Sensitivity and specificity of univariate MRI analysis of experimentally degraded cartilage under clinical imaging conditions

Background To evaluate the sensitivity and specificity of classification of pathomimetically degraded bovine nasal cartilage at 3 Tesla and 37°C using univariate MRI measurements of both pure parameter values and intensities of parameter-weighted images. Methods Pre- and posttrypsin degradation values of T₁, T₂, T₂, magnetization transfer ratio (MTR), and apparent diffusion coefficient (ADC), and corresponding weighted images, were analyzed. Classification based on the Euclidean distance was performed and the quality of classification was assessed through sensitivity, specificity and accuracy (ACC). Results The classifiers with the highest accuracy values were ADC (ACC = 0.82 ± 0.06), MTR (ACC = 0.78 ± 0.06), T₁ (ACC = 0.99 ± 0.01), T₂ derived from a three-dimensional (3D) spin-echo sequence (ACC = 0.74 ± 0.05), and T₂ derived from a 2D spin-echo sequence (ACC = 0.77 ± 0.06), along with two of the diffusion-weighted signal intensities (b = 333 s/mm²: ACC = 0.80 ± 0.05; b = 666 s/mm²: ACC = 0.85 ± 0.04). In particular, T₁ values differed substantially between the groups, resulting in atypically high classification accuracy. The second-best classifier, diffusion weighting with b = 666 s/mm², as well as all other parameters evaluated, exhibited substantial overlap between pre- and postdegradation groups, resulting in decreased accuracies. Conclusion Classification according to T₁ values showed excellent test characteristics (ACC = 0.99), with several other parameters also showing reasonable performance (ACC > 0.70). Of these, diffusion weighting is particularly promising as a potentially practical clinical modality. As in previous work, we again find that highly statistically significant group mean differences do not necessarily translate into accurate clinical classification rules.