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European Radiology

, Volume 29, Issue 10, pp 5539–5548 | Cite as

Permeability measurement using dynamic susceptibility contrast magnetic resonance imaging enhances differential diagnosis of primary central nervous system lymphoma from glioblastoma

  • Ji Ye Lee
  • Atle Bjørnerud
  • Ji Eun ParkEmail author
  • Bo Eun Lee
  • Joo Hyun Kim
  • Ho Sung Kim
Neuro

Abstract

Objectives

To test if adding permeability measurement to perfusion obtained from dynamic susceptibility contrast MRI (DSC-MRI) improves diagnostic performance in the differentiation of primary central nervous system lymphoma (PCNSL) from glioblastoma.

Materials and methods

DSC-MRI was acquired in 145 patients with pathologically proven glioblastoma (n = 89) or PCNSL (n = 56). The permeability metrics of contrast agent extraction fraction (Ex), apparent permeability (Ka), and leakage-corrected perfusion of normalized cerebral blood volume (nCBVres) and cerebral blood flow (nCBFres) were derived from a tissue residue function. For comparison purposes, the leakage-corrected normalized CBV (nCBV) and relative permeability constant (K2) were also obtained using the established Weisskoff-Boxerman leakage correction method. The area under the receiver operating characteristics curve (AUC) and cross-validation were used to compare the diagnostic performance of the single DSC-MRI parameters with the performance obtained with the addition of permeability metrics.

Results

PCNSL demonstrated significantly higher permeability (Ex, p < .001) and lower perfusion (nCBVres, nCBFres, and nCBV, all p < .001) than glioblastoma. The combination of Ex and nCBVres showed the highest performance (AUC, 0.96; 95% confidence interval, 0.92–0.99) for differentiating PCNSL from glioblastoma, which was a significant improvement over the single perfusion (nCBV: AUC, 0.84; nCBVres: AUC, 0.84; nCBFres: AUC, 0.82; all p < .001) or Ex (AUC, 0.80; p < .001) parameters.

Conclusions

Analysis of the combined permeability and perfusion metrics obtained from a single DSC-MRI acquisition improves the diagnostic value for differentiating PCNSL from glioblastoma in comparison with single-parameter nCBV analysis.

Key Points

Permeability measurement can be calculated from DSC-MRI with a tissue residue function-based leakage correction.

Adding Exto CBV aids in the differentiation of PCNSL from glioblastoma.

CBV and Exmeasurements from DSC-MRI were highly reproducible.

Keywords

Glioblastoma Lymphoma Perfusion magnetic resonance imaging Magnetic resonance imaging Permeability 

Abbreviations

AUC

Area under the receiver operating characteristics curve

DCE

Dynamic contrast-enhanced

DSC

Dynamic susceptibility contrast

Ex

Extraction fraction

K2

Relative permeability constant

Ka

Apparent permeability

Ktrans

Contrast agent transfer constant

nCBFres

Leakage-corrected normalized cerebral blood flow from a tissue residue function-based method

nCBV

Normalized cerebral blood volume from a Weisskoff-Boxerman method

nCBVres

Leakage-corrected normalized cerebral blood volume from a tissue residue function-based method

Notes

Funding

This research was supported by the National Research Foundation of Korea (NRF) Grant by the Korean government (MSIP) (grant nos. NRF-2017R1A2A2A05001217 and NRF-2017R1C1B2007258).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Ho Sung Kim.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was waived by the Institutional Review Board.

Ethical approval

Institutional Review Board approval was obtained.

Methodology

• retrospective

• cross-sectional study

• performed at one institution

Supplementary material

330_2019_6097_MOESM1_ESM.docx (20 kb)
ESM 1 (DOCX 19 kb)

References

  1. 1.
    Thompson G, Mills S, Coope D, O’connor J, Jackson A (2011) Imaging biomarkers of angiogenesis and the microvascular environment in cerebral tumours. Br J Radiol 84:S127–S144CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Koeller KK, Smirniotopoulos JG, Jones RV (1997) Primary central nervous system lymphoma: radiologic-pathologic correlation. Radiographics 17:1497–1526CrossRefPubMedGoogle Scholar
  3. 3.
    Hartmann M, Heiland S, Harting I et al (2003) Distinguishing of primary cerebral lymphoma from high-grade glioma with perfusion-weighted magnetic resonance imaging. Neurosci Lett 338:119–122CrossRefPubMedGoogle Scholar
  4. 4.
    Liao W, Liu Y, Wang X et al (2009) Differentiation of primary central nervous system lymphoma and high-grade glioma with dynamic susceptibility contrast-enhanced perfusion magnetic resonance imaging. Acta Radiol 50:217–225CrossRefPubMedGoogle Scholar
  5. 5.
    Xu W, Wang Q, Shao A, Xu B, Zhang J (2017) The performance of MR perfusion-weighted imaging for the differentiation of high-grade glioma from primary central nervous system lymphoma: a systematic review and meta-analysis. PLoS One 12:e0173430CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Kickingereder P, Sahm F, Wiestler B et al (2014) Evaluation of microvascular permeability with dynamic contrast-enhanced MRI for the differentiation of primary CNS lymphoma and glioblastoma: radiologic-pathologic correlation. AJNR Am J Neuroradiol 35:1503–1508CrossRefPubMedGoogle Scholar
  7. 7.
    Zhao J, Yang ZY, Luo BN, Yang JY, Chu JP (2015) Quantitative evaluation of diffusion and dynamic contrast-enhanced MR in tumor parenchyma and peritumoral area for distinction of brain tumors. PLoS One 10:e0138573CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Nguyen TB, Cron GO, Mercier JF et al (2012) Diagnostic accuracy of dynamic contrast-enhanced MR imaging using a phase-derived vascular input function in the preoperative grading of gliomas. Am J Neuroradiol 33:1539–1545CrossRefPubMedGoogle Scholar
  9. 9.
    Bjornerud A, Sorensen AG, Mouridsen K, Emblem KE (2011) T1- and T2*-dominant extravasation correction in DSC-MRI: part I--theoretical considerations and implications for assessment of tumor hemodynamic properties. J Cereb Blood Flow Metab 31:2041–2053CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    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 249:601–613CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Weisskoff R, Boxerman J, Sorensen A, Kulke S, Campbell T, Rosen B (1994) Simultaneous blood volume and permeability mapping using a single Gd-based contrast injection. Proceedings of the Society of Magnetic Resonance, Second Annual Meeting, San Francisco, Calif. Berkeley, pp p.279Google Scholar
  12. 12.
    Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820CrossRefPubMedGoogle Scholar
  13. 13.
    Bjornerud A, Kleppesto M, Batchelor TT, Wen P, Sorensen AG, Emblem KE (2016) Test-retest stability of MTT insensitive CBV leakage correction in DSC-MRI Proceedings of the 24th Annual Meeting of International Society of Magnetic Resonance in Medicine (ISMRM), Singapore, SingaporeGoogle Scholar
  14. 14.
    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. AJNR Am J Neuroradiol 27:859–867PubMedGoogle Scholar
  15. 15.
    Sourbron S, Ingrisch M, Siefert A, Reiser M, Herrmann K (2009) Quantification of cerebral blood flow, cerebral blood volume, and blood-brain-barrier leakage with DCE-MRI. Magn Reson Med 62:205–217CrossRefPubMedGoogle Scholar
  16. 16.
    Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR (1996) High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: mathematical approach and statistical analysis. Magn Reson Med 36:715–725CrossRefPubMedGoogle Scholar
  17. 17.
    Emblem KE, Bjornerud A (2009) An automatic procedure for normalization of cerebral blood volume maps in dynamic susceptibility contrast-based glioma imaging. AJNR Am J Neuroradiol 30:1929–1932CrossRefPubMedGoogle Scholar
  18. 18.
    DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Jain R (2011) Perfusion CT imaging of brain tumors: an overview. AJNR Am J Neuroradiol 32:1570–1577CrossRefPubMedGoogle Scholar
  20. 20.
    Molnar PP, O'Neill BP, Scheithauer BW, Groothuis DR (1999) The blood-brain barrier in primary CNS lymphomas: ultrastructural evidence of endothelial cell death. Neuro Oncol 1:89–100CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Larsson HB, 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 62:1270–1281CrossRefPubMedGoogle Scholar
  22. 22.
    Sorensen AG, Batchelor TT, Zhang WT et al (2009) A “vascular normalization index” as potential mechanistic biomarker to predict survival after a single dose of cediranib in recurrent glioblastoma patients. Cancer Res 69:5296–5300CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Alger JR, Schaewe TJ, Lai TC et al (2009) Contrast agent dose effects in cerebral dynamic susceptibility contrast magnetic resonance perfusion imaging. J Magn Reson Imaging 29:52–64CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Liu HL, Wu YY, Yang WS, Chen CF, Lim KE, Hsu YY (2011) Is Weisskoff model valid for the correction of contrast agent extravasation with combined T1 and T2* effects in dynamic susceptibility contrast MRI? Med Phys 38:802–809CrossRefPubMedGoogle Scholar
  25. 25.
    Emblem KE, Bjornerud A, Mouridsen K et al (2011) T1- and T2*-dominant extravasation correction in DSC-MRI: Part II— predicting patient outcome after a single dose of cediranib in recurrent glioblastoma patients J Cereb Blood Flow Metab 31:2054–2064Google Scholar
  26. 26.
    Grovik E, Redalen KR, Storas TH et al (2017) Dynamic multi-echo DCE- and DSC-MRI in rectal cancer: Low primary tumor Ktrans and ΔR2* peak are significantly associated with lymph node metastasis. J Magn Reson Imaging 46:194–206Google Scholar

Copyright information

© European Society of Radiology 2019

Authors and Affiliations

  1. 1.Department of RadiologyEulji Medical CenterSeoulRepublic of Korea
  2. 2.Department of Diagnostic Physics, Division of Radiology and Nuclear MedicineOslo University HospitalOsloNorway
  3. 3.Department of PhysicsUniversity of OsloOsloNorway
  4. 4.Department of Radiology and Research Institute of Radiology, Asan Medical CenterUniversity of Ulsan College of MedicineSeoulRepublic of Korea
  5. 5.Department of Radiology, Boramae Medical CenterSeoul Metropolitan Government -Seoul National UniversitySeoulRepublic of Korea
  6. 6.NordicNeuroLab, LLCSeoulRepublic of Korea

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