Neuroradiology

, Volume 55, Issue 5, pp 603–613

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
Diagnostic Neuroradiology

DOI: 10.1007/s00234-013-1145-x

Cite this article as:
Roy, B., Gupta, R.K., Maudsley, A.A. et al. Neuroradiology (2013) 55: 603. doi:10.1007/s00234-013-1145-x

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.

Keywords

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

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Bhaswati Roy
    • 1
  • Rakesh K. Gupta
    • 1
  • Andrew A. Maudsley
    • 2
  • Rishi Awasthi
    • 3
  • Sulaiman Sheriff
    • 2
  • Meng Gu
    • 4
  • Nuzhat Husain
    • 5
  • Sudipta Mohakud
    • 3
  • Sanjay Behari
    • 6
  • Chandra M. Pandey
    • 7
  • Ram K. S. Rathore
    • 8
  • Daniel M. Spielman
    • 4
  • Jeffry R. Alger
    • 9
  1. 1.Department of Radiology & ImagingFortis Memorial Research InstituteGurgaonIndia
  2. 2.Department of RadiologyUniversity of MiamiMiamiUSA
  3. 3.Department of RadiodiagnosisSanjay Gandhi Postgraduate Institute of Medical SciencesLucknowIndia
  4. 4.Department of RadiologyStanford UniversityStandfordUSA
  5. 5.Department of PathologyRam Manohar Lohia Institute of Medical SciencesLucknowIndia
  6. 6.Department of NeurosurgerySanjay Gandhi Postgraduate Institute of Medical SciencesLucknowIndia
  7. 7.Department of Biostatistics & Health InformaticsSanjay Gandhi Postgraduate Institute of Medical SciencesLucknowIndia
  8. 8.Department of Mathematics & StatisticsIndian Institute of TechnologyKanpurIndia
  9. 9.Department of Radiological SciencesUCLA School of MedicineLos AngelesUSA

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