Automated Textural Classification of Osteoarthritis Magnetic Resonance Images

Osteoarthritis (OA) is the most common cause of disability in the United Kingdom and United States. Identifying the rate of OA progression remains an important clinical and research challenge for early disease monitoring. Texture analysis of tibial subchondral bone using magnetic resonance imaging (MRI) has demonstrated the ability to discriminate between different stages of OA. This work combines texture analysis with machine learning methods (Lasso, Decision Tree, and Neural Network) to predict radiographic disease progression over 3 years, trained using data from the Osteoarthritis Initiative. We achieved high sensitivity (86%), specificity (64%) and accuracy (74%) for predictions of OA progression.The authors acknowledge research support from the National Institute of Health Research Cambridge Biomedical Research Centre. RT acknowledges the support of the UK Engineering and Physical Sciences Research Council (EPSRC) grant EP/L016516/1 for the University of Cambridge Centre for Doctoral Training, the Cambridge Centre for Analysis. JK and JM acknowledge support by GlaxoSmithKline. AM acknowledges research support from Arthritis Research UK; Tissue Engineering Centre award. FG acknowledges research support from Cancer Research UK