T2analysis of the entire osteoarthritis initiative dataset

While substantial work has been done to understand the relationships between cartilage T(2)relaxation times and osteoarthritis (OA), diagnostic and prognostic abilities of T(2)on a large population yet need to be established. Using 3921 manually annotated 2D multi-slice multi-echo spin-echo magnetic resonance imaging volume, a segmentation model for automatic knee cartilage segmentation was built and evaluated. The optimized model was then used to calculate T(2)values on the entire osteoarthritis initiative (OAI) dataset composed of longitudinal acquisitions of 4796 unique patients, 25 729 magnetic resonance imaging studies in total. Cross-sectional relationships between T(2)values, OA risk factors, radiographic OA, and pain were analyzed in the entire OAI dataset. The performance of T(2)values in predicting the future incidence of radiographic OA as well as total knee replacement (TKR) were also explored. Automatic T(2)values were comparable with manual ones. Significant associations between T(2)relaxation times and demographic and clinical variables were found. Subjects in the highest 25% quartile of tibio-femoral T(2)values had a five times higher risk of radiographic OA incidence 2 years later. Elevation of medial femur T(2)values was significantly associated with TKR after 5 years (coeff = 0.10;P = .036; CI = [0.01,0.20]). Our investigation reinforces the predictive value of T(2)for future incidence OA and TKR. The inclusion of T(2)averages from the automatic segmentation model improved several evaluation metrics when compared to only using demographic and clinical variables.

Automated summary

The study found significant associations between cartilage T(2) relaxation times and osteoarthritis, providing insights into the predictive value of T(2) for future OA and TKR. The automatic segmentation model for T(2) values improved evaluation metrics and demonstrated the potential in diagnosing and predicting OA and TKR.