The DOI matches its Crossref record; 82 of the 82 references it cites were found in a registry or an index.
Liver fat imaging—a clinical overview of ultrasound, CT, and MR imaging
Open accessAbstract
Hepatic steatosis is a frequently encountered imaging finding that may indicate chronic liver disease, the most common of which is non-alcoholic fatty liver disease. Non-alcoholic fatty liver disease is implicated in the development of systemic diseases and its progressive phenotype, non-alcoholic steatohepatitis, leads to increased liver-specific morbidity and mortality. With the rising obesity epidemic and advent of novel therapeutics aimed at altering metabolism, there is a growing need to quantify and monitor liver steatosis. Imaging methods for assessing steatosis range from simple and qualitative to complex and highly accurate metrics. Ultrasound may be appropriate in some clinical instances as a screening modality to identify the presence of abnormal liver morphology. However, it lacks sufficient specificity and sensitivity to constitute a diagnostic modality for instigating and monitoring therapy. Newer ultrasound techniques such as quantitative ultrasound show promise in turning qualitative assessment of steatosis on conventional ultrasound into quantitative measurements. Conventional unenhanced CT is capable of detecting and quantifying moderate to severe steatosis but is inaccurate at diagnosing mild steatosis and involves the use of radiation. Newer CT techniques, like dual energy CT, show potential in expanding the role of CT in quantifying steatosis. MRI proton-density fat fraction is currently the most accurate and precise imaging biomarker to quantify liver steatosis. As such, proton-density fat fraction is the most appropriate noninvasive end point for steatosis reduction in clinical trials and therapy response assessment.
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Cites
Design 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
Nonalcoholic Steatohepatitis: A Proposal for Grading and Staging The Histological Lesions
Brunt, E. M., Janney, C. G., Di Bisceglie, A. M., Neuschwander-Tetri, B. A., & Bacon, B. R. (1999) · American Journal of Gastroenterology · doi:10.1111/j.1572-0241.1999.01377.x · Cited by 3,907
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
Long-term follow-up of patients with NAFLD and elevated liver enzymes
Ekstedt, M., Franzén, L. E., Mathiesen, U. L., Thorelius, L., Holmqvist, M., Bodemar, G., & Kechagias, S. (2006) · Hepatology · doi:10.1002/hep.21327 · Cited by 2,302
Ekstedt, M., Hagström, H., Nasr, P., Fredrikson, M., Stål, P., Kechagias, S., & Hultcrantz, R. (2015) · Hepatology · doi:10.1002/hep.27368 · Cited by 2,287
Williams, C. D., Stengel, J., Asike, M. I., Torres, D. M., Shaw, J., Contreras, M., Landt, C. L., & Harrison, S. A. (2011) · Gastroenterology · doi:10.1053/j.gastro.2010.09.038 · Cited by 2,148
The utility of radiological imaging in nonalcoholic fatty liver disease
Saadeh, S., Younossi, Z. M., Remer, E. M., Gramlich, T., Ong, J. P., Hurley, M., Mullen, K. D., Cooper, J. N., & Sheridan, M. J. (2002) · Gastroenterology · doi:10.1053/gast.2002.35354 · Cited by 2,110
Sampling Variability of Liver Biopsy in Nonalcoholic Fatty Liver Disease
Ratziu, V., Charlotte, F., Heurtier, A., Gombert, S., Giral, P., Bruckert, E., Grimaldi, A., Capron, F., & Poynard, T. (2005) · Gastroenterology · doi:10.1053/j.gastro.2005.03.084 · Cited by 2,106
Obesity and nonalcoholic fatty liver disease: biochemical, metabolic, and clinical implications
Fabbrini, E., Sullivan, S., & Klein, S. (2010) · Hepatology · doi:10.1002/hep.23280 · Cited by 2,100
Cited by
Pirmoazen, A. M., Khurana, A., El Kaffas, A., & Kamaya, A. (2020) · Theranostics · doi:10.7150/thno.40249 · Cited by 165
Machine learning enables new insights into genetic contributions to liver fat accumulation
Haas, M. E., Pirruccello, J. P., Friedman, S. N., Wang, M., Emdin, C. A., Ajmera, V. H., Simon, T. G., Homburger, J. R., Guo, X., Budoff, M., et al. (2021) · Cell Genomics · doi:10.1016/j.xgen.2021.100066 · Cited by 118
Beyazal Polat, H., Beyazal, M., Arpa, M., Kızılkaya, B., Ayaz, T., Gündoğdu, Ö. L., Konur, K., Polat, Z., Beyazal Çeliker, F., & Atasoy, H. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14103513 · Cited by 13
Varmazyar, I., Monazzami, A. A., Moradi, M., & McAinch, A. J. (2024) · BMC Sports Science, Medicine and Rehabilitation · doi:10.1186/s13102-024-00972-9 · Cited by 7
Metadata
- Authors
- Zhang, Y. N., Fowler, K. J., Hamilton, G., Cui, J. Y., Sy, E. Z., Balanay, M., Hooker, J. C., Szeverenyi, N., & Sirlin, C. B.
- Venue
- The British Journal of Radiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2018-06-06
- Volume
- 91
- Issue
- 1089
- Pages
- 20170959
- ISSN
- 0007-1285 (ISSN-L)
- 1748-880X
- OpenAlex
- W2799523744
- Cited by
- 304
- References
- 82
- FWCI
- 12.78
- Type
- journal article
- Language
- en
- Open access
- green
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- Zhang, Y. N., Fowler, K. J., Hamilton, G., Cui, J. Y., Sy, E. Z., Balanay, M., Hooker, J. C., Szeverenyi, N., & Sirlin, C. B. (2018). Liver fat imaging—a clinical overview of ultrasound, CT, and MR imaging. The British Journal of Radiology. https://doi.org/10.1259/bjr.20170959
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