Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome.

• Published in: Scientific Reports
• Main Authors: Turmezei, T D, Treece, G M, Gee, A H, Sigurdsson, S, Jonsson, H, Aspelund, T, Gudnason, V, Poole, K E S
• Other Authors: 1Department of Radiology, Norfolk and Norwich University Hospital, Norwich, UK. [e-mail]. 2Cambridge University Engineering Department, Cambridge, UK. 3Icelandic Heart Association, Kopavogur, Iceland. 4Department of Rheumatology, Landspitalinn University Hospital, Reykjavik, Iceland. 5Department of Medicine, University of Iceland, Reykjavik, Iceland. 6Department of Medicine, University of Cambridge, Cambridge, UK.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Nature Publishing Group 2020
• Subjects: Slitgigt; Myndgreining (læknisfræði); Liðskiptaaðgerðir; Mjaðmaaðgerðir; Batahorfur; Osteoarthritis; Hip; Imaging; Three-Dimensional; Treatment Outcome; Arthroplasty; Replacement; Reykjavík
• Online Access: http://hdl.handle.net/2336/621532; https://doi.org/10.1038/s41598-020-59977-2

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Download 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. National Institute for Health Research (NIHR) Wellcome Trust National Institute on Aging, Bethesda, USA Icelandic Government