Evaluation of dynamic contrast-enhanced T1-weighted perfusion MRI in the differentiation of tumor recurrence from radiation necrosis

Abstract

Introduction

To investigate if perfusion measured with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can be used to differentiate radiation necrosis from tumor recurrence in patients with high-grade glioma.

Methods

The study was approved by the institutional review board and informed consent was obtained from all subjects. 19 patients were recruited following surgery and radiation therapy for glioma. Patients had contrast enhancing lesions, which during the standard MRI examination could not be exclusively determined as recurrence or radiation necrosis. DCE-MRI was used to measure cerebral blood volume (CBV), blood–brain barrier (BBB) permeability and cerebral blood flow (CBF). Subjects also underwent FDG-PET and lesions were classified as either metabolically active or inactive. Follow-up clinical MRI and lesion histology in case of additional tissue resection was used to determine whether lesions were regressing or progressing.

Results

Fourteen enhancing lesions could be classified as progressing (11) or regressing (three). An empirical threshold of 2.0 ml/100 g for CBV allowed detection of regressing lesions with a sensitivity of 100 % and specificity of 100 %. FDG-PET and DCE-MRI agreed in classification of tumor status in 13 out of the 16 cases where an FDG-PET classification was obtained. In two of the remaining three patients, MRI follow-up and histology was available and both indicated that the DCE-MRI answer was correct.

Conclusion

CBV measurements using DCE-MRI may predict the status of contrast enhancing lesions and give results very similar to FDG-PET with regards to differentiation between tumor recurrence and radiation necrosis.

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Abbreviations

AIF:

Arterial input function

BBB:

Blood–brain barrier

CBF:

Cerebral blood flow

CBV:

Cerebral blood volume

DCE-MRI:

Dynamic contrast enhanced magnetic resonance imaging

DSC-MRI:

Dynamic susceptibility contrast magnetic resonance imaging

FDG-PET:

18F-fluorodeoxyglucose positron emission tomography

ICA:

Internal carotid artery

K trans :

Transfer constant

rCBV:

Relative CBV

ROI:

Region of interest

SD:

Standard deviation

TE:

Echo time

WM:

White matter

References

  1. 1.

    Leon SP, Folkerth RD, Black PM (1996) Microvessel density is a prognostic indicator for patients with astroglial brain tumors. Cancer 77:362–372

    PubMed  Article  CAS  Google Scholar 

  2. 2.

    Folkerth RD (2000) Descriptive analysis and quantification of angiogenesis in human brain tumors. J Neurooncol 50:165–172

    PubMed  Article  CAS  Google Scholar 

  3. 3.

    Remler MP, Marcussen WH, Tiller-Borsich J (1986) The late effects of radiation on the blood brain barrier. Int J Radiat oncol biol phys 12:1965–1969

    PubMed  Article  CAS  Google Scholar 

  4. 4.

    Dooms GC, Hecht S, Brant-Zawadzki M et al (1986) Brain radiation lesions. MR-imaging Radiology 158:149–155

    CAS  Google Scholar 

  5. 5.

    Mullins ME, Barest GD, Schaefer PW et al (2005) Radiation necrosis versus glioma recurrence: conventional MR imaging clues to diagnosis. AJNR 26:1967–1672

    PubMed  Google Scholar 

  6. 6.

    Cha S, Knopp EA, Johnson G, Wetzel SG, Litt AW, Zagzag D (2002) Intracranial mass lesions: dynamic contrast-enhanced susceptibility-weighted echo-planar perfusion MR imaging. Radiology 223:11–29

    PubMed  Article  Google Scholar 

  7. 7.

    Nelson SJ (2011) Assessment of therapeutic response and treatment planning for brain tumors using metabolic and physiological MRI. NMR in Biomed 24:734–749

    Google Scholar 

  8. 8.

    Larsson HB, Hansen AE, Berg HK, Rostrup E, Haraldseth O (2008) Dynamic contrast-enhanced quantitative perfusion measurement of the brain using T1-weighted MRI at 3T. J Magn Reson Imaging 27(4):754–762

    PubMed  Article  Google Scholar 

  9. 9.

    Larsson HBW, Courivaud F, Rostrup E, Hansen AE (2009) Measurement of brain perfusion, blood volume, and blood–brain barrier permeability, using dynamic contrast-enhanced T[1]-weighted MRI at 3 Tesla. Magn Reson Med 62:1270–1281

    PubMed  Article  Google Scholar 

  10. 10.

    Bjørnerud A, Emblem KE (2010) A fully automated method for quantitative cerebral hemodynamic analysis using DSC-MRI. J cereb flow metab 30:1066–1078

    Article  Google Scholar 

  11. 11.

    Paulson ES, Schmainda KM (2008) Comparison of dynamic susceptibility-weighted contrast-enhanced MR-methods: Recommendations for measuring relative cerebral blood volume in brain tumors. Radiology 249(2):601–613

    PubMed  Article  Google Scholar 

  12. 12.

    Wang SX, Boethius J, Ericson K (2006) FDG-PET on irradiated brain tumor: ten years’ summary. Acta Radiol [1]: 85–90.

  13. 13.

    Chen W (2007) Clinical applications of PET in brain tumors. J Nucl Med 48:1468–1481

    PubMed  Article  Google Scholar 

  14. 14.

    La Fougère C, Suchorska B, Bartenstein P, Kreth F-W, Tonn J-C (2011) Molecular imaging of gliomas with PET: opportunities and limitations. Neuro-oncology 13(8):806–819

    PubMed  Article  Google Scholar 

  15. 15.

    Ricci PE, Karis JP, Heiserman JE et al (1998) Differentiating recurrent tumor from radiation necrosis: time for re-evaluation of positron emission tomography? AJNR 9:407–413

    Google Scholar 

  16. 16.

    Langleben DD, Segall GM (2000) PET in differentiation of recurrent brain tumor from radiation injury. J Nucl Med 41:1861–1867

    PubMed  CAS  Google Scholar 

  17. 17.

    Hansen AE, Pedersen H, Rostrup E, Larsson HB (2009) Partial volume effect [PVE] on the arterial input function [AIF] in T1-weighted perfusion imaging and limitations of the multiplicative rescaling approach. Magn Reson Med 62:1055–1059

    PubMed  Article  Google Scholar 

  18. 18.

    Lewellen TK, Kohlmyer SG, Miyaoka RS, Kaplan MS (1996) Investigation of the performance of the general electric ADVANCE positron emission tomograph in 3D mode. IEEE Transactions on Nuclear Science 43(4):2199–2206

    Article  Google Scholar 

  19. 19.

    Patlak CS, Blasberg RG (1985) Graphical evaluation of blood-to-brain transfer constants from multiple-time uptake data. Generalizations. J Cereb Blood Flow Metab 5:584–590

    PubMed  Article  CAS  Google Scholar 

  20. 20.

    Macdonald D, Cascino T, Schold SJ, Cairncross J (1990) Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 8:1277–1280

    PubMed  CAS  Google Scholar 

  21. 21.

    Rostrup E, Knudsen GM, Law I, Holm S, Larsson HBW, Paulson OB (2005) The relationship between cerebral blood flow and volume in humans. Neuroimage 24:1–11

    PubMed  Article  Google Scholar 

  22. 22.

    Weybright P, Sundgren PC, Maly P, Hassan DG et al (2005) Differentiation between brain tumor recurrence and radiation injury using MR spectroscopy. Am J Roentgenol 185(6):1471–6

    Article  Google Scholar 

  23. 23.

    Zeng QC, Li CF, Zang K et al (2007) Multivoxel 3D proton MR spectroscopy in the distinction of recurrent glioma from radiation injury. J Neurooncol 84(1):63–69

    PubMed  Article  CAS  Google Scholar 

  24. 24.

    Prat R, Galeano I, Lucas A et al (2010) Relative value of magnetic resonance spectroscopy, magnetic resonance perfusion and 2-(18F)fluoro-2-deoxy-d-glucose positron emission tomography for detection of recurrence or grade increase in gliomas. J Clin Neurosci 17(1):50–53

    PubMed  Article  CAS  Google Scholar 

  25. 25.

    Hu LS, Baxter LC, Smith KA et al (2009) Relative cerebral blood volume values to differentiate high-grade glioma recurrence from posttreatment radiation effect: direct correlation between image-guided tissue histopathology and localized dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging measurements. Am J Neuroradiol 30:552–558

    PubMed  Article  CAS  Google Scholar 

  26. 26.

    Barajas R, Chang JS, Segal MR, Parsa AT, McDermott MW, Berger MS, Cha S (2009) Differentiation of recurrent glioblastoma multiforme from radiation necrosis after external beam radiation therapy with dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 253:486–496

    PubMed  Article  Google Scholar 

  27. 27.

    Bobek-Billewicz B, Stasik-Pres G, Majchrzak H, Zarudzki T (2010) Differentiation between brain tumor recurrence and radiation injury using perfusion, diffusion-weighted imaging and MR spectroscopy. Folia Neuropathologica 48:81–92

    PubMed  Google Scholar 

  28. 28.

    Sugahara T, Korogi Y, Tomiguchi S et al (2000) Posttherapeutic intraaxial brain tumor: the value of perfusion-sensitive contrast-enhanced MR imaging for differentiating tumor recurrence from nonneoplastic contrast-enhancing tissue. Am J neuroradiol 21:901–909

    PubMed  CAS  Google Scholar 

  29. 29.

    Kim YH, Oh SW, Lim YJ et al (2010) Differentiating radiation necrosis from tumor recurrence in high-grade gliomas: assessing the efficacy of 18F-FDG PET, 11C-methionine PET and perfusion MRI. Clin Neurol Neurosurg 112(9):758–65

    PubMed  Article  Google Scholar 

  30. 30.

    Boxerman JL, Schmainda KM, Weisskoff RM (2006) Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade whereas uncorrected maps do not. Am J Neuroradiol 27:859–867

    PubMed  CAS  Google Scholar 

  31. 31.

    Soerensen AG (2008) Perfusion MR imaging: moving forward. Radiology 249(2):416–417

    Article  Google Scholar 

  32. 32.

    Mills SJ, Soh C, O’connor JP et al (2010) Enhancing fraction in glioma and its relationship to the tumoral vascular microenvironment: a dynamic contrast-enhanced MR imaging study. AJNR Am J Neuroradiol 31(4):726–31

    PubMed  Article  CAS  Google Scholar 

  33. 33.

    Dhermain FG, Hau P, Lanfermann H, Jacobs AH, van den Bent MJ (2010) Advanced MRI and PET imaging for assessment of treatment response in patients with gliomas. Lancet 9:906–920

    Article  Google Scholar 

  34. 34.

    Rachinger W, Goetz C, Pöpperl G et al (2005) Positron emission tomography with O-[2-[18F]fluoroethyl]-l-tyrosine versus magnetic resonance imaging in the diagnosis of recurrent gliomas. Neurosurgery 57(3):505–11

    PubMed  Article  Google Scholar 

  35. 35.

    Dandois V, Rommel D, Renard L, Jamart J, Cosnard G (2010) Substitution of 11C-methionine PET by perfusion MRI during the follow-up of treated high-grade gliomas. Preliminary results in clinical practice. J Neuroradiol 37(2):89–97

    PubMed  Article  CAS  Google Scholar 

  36. 36.

    Forsyth PA, Kelly PJ, Cascino TL, Scheithauer BW, Shaw EG, Dinapoli RP (1995) Radiation necrosis or glioma recurrence: is computer-assisted stereotactic biopsy useful? J Neurosurg 82:436–444

    PubMed  Article  CAS  Google Scholar 

  37. 37.

    Chorvath M, Boljesikova E, Pruzincova L et al (2009) Post-therapeutical changes in the brain: novel trends in imaging and their influence on external beam radiotherapy. Neoplasma 56(2):156–62

    PubMed  Article  CAS  Google Scholar 

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We declare that we have no conflict of interest.

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Correspondence to Vibeke A. Larsen.

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Larsen, V.A., Simonsen, H.J., Law, I. et al. Evaluation of dynamic contrast-enhanced T1-weighted perfusion MRI in the differentiation of tumor recurrence from radiation necrosis. Neuroradiology 55, 361–369 (2013). https://doi.org/10.1007/s00234-012-1127-4

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Keywords

  • Brain tumor
  • DCE T1-Perfusion MRI
  • FDG-PET
  • Radiation necrosis