T₂ analysis of the entire osteoarthritis initiative dataset

Abstract

While substantial work has been done to understand the relationships between cartilage T₂ relaxation times and osteoarthritis (OA), diagnostic and prognostic abilities of T₂ 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 T₂ 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 T₂ values, OA risk factors, radiographic OA, and pain were analyzed in the entire OAI dataset. The performance of T₂ values in predicting the future incidence of radiographic OA as well as total knee replacement (TKR) were also explored. Automatic T₂ values were comparable with manual ones. Significant associations between T₂ relaxation times and demographic and clinical variables were found. Subjects in the highest 25% quartile of tibio-femoral T₂ values had a five times higher risk of radiographic OA incidence 2 years later. Elevation of medial femur T₂ 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 T₂ for future incidence OA and TKR. The inclusion of T₂ averages from the automatic segmentation model improved several evaluation metrics when compared to only using demographic and clinical variables.