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Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction
- Source :
- Garzón, B, Emblem, K E, Mouridsen, K, Nedregaard, B, Due-Tønnessen, P, Nome, T, Hald, J, Bjørnerud, A, Håberg, A K & Kvinnsland, Y 2011, ' Multiparametric analysis of magnetic resonance images for glioma grading and patient survival time prediction ', Acta Radiologica, vol. 52, no. 9, pp. 1052-60 . https://doi.org/10.1258/ar.2011.100510
- Publication Year :
- 2011
- Publisher :
- SAGE Publications, 2011.
-
Abstract
- Background A systematic comparison of magnetic resonance imaging (MRI) options for glioma diagnosis is lacking. Purpose To investigate multiple MR-derived image features with respect to diagnostic accuracy in tumor grading and survival prediction in glioma patients. Material and Methods T1 pre- and post-contrast, T2 and dynamic susceptibility contrast scans of 74 glioma patients with histologically confirmed grade were acquired. For each patient, a set of statistical features was obtained from the parametric maps derived from the original images, in a region-of-interest encompassing the tumor volume. A forward stepwise selection procedure was used to find the best combinations of features for grade prediction with a cross-validated logistic model and survival time prediction with a cox proportional-hazards regression. Results Presence/absence of enhancement paired with kurtosis of the FM (first moment of the first-pass curve) was the feature combination that best predicted tumor grade (grade II vs. grade III-IV; median AUC = 0.96), with the main contribution being due to the first of the features. A lower predictive value (median AUC = 0.82) was obtained when grade IV tumors were excluded. Presence/absence of enhancement alone was the best predictor for survival time, and the regression was significant ( P < 0.0001). Conclusion Presence/absence of enhancement, reflecting transendothelial leakage, was the feature with highest predictive value for grade and survival time in glioma patients.
- Subjects :
- Adult
Male
Pathology
medicine.medical_specialty
Logistic regression
Glioma
Humans
Medicine
Radiology, Nuclear Medicine and imaging
neoplasms
Aged
Radiological and Ultrasound Technology
medicine.diagnostic_test
Brain Neoplasms
business.industry
Multiparametric Analysis
Magnetic resonance imaging
General Medicine
Middle Aged
Stepwise regression
equipment and supplies
medicine.disease
Magnetic Resonance Imaging
Regression
nervous system diseases
Glioma grading
Kurtosis
Female
Neoplasm Grading
business
Nuclear medicine
human activities
Subjects
Details
- ISSN :
- 16000455 and 02841851
- Volume :
- 52
- Database :
- OpenAIRE
- Journal :
- Acta Radiologica
- Accession number :
- edsair.doi.dedup.....569ff7135adcfa7974d59661791430b0