European Radiology

, Volume 28, Issue 9, pp 3801–3810 | Cite as

Differentiation between primary CNS lymphoma and glioblastoma: qualitative and quantitative analysis using arterial spin labeling MR imaging

  • Sung-Hye You
  • Tae Jin YunEmail author
  • Hye Jeong Choi
  • Roh-Eul Yoo
  • Koung Mi Kang
  • Seung Hong Choi
  • Ji-hoon Kim
  • Chul-Ho Sohn



To evaluate the diagnostic performance of arterial spin labelling perfusion weighted images (ASL-PWIs) to differentiate primary CNS lymphoma (PCNSL) from glioblastoma (GBM).


ASL-PWIs of pathologically confirmed PCNSL (n = 21) or GBM (n = 93) were analysed. For qualitative analysis, tumours were visually scored into five categories based on ASL-CBF maps. For quantitative analysis, normalised CBF values were derived by contralateral grey matter (GM) in intra- and peritumoral areas (nCBFintratumoral and nCBFperitumoral, respectively). Visual scoring scales and quantitative parameters from PCNSL and GBM were compared. In addition, the area under the receiver-operating characteristic (ROC) curve was used to determine the diagnostic accuracy of ASL-PWI for differentiating PCNSL from GBM. Weighted kappa or intraclass correlation coefficients (ICCs) were used to assess reliability between two observers.


In qualitative analysis, scores 5 (CBFintratumoral>CBFGM, 68.8% [64/93]) and 4 (CBFintratumoral ≈ CBFGM, 47.6% [10/21]) were the most frequently reported scores for GBM and PCNSL, respectively. In quantitative analysis, both nCBFintratumoral and nCBFperitumoral in PCNSL were significantly lower than those in the GBM (nCBFintratumoral, 0.89 ± 0.59 [mean and SD] vs. 2.68 ± 1.89, p < 0.001; nCBFperitumoral, 0.17 ± 0.08 vs. 0.45 ± 0.28, p < 0.001). nCBFperitumoral demonstrated the best diagnostic performance (area under the ROC curve: visual scoring, 0.814; nCBFintratumoral, 0.849; nCBFperitumoral, 0.908; p < 0.001 for all). Interobserver agreements for visual scoring (weighted kappa = 0.869), nCBFintratumoral_GM (ICC = 0.958) and nCBFperitumoral_GM (ICC = 0.947) were all excellent.


ASL-PWI performs well in differentiating PCNSL from GBM in both qualitative and quantitative analyses.

Key Points

ASL-PWI performs well (AUC > 0.8) in differentiating PCNSL from GBM.

The visual scoring template demonstrated good diagnostic performance, similar to quantitative analysis.

nCBFperitumoral demonstrated better diagnostic performance than nCBFintratumoral or visual scoring.


Lymphoma Glioblastoma Brain neoplasms Magnetic resonance imaging Perfusion imaging 



Arterial spin labelling perfusion weighted images


Intratumoral CBF


Peritumoral CBF


Contralateral grey matter CBF


Contralateral white matter CBF




CBFintratumoral/CBFcontralateral grey matter


CBFintratumoral/CBFcontralateral white matter


CBFperitumoral/CBFcontralateral grey matter


CBFperitumoral/CBFcontralateral white matter


Primary CNS lymphoma



The authors state that this work has not received any funding.

Compliance with ethical standards


The scientific guarantor of this publication is Tae Jin Yun.

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.


• retrospective

• diagnostic or prognostic study

• performed at one institution

Supplementary material

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  1. 1.
    Ferreri AJ, Reni M (2007) Primary central nervous system lymphoma. Crit Rev Oncol Hematol 63:257–268CrossRefPubMedGoogle Scholar
  2. 2.
    Stupp R, Mason WP, Van Den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996CrossRefPubMedGoogle Scholar
  3. 3.
    Haldorsen I, Espeland A, Larsson E-M (2011) Central nervous system lymphoma: characteristic findings on traditional and advanced imaging. AJNR Am J Neuroradiol 32:984–992CrossRefPubMedGoogle Scholar
  4. 4.
    Nakajima S, Okada T, Yamamoto A et al (2015) Differentiation between primary central nervous system lymphoma and glioblastoma: a comparative study of parameters derived from dynamic susceptibility contrast-enhanced perfusion-weighted MRI. Clin Radiol 70:1393–1399CrossRefPubMedGoogle Scholar
  5. 5.
    Hakyemez B, Erdogan C, Bolca N, Yildirim N, Gokalp G, Parlak M (2006) Evaluation of different cerebral mass lesions by perfusion-weighted MR imaging. J Magn Reson Imaging 24:817–824CrossRefPubMedGoogle Scholar
  6. 6.
    Calli C, Kitis O, Yunten N, Yurtseven T, Islekel S, Akalin T (2006) Perfusion and diffusion MR imaging in enhancing malignant cerebral tumors. Eur J Radiol 58:394–403CrossRefPubMedGoogle Scholar
  7. 7.
    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
  8. 8.
    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 1. Radiology 223:11–29CrossRefPubMedGoogle Scholar
  9. 9.
    Lin X, Lee M, Buck O et al (2017) Diagnostic accuracy of T1-weighted dynamic contrast-enhanced–MRI and DWI-ADC for differentiation of glioblastoma and primary CNS lymphoma. AJNR Am J Neuroradiol 38:485–491CrossRefPubMedGoogle Scholar
  10. 10.
    Lu S, Gao Q, Yu J et al (2016) Utility of dynamic contrast-enhanced magnetic resonance imaging for differentiating glioblastoma, primary central nervous system lymphoma and brain metastatic tumor. Eur J Radiol 85:1722–1727CrossRefPubMedGoogle Scholar
  11. 11.
    Noguchi T, Yoshiura T, Hiwatashi A et al (2008) Perfusion imaging of brain tumors using arterial spin-labeling: correlation with histopathologic vascular density. AJNR Am J Neuroradiol 29:688–693CrossRefPubMedGoogle Scholar
  12. 12.
    White CM, Pope WB, Zaw T et al (2014) Regional and voxel-wise comparisons of blood flow measurements between dynamic susceptibility contrast magnetic resonance imaging (DSC-MRI) and arterial spin labeling (ASL) in brain tumors. J Neuroimaging 24:23–30CrossRefPubMedGoogle Scholar
  13. 13.
    Lehmann P, Monet P, De Marco G et al (2010) A comparative study of perfusion measurement in brain tumours at 3 Tesla MR: arterial spin labeling versus dynamic susceptibility contrast-enhanced MRI. Eur Neurol 64:21–26CrossRefPubMedGoogle Scholar
  14. 14.
    Järnum H, Steffensen EG, Knutsson L et al (2010) Perfusion MRI of brain tumours: a comparative study of pseudo-continuous arterial spin labelling and dynamic susceptibility contrast imaging. Neuroradiology 52:307–317CrossRefPubMedGoogle Scholar
  15. 15.
    Warmuth C, Gunther M, Zimmer C (2003) Quantification of blood flow in brain tumors: comparison of arterial spin labeling and dynamic susceptibility-weighted contrast-enhanced MR imaging 1. Radiology 228:523–532CrossRefPubMedGoogle Scholar
  16. 16.
    Yamashita K, Yoshiura T, Hiwatashi A et al (2013) Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and 18F-fluorodeoxyglucose positron emission tomography. Neuroradiology 55:135–143CrossRefPubMedGoogle Scholar
  17. 17.
    Yoo RE, Choi SH, Cho HR et al (2013) Tumor blood flow from arterial spin labeling perfusion MRI: A key parameter in distinguishing high-grade gliomas from primary cerebral lymphomas, and in predicting genetic biomarkers in high-grade gliomas. J Magn Reson Imaging 38:852–860CrossRefPubMedGoogle 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 44:837–845CrossRefPubMedGoogle Scholar
  19. 19.
    Yamashita K, Hiwatashi A, Togao O et al (2016) Diagnostic utility of intravoxel incoherent motion mr imaging in differentiating primary central nervous system lymphoma from glioblastoma multiforme. J Magn Reson Imaging 44:1256–1261CrossRefPubMedGoogle Scholar
  20. 20.
    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
  21. 21.
    Boxerman J, Schmainda K, Weisskoff R (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
  22. 22.
    Engelhorn T, Savaskan NE, Schwarz MA et al (2009) Cellular characterization of the peritumoral edema zone in malignant brain tumors. Cancer Sci 100:1856–1862CrossRefPubMedGoogle Scholar
  23. 23.
    Watanabe M, Tanaka R, Takeda N (1992) Magnetic resonance imaging and histopathology of cerebral gliomas. Neuroradiology 34:463–469CrossRefPubMedGoogle Scholar
  24. 24.
    Petersen E, Zimine I, Ho YL, Golay X (2006) Non-invasive measurement of perfusion: a critical review of arterial spin labelling techniques. Br J Radiol 79:688–701Google Scholar
  25. 25.
    Van Gelderen P, De Zwart J, Duyn J (2008) Pittfalls of MRI measurement of white matter perfusion based on arterial spin labeling. Magn Reson Med 59:788–795CrossRefPubMedGoogle Scholar
  26. 26.
    Bastos-Leite A, Kuijer J, Rombouts S et al (2008) Cerebral blood flow by using pulsed arterial spin-labeling in elderly subjects with white matter hyperintensities. AJNR Am J Neuroradiol 29:1296–1301CrossRefPubMedGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Institute of Radiation MedicineSeoul National University Medical Research CenterSeoulRepublic of Korea
  2. 2.Department of RadiologySeoul National University HospitalSeoulKorea

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