Opin vísindi

Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome

Show simple item record

dc.contributor Háskóli Íslands
dc.contributor University of Iceland
dc.contributor.author Turmezei, Thomas
dc.contributor.author Treece, Graham
dc.contributor.author Gee, A. H.
dc.contributor.author Sigurðsson, Sigurður
dc.contributor.author Jonsson, Helgi
dc.contributor.author Aspelund, Thor
dc.contributor.author Gudnason, Vilmundur
dc.contributor.author Poole, Kenneth E.S.
dc.date.accessioned 2020-11-02T13:38:13Z
dc.date.available 2020-11-02T13:38:13Z
dc.date.issued 2020-03-05
dc.identifier.citation Turmezei, T.D., Treece, G.M., Gee, A.H. et al. Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome. Scientific Reports 10, 4127 (2020). https://doi.org/10.1038/s41598-020-59977-2
dc.identifier.issn 2045-2322
dc.identifier.uri https://hdl.handle.net/20.500.11815/2155
dc.description Publisher's version (útgefin grein)
dc.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.
dc.description.sponsorship K.P. acknowledges the support of Cambridge NIHR Biomedical Research Centre. T.T. thanks the Wellcome Trust for funding support (100676/Z/12/Z) for part of this work. All authors acknowledge funding support grants from the National Institute on Aging (NO1-AG-1-2100), Bethesda, USA, and the Icelandic Government. All authors thank Dr Ilya Burkov, formerly PhD student at the University of Cambridge, for his work on segmentation of the proximal femur from CT data, Professor Lee Shepstone, University of East Anglia, for guidance with generalised estimating equation analysis, and Professor Karl Friston, University College London, for guidance with statistical parametric mapping analysis.
dc.format.extent 4127
dc.language.iso en
dc.publisher Springer Science and Business Media LLC
dc.relation.ispartofseries Scientific Reports;10(1)
dc.rights info:eu-repo/semantics/openAccess
dc.subject 3D
dc.subject Osteoarthritis
dc.subject Therapy development
dc.subject Slitgigt
dc.subject Þrívídd
dc.subject Liðamót
dc.title Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome
dc.type info:eu-repo/semantics/article
dcterms.license Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
dc.description.version Peer Reviewed
dc.identifier.journal Scientific Reports
dc.identifier.doi 10.1038/s41598-020-59977-2
dc.relation.url https://www.nature.com/articles/s41598-020-59977-2
dc.contributor.department Læknadeild (HÍ)
dc.contributor.department Faculty of Medicine (UI)
dc.contributor.school Heilbrigðisvísindasvið (HÍ)
dc.contributor.school School of Health Sciences (UI)

Files in this item

This item appears in the following Collection(s)

Show simple item record