Advertisement

Diagnostic performance of DWI for differentiating primary central nervous system lymphoma from glioblastoma: a systematic review and meta-analysis

  • Xiaoyang Lu
  • Weilin Xu
  • Yuyu Wei
  • Tao Li
  • Liansheng Gao
  • Xiongjie Fu
  • Yuan Yao
  • Lin WangEmail author
Original Article
  • 20 Downloads

Abstract

Objective

The purpose of this meta-analysis was to evaluate the diagnostic performance of diffusion-weighted imaging (DWI) for differentiating primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM).

Materials and methods

A thorough search of the databases including PubMed, EMBASE, and Cochrane Library was carried out and the data acquired were up to November 1, 2017. The quality of the studies involved was evaluated using QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies, revised version). Multiple analytic values including sensitivity (SEN), specificity (SPE), positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and the summary receiver operating characteristic (SROC) curve were calculated and pooled for the statistical analysis. The subgroup analysis was also performed to explore the heterogeneity.

Results

Eight retrospective studies (461 patients with 461 lesions) were included. The pooled SEN, SPE, PLR, NLR, and DOR with 95% confidence interval (CI) were 0.82 [95% CI 0.70–0.90], 0.84 [95% CI 0.75–0.90], 4.96 [95% CI 3.20–7.69], 0.22 [95% CI 0.13–0.37], and 22.85 [95% CI 10.42–50.11], respectively. The area under the curve (AUC) given by SROC curve was 0.90 [95% CI 0.87–0.92]. The subgroup analysis indicated the slice thickness of the images (> 3 mm versus ≤ 3 mm) was a significant factor affecting the heterogeneity. No existence of significant publication bias was confirmed with Deeks’ test.

Conclusions

DWI showed moderate diagnostic performance for differentiating primary central nervous system lymphoma (PCNSL) from glioblastoma (GBM). Moreover, it is of clinical significance using DWI combined with conventional MRI to differentiate PCNSL from GBM.

Keywords

DWI Lymphoma Glioblastoma Meta-analysis 

Notes

Acknowledgments

This work was supported by the Zhejiang Provincial Natural Science Foundation of China (grant LY18H090007). All the authors confirmed that there is no conflict of interest.

Supplementary material

10072_2019_3732_MOESM1_ESM.doc (64 kb)
ESM 1 (DOC 64 kb)

References

  1. 1.
    Deangelis LM (2001) Brain tumors. N Engl J Med 344(344):114–123CrossRefGoogle Scholar
  2. 2.
    Norden AD, Jan D, Wen PY, Claus EB (2011) Survival among patients with primary central nervous system lymphoma, 1973-2004. J Neuro-Oncol 101(3):487–493CrossRefGoogle Scholar
  3. 3.
    Schultz CJ, Bovi J (2010) Current management of primary central nervous system lymphoma. Int J Radiat Oncol Biol Phys 76(3):666–678CrossRefGoogle Scholar
  4. 4.
    Schlegel U (2009) Primary CNS lymphoma. Ther Adv Neurol Disord 2(2):93–104CrossRefGoogle Scholar
  5. 5.
    Stupp R, Mason WP, Van DB, Martin J et al (2008) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. Clin Med Oncol 2(10):421–422Google Scholar
  6. 6.
    Philipp K, Benedikt W, Felix S et al (2014) Primary central nervous system lymphoma and atypical glioblastoma: multiparametric differentiation by using diffusion-, perfusion-, and susceptibility-weighted MR imaging. Radiology 272(3):843–850CrossRefGoogle Scholar
  7. 7.
    Kitis O, Altay H, Calli C, Yunten N, Akalin T, Yurtseven T (2005) Minimum apparent diffusion coefficients in the evaluation of brain tumors. Eur J Radiol 55(3):393–400CrossRefGoogle Scholar
  8. 8.
    Yamasaki F, Kurisu K, Satoh K, Arita K, Sugiyama K, Ohtaki M, Takaba J, Tominaga A, Hanaya R, Yoshioka H, Hama S, Ito Y, Kajiwara Y, Yahara K, Saito T, Thohar MA (2005) Apparent diffusion coefficient of human brain tumors at MR imaging. Radiology 235(3):985–991CrossRefGoogle Scholar
  9. 9.
    Server A, Kulle B, Maehlen J et al (2009) Quantitative apparent diffusion coefficients in the characterization of brain tumors and associated peritumoral edema. Acta Radiol 50(6):682–689CrossRefGoogle Scholar
  10. 10.
    Rizzo L, Crasto SG, Moruno PG, Cassoni P, Rudà R, Boccaletti R, Brosio M, de Lucchi R, Fava C (2009) Role of diffusion- and perfusion-weighted MR imaging for brain tumour characterisation. Radiol Med 114(4):645–659CrossRefGoogle Scholar
  11. 11.
    Maeda M, Matsushima N, Takeda K (2010) Relationship between FDG uptake and apparent diffusion coefficients in solitary brain tumor. Neuroradiol J 23:390–391Google Scholar
  12. 12.
    Moher D, Liberati A, Tetzlaff J, Altman DG (2009) Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Open Med 3(3):e123Google Scholar
  13. 13.
    Whiting PF, Rutjes AW, Westwood ME et al (2011) QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 155(8):529–536CrossRefGoogle Scholar
  14. 14.
    Higgins JP, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21(11):1539–1558CrossRefGoogle Scholar
  15. 15.
    Rutjes AWS, Zwinderman AH, Reitsma JB, Glas AS, Bossuyt PM, Scholten RJPM (2005) Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J Clin Epidemiol 58(10):982–990CrossRefGoogle Scholar
  16. 16.
    Deeks JJ, Petra M, Les I (2005) The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol 58(9):882–893CrossRefGoogle Scholar
  17. 17.
    Deeks JJ, Macaskill P, Irwig L (2005) The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J Clin Epidemiol 58(9):882–893CrossRefGoogle Scholar
  18. 18.
    Ahn SJ, Shin HJ, Chang JH, Lee SK (2014) Differentiation between primary cerebral lymphoma and glioblastoma using the apparent diffusion coefficient: comparison of three different ROI methods. PLoS One 9(11):e112948CrossRefGoogle Scholar
  19. 19.
    Doskaliyev A, Yamasaki F, Ohtaki M, Kajiwara Y, Takeshima Y, Watanabe Y, Takayasu T, Amatya VJ, Akiyama Y, Sugiyama K, Kurisu K (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(2):339–344CrossRefGoogle Scholar
  20. 20.
    Ko CC, Tai MH, Li CF, Chen TY, Chen JH, Shu G, Kuo YT, Lee YC (2016) Differentiation between glioblastoma multiforme and primary cerebral lymphoma: additional benefits of quantitative diffusion-weighted MR imaging. PLoS One 11(9):e0162565CrossRefGoogle Scholar
  21. 21.
    Lin X, Lee M, Buck O, Woo KM, Zhang Z, Hatzoglou V, Omuro A, Arevalo-Perez J, Thomas AA, Huse J, Peck K, Holodny AI, Young RJ (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(3):485–491CrossRefGoogle Scholar
  22. 22.
    Lu S, Wang S, Gao Q, Zhou M, Li Y, Cao P, Hong X, Shi H (2017) Quantitative evaluation of diffusion and dynamic contrast-enhanced magnetic resonance imaging for differentiation between primary central nervous system lymphoma and glioblastoma. J Comput Assist Tomogr 41(6):898–903CrossRefGoogle Scholar
  23. 23.
    Nakajima S, Okada T, Yamamoto A, Kanagaki M, Fushimi Y, Okada T, Arakawa Y, Takagi Y, Miyamoto S, Togashi K (2015) Primary central nervous system lymphoma and glioblastoma: differentiation using dynamic susceptibility-contrast perfusion-weighted imaging, diffusion-weighted imaging, and (18)F-fluorodeoxyglucose positron emission tomography. Clin Imaging 39(3):390–395CrossRefGoogle Scholar
  24. 24.
    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(3):471–475CrossRefGoogle Scholar
  25. 25.
    Yamashita K, Yoshiura T, Hiwatashi A, Togao O, Yoshimoto K, Suzuki SO, Abe K, Kikuchi K, Maruoka Y, Mizoguchi M, Iwaki T, Honda H (2013) Differentiating primary CNS lymphoma from glioblastoma multiforme: assessment using arterial spin labeling, diffusion-weighted imaging, and (1)(8)F-fluorodeoxyglucose positron emission tomography. Neuroradiology 55(2):135–143CrossRefGoogle Scholar
  26. 26.
    Matsukado Y, Maccarty CS, Kernohan JW (1961) The growth of glioblastoma multiforme (astrocytomas, grades 3 and 4) in neurosurgical practice. J Neurosurg 18(5):636–644CrossRefGoogle Scholar
  27. 27.
    Scherer HJ (1940) The forms of growth in gliomas and their practical significance. Brain Part 63(1):1–35CrossRefGoogle Scholar
  28. 28.
    Aho R, Ekfors T, Haltia M, Kalimo H (1993) Pathogenesis of primary central nervous system lymphoma: invasion of malignant lymphoid cells into and within the brain parenchyme. Acta Neuropathol 86(1):71–76CrossRefGoogle Scholar
  29. 29.
    Zhang H, Ma L, Wang Q, Zheng X, Wu C, Xu BN (2014) Role of magnetic resonance spectroscopy for the differentiation of recurrent glioma from radiation necrosis: a systematic review and meta-analysis. Eur J Radiol 83(12):2181–2189CrossRefGoogle Scholar
  30. 30.
    Sugahara T, Korogi Y, Kochi M, Ikushima I, Shigematu Y, Hirai T, Okuda T, Liang L, Ge Y, Komohara Y, Ushio Y, Takahashi M (1999) Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging 9(1):53–60CrossRefGoogle Scholar
  31. 31.
    Horger M, Fenchel M, Nagele T et al (2009) Water diffusivity: comparison of primary CNS lymphoma and astrocytic tumor infiltrating the corpus callosum. AJR Am J Roentgenol 193(5):1384–1387CrossRefGoogle Scholar
  32. 32.
    Kang Y, Choi SH, Kim YJ, Kim KG, Sohn CH, Kim JH, Yun TJ, Chang KH (2011) Gliomas: histogram analysis of apparent diffusion coefficient maps with standard- or high-b-value diffusion-weighted MR imaging--correlation with tumor grade. Radiology 261(3):882–890CrossRefGoogle Scholar
  33. 33.
    Villano JL, Koshy M, Shaikh H, Dolecek TA, McCarthy BJ (2011) Age, gender, and racial differences in incidence and survival in primary CNS lymphoma. Br J Cancer 105(9):1414–1418CrossRefGoogle Scholar
  34. 34.
    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(3):e0173430CrossRefGoogle Scholar
  35. 35.
    Aburano H, Ueda F, Yoshie Y et al (2015) Differences between glioblastomas and primary central nervous system lymphomas in 1 H-magnetic resonance spectroscopy. Jpn J Radiol 33(7):1–12CrossRefGoogle Scholar
  36. 36.
    Poptani H, Gupta RK, Roy R, Pandey R, Jain VK, Chhabra DK (1995) Characterization of intracranial mass lesions with in vivo proton MR spectroscopy. AJNR Am J Neuroradiol 16(8):1593–1603Google Scholar
  37. 37.
    Chawla S, Zhang Y, Wang S, Chaudhary S, Chou C, O'Rourke DM, Vossough A, Melhem ER, Poptani H (2010) Proton magnetic resonance spectroscopy in differentiating glioblastomas from primary cerebral lymphomas and brain metastases. J Comput Assist Tomogr 34(6):836–841CrossRefGoogle Scholar
  38. 38.
    Guo AC, Cummings TJ, Dash RC, Provenzale JM (2002) Lymphomas and high-grade astrocytomas: comparison of water diffusibility and histologic characteristics. Radiology 224(1):177–183CrossRefGoogle Scholar
  39. 39.
    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(3):394–403CrossRefGoogle Scholar
  40. 40.
    Deeks JJ (2001) Systematic reviews in health care: systematic reviews of evaluations of diagnostic and screening tests. BMJ 323(7322):157–162CrossRefGoogle Scholar

Copyright information

© Fondazione Società Italiana di Neurologia 2019

Authors and Affiliations

  1. 1.Department of Neurosurgery, Second Affiliated HospitalZhejiang University School of MedicineZhejiangChina

Personalised recommendations