Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome

Turmezei, Thomas, Treece, Graham M., Gee, Andrew H., Sigurdsson, S., Jonsson, H., Aspelund, T., Gudnason, V. and Poole, Kenneth E. S. (2020) Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome. Scientific Reports, 10. ISSN 2045-2322

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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.

Item Type:	Article
Uncontrolled Keywords:	sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School
Depositing User:	LivePure Connector
Date Deposited:	02 Jul 2020 00:03
Last Modified:	22 Oct 2022 06:24
URI:	https://ueaeprints.uea.ac.uk/id/eprint/75863
DOI:	10.1038/s41598-020-59977-2

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