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Utility of multiparametric 3-T MRI for glioma characterization

  • Diagnostic Neuroradiology
  • Published:
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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.

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Acknowledgments

This work was supported in part by Indo-US Science and Technology Forum award no. 20-2009. BR received financial assistance from the University Grant Commission, New Delhi, India. RA received financial assistance from the Indian Council of Medical Research, New Delhi, India. Sequence and software development was carried out under NIH grant R01EB000822.

Conflict of interest

We declare that we have no conflict of interest.

Author information

Authors and Affiliations

  1. Department of Radiology & Imaging, Fortis Memorial Research Institute, Gurgaon, Haryana, India, 122002

    Bhaswati Roy & Rakesh K. Gupta

  2. Department of Radiology, University of Miami, Miami, USA

    Andrew A. Maudsley & Sulaiman Sheriff

  3. Department of Radiodiagnosis, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India

    Rishi Awasthi & Sudipta Mohakud

  4. Department of Radiology, Stanford University, Standford, USA

    Meng Gu & Daniel M. Spielman

  5. Department of Pathology, Ram Manohar Lohia Institute of Medical Sciences, Lucknow, India

    Nuzhat Husain

  6. Department of Neurosurgery, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India

    Sanjay Behari

  7. Department of Biostatistics & Health Informatics, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India

    Chandra M. Pandey

  8. Department of Mathematics & Statistics, Indian Institute of Technology, Kanpur, India

    Ram K. S. Rathore

  9. Department of Radiological Sciences, UCLA School of Medicine, Los Angeles, USA

    Jeffry R. Alger

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  1. Bhaswati Roy
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  2. Rakesh K. Gupta
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  8. Sudipta Mohakud
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  11. Ram K. S. Rathore
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Correspondence to Rakesh K. Gupta.

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Roy, B., Gupta, R.K., Maudsley, A.A. et al. Utility of multiparametric 3-T MRI for glioma characterization. Neuroradiology 55, 603–613 (2013). https://doi.org/10.1007/s00234-013-1145-x

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  • DOI: https://doi.org/10.1007/s00234-013-1145-x

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