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

Vanessa A. Lukas, Kenneth W. Fishbein, David A. Reiter, Ping Chang Lin, Erika Schneider, Richard G. Spencer

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)136-144
Number of pages9
JournalJournal of Magnetic Resonance Imaging
Volume42
Issue number1
DOIs
StatePublished - Jul 1 2015

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Cartilage
Sensitivity and Specificity
Nasal Cartilages

Keywords

  • bovine nasal cartilage
  • classification
  • osteoarthritis
  • quantitative MRI
  • sensitivity
  • specificity

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Cite this

Sensitivity and specificity of univariate MRI analysis of experimentally degraded cartilage under clinical imaging conditions. / Lukas, Vanessa A.; Fishbein, Kenneth W.; Reiter, David A.; Lin, Ping Chang; Schneider, Erika; Spencer, Richard G.

In: Journal of Magnetic Resonance Imaging, Vol. 42, No. 1, 01.07.2015, p. 136-144.

Research output: Contribution to journalArticle

Lukas, Vanessa A. ; Fishbein, Kenneth W. ; Reiter, David A. ; Lin, Ping Chang ; Schneider, Erika ; Spencer, Richard G. / Sensitivity and specificity of univariate MRI analysis of experimentally degraded cartilage under clinical imaging conditions. In: Journal of Magnetic Resonance Imaging. 2015 ; Vol. 42, No. 1. pp. 136-144.
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abstract = "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.",
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N2 - 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.

AB - 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.

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