The image features model yielded an ROC AUC of 0.80 (CI: 0.73–0.87). An ROC AUC of 0.68 (95% confidence interval (CI): 0.60–0.75) was obtained using the covariate model. Of 665 analyzed knees, 76 (11.4%) had osteoarthritis. Performance of the covariate (age and body mass index), image features, and combined covariate + image features models were assessed using the area under the receiver operating characteristic curve (ROC AUC). Machine learning–based Elastic Net models with 10-fold cross-validation were used to distinguish between knees without and with MRI Osteoarthritis Knee Score (MOAKS)–based tibiofemoral osteoarthritis. Radiomic features related to the shape and texture were calculated from six volumes of interests (VOIs) in the proximal tibia. Tibial bone was segmented using a method that combines multi-atlas and appearance models. ![]() ![]() A fast imaging employing steady-state acquisition sequence was used for the quantitative bone analyses. The right knees of 665 females from the population-based Rotterdam Study scanned with 1.5T MRI were analyzed. Our aim was to assess the ability of semi-automatically extracted magnetic resonance imaging (MRI)–based radiomic features from tibial subchondral bone to distinguish between knees without and with osteoarthritis.
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