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1 Department of Computer Science, Faculty of Science, Københavns Universitet 2 University of California 3 Administration, Department of Computer Science, Faculty of Science, Københavns Universitet 4 Biomediq A/S (Biz) 5 Department of Computer Science, Faculty of Science, Københavns Universitet 6 Administration, Department of Computer Science, Faculty of Science, Københavns Universitet
A longitudinal study was used to investigate the quantification of osteoarthritis and prediction of tibial cartilage loss by analysis of the tibia trabecular bone from magnetic resonance images of knees. The Kellgren Lawrence (KL) grades were determined by radiologists and the levels of cartilage loss were assessed by a segmentation process. Aiming to quantify and potentially capture the structure of the trabecular bone anatomy, a machine learning approach used a set of texture features for training a classifier to recognize the trabecular bone of a knee with radiographic osteoarthritis. Using cross-validation, the bone structure marker was used to estimate for each knee both the probability of having radiographic osteoarthritis (KL >1) and the probability of rapid cartilage volume loss. The diagnostic ability reached a median area under the receiver-operator-characteristics curve of 0.92 (P <0.0001), and the prognosis had odds ratio of 3.9 (95% confidence interval: 2.4-6.5). The medians of cartilage loss of the subjects classified as slow and rapid progressors were 1.1% and 4.9% per year, respectively. A preliminary radiological reading of the high and low risk knees put forward an hypothesis of which pathologies the bone marker could be capturing to define the prognosis of cartilage loss. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.
Magnetic Resonance in Medicine, 2013, Vol 70, Issue 2, p. 568-575
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