Statistical shape modelling reveals large and distinct subchondral bony differences in osteoarthritic knees.

Autor: Lynch JT; Australian National University, Australia; Trauma and Orthopaedic Research Unit, Canberra Hospital and Health Services, Australia. Electronic address: Joseph.lynch@anu.edu.au., Schneider MTY; Auckland Bioengineering Institute, University of Auckland, New Zealand. Electronic address: msch153@aucklanduni.ac.nz., Perriman DM; Australian National University, Australia; Trauma and Orthopaedic Research Unit, Canberra Hospital and Health Services, Australia; University of Canberra, Australia. Electronic address: diana.perriman@anu.edu.au., Scarvell JM; Australian National University, Australia; University of Canberra, Australia. Electronic address: jennie.scarvell@canberra.edu.au., Pickering MR; School of Engineering and Information Technology, University of New South Wales, Canberra, Australia. Electronic address: m.pickering@unsw.edu.au., Asikuzzaman M; School of Engineering and Information Technology, University of New South Wales, Canberra, Australia. Electronic address: m.asikuzzaman@unsw.edu.au., Galvin CR; Australian National University, Australia; Trauma and Orthopaedic Research Unit, Canberra Hospital and Health Services, Australia. Electronic address: catherine.galvin@anu.edu.au., Besier TF; Auckland Bioengineering Institute, University of Auckland, New Zealand. Electronic address: t.besier@auckland.ac.nz., Smith PN; Australian National University, Australia; Trauma and Orthopaedic Research Unit, Canberra Hospital and Health Services, Australia. Electronic address: psmith.admin@orthoact.com.au.
Jazyk: angličtina
Zdroj: Journal of biomechanics [J Biomech] 2019 Aug 27; Vol. 93, pp. 177-184. Date of Electronic Publication: 2019 Jul 10.
DOI: 10.1016/j.jbiomech.2019.07.003
Abstrakt: Knee osteoarthritis (OA) results in changes such as joint-space narrowing and osteophyte formation. Radiographic classification systems group patients by the presence or absence of these gross anatomical features but are poorly correlated to function. Statistical-shape modelling (SSM) can detect subtle differences in 3D-bone geometry, providing an opportunity for accurate predictive models. The aim of this study was to describe and quantify the main modes of shape variation which distinguish end-stage OA from asymptomatic knees. Seventy-six patients with OA and 77 control participants received a CT of their knee. 3D models of the joint were created by manual segmentation. A template mesh was fitted to all meshes and rigidly aligned resulting in a set of correspondent meshes. Principal Component Analysis (PCA) was performed to create the SSM. Logistic regression was performed on the PCA weights to distinguish morphological features of the two groups. The first 7 modes of the SSM captured >90% shape variation with 6 modes best distinguishing between OA and asymptomatic knees. OA knees displayed sub-chondral bone expansion particularly in the condyles and posterior medial tibial plateau of up to 10 mm. The model classified the two groups with 95% accuracy, 96% sensitivity, 94% specificity, and 97% AUC. There were distinct features which differentiated OA from asymptomatic knees. Further research will elucidate how magnitude and location of shape changes in the knee influence clinical and functional outcomes.
(Copyright © 2019 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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