Mechanical metrics may show improved ability to predict osteoarthritis compared to T1rho mapping

Abstract

Changes in cartilage structure and composition are commonly observed during the progression of osteoarthritis (OA). Importantly, quantitative magnetic resonance imaging (MRI) methods, such as T1rho relaxation imaging, can noninvasively provide in vivo metrics that reflect changes in cartilage composition and therefore have the potential for use in early OA detection. Changes in cartilage mechanical properties are also hallmarks of OA cartilage; thus, measurement of cartilage mechanical properties may also be beneficial for earlier OA detection. However, the relative predictive ability of compositional versus mechanical properties in detecting OA has yet to be determined. Therefore, we developed logistic regression models predicting OA status in an ex vivo environment using several mechanical and compositional metrics to assess which metrics most effectively predict OA status. Specifically, in this study the compositional metric analyzed was the T1rho relaxation time, while the mechanical metrics analyzed were the stiffness and recovery (defined as a measure of how quickly cartilage returns to its original shape after loading) of the cartilage. Cartilage recovery had the best predictive ability of OA status both alone and in a multivariate model including the T1rho relaxation time. These findings highlight the potential of cartilage recovery as a non-invasive marker of in vivo cartilage health and motivate future investigation of this metric clinically.

Keywords: Biomarker; Cartilage mechanical properties; Cartilage recovery; Cartilage stiffness; OA diagnosis; Quantitative MRI.

Figure 1:

Overall data collection procedure. Joints were procured, MR imaged, dissected, cartilage explants were harvested, and then MR imaged again. Subsequently, cartilage explants were mechanically tested.

Figure 2:

Cartilage defect (red) measurement procedure, showing a) segmentation of the tibial cortex and tibial articular cartilage explant harvest sites, b) compilation of segmentations across several slices, and c) 3D surface mesh model creation.

Figure 3:

Differences across OA status in each continuous metric. Error bars represent 95% confidence intervals. *Significant main effect of OA status, p < 0.05.

Figure 4:

Bivariate logistic regression models of the probability of OA (where 1 = OA, 0 = healthy) based on each of the three continuous metrics.

Figure 5:

Receiver operating characteristic curves for the parsimonious multivariate and bivariate models, representing the relationship between the true positive proportion and the false positive proportion for predicting OA status. A) Comparison of the parsimonious multivariate model and the bivariate model using the recovery. B) Comparison of all three bivariate models.