Download PDF
Cent Eur Neurosurg 2011; 72(2): 63-69
DOI: 10.1055/s-0030-1253410
Original Article

© Georg Thieme Verlag KG Stuttgart · New York

Magnetic Resonance Spectroscopic Imaging for Visualization of the Infiltration Zone of Glioma

A. Stadlbauer1 , M. Buchfelder1 , M. T. Doelken2 , T. Hammen3 , O. Ganslandt1
  • 1University of Erlangen-Nuremberg, Department of Neurosurgery, Erlangen, Germany
  • 2University of Erlangen-Nuremberg, Department of Neuroradiology, Erlangen, Germany
  • 3University of Erlangen-Nuremberg, Department of Neurology, Epilepsy Center (ZEE), Erlangen, Germany
Further Information

Publication History

Publication Date:
15 July 2010 (online)

    Permissions and Reprints

Abstract

Background and Purpose: In conventional MR imaging, it is often difficult to delineate the heterogeneous structure of gliomas. Proton magnetic resonance spectroscopic imaging (1H-MRSI) is a noninvasive tool for investigating the spatial distribution of metabolic changes in brain lesions. The aim of this study was to assess the improvements in delineation of gliomas based on segmentation of metabolic changes measured with 1H-MRSI.

Material and Methods: Twenty patients with gliomas (WHO grade II and III) were examined using a standard 1H-MRSI sequence. Metabolic maps for choline (Cho), N-acetyl-aspartate (NAA) and Cho/NAA ratios were calculated and segmented based on the assumption of a Gaussian distribution of the Cho/NAA values for normal brain. Areas of hyperintensity on T2-weighted (T2w) MR images were compared with the areas of the segmented tumor on Cho/NAA maps. Stereotactic biopsies were obtained from the MRSI/T2w difference areas.

Results: In all patients, the segmented MRSI tumor areas were greater than the T2w hyperintense areas, on average, by 20% (range 6–34%). In nine patients, biopsy sampling from the MRSI/T2w difference areas showed tumor infiltration ranging from 4–17% (mean 9%) tumor cells, in the areas detected only by MRSI.

Discussion and Conclusion: Our method for automated segmentation of the lesion-related metabolic changes achieved significantly improved delineation for gliomas compared to routine clinical methods. We demonstrate that this method can improve delineation of tumor borders compared to routine imaging strategies in clinics. Metabolic images of the segmented tumor may thus be helpful for therapeutic planning.

Key words

glioma - MR imaging - MR spectroscopy - tumor border

Comment to this article:

Comment to the article:Cent Eur Neurosurg 2011; 72(02): 70-70
DOI: 10.1055/s-0030-1254104

Comment to this article:

Comment to the article:Cent Eur Neurosurg 2011; 72(02): 70-70
DOI: 10.1055/s-0030-1254103

References

  • 1 Dowling C, Bollen AW, Noworolski SM. et al . Preoperative proton MR spectroscopic imaging of brain tumors: correlation with histopathologic analysis of resection specimens.  AJNR Am J Neuroradiol. 2001;  22 604-612
    Google Scholar
  • 2 Kondziolka D, Lunsford LD, Martinez AJ. Unreliability of contemporary neurodiagnostic imaging in evaluating suspected adult supratentorial (low-grade) astrocytoma.  J Neurosurg. 1993;  79 533-536
    Google Scholar
  • 3 Majos C, Alonso J, Aguilera C. et al . Adult primitive neuroectodermal tumor: proton MR spectroscopic findings with possible application for differential diagnosis.  Radiology. 2002;  225 556-566
    Google Scholar
  • 4 Negendank WG, Sauter R, Brown TR. et al . Proton magnetic resonance spectroscopy in patients with glial tumors: a multicenter study.  J Neurosurg. 1996;  84 449-458
    Google Scholar
  • 5 Michaelis T, Merboldt KD, Bruhn H. et al . Absolute concentrations of metabolites in the adult human brain in vivo: quantification of localized proton MR spectra.  Radiology. 1993;  187 219-227
    Google Scholar
  • 6 Urenjak J, Williams SR, Gadian DG. et al . Proton nuclear magnetic resonance spectroscopy unambiguously identifies different neural cell types.  J Neurosci. 1993;  13 981-989
    Google Scholar
  • 7 Kemp GJ. Non-invasive methods for studying brain energy metabolism: what they show and what it means.  Dev Neurosci. 2000;  22 418-428
    Google Scholar
  • 8 Stadlbauer A, Gruber S, Nimsky C. et al . Preoperative grading of gliomas by using metabolite quantification with high-spatial-resolution proton MR spectroscopic imaging.  Radiology. 2006;  238 958-969
    Google Scholar
  • 9 Stadlbauer A, Moser E, Gruber S. et al . Improved delineation of brain tumors: an automated method for segmentation based on pathologic changes of 1H-MRSI metabolites in gliomas.  Neuroimage. 2004;  23 454-461
    Google Scholar
  • 10 David HA, Hartley HO, Pearson ES. The distribution of the ratio, in a single normal sample, of range to standard deviation.  Biometrika. 1954;  41 482-493
    Google Scholar
  • 11 Provencher SW. Estimation of metabolite concentrations from localized in vivo proton NMR spectra.  Magn Reson Med. 1993;  30 672-679
    Google Scholar
  • 12 Stadlbauer A, Moser E, Gruber S. et al . Integration of biochemical images of a tumor into frameless stereotaxy achieved using a magnetic resonance imaging/magnetic resonance spectroscopy hybrid data set.  J Neurosurg. 2004;  101 287-294
    Google Scholar
  • 13 Blumcke I, Becker AJ, Normann S. et al . Distinct expression pattern of microtubule-associated protein-2 in human oligodendrogliomas and glial precursor cells.  J Neuropathol Exp Neurol. 2001;  60 984-993
    Google Scholar
  • 14 Blumcke I, Muller S, Buslei R. et al . Microtubule-associated protein-2 immunoreactivity: a useful tool in the differential diagnosis of low-grade neuroepithelial tumors.  Acta Neuropathol (Berl). 2004;  108 89-96
    Google Scholar
  • 15 Pirzkall A, Nelson SJ, McKnight TR. et al . Metabolic imaging of low-grade gliomas with three-dimensional magnetic resonance spectroscopy.  Int J Radiat Oncol Biol Phys. 2002;  53 1254-1264
    Google Scholar
  • 16 Li X, Lu Y, Pirzkall A. et al . Analysis of the spatial characteristics of metabolic abnormalities in newly diagnosed glioma patients.  J Magn Reson Imaging. 2002;  16 229-237
    Google Scholar
  • 17 De Edelenyi FS, Rubin C, Esteve F. et al . A new approach for analyzing proton magnetic resonance spectroscopic images of brain tumors: nosologic images.  Nat Med. 2000;  6 1287-1289
    Google Scholar
  • 18 McKnight TR, von dem Bussche MH, Vigneron DB. et al . Histopathological validation of a three-dimensional magnetic resonance spectroscopy index as a predictor of tumor presence.  J Neurosurg. 2002;  97 794-802
    Google Scholar
  • 19 Pirzkall A, McKnight TR, Graves EE. et al . MR-spectroscopy guided target delineation for high-grade gliomas.  Int J Radiat Oncol Biol Phys. 2001;  50 915-928
    Google Scholar
  • 20 Nelson SJ, Graves E, Pirzkall A. et al . In vivo molecular imaging for planning radiation therapy of gliomas: An application of 1H MRSI.  J Magn Reson Imaging. 2002;  16 464-476
    Google Scholar

Correspondence

Dr. A. Stadlbauer

University of Erlangen-Nuremberg

Department of Neurosurgery

Schwabachanlage 6

91054 Erlangen

Germany

Phone: +49/9131/85 34259

Fax: +49/9131/85 34271

Email: andi@nmr.at

      >