Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome
Abstract: Osteoarthritis is an increasingly important health problem for which the main treatment remains joint replacement. Therapy developments have been hampered by a lack of biomarkers that can reliably predict disease, while 2D radiographs interpreted by human observers are still the gold stand...
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ftdatacite:10.17863/cam.65553 2023-05-15T18:07:00+02:00 Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome Turmezei, T. D. Treece, G. M. Gee, A. H. Sigurdsson, S. Jonsson, H. Aspelund, T. Gudnason, V. Poole, K. E. S. 2020 https://dx.doi.org/10.17863/cam.65553 https://www.repository.cam.ac.uk/handle/1810/318439 unknown Apollo - University of Cambridge Repository Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Article /692/53/2423 /692/4023/1670/407 /639/166/985 /639/705/531 /123 /141 /139 article Text Article article-journal ScholarlyArticle 2020 ftdatacite https://doi.org/10.17863/cam.65553 2021-11-05T12:55:41Z Abstract: Osteoarthritis is an increasingly important health problem for which the main treatment remains joint replacement. Therapy developments have been hampered by a lack of biomarkers that can reliably predict disease, while 2D radiographs interpreted by human observers are still the gold standard for clinical trial imaging assessment. We propose a 3D approach using computed tomography—a fast, readily available clinical technique—that can be applied in the assessment of osteoarthritis using a new quantitative 3D analysis technique called joint space mapping (JSM). We demonstrate the application of JSM at the hip in 263 healthy older adults from the AGES-Reykjavík cohort, examining relationships between 3D joint space width, 3D joint shape, and future joint replacement. Using JSM, statistical shape modelling, and statistical parametric mapping, we show an 18% improvement in prediction of joint replacement using 3D metrics combined with radiographic Kellgren & Lawrence grade (AUC 0.86) over the existing 2D FDA-approved gold standard of minimum 2D joint space width (AUC 0.73). We also show that assessment of joint asymmetry can reveal significant differences between individuals destined for joint replacement versus controls at regions of the joint that are not captured by radiographs. This technique is immediately implementable with standard imaging technologies. Text Reykjavík Reykjavík DataCite Metadata Store (German National Library of Science and Technology) Reykjavík |
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Article /692/53/2423 /692/4023/1670/407 /639/166/985 /639/705/531 /123 /141 /139 article |
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Article /692/53/2423 /692/4023/1670/407 /639/166/985 /639/705/531 /123 /141 /139 article Turmezei, T. D. Treece, G. M. Gee, A. H. Sigurdsson, S. Jonsson, H. Aspelund, T. Gudnason, V. Poole, K. E. S. Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome |
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Article /692/53/2423 /692/4023/1670/407 /639/166/985 /639/705/531 /123 /141 /139 article |
description |
Abstract: Osteoarthritis is an increasingly important health problem for which the main treatment remains joint replacement. Therapy developments have been hampered by a lack of biomarkers that can reliably predict disease, while 2D radiographs interpreted by human observers are still the gold standard for clinical trial imaging assessment. We propose a 3D approach using computed tomography—a fast, readily available clinical technique—that can be applied in the assessment of osteoarthritis using a new quantitative 3D analysis technique called joint space mapping (JSM). We demonstrate the application of JSM at the hip in 263 healthy older adults from the AGES-Reykjavík cohort, examining relationships between 3D joint space width, 3D joint shape, and future joint replacement. Using JSM, statistical shape modelling, and statistical parametric mapping, we show an 18% improvement in prediction of joint replacement using 3D metrics combined with radiographic Kellgren & Lawrence grade (AUC 0.86) over the existing 2D FDA-approved gold standard of minimum 2D joint space width (AUC 0.73). We also show that assessment of joint asymmetry can reveal significant differences between individuals destined for joint replacement versus controls at regions of the joint that are not captured by radiographs. This technique is immediately implementable with standard imaging technologies. |
format |
Text |
author |
Turmezei, T. D. Treece, G. M. Gee, A. H. Sigurdsson, S. Jonsson, H. Aspelund, T. Gudnason, V. Poole, K. E. S. |
author_facet |
Turmezei, T. D. Treece, G. M. Gee, A. H. Sigurdsson, S. Jonsson, H. Aspelund, T. Gudnason, V. Poole, K. E. S. |
author_sort |
Turmezei, T. D. |
title |
Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome |
title_short |
Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome |
title_full |
Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome |
title_fullStr |
Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome |
title_full_unstemmed |
Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome |
title_sort |
quantitative 3d imaging parameters improve prediction of hip osteoarthritis outcome |
publisher |
Apollo - University of Cambridge Repository |
publishDate |
2020 |
url |
https://dx.doi.org/10.17863/cam.65553 https://www.repository.cam.ac.uk/handle/1810/318439 |
geographic |
Reykjavík |
geographic_facet |
Reykjavík |
genre |
Reykjavík Reykjavík |
genre_facet |
Reykjavík Reykjavík |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.17863/cam.65553 |
_version_ |
1766178800979148800 |