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T2 map signal variation predicts symptomatic osteoarthritis progression: data from the Osteoarthritis Initiative

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Abstract

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.

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Acknowledgments

The project was supported by the National Institutes of Health through grant number KL2 TR000146 and the Orthopaedic Research Education Foundation. Additional support was provided in part by the Woodward Family Endowment in Biomedical Engineering to the Penn State University. The OAI is a public–private partnership comprising five contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262) funded by the National Institutes of Health, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health. This manuscript was prepared using an OAI public use data set and does not necessarily reflect the opinions or views of the OAI investigators, the NIH, or the private funding partners.

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Authors and Affiliations

  1. Department of Electrical Engineering, The Pennsylvania State University, 227C Electrical Engineering West, University Park, PA, USA

    Haoti Zhong & David J. Miller

  2. The Bone & Joint Center, Magee Womens Hospital of the University of Pittsburgh Medical Center, Pittsburgh, PA, USA

    Kenneth L. Urish

  3. Department of Orthopaedic Surgery, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15212, USA

    Kenneth L. Urish

Authors
  1. Haoti Zhong
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  2. David J. Miller
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  3. Kenneth L. Urish
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Correspondence to Kenneth L. Urish.

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All of the authors were involved in the experimental or software design, data analysis, and manuscript preparation.

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Supplemental Table 1

WOMAC pain questionnaire. (XLSX 35 kb)

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Zhong, H., Miller, D.J. & Urish, K.L. T2 map signal variation predicts symptomatic osteoarthritis progression: data from the Osteoarthritis Initiative. Skeletal Radiol 45, 909–913 (2016). https://doi.org/10.1007/s00256-016-2360-4

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  • DOI: https://doi.org/10.1007/s00256-016-2360-4

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