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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