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DOI: 10.1055/s-0043-1772466
The Role of Multimodal Imaging in Differentiating Vasogenic from Infiltrative Edema: A Systematic Review
Funding None.Abstract
Background High-grade gliomas (HGGs) are the most prevalent primary malignancy of the central nervous system. The tumor results in vasogenic and infiltrative edema . Exact anatomical differentiation of these edemas is so important for surgical planning. Multimodal imaging could be used to differentiate the edema type.
Purpose The aim of this study was to investigate the role of multimodal imaging in the differentiation of vasogenic edema from infiltrative edema in patients with HGG (grade III and grade IV).
Data Sources A search on PubMed, EMBASE, Scopus, and ISI Web of Science Core Collection up to June 2022 using terms related to (a) multimodal imaging AND (b) HGG AND (c) edema. (PROSPERO registration number: CRD42022336131)
Study Selection Two reviewers screened the articles and independently extracted the data. We included original articles assessing the role of multimodal imaging in differentiating vasogenic from infiltrative edema in patients with HGG. Six high-quality articles remained for the narrative synthesis.
Data Synthesis Dynamic susceptibility contrast imaging showed that relative cerebral blood volume and relative cerebral blood flow were higher in the infiltrative edema component than in the vasogenic edema component. Diffusion tensor imaging revealed a dispute on fractional anisotropy. The apparent diffusion coefficient was comparable between the two edematous components. Magnetic resonance spectroscopy exhibited an increment in choline/creatinine ratio and choline/N-acetyl aspartate ratio in the infiltrative edema component.
Limitations Strict study selection, low sample size of relevant published studies, and heterogeneity in endpoint variables were the major drawbacks.
Conclusions Multimodal imaging, including dynamic susceptibility contrast and magnetic resonance spectroscopy, might help differentiate between vasogenic and infiltrative edema.
Author Contributions
AH was involved in data curation, investigation, drafting, and revision; HSM helped in data curation, investigation, drafting, and revision; MSh contributed to data curation, investigation, supervision, and revision; AHJ helped in conceptualization, methodology, and supervision; KF contributed to conceptualization, supervision, project administration, and revision. All authors read and approved the final manuscript.
Data Availability Statement
This article is a systematic review using previously published articles.
* A.H and H.S.M contributed equally to this study.
Publikationsverlauf
Artikel online veröffentlicht:
21. August 2023
© 2023. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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References
- 1 de Groot JF. High-grade gliomas. Continuum (Minneap Minn) 2015; 21 (2 Neuro-oncology): 332-344
- 2 Ostrom QT, Gittleman H, Liao P. et al. CBTRUS Statistical Report: Primary brain and other central nervous system tumors diagnosed in the United States in 2010-2014. Neuro-oncol 2017; 19 (suppl_5): v1-v88
- 3 Ostrom QT, Gittleman H, Stetson L, Virk SM, Barnholtz-Sloan JS. Epidemiology of gliomas. Cancer Treat Res 2015; 163: 1-14
- 4 Linkous AG, Yazlovitskaya EM. Angiogenesis in glioblastoma multiforme: navigating the maze. Anticancer Agents Med Chem 2011; 11 (08) 712-718
- 5 De Bonis P, Lofrese G, Anile C, Pompucci A, Vigo V, Mangiola A. Radioimmunotherapy for high-grade glioma. Immunotherapy 2013; 5 (06) 647-659
- 6 Klatzo I. Pathophysiological aspects of brain edema. Acta Neuropathol 1987; 72 (03) 236-239
- 7 Kuroiwa T, Cahn R, Juhler M, Goping G, Campbell G, Klatzo I. Role of extracellular proteins in the dynamics of vasogenic brain edema. Acta Neuropathol 1985; 66 (01) 3-11
- 8 Strugar J, Rothbart D, Harrington W, Criscuolo GR. Vascular permeability factor in brain metastases: correlation with vasogenic brain edema and tumor angiogenesis. J Neurosurg 1994; 81 (04) 560-566
- 9 Reulen HJ, Graham R, Spatz M, Klatzo I. Role of pressure gradients and bulk flow in dynamics of vasogenic brain edema. J Neurosurg 1977; 46 (01) 24-35
- 10 Kelly PJ, Daumas-Duport C, Scheithauer BW, Kall BA, Kispert DB. Stereotactic histologic correlations of computed tomography- and magnetic resonance imaging-defined abnormalities in patients with glial neoplasms. Mayo Clin Proc 1987; 62 (06) 450-459
- 11 Bertossi M, Virgintino D, Maiorano E, Occhiogrosso M, Roncali L. Ultrastructural and morphometric investigation of human brain capillaries in normal and peritumoral tissues. Ultrastruct Pathol 1997; 21 (01) 41-49
- 12 Stadlbauer A, Prante O, Nimsky C. et al. Metabolic imaging of cerebral gliomas: spatial correlation of changes in O-(2-18F-fluoroethyl)-L-tyrosine PET and proton magnetic resonance spectroscopic imaging. J Nucl Med 2008; 49 (05) 721-729
- 13 Di Costanzo A, Scarabino T, Trojsi F. et al. Proton MR spectroscopy of cerebral gliomas at 3 T: spatial heterogeneity, and tumour grade and extent. Eur Radiol 2008; 18 (08) 1727-1735
- 14 Chen W. Clinical applications of PET in brain tumors. J Nucl Med 2007; 48 (09) 1468-1481
- 15 Barajas Jr RF, Hodgson JG, Chang JS. et al. Glioblastoma multiforme regional genetic and cellular expression patterns: influence on anatomic and physiologic MR imaging. Radiology 2010; 254 (02) 564-576
- 16 Barajas Jr RF, Phillips JJ, Parvataneni R. et al. Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging. Neuro-oncol 2012; 14 (07) 942-954
- 17 Fudaba H, Shimomura T, Abe T. et al. Comparison of multiple parameters obtained on 3T pulsed arterial spin-labeling, diffusion tensor imaging, and MRS and the Ki-67 labeling index in evaluating glioma grading. Am J Neuroradiol 2014; 35 (11) 2091-2098
- 18 Kitis O, Altay H, Calli C, Yunten N, Akalin T, Yurtseven T. Minimum apparent diffusion coefficients in the evaluation of brain tumors. Eur J Radiol 2005; 55 (03) 393-400
- 19 Server A, Graff BA, Josefsen R. et al. Analysis of diffusion tensor imaging metrics for gliomas grading at 3 T. Eur J Radiol 2014; 83 (03) e156-e165
- 20 Patronas NJ, Di Chiro G, Brooks RA. et al. Work in progress: [18F] fluorodeoxyglucose and positron emission tomography in the evaluation of radiation necrosis of the brain. Radiology 1982; 144 (04) 885-889
- 21 Di Chiro G, DeLaPaz RL, Brooks RA. et al. Glucose utilization of cerebral gliomas measured by [18F] fluorodeoxyglucose and positron emission tomography. Neurology 1982; 32 (12) 1323-1329
- 22 Munn Z, Barker TH, Moola S. et al. Methodological quality of case series studies: an introduction to the JBI critical appraisal tool. JBI Evid Synth 2020; 18 (10) 2127-2133
- 23 Artzi M, Bokstein F, Blumenthal DT. et al. Differentiation between vasogenic-edema versus tumor-infiltrative area in patients with glioblastoma during bevacizumab therapy: a longitudinal MRI study. Eur J Radiol 2014; 83 (07) 1250-1256
- 24 Wu H, Tong H, Du X. et al. Vascular habitat analysis based on dynamic susceptibility contrast perfusion MRI predicts IDH mutation status and prognosis in high-grade gliomas. Eur Radiol 2020; 30 (06) 3254-3265
- 25 Valentini MC, Mellai M, Annovazzi L. et al. Comparison among conventional and advanced MRI, 18F-FDG PET/CT, phenotype and genotype in glioblastoma. Oncotarget 2017; 8 (53) 91636-91653
- 26 Artzi M, Blumenthal DT, Bokstein F. et al. Classification of tumor area using combined DCE and DSC MRI in patients with glioblastoma. J Neurooncol 2015; 121 (02) 349-357
- 27 Oh J, Cha S, Aiken AH. et al. Quantitative apparent diffusion coefficients and T2 relaxation times in characterizing contrast enhancing brain tumors and regions of peritumoral edema. J Magn Reson Imaging 2005; 21 (06) 701-708
- 28 Molina-Romero M, Wiestler B, Gomez PA, Menzel MI, Menze BH. in 21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). vol. 11072. Granada, Spain: Springer International Publishing Ag; 2018: 98-106
- 29 Selker RG, Mendelow H, Walker M, Sheptak PE, Phillips JG. Pathological correlation of CT ring in recurrent, previously treated gliomas. Surg Neurol 1982; 17 (04) 251-254
- 30 Kelly PJ, Daumas-Duport C, Kispert DB, Kall BA, Scheithauer BW, Illig JJ. Imaging-based stereotaxic serial biopsies in untreated intracranial glial neoplasms. J Neurosurg 1987; 66 (06) 865-874
- 31 Ramakrishna R, Barber J, Kennedy G. et al. Imaging features of invasion and preoperative and postoperative tumor burden in previously untreated glioblastoma: correlation with survival. Surg Neurol Int 2010;
- 32 Engelhorn T, Savaskan NE, Schwarz MA. et al. Cellular characterization of the peritumoral edema zone in malignant brain tumors. Cancer Sci 2009; 100 (10) 1856-1862
- 33 Law M, Cha S, Knopp EA, Johnson G, Arnett J, Litt AW. High-grade gliomas and solitary metastases: differentiation by using perfusion and proton spectroscopic MR imaging. Radiology 2002; 222 (03) 715-721
- 34 Nelson SJ. Multivoxel magnetic resonance spectroscopy of brain tumors. Mol Cancer Ther 2003; 2 (05) 497-507
- 35 Schiffer D, Mellai M, Annovazzi L. et al. Stem cell niches in glioblastoma: a neuropathological view. BioMed Res Int 2014; 2014: 725921
- 36 Barajas Jr RF, Cha S. Benefits of dynamic susceptibility-weighted contrast-enhanced perfusion MRI for glioma diagnosis and therapy. CNS Oncol 2014; 3 (06) 407-419
- 37 Morrice JR, Gregory-Evans CY, Shaw CA. Animal models of amyotrophic lateral sclerosis: a comparison of model validity. Neural Regen Res 2018; 13 (12) 2050-2054
- 38 Maia Jr AC, Malheiros SM, da Rocha AJ. et al. MR cerebral blood volume maps correlated with vascular endothelial growth factor expression and tumor grade in nonenhancing gliomas. Am J Neuroradiol 2005; 26 (04) 777-783
- 39 Cha S, Lupo JM, Chen MH. et al. Differentiation of glioblastoma multiforme and single brain metastasis by peak height and percentage of signal intensity recovery derived from dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Am J Neuroradiol 2007; 28 (06) 1078-1084
- 40 McKnight TR, von dem Bussche MH, Vigneron DB. et al. Histopathological validation of a three-dimensional magnetic resonance spectroscopy index as a predictor of tumor presence. J Neurosurg 2002; 97 (04) 794-802
- 41 Croteau D, Scarpace L, Hearshen D. et al. Correlation between magnetic resonance spectroscopy imaging and image-guided biopsies: semiquantitative and qualitative histopathological analyses of patients with untreated glioma. Neurosurgery 2001; 49 (04) 823-829
- 42 Pirzkall A, Li X, Oh J. et al. 3D MRSI for resected high-grade gliomas before RT: tumor extent according to metabolic activity in relation to MRI. Int J Radiat Oncol Biol Phys 2004; 59 (01) 126-137
- 43 Stadlbauer A, Nimsky C, Buslei R. et al. Proton magnetic resonance spectroscopic imaging in the border zone of gliomas: correlation of metabolic and histological changes at low tumor infiltration–initial results. Invest Radiol 2007; 42 (04) 218-223
- 44 Molina-Romero M, Wiestler B, Gómez PA, Menzel MI, Menze BH. in Medical Image Computing and Computer Assisted Intervention – MICCAI 2018,. Frangi AF, Schnabel JA, Davatzikos C, Alberola-López C, Fichtinger G. eds. Springer International Publishing 2018: 98-106
- 45 Piyapittayanan S, Chawalparit O, Tritakarn SO. et al. Value of diffusion tensor imaging in differentiating high-grade from low-grade gliomas. J Med Assoc Thai 2013; 96 (06) 716-721
- 46 Alexiou GA, Zikou A, Tsiouris S. et al. Correlation of diffusion tensor, dynamic susceptibility contrast MRI and (99m)Tc-Tetrofosmin brain SPECT with tumour grade and Ki-67 immunohistochemistry in glioma. Clin Neurol Neurosurg 2014; 116: 41-45
- 47 Price SJ, Gillard JH. Imaging biomarkers of brain tumour margin and tumour invasion. Br J Radiol 2011; 84 (Spec No 2): S159-S167
- 48 Pauleit D, Langen KJ, Floeth F. et al. Can the apparent diffusion coefficient be used as a noninvasive parameter to distinguish tumor tissue from peritumoral tissue in cerebral gliomas?. J Magn Reson Imaging 2004; 20 (05) 758-764
- 49 Price SJ, Jena R, Burnet NG, Carpenter TA, Pickard JD, Gillard JH. Predicting patterns of glioma recurrence using diffusion tensor imaging. Eur Radiol 2007; 17 (07) 1675-1684
- 50 Yin Y, Tong D, Liu XY. et al. Correlation of apparent diffusion coefficient with Ki-67 in the diagnosis of gliomas. Zhongguo Yi Xue Ke Xue Yuan Xue Bao 2012; 34 (05) 503-508
- 51 Zikou AK, Alexiou GA, Kosta P. et al. Diffusion tensor and dynamic susceptibility contrast MRI in glioblastoma. Clin Neurol Neurosurg 2012; 114 (06) 607-612
- 52 Kalpathy-Cramer J, Gerstner ER, Emblem KE, Andronesi O, Rosen B. Advanced magnetic resonance imaging of the physical processes in human glioblastoma. Cancer Res 2014; 74 (17) 4622-4637
- 53 Rakheja R, Chandarana H, DeMello L. et al. Correlation between standardized uptake value and apparent diffusion coefficient of neoplastic lesions evaluated with whole-body simultaneous hybrid PET/MRI. Am J Roentgenol 2013; 201 (05) 1115-1119
- 54 Law M, Young R, Babb J. et al. Comparing perfusion metrics obtained from a single compartment versus pharmacokinetic modeling methods using dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. Am J Neuroradiol 2006; 27 (09) 1975-1982
- 55 Cha S. Update on brain tumor imaging: from anatomy to physiology. Am J Neuroradiol 2006; 27 (03) 475-487
- 56 Neska-Matuszewska M, Bladowska J, Sąsiadek M, Zimny A. Differentiation of glioblastoma multiforme, metastases and primary central nervous system lymphomas using multiparametric perfusion and diffusion MR imaging of a tumor core and a peritumoral zone-Searching for a practical approach. PLoS One 2018; 13 (01) e0191341