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Abstract #0301

Clinical Decision Rules for Detection of Cartilage Degradation Based on Univariate MR Parameter Analysis

Richard G. Spencer 1 , Vanessa A. Lukas 1 , Benjamin D. Cortese 2 , David A. Reiter 1 , Kenneth W. Fishbein 1 , Nancy Pleshko 3 , and Bimal Sinha 4

1 Magnetic Resonance Imaging and Spectroscopy Section, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States, 2 Department of Mathematics, Syracuse University, Syracuse, New York, United States, 3 Tissue Imaging and Spectroscopy Laboratory, Bioengineering Department, Temple University, Philadelphia, Pennsylvania, United States, 4 Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, Maryland, United States

Little work has been done to translate cartilage-sensitive MR outcome measures to clinical decision rules. The goal of this investigation is to develop and apply clinical classification rules based on group differences between cartilage-matrix sensitive MR measurements. We develop two distinct methods, one based on the Euclidean distance metric and one based on the likelihood ratio approach. We derive closed-form expressions for the sensitivity and specificity these decision rules, and present analyses of both a cartilage degradation dataset and literature results. We find that even highly statistically significant group differences may not lead to high-quality clinical decision rules.

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