Utility of multiparametric 3-T MRI for glioma characterization

Bhaswati Roy, Rakesh K. Gupta, Andrew A Maudsley, Rishi Awasthi, Sulaiman Sheriff, Meng Gu, Nuzhat Husain, Sudipta Mohakud, Sanjay Behari, Chandra M. Pandey, Ram K S Rathore, Daniel M. Spielman, Jeffry R. Alger

Research output: Contribution to journalArticle

59 Citations (Scopus)

Abstract

Introduction: Accurate grading of cerebral glioma using conventional structural imaging techniques remains challenging due to the relatively poor sensitivity and specificity of these methods. The purpose of this study was to evaluate the relative sensitivity and specificity of structural magnetic resonance imaging and MR measurements of perfusion, diffusion, and whole-brain spectroscopic parameters for glioma grading. Methods: Fifty-six patients with radiologically suspected untreated glioma were studied with T1- and T2-weighted MR imaging, dynamic contrast-enhanced MR imaging, diffusion tensor imaging, and volumetric whole-brain MR spectroscopic imaging. Receiver-operating characteristic analysis was performed using the relative cerebral blood volume (rCBV), apparent diffusion coefficient, fractional anisotropy, and multiple spectroscopic parameters to determine optimum thresholds for tumor grading and to obtain the sensitivity, specificity, and positive and negative predictive values for identifying high-grade gliomas. Logistic regression was performed to analyze all the parameters together. Results: The rCBV individually classified glioma as low and high grade with a sensitivity and specificity of 100 and 88 %, respectively, based on a threshold value of 3.34. On combining all parameters under consideration, the classification was achieved with 2 % error and sensitivity and specificity of 100 and 96 %, respectively. Conclusion: Individually, CBV measurement provides the greatest diagnostic performance for predicting glioma grade; however, the most accurate classification can be achieved by combining all of the imaging parameters.

Original languageEnglish
Pages (from-to)603-613
Number of pages11
JournalNeuroradiology
Volume55
Issue number5
DOIs
StatePublished - May 1 2013

Fingerprint

Glioma
Sensitivity and Specificity
Diffusion Tensor Imaging
Neoplasm Grading
Anisotropy
Brain
ROC Curve
Perfusion
Logistic Models
Magnetic Resonance Imaging
Cerebral Blood Volume

Keywords

  • Diffusion tensor imaging
  • Dynamic contrast enhance MR
  • Glioma grading
  • Multiparametric MRI
  • Whole-brain MRSI

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine

Cite this

Roy, B., Gupta, R. K., Maudsley, A. A., Awasthi, R., Sheriff, S., Gu, M., ... Alger, J. R. (2013). Utility of multiparametric 3-T MRI for glioma characterization. Neuroradiology, 55(5), 603-613. https://doi.org/10.1007/s00234-013-1145-x

Utility of multiparametric 3-T MRI for glioma characterization. / Roy, Bhaswati; Gupta, Rakesh K.; Maudsley, Andrew A; Awasthi, Rishi; Sheriff, Sulaiman; Gu, Meng; Husain, Nuzhat; Mohakud, Sudipta; Behari, Sanjay; Pandey, Chandra M.; Rathore, Ram K S; Spielman, Daniel M.; Alger, Jeffry R.

In: Neuroradiology, Vol. 55, No. 5, 01.05.2013, p. 603-613.

Research output: Contribution to journalArticle

Roy, B, Gupta, RK, Maudsley, AA, Awasthi, R, Sheriff, S, Gu, M, Husain, N, Mohakud, S, Behari, S, Pandey, CM, Rathore, RKS, Spielman, DM & Alger, JR 2013, 'Utility of multiparametric 3-T MRI for glioma characterization', Neuroradiology, vol. 55, no. 5, pp. 603-613. https://doi.org/10.1007/s00234-013-1145-x
Roy, Bhaswati ; Gupta, Rakesh K. ; Maudsley, Andrew A ; Awasthi, Rishi ; Sheriff, Sulaiman ; Gu, Meng ; Husain, Nuzhat ; Mohakud, Sudipta ; Behari, Sanjay ; Pandey, Chandra M. ; Rathore, Ram K S ; Spielman, Daniel M. ; Alger, Jeffry R. / Utility of multiparametric 3-T MRI for glioma characterization. In: Neuroradiology. 2013 ; Vol. 55, No. 5. pp. 603-613.
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AU - Gu, Meng

AU - Husain, Nuzhat

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AU - Behari, Sanjay

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AB - Introduction: Accurate grading of cerebral glioma using conventional structural imaging techniques remains challenging due to the relatively poor sensitivity and specificity of these methods. The purpose of this study was to evaluate the relative sensitivity and specificity of structural magnetic resonance imaging and MR measurements of perfusion, diffusion, and whole-brain spectroscopic parameters for glioma grading. Methods: Fifty-six patients with radiologically suspected untreated glioma were studied with T1- and T2-weighted MR imaging, dynamic contrast-enhanced MR imaging, diffusion tensor imaging, and volumetric whole-brain MR spectroscopic imaging. Receiver-operating characteristic analysis was performed using the relative cerebral blood volume (rCBV), apparent diffusion coefficient, fractional anisotropy, and multiple spectroscopic parameters to determine optimum thresholds for tumor grading and to obtain the sensitivity, specificity, and positive and negative predictive values for identifying high-grade gliomas. Logistic regression was performed to analyze all the parameters together. Results: The rCBV individually classified glioma as low and high grade with a sensitivity and specificity of 100 and 88 %, respectively, based on a threshold value of 3.34. On combining all parameters under consideration, the classification was achieved with 2 % error and sensitivity and specificity of 100 and 96 %, respectively. Conclusion: Individually, CBV measurement provides the greatest diagnostic performance for predicting glioma grade; however, the most accurate classification can be achieved by combining all of the imaging parameters.

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