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A search for new MRI criteria for dissemination in space in subjects with a clinically isolated syndrome.

Korteweg T, Tintore M, Uitdehaag BM, Knol DL, Vrenken H, Rovira A, Frederiksen J, Miller DH, Fernando K, Filippi M, Agosta F, Rocca MA, Fazekas F, Enzinger C, Parry A, Polman CH, Montalban X, Barkhof F - Eur Radiol (2009)

Bottom Line: Although specific for MS, the B/T criteria were criticised for their low sensitivity and relative complexity in clinical use.We used lesion characteristics at onset from 349 CIS patients in logistic regression and recursive partitioning modelling in a search for simpler and more sensitive criteria, while maintaining current specificity.Apparently, findings from contrast-enhanced and follow-up magnetic resonance scans are needed to improve the diagnostic algorithm.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, VU University Medical Centre, De Boelelaan 1118, Amsterdam 1081 HV, The Netherlands. T.Korteweg@vumc.nl

ABSTRACT
The International Panel on the Diagnosis of Multiple Sclerosis (MS) incorporated the Barkhof/Tintoré (B/T) magnetic resonance criteria into their diagnostic scheme to provide evidence of dissemination in space of central nervous system lesions, a prerequisite for diagnosing MS in patients who present with clinically isolated syndromes (CIS). Although specific for MS, the B/T criteria were criticised for their low sensitivity and relative complexity in clinical use. We used lesion characteristics at onset from 349 CIS patients in logistic regression and recursive partitioning modelling in a search for simpler and more sensitive criteria, while maintaining current specificity. The resulting models, all based on the presence of periventricular and deep white matter lesions, performed roughly in agreement with the B/T criteria, but were unable to provide higher diagnostic accuracy based on information from a single scan. Apparently, findings from contrast-enhanced and follow-up magnetic resonance scans are needed to improve the diagnostic algorithm.

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Related in: MedlinePlus

Classification tree derived from the training set data. Values represent number of CIS or CDMS cases. The predicted class is displayed in each terminal node of the tree in bold
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Fig1: Classification tree derived from the training set data. Values represent number of CIS or CDMS cases. The predicted class is displayed in each terminal node of the tree in bold

Mentions: The classification tree analysis included the same predictors as those found using the regression analysis. The first split was made using the presence of one or more deep white matter lesions, dividing the development dataset into a group of 94 and 136 cases (Fig. 1). The risk of conversion was 15% (14/94) in the left node without deep white matter lesions versus 56% (76/136) in the right node with deep white matter lesions. A final split was made within the group with deep white matter lesions, based on the presence of a periventricular lesion. This split increased the risk of conversion to 60% in the right lower node with a periventricular lesion. When applied to the test set, this model showed sensitivity of 0.64 (95% CI: 0.48–0.78), specificity of 0.70 (95% CI: 0.59–0.80) and accuracy of 0.68 (95% CI: 0.59–0.76). No improvement in accuracy was found using Random Forest analysis. Table 2 summarises the performance of the models in the test set, including the original B/T criteria. Similar to the regression analysis, including the total number of lesions as an independent covariate resulted in a tree with a single split based on the presence of four lesions regardless of their topographical location.Fig. 1


A search for new MRI criteria for dissemination in space in subjects with a clinically isolated syndrome.

Korteweg T, Tintore M, Uitdehaag BM, Knol DL, Vrenken H, Rovira A, Frederiksen J, Miller DH, Fernando K, Filippi M, Agosta F, Rocca MA, Fazekas F, Enzinger C, Parry A, Polman CH, Montalban X, Barkhof F - Eur Radiol (2009)

Classification tree derived from the training set data. Values represent number of CIS or CDMS cases. The predicted class is displayed in each terminal node of the tree in bold
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2719077&req=5

Fig1: Classification tree derived from the training set data. Values represent number of CIS or CDMS cases. The predicted class is displayed in each terminal node of the tree in bold
Mentions: The classification tree analysis included the same predictors as those found using the regression analysis. The first split was made using the presence of one or more deep white matter lesions, dividing the development dataset into a group of 94 and 136 cases (Fig. 1). The risk of conversion was 15% (14/94) in the left node without deep white matter lesions versus 56% (76/136) in the right node with deep white matter lesions. A final split was made within the group with deep white matter lesions, based on the presence of a periventricular lesion. This split increased the risk of conversion to 60% in the right lower node with a periventricular lesion. When applied to the test set, this model showed sensitivity of 0.64 (95% CI: 0.48–0.78), specificity of 0.70 (95% CI: 0.59–0.80) and accuracy of 0.68 (95% CI: 0.59–0.76). No improvement in accuracy was found using Random Forest analysis. Table 2 summarises the performance of the models in the test set, including the original B/T criteria. Similar to the regression analysis, including the total number of lesions as an independent covariate resulted in a tree with a single split based on the presence of four lesions regardless of their topographical location.Fig. 1

Bottom Line: Although specific for MS, the B/T criteria were criticised for their low sensitivity and relative complexity in clinical use.We used lesion characteristics at onset from 349 CIS patients in logistic regression and recursive partitioning modelling in a search for simpler and more sensitive criteria, while maintaining current specificity.Apparently, findings from contrast-enhanced and follow-up magnetic resonance scans are needed to improve the diagnostic algorithm.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiology, VU University Medical Centre, De Boelelaan 1118, Amsterdam 1081 HV, The Netherlands. T.Korteweg@vumc.nl

ABSTRACT
The International Panel on the Diagnosis of Multiple Sclerosis (MS) incorporated the Barkhof/Tintoré (B/T) magnetic resonance criteria into their diagnostic scheme to provide evidence of dissemination in space of central nervous system lesions, a prerequisite for diagnosing MS in patients who present with clinically isolated syndromes (CIS). Although specific for MS, the B/T criteria were criticised for their low sensitivity and relative complexity in clinical use. We used lesion characteristics at onset from 349 CIS patients in logistic regression and recursive partitioning modelling in a search for simpler and more sensitive criteria, while maintaining current specificity. The resulting models, all based on the presence of periventricular and deep white matter lesions, performed roughly in agreement with the B/T criteria, but were unable to provide higher diagnostic accuracy based on information from a single scan. Apparently, findings from contrast-enhanced and follow-up magnetic resonance scans are needed to improve the diagnostic algorithm.

Show MeSH
Related in: MedlinePlus