Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome

• Published in: Scientific Reports
• Main Authors: Turmezei, Thomas, Treece, Graham, Gee, A. H., Sigurðsson, Sigurður, Jonsson, Helgi, Aspelund, Thor, Gudnason, Vilmundur, Poole, Kenneth E.S.
• Other Authors: Læknadeild (HÍ), Faculty of Medicine (UI), Heilbrigðisvísindasvið (HÍ), School of Health Sciences (UI), Háskóli Íslands, University of Iceland
• Format: Article in Journal/Newspaper
• Language: English
• Published: Springer Science and Business Media LLC 2020
• Subjects: 3D; Osteoarthritis; Therapy development; Slitgigt; Þrívídd; Liðamót; Reykjavík
• Online Access: https://hdl.handle.net/20.500.11815/2155; https://doi.org/10.1038/s41598-020-59977-2

Publisher's version (útgefin grein) 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. 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 ...