advancements in the therapeutic modalities used for the treatment of chronic liver diseases, the accurate assessment of liver fibrosis is a vital need for successful individualized management of disease activity in patients. The lack of accurate, reproducible and easily applied methods for fibrosis assessment has been the major limitation in both the clinical management and for research in liver diseases. However, the problem of the development of biomarkers capable of non-invasive staging of fibrosis in the liver is difficult due to the fact that the process of fibrogenesis is a component of the normal healing response to injury, invasion by pathogens, and many other etiologic factors. Current non-invasive methods range from serum biomarker assays to advanced imaging techniques such as transient elastography and magnetic resonance imaging (MRI). Among non-invasive methods that gain strongest clinical foothold are FibroScan elastometry and serum-based APRI and FibroTest. There are many other tests that are not yet widely validated, but are none the less, promising. The rate of adoption of non-invasive diagnostic tests for liver fibrosis differs from country to country, but remains limited. At the present time, use of non-invasive procedures could be recommended as pre-screening that may allow physicians to narrow down the patients' population before definitive testing of liver fibrosis by biopsy of the liver. This review provides a systematic overview of these techniques, as well as both direct and indirect biomarkers based approaches used to stage fibrosis and covers recent developments in this rapidly advancing area.
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.
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
Sterling, R. K., Lissen, E., Clumeck, N., Sola, R., Correa, M. C., Montaner, J., S. Sulkowski, M., Torriani, F. J., Dieterich, D. T., Thomas, D. L., et al. (2006) · Hepatology · doi:10.1002/hep.21178 · Cited by 5,021
Bataller, R., & Brenner, D. A. (2005) · Journal of Clinical Investigation · doi:10.1172/jci24282 · Cited by 4,958
An Algorithm for the Grading of Activity in Chronic Hepatitis C
Bedossa, P., & Poynard, T. (1996) · Hepatology · doi:10.1002/hep.510240201 · Cited by 4,088
Knodell, R. G., Ishak, K. G., Black, W. C., Chen, T. S., Craig, R., Kaplowitz, N., Kiernan, T. W., & Wollman, J. (1981) · Hepatology · doi:10.1002/hep.1840010511 · Cited by 3,522
Angulo, P., Hui, J. M., Marchesini, G., Bugianesi, E., George, J., Farrell, G. C., Enders, F., Saksena, S., Burt, A. D., Bida, J. P., et al. (2007) · Hepatology · doi:10.1002/hep.21496 · Cited by 3,203
Hepatic Stellate Cells: Protean, Multifunctional, and Enigmatic Cells of the Liver
Friedman, S. L. (2008) · Physiological Reviews · doi:10.1152/physrev.00013.2007 · Cited by 2,842
Classification of chronic hepatitis: Diagnosis, grading and staging
Desmet, V. J., Gerber, M., Hoofnagle, J. H., Manns, M., & Scheuer, P. J. (1994) · Hepatology · doi:10.1002/hep.1840190629 · Cited by 2,593
Castéra, L., Vergniol, J., Foucher, J., Le Bail, B., Chanteloup, E., Haaser, M., Darriet, M., Couzigou, P., & de Lédinghen, V. (2005) · Gastroenterology · doi:10.1053/j.gastro.2004.11.018 · Cited by 2,487
Sampling Variability of Liver Fibrosis in Chronic Hepatitis C
Bedossa, P., Dargère, D., & Paradis, V. (2003) · Hepatology · doi:10.1016/j.hep.2003.09.022 · Cited by 2,376
Vallet-Pichard, A., Mallet, V., Nalpas, B., Verkarre, V., Nalpas, A., Dhalluin-Venier, V., Fontaine, H., & Pol, S. (2007) · Hepatology · doi:10.1002/hep.21669 · Cited by 2,077
Cited by
Clapper, J. R., Hendricks, M. D., Gu, G., Wittmer, C., Dolman, C. S., Herich, J., Athanacio, J., Villescaz, C., Ghosh, S. S., Heilig, J. S., et al. (2013) · American Journal of Physiology-Gastrointestinal and Liver Physiology · doi:10.1152/ajpgi.00079.2013 · Cited by 294
Cusi, K., Chang, Z., Harrison, S., Lomonaco, R., Bril, F., Orsak, B., Ortiz-Lopez, C., Hecht, J., Feldstein, A. E., Webb, A., et al. (2014) · Journal of Hepatology · doi:10.1016/j.jhep.2013.07.042 · Cited by 273
Collagen biology and non‐invasive biomarkers of liver fibrosis
Karsdal, M. A., Daniels, S. J., Holm Nielsen, S., Bager, C., Rasmussen, D. G. K., Loomba, R., Surabattula, R., Villesen, I. F., Luo, Y., Shevell, D., et al. (2020) · Liver International · doi:10.1111/liv.14390 · Cited by 197
Sick fat: the good and the bad of old and new circulating markers of adipose tissue inflammation
Barchetta, I., Cimini, F. A., Ciccarelli, G., Baroni, M. G., & Cavallo, M. G. (2019) · Journal of Endocrinological Investigation · doi:10.1007/s40618-019-01052-3 · Cited by 77
Wang, P.-P., Xie, D.-Y., Liang, X.-J., Peng, L., Zhang, G.-L., Ye, Y.-N., Xie, C., & Gao, Z.-L. (2012) · PLoS ONE · doi:10.1371/journal.pone.0043408 · Cited by 55
Metadata
- Authors
- Baranova, A., Lal, P., Birerdinc, A., & Younossi, Z. M.
- Venue
- BMC Gastroenterology
- Published
- 2011-08-17
- Volume
- 11
- Issue
- 1
- Pages
- 91-91
- ISSN
- 1471-230X (ISSN-L)
- OpenAlex
- W1993794153
- Cited by
- 297
- References
- 94
- FWCI
- 17.51
- Type
- journal article
- Language
- en
- Open access
- gold
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Non-Invasive markers for hepatic fibrosis. https://app.cochat.ai/papers/10.1186/1471-230x-11-91
- The paper
- Baranova, A., Lal, P., Birerdinc, A., & Younossi, Z. M. (2011). Non-Invasive markers for hepatic fibrosis. BMC Gastroenterology. https://doi.org/10.1186/1471-230x-11-91
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.