Perfusion MR Imaging in Gliomas: Comparison with Histologic Tumor Grade

  • Sun Joo Lee (Department of Radiology, Gyeongsang National University College of Medicine) ;
  • Jae Hyoung Kim (Department of Radiology, Gyeongsang National University College of Medicine) ;
  • Young Mee Kim (Department of Radiology, Gyeongsang National University College of Medicine) ;
  • Gyung Kyu Lee (Department of Radiology, Gyeongsang National University College of Medicine) ;
  • Eun Ja Lee (Department of Radiology, Gyeongsang National University College of Medicine) ;
  • In Sung Park (Department of Electronic Engineering, Gyeongsang National University College of Engineering) ;
  • Jin-Myung Jung (Department of Electronic Engineering, Gyeongsang National University College of Engineering) ;
  • Kyeong Hun Kang (Department of Neurosurgery, Gyeongsang National University College of Medicine) ;
  • Taemin Shin (Department of Neurosurgery, Gyeongsang National University College of Medicine)
  • Received : 2000.10.19
  • Accepted : 2000.12.20
  • Published : 2001.03.31

Abstract

Objective: To determine the usefulness of perfusion MR imaging in assessing the histologic grade of cerebral gliomas. Materials and Methods: In order to determine relative cerebral blood volume (rCBV), 22 patients with pathologically proven gliomas (9 glioblastomas, 9 anaplastic gliomas and 4 low-grade gliomas) underwent dynamic contrast-enhanced T2*-weighted and conventional T1- and T2-weighted imaging. rCBV maps were obtained by fitting a gamma-variate function to the contrast material concentration versus time curve. rCBV ratios between tumor and normal white matter (maximum rCBV of tumor / rCBV of contralateral white matter) were calculated and compared between glioblastomas, anaplastic gliomas and low-grade gliomas. Results: Mean rCBV ratios were 4.90°±1.01 for glioblastomas, 3.97°±0.56 for anaplastic gliomas and 1.75°±1.51 for low-grade gliomas, and were thus significantly different; p < .05 between glioblastomas and anaplastic gliomas, p < .05 between anaplastic gliomas and low-grade gliomas, p < .01 between glioblastomas and low-grade gliomas. The rCBV ratio cutoff value which permitted discrimination between high-grade (glioblastomas and anaplastic gliomas) and low-grade gliomas was 2.60, and the sensitivity and specificity of this value were 100% and 75%, respectively. Conclusion: Perfusion MR imaging is a useful and reliable technique for estimating the histologic grade of gliomas.

Keywords

Acknowledgement

This study was supported by grant number HMP-97-NM-2-0038 from the Good Health R&D Project, Ministry of Health and Welfare, and the Brain Korea 21 Project, Ministry of Education, South Korea.

References

  1. Russell D, Rubinstein L. Tumours of central neuroepithelial origin. In Rubinstein LJ, ed. Pathology of tumours of the central nervous system. Baltimore, Md.: Williams & Wilkins, 1989;83-350 
  2. Van Kirk OC, Cornell SH, Jacoby CG. Posterior fossa intraaxial tumors: a comparision of computed tomography with other imaging methods. J Comput Assist Tomogr 1979;3:31-39 
  3. Joyce P, Bentson J, Takahashi M, Winter J, Wilson G, Byrd S. The accuracy of predicting histologic grades of supratentorial astrocytomas on the basis of computerized tomography and cerebral angiography. Neuroradiology 1978;16:346-348 
  4. Seeger JF, Burke DP, Knake JE, Gabrielsen TO. Computed tomographic and angiographic evaluation of hemangioblastoma. Radiology 1981;138:65-73 
  5. Aronen HJ, Gazit IE, Louis DN, et al. Cerebral blood volume maps of gliomas: comparision with tumor grade and histologic findings. Radiology 1994;191:41-51 
  6. Edelman RR, Mattle HP, Atkinson DJ, et al. Cerebral blood flow: assessment with dynamic contrast-enhanced T2*-weighted MR imaging at 1.5T. Radiology 1990;176:211-220 
  7. Rosen BR, Belliveau JW, Aronen HJ, et al. Susceptibility contrast imaging of cerebral blood volume: human experience. Magn Reson Med 1991;22:293-299 
  8. Aronen HJ, Cohen MS, Belliveau JW, Fordham JA, Rosen BR. Ultrafast imaging of brain tumors. Top Magn Reson Imaging 1993;5:14-24 
  9. Le Bihan D, Douek P, Argyropoulou M, Turner R, Patronas N, Fulham M. Diffusion and perfusion magnetic resonance imaging in brain tumors. Top Magn Reson Imaging 1993;5:25-31 
  10. Maeda M, Itoh S, Kimura H, et al. Tumor vascularity in the brain: evaluation with dynamic susceptibility-contrast MR imaging. Radiology 1993;189:233-238 
  11. Maeda M, Itoh S, Kimura H, et al. Vascularity of meningioma and neuroma: assessment with dynamic susceptibility-contrast MR imaging. AJR 1994;163:181-186 
  12. Kim JS, Lee GK, Kim JH, et al. Blood volume of intraaxial brain tumor: evaluation with dynamic contrast-enhanced T2*-weighted MR imaging. J Korean Radiol Soc 1997;37:783-788 
  13. Kim HD, Chang KH, Song IC, et al. Perfusion MR imaging of the brain tumor: preliminary report. J Korean Soc Magn Reson Med 1997;1:119-124 
  14. Choi JY, Sun JS, Kim SY, et al. Effect of steroid on brain tumors and surround edemas: observation with regional cerebral blood volume (rCBV) maps of perfusion MRI. J Korean Radiol Soc 2000;42:15-21 
  15. Sugahara T, Korogi Y, Kochi M, et al. Correlation of MR imaging-determined cerebral blood volume maps with histologic and angiographic determination of vascularity of gliomas. AJR 1998;171:1479-1486 
  16. Knopp EA, Cha S, Johnson G, et al. Glial neoplasms: dynamic contrast-enhanced T2*-weighted MR imaging. Radiology 1999;211:791-798 
  17. Kleihues P, Burger PC, Scheithauer BW. Histologic typing of tumours of the central nervous system. 2nd ed. Berlin, Germany: Springer-Verlag, 1993;11-30 
  18. Thompson HK, Starmer CF, Whalen RE, McIntosh HD. Indicator transit time considered as a gamma variate. Circ Res 1964;14:502-515 
  19. Benner T, Heiland S, Erb G, Forsting M, Sartor K. Accuracy of gamma-variate fits to concentration-time curves from dynamic susceptibility-contrast MRI: influence of time resolution, maximal signal drop and signal-to-noise. Magn Reson Imaging 1997;15:307-317 
  20. Rosen BR, Belliveau JW, Vevea JM, et al. Perfusion MR imaging with NMR contrast agents. Magn Reson Med 1990;14:249-265 
  21. Brem S, Cotran R, Folkman J. Tumor angiogenesis: a quantitative method for histologic grading. J Natl Cancer Inst 1972;48:347-356 
  22. Edelman RR, Siewert B, Darby DG, et al. Quantitative mapping of cerebral blood flow and functional localization with echo-planar MR imaging and signal targeting with alternating radiofrequency. Radiology 1994;192:513-520 
  23. Belliveau JW, Rosen BR, Kantor HL, et al. Functional cerebral imaging by susceptibility contrast NMR. Magn Reson Med 1990;14:538-546 
  24. Guckel F, Brix G, Rempp K, Deimling M, Rother J, Georgi M. Assessment of cerebral blood volume with dynamic susceptibility contrast enhanced gradient echo imaging. J Comput Assist Tomogr 1994;18:344-351 
  25. Wenz F, Rempp K, Hess T, et al. Effect of radiation on blood volume in low-grade astrocytomas and normal brain tissue: quantification with dynamic susceptibility contrast MR imaging. AJR 1996;166:187-193 
  26. Cha S, Knopp EA, Johnson G, et al. Dynamic contrast-enhanced T2*-weighted MR imaging of recurrent malignant gliomas treated with thalidomide and carboplatin. AJNR 2000;21:881-890 
  27. Sugahara T, Korogi Y, Tomiguchi S, et al. Posttherapeutic intraaxial brain tumor: the value of perfusion-sensitive contrast-enhanced MR imaging for differentiating tumor recurrence from nonneoplastic contrast-enhancing tissue. AJNR 2000;21:901-909