Quantification of Liver Iron Overload with MRI: Review and Guidelines from the ESGAR and SAR Article Swipe
Scott B. Reeder
,
Takeshi Yokoo
,
Manuela França
,
Diego Hernando
,
Ángel Alberich‐Bayarri
,
J.M. Alústiza
,
Y. Gandon
,
Benjamin Henninger
,
Claudia M. Hillenbrand
,
Kartik Jhaveri
,
Muşturay Karçaaltıncaba
,
Jens‐Peter Kühn
,
Amirkasra Mojtahed
,
Suraj D. Serai
,
Richard Ward
,
John C. Wood
,
Jin Yamamura
,
Luis Martí‐Bonmatí
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1148/radiol.221856
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1148/radiol.221856
Accumulation of excess iron in the body, or systemic iron overload, results from a variety of causes. The concentration of iron in the liver is linearly related to the total body iron stores and, for this reason, quantification of liver iron concentration (LIC) is widely regarded as the best surrogate to assess total body iron. Historically assessed using biopsy, there is a clear need for noninvasive quantitative imaging biomarkers of LIC. MRI is highly sensitive to the presence of tissue iron and has been increasingly adopted as a noninvasive alternative to biopsy for detection, severity grading, and treatment monitoring in patients with known or suspected iron overload. Multiple MRI strategies have been developed in the past 2 decades, based on both gradient-echo and spin-echo imaging, including signal intensity ratio and relaxometry strategies. However, there is a general lack of consensus regarding the appropriate use of these methods. The overall goal of this article is to summarize the current state of the art in the clinical use of MRI to quantify liver iron content and to assess the overall level of evidence of these various methods. Based on this summary, expert consensus panel recommendations on best practices for MRI-based quantification of liver iron are provided.
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Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1148/radiol.221856
- OA Status
- green
- Cited By
- 121
- References
- 90
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4321447289Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1148/radiol.221856Digital Object Identifier
- Title
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Quantification of Liver Iron Overload with MRI: Review and Guidelines from the ESGAR and SARWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-02-21Full publication date if available
- Authors
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Scott B. Reeder, Takeshi Yokoo, Manuela França, Diego Hernando, Ángel Alberich‐Bayarri, J.M. Alústiza, Y. Gandon, Benjamin Henninger, Claudia M. Hillenbrand, Kartik Jhaveri, Muşturay Karçaaltıncaba, Jens‐Peter Kühn, Amirkasra Mojtahed, Suraj D. Serai, Richard Ward, John C. Wood, Jin Yamamura, Luis Martí‐BonmatíList of authors in order
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https://doi.org/10.1148/radiol.221856Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://pmc.ncbi.nlm.nih.gov/articles/PMC10068892/pdf/radiol.221856.pdfDirect OA link when available
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Medicine, Magnetic resonance imaging, Liver biopsy, Radiology, Grading (engineering), Biopsy, Engineering, Civil engineeringTop concepts (fields/topics) attached by OpenAlex
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121Total citation count in OpenAlex
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2025: 59, 2024: 45, 2023: 17Per-year citation counts (last 5 years)
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90Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.with | 101 |
| abstract_inverted_index.(LIC) | 42 |
| abstract_inverted_index.Based | 185 |
| abstract_inverted_index.based | 118 |
| abstract_inverted_index.body, | 6 |
| abstract_inverted_index.clear | 62 |
| abstract_inverted_index.iron. | 54 |
| abstract_inverted_index.known | 102 |
| abstract_inverted_index.level | 178 |
| abstract_inverted_index.liver | 23, 39, 170, 200 |
| abstract_inverted_index.panel | 191 |
| abstract_inverted_index.ratio | 128 |
| abstract_inverted_index.state | 158 |
| abstract_inverted_index.there | 59, 133 |
| abstract_inverted_index.these | 145, 182 |
| abstract_inverted_index.total | 29, 52 |
| abstract_inverted_index.using | 57 |
| abstract_inverted_index.assess | 51, 175 |
| abstract_inverted_index.biopsy | 91 |
| abstract_inverted_index.excess | 2 |
| abstract_inverted_index.expert | 189 |
| abstract_inverted_index.highly | 73 |
| abstract_inverted_index.signal | 126 |
| abstract_inverted_index.stores | 32 |
| abstract_inverted_index.tissue | 79 |
| abstract_inverted_index.widely | 44 |
| abstract_inverted_index.adopted | 85 |
| abstract_inverted_index.article | 152 |
| abstract_inverted_index.biopsy, | 58 |
| abstract_inverted_index.causes. | 16 |
| abstract_inverted_index.content | 172 |
| abstract_inverted_index.current | 157 |
| abstract_inverted_index.general | 136 |
| abstract_inverted_index.imaging | 67 |
| abstract_inverted_index.overall | 148, 177 |
| abstract_inverted_index.reason, | 36 |
| abstract_inverted_index.related | 26 |
| abstract_inverted_index.results | 11 |
| abstract_inverted_index.variety | 14 |
| abstract_inverted_index.various | 183 |
| abstract_inverted_index.However, | 132 |
| abstract_inverted_index.Multiple | 107 |
| abstract_inverted_index.assessed | 56 |
| abstract_inverted_index.clinical | 164 |
| abstract_inverted_index.decades, | 117 |
| abstract_inverted_index.evidence | 180 |
| abstract_inverted_index.grading, | 95 |
| abstract_inverted_index.imaging, | 124 |
| abstract_inverted_index.linearly | 25 |
| abstract_inverted_index.methods. | 146, 184 |
| abstract_inverted_index.patients | 100 |
| abstract_inverted_index.presence | 77 |
| abstract_inverted_index.quantify | 169 |
| abstract_inverted_index.regarded | 45 |
| abstract_inverted_index.severity | 94 |
| abstract_inverted_index.summary, | 188 |
| abstract_inverted_index.systemic | 8 |
| abstract_inverted_index.MRI-based | 197 |
| abstract_inverted_index.consensus | 139, 190 |
| abstract_inverted_index.developed | 112 |
| abstract_inverted_index.including | 125 |
| abstract_inverted_index.intensity | 127 |
| abstract_inverted_index.overload, | 10 |
| abstract_inverted_index.overload. | 106 |
| abstract_inverted_index.practices | 195 |
| abstract_inverted_index.provided. | 203 |
| abstract_inverted_index.regarding | 140 |
| abstract_inverted_index.sensitive | 74 |
| abstract_inverted_index.spin-echo | 123 |
| abstract_inverted_index.summarize | 155 |
| abstract_inverted_index.surrogate | 49 |
| abstract_inverted_index.suspected | 104 |
| abstract_inverted_index.treatment | 97 |
| abstract_inverted_index.biomarkers | 68 |
| abstract_inverted_index.detection, | 93 |
| abstract_inverted_index.monitoring | 98 |
| abstract_inverted_index.strategies | 109 |
| abstract_inverted_index.alternative | 89 |
| abstract_inverted_index.appropriate | 142 |
| abstract_inverted_index.noninvasive | 65, 88 |
| abstract_inverted_index.relaxometry | 130 |
| abstract_inverted_index.strategies. | 131 |
| abstract_inverted_index.Accumulation | 0 |
| abstract_inverted_index.Historically | 55 |
| abstract_inverted_index.increasingly | 84 |
| abstract_inverted_index.quantitative | 66 |
| abstract_inverted_index.concentration | 18, 41 |
| abstract_inverted_index.gradient-echo | 121 |
| abstract_inverted_index.quantification | 37, 198 |
| abstract_inverted_index.recommendations | 192 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 18 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.99879837 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |