T<sub>2</sub> analysis of the entire osteoarthritis initiative dataset
Alaleh Razmjoo, Francesco Calivá, Jinhee Lee, Felix Liu, Gabby B. Joseph, Thomas M. Link, Sharmila Majumdar, Valentina Pedoia
Abstract
Abstract 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.