Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress
Clinical Collections
Advances in Alzheimer’s

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.
ADVANCED SEARCH
Log in
Top
Neurological Sciences
Published in: Neurological Sciences 5/2019

01-05-2019 | Glioblastoma | Original Article

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

Authors: Xiaoyang Lu, Weilin Xu, Yuyu Wei, Tao Li, Liansheng Gao, Xiongjie Fu, Yuan Yao, Lin Wang

Published in: Neurological Sciences | Issue 5/2019

Login to get access

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.
Appendix
Available only for authorised users
Literature
1.
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):487–493CrossRef
3.
Schultz CJ, Bovi J (2010) Current management of primary central nervous system lymphoma. Int J Radiat Oncol Biol Phys 76(3):666–678CrossRefPubMed
4.
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):421–422
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):843–850CrossRef
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):393–400CrossRefPubMed
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):985–991CrossRefPubMed
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):682–689CrossRefPubMed
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):645–659CrossRefPubMed
11.
Maeda M, Matsushima N, Takeda K (2010) Relationship between FDG uptake and apparent diffusion coefficients in solitary brain tumor. Neuroradiol J 23:390–391
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):e123PubMedPubMedCentral
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):529–536CrossRefPubMed
14.
Higgins JP, Thompson SG (2002) Quantifying heterogeneity in a meta-analysis. Stat Med 21(11):1539–1558CrossRefPubMed
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):982–990CrossRefPubMed
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):882–893CrossRefPubMed
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):882–893CrossRefPubMed
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):e112948CrossRefPubMedPubMedCentral
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):339–344CrossRefPubMed
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):e0162565CrossRefPubMedPubMedCentral
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):485–491CrossRefPubMedPubMedCentral
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):898–903CrossRefPubMed
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):390–395CrossRefPubMed
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):471–475CrossRefPubMedPubMedCentral
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):135–143CrossRefPubMed
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):636–644CrossRefPubMed
27.
Scherer HJ (1940) The forms of growth in gliomas and their practical significance. Brain Part 63(1):1–35CrossRef
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):71–76CrossRefPubMed
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):2181–2189CrossRefPubMed
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):53–60CrossRefPubMed
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):1384–1387CrossRefPubMed
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):882–890CrossRefPubMed
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):1414–1418CrossRefPubMedPubMedCentral
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):e0173430CrossRefPubMedPubMedCentral
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):1–12CrossRef
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):1593–1603PubMedPubMedCentral
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):836–841CrossRefPubMed
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):177–183CrossRefPubMed
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):394–403CrossRefPubMed
40.
Metadata
Title
Diagnostic performance of DWI for differentiating primary central nervous system lymphoma from glioblastoma: a systematic review and meta-analysis
Authors
Xiaoyang Lu
Weilin Xu
Yuyu Wei
Tao Li
Liansheng Gao
Xiongjie Fu
Yuan Yao
Lin Wang
Publication date
01-05-2019
Publisher
Springer International Publishing
Published in
Neurological Sciences / Issue 5/2019
Print ISSN: 1590-1874
Electronic ISSN: 1590-3478
DOI
https://doi.org/10.1007/s10072-019-03732-7

Other articles of this Issue 5/2019

Neurological Sciences 5/2019 Go to the issue

Correction

Correction to: Cognitive assessment in multiple sclerosis—an Italian consensus

Correction

Correction to: Normative values of the Rao’s Brief Repeatable Battery in an Italian young adolescent population: the influence of age, gender, and education

Original Article

Source localization of epileptiform discharges in childhood absence epilepsy using a distributed source model: a standardized, low-resolution, brain electromagnetic tomography (sLORETA) study

Original Article

Stroke during sleep and obstructive sleep apnea: there is a link

Letter to the Editor

L-type calcium channel blockers and a symptom complex mimicking de Melo-Souza’s syndrome

Original Article

Methylation of cation–chloride cotransporters NKCC1 and KCC2 in patients with juvenile myoclonic epilepsy