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Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction

Multiparametric analysis of magnetic resonance images for glioma grading and... BackgroundA systematic comparison of magnetic resonance imaging (MRI) options forglioma diagnosis is lacking.PurposeTo investigate multiple MR-derived image features with respect to diagnosticaccuracy in tumor grading and survival prediction in glioma patients.Material and MethodsT1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74glioma patients with histologically confirmed grade were acquired. For eachpatient, a set of statistical features was obtained from the parametric mapsderived from the original images, in a region-of-interest encompassing thetumor volume. A forward stepwise selection procedure was used to find thebest combinations of features for grade prediction with a cross-validatedlogistic model and survival time prediction with a cox proportional-hazardsregression.ResultsPresence/absence of enhancement paired with kurtosis of the FM (first momentof the first-pass curve) was the feature combination that best predictedtumor grade (grade II vs. grade III-IV; median AUC = 0.96), with the maincontribution being due to the first of the features. A lower predictivevalue (median AUC = 0.82) was obtained when grade IV tumors were excluded.Presence/absence of enhancement alone was the best predictor for survivaltime, and the regression was significant (P <0.0001).ConclusionPresence/absence of enhancement, reflecting transendothelial leakage, was thefeature with highest predictive value for grade and survival time in gliomapatients. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Acta Radiologica SAGE

Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction

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References (1)

Publisher
SAGE
Copyright
© 2011 The Foundation Acta Radiologica
ISSN
0284-1851
eISSN
1600-0455
DOI
10.1258/ar.2011.100510
Publisher site
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Abstract

BackgroundA systematic comparison of magnetic resonance imaging (MRI) options forglioma diagnosis is lacking.PurposeTo investigate multiple MR-derived image features with respect to diagnosticaccuracy in tumor grading and survival prediction in glioma patients.Material and MethodsT1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74glioma patients with histologically confirmed grade were acquired. For eachpatient, a set of statistical features was obtained from the parametric mapsderived from the original images, in a region-of-interest encompassing thetumor volume. A forward stepwise selection procedure was used to find thebest combinations of features for grade prediction with a cross-validatedlogistic model and survival time prediction with a cox proportional-hazardsregression.ResultsPresence/absence of enhancement paired with kurtosis of the FM (first momentof the first-pass curve) was the feature combination that best predictedtumor grade (grade II vs. grade III-IV; median AUC = 0.96), with the maincontribution being due to the first of the features. A lower predictivevalue (median AUC = 0.82) was obtained when grade IV tumors were excluded.Presence/absence of enhancement alone was the best predictor for survivaltime, and the regression was significant (P <0.0001).ConclusionPresence/absence of enhancement, reflecting transendothelial leakage, was thefeature with highest predictive value for grade and survival time in gliomapatients.

Journal

Acta Radiologica – SAGE

Published: Nov 1, 2011

Keywords: MR imaging; perfusion; brain; primary neoplasms

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