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Evaluation of activity of Graves’ orbitopathy with multiparameter orbital magnetic resonance imaging (MRI)
BACKGROUND: When quantitative magnetic resonance imaging (MRI) is used to assess the activity of Graves’ orbitopathy (GO), the examination is generally focused on a specific orbital tissue, especially the extraocular muscles (EOMs). However, GO usually involves the entire intraorbital soft tissue. T...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10167472/ https://www.ncbi.nlm.nih.gov/pubmed/37179934 http://dx.doi.org/10.21037/qims-22-814 |
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author | Cheng, Jingyi Zhang, Xiuying Lian, Jianxiu Piao, Zhenyu Zhou, Lingli Gou, Xinyi Chen, Chuhan Chen, Lei Jiang, Ke Cheng, Jin Ji, Linong Hong, Nan |
author_facet | Cheng, Jingyi Zhang, Xiuying Lian, Jianxiu Piao, Zhenyu Zhou, Lingli Gou, Xinyi Chen, Chuhan Chen, Lei Jiang, Ke Cheng, Jin Ji, Linong Hong, Nan |
author_sort | Cheng, Jingyi |
collection | PubMed |
description | BACKGROUND: When quantitative magnetic resonance imaging (MRI) is used to assess the activity of Graves’ orbitopathy (GO), the examination is generally focused on a specific orbital tissue, especially the extraocular muscles (EOMs). However, GO usually involves the entire intraorbital soft tissue. The aim of this study was to use multiparameter MRI on multiple orbital tissues to distinguish the active and inactive GO. METHODS: From May 2021 to March 2022, consecutive patients with GO were prospectively enrolled at Peking University People’s Hospital (Beijing, China) and divided into those with active disease and those with inactive disease based on a clinical activity score. Patients then underwent MRI, including sequences of conventional imaging, T1 mapping, T2 mapping, and mDIXON Quant. Width, T2 signal intensity ratio (SIR), T1 values, T2 values, and fat fraction of EOMs, as well as water fraction (WF) of orbital fat (OF), were measured. Parameters were compared between the 2 groups, and a combined diagnostic model was constructed using logistic regression analysis. Receiver operating characteristic analysis was used to test the diagnostic performance of the model. RESULTS: Sixty-eight patients with GO (27 with active GO, 41 with inactive GO) were included in the study. The active GO group had higher values of EOM thickness, T2 SIR, and T2 values, as well as higher WF of OF. The diagnostic model, which included EOM T2 value and WF of OF, demonstrated a good ability to distinguish between active and inactive GO (area under the curve, 0.878; 95% CI: 0.776–0.945; sensitivity, 88.89%; specificity, 75.61%). CONCLUSIONS: A combined model incorporating the T2 value of EOMs and the WF of OF was able to identify cases of active GO, potentially offering an effective and noninvasive method to assess pathological changes in this disease. |
format | Online Article Text |
id | pubmed-10167472 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-101674722023-05-10 Evaluation of activity of Graves’ orbitopathy with multiparameter orbital magnetic resonance imaging (MRI) Cheng, Jingyi Zhang, Xiuying Lian, Jianxiu Piao, Zhenyu Zhou, Lingli Gou, Xinyi Chen, Chuhan Chen, Lei Jiang, Ke Cheng, Jin Ji, Linong Hong, Nan Quant Imaging Med Surg Original Article BACKGROUND: When quantitative magnetic resonance imaging (MRI) is used to assess the activity of Graves’ orbitopathy (GO), the examination is generally focused on a specific orbital tissue, especially the extraocular muscles (EOMs). However, GO usually involves the entire intraorbital soft tissue. The aim of this study was to use multiparameter MRI on multiple orbital tissues to distinguish the active and inactive GO. METHODS: From May 2021 to March 2022, consecutive patients with GO were prospectively enrolled at Peking University People’s Hospital (Beijing, China) and divided into those with active disease and those with inactive disease based on a clinical activity score. Patients then underwent MRI, including sequences of conventional imaging, T1 mapping, T2 mapping, and mDIXON Quant. Width, T2 signal intensity ratio (SIR), T1 values, T2 values, and fat fraction of EOMs, as well as water fraction (WF) of orbital fat (OF), were measured. Parameters were compared between the 2 groups, and a combined diagnostic model was constructed using logistic regression analysis. Receiver operating characteristic analysis was used to test the diagnostic performance of the model. RESULTS: Sixty-eight patients with GO (27 with active GO, 41 with inactive GO) were included in the study. The active GO group had higher values of EOM thickness, T2 SIR, and T2 values, as well as higher WF of OF. The diagnostic model, which included EOM T2 value and WF of OF, demonstrated a good ability to distinguish between active and inactive GO (area under the curve, 0.878; 95% CI: 0.776–0.945; sensitivity, 88.89%; specificity, 75.61%). CONCLUSIONS: A combined model incorporating the T2 value of EOMs and the WF of OF was able to identify cases of active GO, potentially offering an effective and noninvasive method to assess pathological changes in this disease. AME Publishing Company 2023-02-06 2023-05-01 /pmc/articles/PMC10167472/ /pubmed/37179934 http://dx.doi.org/10.21037/qims-22-814 Text en 2023 Quantitative Imaging in Medicine and Surgery. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Cheng, Jingyi Zhang, Xiuying Lian, Jianxiu Piao, Zhenyu Zhou, Lingli Gou, Xinyi Chen, Chuhan Chen, Lei Jiang, Ke Cheng, Jin Ji, Linong Hong, Nan Evaluation of activity of Graves’ orbitopathy with multiparameter orbital magnetic resonance imaging (MRI) |
title | Evaluation of activity of Graves’ orbitopathy with multiparameter orbital magnetic resonance imaging (MRI) |
title_full | Evaluation of activity of Graves’ orbitopathy with multiparameter orbital magnetic resonance imaging (MRI) |
title_fullStr | Evaluation of activity of Graves’ orbitopathy with multiparameter orbital magnetic resonance imaging (MRI) |
title_full_unstemmed | Evaluation of activity of Graves’ orbitopathy with multiparameter orbital magnetic resonance imaging (MRI) |
title_short | Evaluation of activity of Graves’ orbitopathy with multiparameter orbital magnetic resonance imaging (MRI) |
title_sort | evaluation of activity of graves’ orbitopathy with multiparameter orbital magnetic resonance imaging (mri) |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10167472/ https://www.ncbi.nlm.nih.gov/pubmed/37179934 http://dx.doi.org/10.21037/qims-22-814 |
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