Literature DB >> 24306109

A novel method for bone area measurement provides new insights into osteoarthritis and its progression.

Michael A Bowes1, Graham R Vincent1, Christopher B Wolstenholme1, Philip G Conaghan2.   

Abstract

BACKGROUND: Modern image analysis enables the accurate quantification of knee osteoarthritis (OA) bone using MRI. We hypothesised that three-dimensional changes in bone would be characteristic of OA and provide a responsive measure of progression.
METHODS: 1312 participants with radiographic knee OA, and 885 non-OA controls with MRIs at baseline, 1, 2 and 4 years were selected from the NIH Osteoarthritis Initiative. Automated segmentation of all knee bones and calculation of bone area was performed using active appearance models. In a subset of 352 participants, responsiveness of bone area change was compared with change in radiographic joint space width (JSW) and MRI cartilage thickness over a 2-year period.
RESULTS: All OA knee compartments showed increased bone area over time compared with non-OA participants: for example, the 4-year percentage change from baseline in medial femur area for OA (95% CI) was 1.87(0.13), non-OA 0.43 (0.07); p<0.0001. Bone area change was more responsive than cartilage thickness or JSW; 2-year SRM for bone area in the medial femur was 0.83, for the most responsive cartilage thickness measure central medial femorotibial composite (cMFTC): 0.38, JSW: 0.35. Almost half of all knees had change greater than smallest detectable difference at 2 years. Body mass index, gender and alignment had only a small effect on the rate of change of bone area.
CONCLUSIONS: Changes in bone area discriminated people with OA from controls and was more responsive than the current and impending standards for assessing OA progression. The shape change in OA bone provides a new window on OA pathogenesis and a focus for clinical trials. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Entities:  

Keywords:  Disease Activity; Knee Osteoarthritis; Magnetic Resonance Imaging

Mesh:

Year:  2013        PMID: 24306109     DOI: 10.1136/annrheumdis-2013-204052

Source DB:  PubMed          Journal:  Ann Rheum Dis        ISSN: 0003-4967            Impact factor:   19.103


  20 in total

1.  Automated Segmentation of Tissues Using CT and MRI: A Systematic Review.

Authors:  Leon Lenchik; Laura Heacock; Ashley A Weaver; Robert D Boutin; Tessa S Cook; Jason Itri; Christopher G Filippi; Rao P Gullapalli; James Lee; Marianna Zagurovskaya; Tara Retson; Kendra Godwin; Joey Nicholson; Ponnada A Narayana
Journal:  Acad Radiol       Date:  2019-08-10       Impact factor: 3.173

Review 2.  Imaging of cartilage and bone: promises and pitfalls in clinical trials of osteoarthritis.

Authors:  F Eckstein; A Guermazi; G Gold; J Duryea; M-P Hellio Le Graverand; W Wirth; C G Miller
Journal:  Osteoarthritis Cartilage       Date:  2014-10       Impact factor: 6.576

3.  Study of the interactions between proximal femur 3d bone shape, cartilage health, and biomechanics in patients with hip Osteoarthritis.

Authors:  Valentina Pedoia; Michael A Samaan; Gaurav Inamdar; Matthew C Gallo; Richard B Souza; Sharmila Majumdar
Journal:  J Orthop Res       Date:  2017-08-11       Impact factor: 3.494

4.  A Machine Learning Model to Predict Knee Osteoarthritis Cartilage Volume Changes over Time Using Baseline Bone Curvature.

Authors:  Hossein Bonakdari; Jean-Pierre Pelletier; François Abram; Johanne Martel-Pelletier
Journal:  Biomedicines       Date:  2022-05-26

5.  Fully Automatic Knee Joint Segmentation and Quantitative Analysis for Osteoarthritis from Magnetic Resonance (MR) Images Using a Deep Learning Model.

Authors:  Xiongfeng Tang; Deming Guo; Aie Liu; Dijia Wu; Jianhua Liu; Nannan Xu; Yanguo Qin
Journal:  Med Sci Monit       Date:  2022-06-14

6.  Learning osteoarthritis imaging biomarkers from bone surface spherical encoding.

Authors:  Alejandro Morales Martinez; Francesco Caliva; Io Flament; Felix Liu; Jinhee Lee; Peng Cao; Rutwik Shah; Sharmila Majumdar; Valentina Pedoia
Journal:  Magn Reson Med       Date:  2020-04-03       Impact factor: 4.668

7.  Deep convolutional neural network for segmentation of knee joint anatomy.

Authors:  Zhaoye Zhou; Gengyan Zhao; Richard Kijowski; Fang Liu
Journal:  Magn Reson Med       Date:  2018-05-17       Impact factor: 4.668

8.  Meniscal Transplantation and its Effect on Osteoarthritis Risk: an abridged protocol for the MeTEOR study: a comprehensive cohort study incorporating a pilot randomised controlled trial.

Authors:  N A Smith; J Achten; N Parsons; D Wright; B Parkinson; P Thompson; C E Hutchinson; T Spalding; M L Costa
Journal:  Bone Joint Res       Date:  2015-06       Impact factor: 5.853

9.  New Drug Treatments for Osteoarthritis: What is on the Horizon?

Authors:  Fiona E Watt; Malvika Gulati
Journal:  Eur Med J Rheumatol       Date:  2017-03-02

Review 10.  A systematic review of the relationship between subchondral bone features, pain and structural pathology in peripheral joint osteoarthritis.

Authors:  Andrew J Barr; T Mark Campbell; Devan Hopkinson; Sarah R Kingsbury; Mike A Bowes; Philip G Conaghan
Journal:  Arthritis Res Ther       Date:  2015-08-25       Impact factor: 5.156

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