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3D pseudo-continuous arterial spin labeling-MRI (3D PCASL-MRI) in the differential diagnosis between glioblastomas and primary central nervous system lymphomas

  • Diagnostic Neuroradiology
  • Published:
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Abstract

Purpose

The aim of the study was to compare the parameters of blood flow in glioblastomas and primary central nervous system lymphomas (PCNSLs), measured by pseudo-continuous arterial spin labeling MRI (3D PCASL), and to determine the informativeness of this method in the differential diagnosis between these lesions.

Methods

The study included MRI data of 139 patients with PCNSL (n = 21) and glioblastomas (n = 118), performed in the Burdenko Neurosurgical Center. No patients received chemotherapy, hormone therapy, or radiation therapy prior to MRI. On the 3D PCASL perfusion map, the absolute and normalized values of tumor blood flow were calculated in the glioblastoma and PCNSL groups (maxTBFmean and nTBF).

Results

MaxTBFmean and nTBF in the glioblastoma group were significantly higher than those in the PCNSL group: 168.9 ml/100 g/min versus 65.6 and 9.3 versus 3.7, respectively (p < 0.001). Arterial spin labeling perfusion had high sensitivity (86% for maxTBFmean, 95% for nTBF) and specificity (77% for maxTBFmean, 73% for nTBF) in the differential diagnosis between PCNSL and glioblastomas. Blood flow thresholds were 98.9 ml/100 g/min using absolute blood flow values and 6.1 using normalized values, AUC > 0.88.

Conclusion

The inclusion of 3D PCASL in the standard MRI protocol can increase the specificity of the differential diagnosis between glioblastomas and PCNSL.

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Data availability

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to the fact that they could contain sensitive information about patients.

Code availability

The post-processing of the obtained data was carried out using the ReadyView software package (GE Healthcare).

References

  1. 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 58:394–403

    Article  Google Scholar 

  2. Ma JH, Kim HS, Rim NJ, Kim SH, Cho KG (2010) Differentiation among glioblastoma multiforme, solitary metastatic tumor, and lymphoma using whole-tumor histogram analysis of the normalized cerebral blood volume in enhancing and perienhancing lesions. AJNR Am J Neuroradiol 31:1699–1706

    Article  CAS  Google Scholar 

  3. Alifieris C, Trafalis DT (2015) Glioblastoma multiforme: pathogenesis and treatment. Pharmacol Ther 152:63–82. https://doi.org/10.1016/j.pharmthera.2015.05.005

    Article  CAS  PubMed  Google Scholar 

  4. Bataille B, Delwail V, Menet E et al (2000) Primary intracerebral malignant lymphoma: report of 248 cases. J Neurosurg 92:261–266

    Article  CAS  Google Scholar 

  5. DeAngelis LM (1999) Primary CNS lymphoma: treatment with combined chemotherapy and radiotherapy. J Neurooncol 43:249–257

    Article  CAS  Google Scholar 

  6. Labak CM, Holdhoff M, Bettegowda C et al (2019) Surgical resection for primary central nervous system lymphoma: a systematic review. World Neurosurg 126:e1436–e1448. https://doi.org/10.1016/j.wneu.2019.02.252

    Article  PubMed  Google Scholar 

  7. Hoang Xuan K, Bessell E, Bromberg J, Hottinger AF, Preusser M, Rudà R, Schlegel U, Siegal T, Soussain C, Abacioglu U et al (2015) European Association for Neuro Oncology Task Force on Primary CNS Lymphoma: Diagnosis and treatment of primary CNS lymphoma in immunocompetent patients: guidelines from the European Association for Neuro-oncology. Lancet Oncol 16:e322–e332

    Article  Google Scholar 

  8. Ferreri AJ, Reni M, Foppoli M, Martelli M, Pangalis GA, Frezzato M, Cabras MG, Fabbri A, Corazzelli G, Ilariucci F et al (2009) International Extranodal Lymphoma Study Group (IELSG): High-dose cytarabine plus high-dose methotrexate versus high-dose methotrexate alone in patients with primary CNS lymphoma: a randomised phase 2 trial. Lancet 374:1512–1520

    Article  CAS  Google Scholar 

  9. Schlegel U (2009) Primary CNS lymphoma. Ther Adv Neurol Disord 2(2):93–104. https://doi.org/10.1177/1756285608101222

    Article  PubMed  PubMed Central  Google Scholar 

  10. Malikova H, Koubska E, Weichet J et al (2016) Can morphological MRI differentiate between primary central nervous system lymphoma and glioblastoma? Cancer Imaging 16:40

    Article  CAS  Google Scholar 

  11. Naka 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 38:485–491

    Article  Google Scholar 

  12. Guo AC, Cummings TJ, Dash RC, Provenzale JM (2002) Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics. Radiology 224:177–183

    Article  Google Scholar 

  13. Doskaliyev A, Yamasaki F, Ohtaki M et al (2012) Lymphomas and glioblastomas: differences in the apparent diffusion coefficient evaluated with high b-value diffusion-weighted magnetic resonance imaging at 3T. Eur J Radiol 81:339–344

    Article  Google Scholar 

  14. Wang S, Kim S, Chawla S et al (2011) Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging. AJNR Am J Neuroradiol 32:507–514

    Article  CAS  Google Scholar 

  15. Toh CH, Castillo M, Wong AM et al (2008) Primary cerebral lymphoma and glioblastoma multiforme: differences in diffusion characteristics evaluated with diffusion tensor imaging. AJNR Am J Neuroradiol 29:471–475

    Article  Google Scholar 

  16. Kinoshita M, Hashimoto N, Goto T et al (2008) Fractional anisotropy and tumor cell density of the tumor core show positive correlation in diffusion tensor magnetic resonance imaging of malignant brain tumors. Neuroimage 43:29–35

    Article  CAS  Google Scholar 

  17. Beppu T, Inoue T, Shibata Y et al (2003) Measurement of fractional anisotropy using diffusion tensor MRI in supratentorial astrocytic tumors. J Neurooncol 63:109–116

    Article  Google Scholar 

  18. Toh CH, Wei KC, Chang CN, Ng SH, Wong HF (2013) Differentiation of primary central nervous system lymphomas and glioblastomas: comparisons of diagnostic performance of dynamic susceptibility contrast-enhanced perfusion MR imaging without and with contrast-leakage correction. AJNR Am J Neuroradiol 34:1145–1149

    Article  CAS  Google Scholar 

  19. 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 50:217–225

    Article  Google Scholar 

  20. Murayama K, Nishiyama Y, Hirose Y et al (2018) Differentiating between central nervous system lymphoma and high-grade glioma using dynamic susceptibility contrast and dynamic contrast-enhanced MR imaging with histogram analysis. Magn Reson Med Sci 17:42–49

    Article  Google Scholar 

  21. Choi YS, Lee HJ, Ahn SS et al (2017) Primary central nervous system lymphoma and atypical glioblastoma: differentiation using the initial area under the curve derived from dynamic contrast-enhanced MR and the apparent diffusion coefficient. Eur Radiol 27:1344–1351

    Article  Google Scholar 

  22. Batalov AI, Zakharova NE, Pogosbekyan EL et al (2018) Beskontrastnaia ASL-perfuziia v predoperatsionnoĭ diagnostike supratentorial’nykh gliom [Non-contrast ASL perfusion in preoperative diagnosis of supratentorial gliomas]. Zh Vopr Neirokhir Im N N Burdenko 82(6):15–22. https://doi.org/10.17116/neiro20188206115

    Article  CAS  PubMed  Google Scholar 

  23. Lee JY, Bjørnerud A, Park JE, Lee BE, Kim JH, Kim HS (2019) Permeability measurement using dynamic susceptibility contrast magnetic resonance imaging enhances differential diagnosis of primary central nervous system lymphoma from glioblastoma. Eur Radiol 29(10):5539–5548. https://doi.org/10.1007/s00330-019-06097-9

    Article  PubMed  Google Scholar 

  24. Lee I et al (2010) Analysis of perfusion weighted image of CNS lymphoma. Eur J Radiol 76:48–51

    Article  Google Scholar 

  25. Hartmann M et al (2003) Distinguishing of primary cerebral lymphoma from HGG with perfusion-weighted MRI. Neurosci Lett 338:119–122

    Article  CAS  Google Scholar 

  26. 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(2):135–143. https://doi.org/10.1007/s00234-012-1089-6

    Article  PubMed  Google Scholar 

  27. 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 70:1393–1399

    Article  CAS  Google Scholar 

  28. 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 24:817–824

    Article  Google Scholar 

  29. 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 44:1256–1261

    Article  Google Scholar 

  30. 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 35:1503–1508

    Article  CAS  Google Scholar 

  31. Yoo RE, Choi SH, Cho HR, Kim TM, Lee SH, Park C-K, Park S, Kim IH, Yun TJ, Kim JH, Sohn C, Han MH, Chang KH (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(4):852–860. https://doi.org/10.1002/jmri.24026

    Article  PubMed  Google Scholar 

  32. You S, Yun TJ, Choi HJ et al (2018) Differentiation between primary CNS lymphoma and glioblastoma: qualitative and quantitative analysis using arterial spin labeling MR imaging. Eur Radiol 28:3801–3810. https://doi.org/10.1007/s00330-018-5359-5

    Article  PubMed  Google Scholar 

  33. Rau M, Braun C, Skardelly M, Schittenhelm J, Paulsen F, Bender B, Ernemann U, Bisdas S (2014) Prognostic value of blood flow estimated by arterial spin labeling and dynamic susceptibility contrast-enhanced MR imaging in high grade gliomas. J Neurooncol 120(3):557–566. https://doi.org/10.1007/s11060-014-1586-z

    Article  PubMed  Google Scholar 

  34. Jiang J, Zhao L, Zhang Y, Zhang S, Yao Y, Qin Y, Wang CY, Zhu W (2014) Comparative analysis of arterial spin labeling and dynamic susceptibility contrast perfusion imaging for quantitative perfusion measurements of brain tumors. Int J Clin Exp Pathol 7(6):2790–2799

    PubMed  PubMed Central  Google Scholar 

  35. Qiu D, Straka M, Zun Z, Bammer R, Moseley M, Zaharchuk G (2012) CBF measurements using multidelay pseudocontinuous and velocity-selective arterial spin labeling in patients with long arterial transit delays: comparison with xenon CT CBF. J Magn Reson Imaging 36(1):110–119. https://doi.org/10.1002/jmri.23613

    Article  PubMed  PubMed Central  Google Scholar 

  36. Ma H, Wang Z, Xu K, Shao Z, Yang C, Xu P, Liu X, Hu C, Lu X, Rong Y (2017) Three-dimensional arterial spin labeling imaging and dynamic susceptibility contrast perfusion-weighted imaging value in diagnosing glioma grade prior to surgery. Exp Ther Med 13(6):2691–2698. https://doi.org/10.3892/etm.2017.4370

    Article  PubMed  PubMed Central  Google Scholar 

  37. Xiao H, Chen Z, Lou X, Wang Y, Gui Q, Wang Y, Shi K, Zhou Z, Zheng D, Wang D, Ma L (2015) Astrocytic tumour grading: a comparative study of three-dimensional pseudocontinuous arterial spin labelling, dynamic susceptibility contrast-enhanced perfusion-weighted imaging, and diffusion-weighted imaging. Eur Radiol 25(12):3423–3430. https://doi.org/10.1007/s00330-015-3768-2

    Article  PubMed  PubMed Central  Google Scholar 

  38. Di N, Cheng W, Chen H, Zhai F, Liu Y, Mu X, Chu Z, Lu N, Liu X, Wang B (2019) Utility of arterial spin labelling MRI for discriminating atypical high-grade glioma from primary central nervous system lymphoma. Clin Radiol 74(2):165.e1-165.e9. https://doi.org/10.1016/j.crad.2018.10.003

    Article  CAS  Google Scholar 

  39. Xi YB, Kang XW, Wang N, Liu TT, Zhu YQ, Cheng G, Wang K, Li C, Guo F, Yin H (2019) Differentiation of primary central nervous system lymphoma from high-grade glioma and brain metastasis using arterial spin labeling and dynamic contrast-enhanced magnetic resonance imaging. Eur J Radiol 112:59–64. https://doi.org/10.1016/j.ejrad.2019.01.008

    Article  PubMed  Google Scholar 

  40. Hatakeyama J, Ono T, Takahashi M, Oda M, Shimizu H (2021) Differentiating between primary central nervous system lymphoma and glioblastoma: the diagnostic value of combining 18F-fluorodeoxyglucose positron emission tomography with arterial spin labeling. Neurol Med Chir (Tokyo). 61(6):367–375. https://doi.org/10.2176/nmc.oa.2020-0375

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This study was supported by the Ministry of Higher Education Agreement 075-15-2021-1343.

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Authors and Affiliations

  1. Federal State Autonomous Institution N.N. Burdenko National Medical Research Center of Neurosurgery of the Ministry of Health of the Russian Federation, Moscow, Russian Federation

    A. I. Batalov, R. M. Afandiev, N. E. Zakharova, E. L. Pogosbekyan, A. A. Shulgina, G. L. Kobyakov, A. A. Potapov & I. N. Pronin

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  1. A. I. Batalov
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  2. R. M. Afandiev
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  3. N. E. Zakharova
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  5. A. A. Shulgina
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  6. G. L. Kobyakov
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Corresponding author

Correspondence to R. M. Afandiev.

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The authors declare no conflict of interest.

Ethics approval

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of N. N. Burdenko National Medical Research Center of Neurosurgery (protocol code 09/2021, date of approval 10 September 2021).

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Informed consent was obtained from all subjects involved in the study.

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All patients were informed that data could be used in publications.

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Batalov, A.I., Afandiev, R.M., Zakharova, N.E. et al. 3D pseudo-continuous arterial spin labeling-MRI (3D PCASL-MRI) in the differential diagnosis between glioblastomas and primary central nervous system lymphomas. Neuroradiology 64, 1539–1545 (2022). https://doi.org/10.1007/s00234-021-02888-4

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  • DOI: https://doi.org/10.1007/s00234-021-02888-4

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