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.
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging