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1. Visualizing pre‐osteoarthritis: Integrating MRI UTE‐T2* with mechanics and biology to combat osteoarthritis—The 2019 Elizabeth Winston Lanier Kappa Delta Award

   https://doi.org/10.1002/jor.25045  

   Chu, Constance R. ; Williams, Ashley A. ; Erhart‐Hledik, Jennifer C. ; Titchenal, Matthew R. ; Qian, Yongxian ; Andriacchi, Thomas P. ( April 2021 , Journal of Orthopaedic Research)
2. Novel composite knee structure metrics of disease activity and cumulative damage predict the development of accelerated knee osteoarthritis: data from the osteoarthritis initiative

   https://doi.org/10.1016/j.joca.2019.02.738  

   Harkey, M. ; Davis, J. ; Lu, B. ; Price, L. ; Ward, R.J. ; MacKay, J.W. ; Eaton, C.B. ; Lo, G.H. ; Barbe, M.F. ; Zhang, M. ; et al ( April 2019 , Osteoarthritis and Cartilage)
3. Composite quantitative knee structure metrics predict the development of accelerated knee osteoarthritis: data from the osteoarthritis initiative

   https://doi.org/10.1186/s12891-020-03338-7  

   Harkey, Matthew S. ; Davis, Julie E. ; Price, Lori Lyn ; Ward, Robert J. ; MacKay, James W. ; Eaton, Charles B. ; Lo, Grace H. ; Barbe, Mary F. ; Zhang, Ming ; Pang, Jincheng ; et al ( December 2020 , BMC Musculoskeletal Disorders)

   Abstract Background We aimed to determine if composite structural measures of knee osteoarthritis (KOA) progression on magnetic resonance (MR) imaging can predict the radiographic onset of accelerated knee osteoarthritis. Methods We used data from a nested case-control study among participants from the Osteoarthritis Initiative without radiographic KOA at baseline. Participants were separated into three groups based on radiographic disease progression over 4 years: 1) accelerated (Kellgren-Lawrence grades [KL] 0/1 to 3/4), 2) typical (increase in KL, excluding accelerated osteoarthritis), or 3) no KOA (no change in KL). We assessed tibiofemoral cartilage damage (four regions: medial/lateral tibia/femur), bone marrow lesion (BML) volume (four regions: medial/lateral tibia/femur), and whole knee effusion-synovitis volume on 3 T MR images with semi-automated programs. We calculated two MR-based composite scores. Cumulative damage was the sum of standardized cartilage damage. Disease activity was the sum of standardized volumes of effusion-synovitis and BMLs. We focused on annual images from 2 years before to 2 years after radiographic onset (or a matched time for those without knee osteoarthritis). To determine between group differences in the composite metrics at all time points, we used generalized linear mixed models with group (3 levels) and time (up to 5 levels). For our prognostic analysis,more »we used multinomial logistic regression models to determine if one-year worsening in each composite metric change associated with future accelerated knee osteoarthritis (odds ratios [OR] based on units of 1 standard deviation of change). Results Prior to disease onset, the accelerated KOA group had greater average disease activity compared to the typical and no KOA groups and this persisted up to 2 years after disease onset. During a pre-radiographic disease period, the odds of developing accelerated KOA were greater in people with worsening disease activity [versus typical KOA OR (95% confidence interval [CI]): 1.58 (1.08 to 2.33); versus no KOA: 2.39 (1.55 to 3.71)] or cumulative damage [versus typical KOA: 1.69 (1.14 to 2.51); versus no KOA: 2.11 (1.41 to 3.16)]. Conclusions MR-based disease activity and cumulative damage metrics may be prognostic markers to help identify people at risk for accelerated onset and progression of knee osteoarthritis.« less
4. Spatial Gradients of Quantitative MRI as Biomarkers for Early Detection of Osteoarthritis: Data From Human Explants and the Osteoarthritis Initiative

   https://doi.org/10.1002/jmri.28471  

   Wilson, Robert L. ; Emery, Nancy C. ; Pierce, David M. ; Neu, Corey P. ( October 2022 , Journal of Magnetic Resonance Imaging)

   Healthy articular cartilage presents structural gradients defined by distinct zonal patterns through the thickness, which may be disrupted in the pathogenesis of several disorders. Analysis of textural patterns using quantitative MRI data may identify structural gradients of healthy or degenerating tissue that correlate with early osteoarthritis (OA).

   To quantify spatial gradients and patterns in MRI data, and to probe new candidate biomarkers for early severity of OA.

   Retrospective study.

   Fourteen volunteers receiving total knee replacement surgery (eight males/two females/four unknown, average age ± standard deviation: 68.1 ± 9.6 years) and 10 patients from the OA Initiative (OAI) with radiographic OA onset (two males/eight females, average age ± standard deviation: 57.7 ± 9.4 years; initial Kellgren‐Lawrence [KL] grade: 0; final KL grade: 3 over the 10‐year study).

   3.0‐T and 14.1‐T, biomechanics‐based displacement‐encoded imaging, fast spin echo, multi‐slice multi‐echoT2mapping.

   We studied structure and strain in cartilage explants from volunteers receiving total knee replacement, or structure in cartilage of OAI patients with progressive OA. We calculated spatial gradients of quantitative MRI measures (eg, T2) normal to the cartilage surface to enhance zonal variations. We compared gradient values against histologically OA severity, conventional relaxometry, and/or KL grades.

   Multiparametric linear regression for evaluation of the relationship between residuals of the mixedmore »effects models and histologically determined OA severity scoring, with a significance threshold atα = 0.05.

   Gradients of individual relaxometry and biomechanics measures significantly correlated with OA severity, outperforming conventional relaxometry and strain metrics. In human explants, analysis of spatial gradients provided the strongest relationship to OA severity (R² = 0.627). Spatial gradients of T2 from OAI data identified variations in radiographic (KL Grade 2) OA severity in single subjects, while conventional T2 alone did not.

   Spatial gradients of quantitative MRI data may improve the predictive power of noninvasive imaging for early‐stage degeneration.

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5. A Novel Method to Predict Knee Osteoarthritis Progression on MRI Using Machine Learning Methods

   https://doi.org/10.1109/TNB.2018.2840082  

   Du, Yaodong ; Almajalid, Rania ; Shan, Juan ; Zhang, Ming ( January 2018 , IEEE Transactions on NanoBioscience)