T2 map signal variation predicts symptomatic osteoarthritis progression: data from the Osteoarthritis Initiative.
Objective: The aim of this work is to use quantitative magnetic resonance imaging (MRI) to identify patients at risk for symptomatic osteoarthritis (OA) progression. We hypothesized that classification of signal variation on T2 maps might predict symptomatic OA progression.Methods: Patients were sel...
| Published in: | Skeletal Radiology Vol. 45; no. 7; pp. 909 - 914 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Jul2016
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115528479&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115528479 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03642348 O14 jtl: Skeletal Radiology issn: 03642348 maglogo: N pubinfo: dt: Jul2016 vid: 45 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 115528479 115528479 NLM26992910 10.1007/s00256-016-2360-4 NLM26992910 PMC4876054 [Available on 07/01/17] 115528479 ppf: 909 ppct: 5 formats: fmt: @attributes: type: P tig: atl: T2 map signal variation predicts symptomatic osteoarthritis progression: data from the Osteoarthritis Initiative. aug: au: Zhong, Haoti Miller, David Urish, Kenneth Miller, David J Urish, Kenneth L affil: Department of Electrical Engineering, The Pennsylvania State University, 227C Electrical Engineering West University Park USA sug: subj: Osteoarthritis, Knee Disease Progression Radiography Cartilage, Articular Case Control Studies Knee Joint Cartilage, Articular Pathology Prospective Studies Magnetic Resonance Imaging Knee Joint Pathology Ontario Arthritis Impact Measurement Scales Scales ab: Objective: The aim of this work is to use quantitative magnetic resonance imaging (MRI) to identify patients at risk for symptomatic osteoarthritis (OA) progression. We hypothesized that classification of signal variation on T2 maps might predict symptomatic OA progression.Methods: Patients were selected from the Osteoarthritis Initiative (OAI), a prospective cohort. Two groups were identified: a symptomatic OA progression group and a control group. At baseline, both groups were asymptomatic (Western Ontario and McMaster Universities Arthritis [WOMAC] pain score total <10) with no radiographic evidence of OA (Kellgren-Lawrence [KL] score ≤ 1). The OA progression group (n = 103) had a change in total WOMAC score greater than 10 by the 3-year follow-up. The control group (n = 79) remained asymptomatic, with a change in total WOMAC score less than 10 at the 3-year follow-up. A classifier was designed to predict OA progression in an independent population based on T2 map cartilage signal variation. The classifier was designed using a nearest neighbor classification based on a Gaussian Mixture Model log-likelihood fit of T2 map cartilage voxel intensities.Results: The use of T2 map signal variation to predict symptomatic OA progression in asymptomatic individuals achieved a specificity of 89.3 %, a sensitivity of 77.2 %, and an overall accuracy rate of 84.2 %.Conclusion: T2 map signal variation can predict symptomatic knee OA progression in asymptomatic individuals, serving as a possible early OA imaging biomarker. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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