Vol 53, No 3 (2019)
Research paper
Published online: 2019-06-10
Submitted: 2019-03-22
Accepted: 2019-06-03
Get Citation

Multiparametric MRI in differentiating solitary brain metastasis from high-grade glioma: diagnostic value of the combined use of diffusion-weighted imaging, dynamic susceptibility contrast imaging, and magnetic resonance spectroscopy parameters

Kerim Aslan, Hediye Pinar Gunbey, Leman Tomak, Lutfi Incesu
DOI: 10.5603/PJNNS.a2019.0024
·
Pubmed: 31180131
·
Neurol Neurochir Pol 2019;53(3):227-237.

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Vol 53, No 3 (2019)
Research paper
Published online: 2019-06-10
Submitted: 2019-03-22
Accepted: 2019-06-03

Abstract

Objective. The purpose of this study was to determine whether the combined use of diffusion weighted imaging (DWI), magnetic resonance spectroscopy (MRS), and dynamic susceptibility contrast imaging (DSCI) parameters could provide a more accurate diagnosis in the differentiation of high-grade glioma (HGG) from solitary brain metastasis (SBM) in the enhancing tumour and in the peritumoural region.

Materials and methods. Fifty-six patients who received DWI, DSCI, and MRS before surgery were assessed. In differentiating SBM from HGG, the cutoff values of the DWI-apparent diffusion coefficient (ADCmin, ADCmax, and ADCmean), DSCI-relative cerebral blood volume (rCBV), and MRS-Cho/Cr, Cho/NAA, and NAA/Cr parameters for the peritumoural region were determined with ROC. The combined ROC curve was used for the different combinations of the peritumoural region DWI, DSCI, and MRS parameters in differentiating between the two tumours, and the best model combination was formed. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments. This study was approved by the Institutional Review Board at our institutes.

Results. In the enhancing tumour, all the parameters except NAA/Cr (P = 0.024) exhibited no statistical difference in differentiating between these two groups (P > 0.05). AUC values for ADCmin, ADCmax, ADCmean, rADCmin, rADCmax, rADCmean, rCBV, Cho/Cr, Cho/NAA, and NAA/Cr parameters in the peritumoural region in differentiating SBM from HGG were 0.860, 0.822, 0.848, 0.822, 0.801, 0.822, 0.906, 0.851, 0.903, and 0.784, respectively. In differentiating HGG from SBM, the best model consisted of the
combination of peritumoural ADCmin, rCBV, and Cho/NAA parameters. AUC values were 0.970.

Conclusions. The combination of peritumoural region ADCmin, rCBV, and Cho/NAA parameters can help in differentiating SBM from HGG, with a diagnostic accuracy of 97%.

Abstract

Objective. The purpose of this study was to determine whether the combined use of diffusion weighted imaging (DWI), magnetic resonance spectroscopy (MRS), and dynamic susceptibility contrast imaging (DSCI) parameters could provide a more accurate diagnosis in the differentiation of high-grade glioma (HGG) from solitary brain metastasis (SBM) in the enhancing tumour and in the peritumoural region.

Materials and methods. Fifty-six patients who received DWI, DSCI, and MRS before surgery were assessed. In differentiating SBM from HGG, the cutoff values of the DWI-apparent diffusion coefficient (ADCmin, ADCmax, and ADCmean), DSCI-relative cerebral blood volume (rCBV), and MRS-Cho/Cr, Cho/NAA, and NAA/Cr parameters for the peritumoural region were determined with ROC. The combined ROC curve was used for the different combinations of the peritumoural region DWI, DSCI, and MRS parameters in differentiating between the two tumours, and the best model combination was formed. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments. This study was approved by the Institutional Review Board at our institutes.

Results. In the enhancing tumour, all the parameters except NAA/Cr (P = 0.024) exhibited no statistical difference in differentiating between these two groups (P > 0.05). AUC values for ADCmin, ADCmax, ADCmean, rADCmin, rADCmax, rADCmean, rCBV, Cho/Cr, Cho/NAA, and NAA/Cr parameters in the peritumoural region in differentiating SBM from HGG were 0.860, 0.822, 0.848, 0.822, 0.801, 0.822, 0.906, 0.851, 0.903, and 0.784, respectively. In differentiating HGG from SBM, the best model consisted of the
combination of peritumoural ADCmin, rCBV, and Cho/NAA parameters. AUC values were 0.970.

Conclusions. The combination of peritumoural region ADCmin, rCBV, and Cho/NAA parameters can help in differentiating SBM from HGG, with a diagnostic accuracy of 97%.

Get Citation

Keywords

Solitary brain metastasis, high-grade glioma, diffusion-weighted imaging, dynamic susceptibility contrast

About this article
Title

Multiparametric MRI in differentiating solitary brain metastasis from high-grade glioma: diagnostic value of the combined use of diffusion-weighted imaging, dynamic susceptibility contrast imaging, and magnetic resonance spectroscopy parameters

Journal

Neurologia i Neurochirurgia Polska

Issue

Vol 53, No 3 (2019)

Pages

227-237

Published online

2019-06-10

DOI

10.5603/PJNNS.a2019.0024

Pubmed

31180131

Bibliographic record

Neurol Neurochir Pol 2019;53(3):227-237.

Keywords

Solitary brain metastasis
high-grade glioma
diffusion-weighted imaging
dynamic susceptibility contrast

Authors

Kerim Aslan
Hediye Pinar Gunbey
Leman Tomak
Lutfi Incesu

References (48)
  1. Elmariah SB, Huse J, Mason B, et al. Multicentric glioblastoma multiforme in a patient with BRCA-1 invasive breast cancer. Breast J. 2006; 12(5): 470–474.
  2. Single Brain Metastasis. Curr Treat Options Neurol. 2001; 3(1): 89–99.
  3. Campos S, Davey P, Hird A, et al. Brain metastasis from an unknown primary, or primary brain tumour? A diagnostic dilemma. Curr Oncol. 2009; 16(1): 62–66.
  4. 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(6): 701–708.
  5. Lee EJa, terBrugge K, Mikulis D, et al. Diagnostic value of peritumoral minimum apparent diffusion coefficient for differentiation of glioblastoma multiforme from solitary metastatic lesions. AJR Am J Roentgenol. 2011; 196(1): 71–76.
  6. Byrnes TJD, Barrick TR, Bell BA, et al. Diffusion tensor imaging discriminates between glioblastoma and cerebral metastases in vivo. NMR Biomed. 2011; 24(1): 54–60.
  7. Lu S, Ahn D, Johnson G, et al. Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors. AJNR Am J Neuroradiol. 2003; 24(5): 937–941.
  8. Pavlisa G, Rados M, Pavlisa G, et al. The differences of water diffusion between brain tissue infiltrated by tumor and peritumoral vasogenic edema. Clin Imaging. 2009; 33(2): 96–101.
  9. Lemercier P, Paz Maya S, Patrie JT, et al. Gradient of apparent diffusion coefficient values in peritumoral edema helps in differentiation of glioblastoma from solitary metastatic lesions. AJR Am J Roentgenol. 2014; 203(1): 163–169.
  10. Server A, Kulle B, Maehlen J, et al. Quantitative apparent diffusion coefficients in the characterization of brain tumors and associated peritumoral edema. Acta Radiol. 2009; 50(6): 682–689.
  11. Huang J, Luo J, Peng J, et al. Cerebral schistosomiasis: diffusion-weighted imaging helps to differentiate from brain glioma and metastasis. Acta Radiol. 2017; 58(11): 1371–1377.
  12. Tan Y, Wang XC, Zhang H, et al. Differentiation of high-grade-astrocytomas from solitary-brain-metastases: Comparing diffusion kurtosis imaging and diffusion tensor imaging. Eur J Radiol. 2015; 84(12): 2618–2624.
  13. Han C, Huang S, Guo J, et al. Use of a high b-value for diffusion weighted imaging of peritumoral regions to differentiate high-grade gliomas and solitary metastases. J Magn Reson Imaging. 2015; 42(1): 80–86.
  14. Caravan I, Ciortea CA, Contis A, et al. Diagnostic value of apparent diffusion coefficient in differentiating between high-grade gliomas and brain metastases. Acta Radiol. 2018; 59(5): 599–605.
  15. Suh CH, Kim HS, Jung SC, et al. Diffusion-Weighted Imaging and Diffusion Tensor Imaging for Differentiating High-Grade Glioma from Solitary Brain Metastasis: A Systematic Review and Meta-Analysis. AJNR Am J Neuroradiol. 2018; 39(7): 1208–1214.
  16. Ishimaru H, Morikawa M, Iwanaga S, et al. Differentiation between high-grade glioma and metastatic brain tumor using single-voxel proton MR spectroscopy. Eur Radiol. 2001; 11(9): 1784–1791.
  17. Fan G, Sun B, Wu Z, et al. In vivo single-voxel proton MR spectroscopy in the differentiation of high-grade gliomas and solitary metastases. Clin Radiol. 2004; 59(1): 77–85.
  18. Server A, Josefsen R, Kulle B, et al. Proton magnetic resonance spectroscopy in the distinction of high-grade cerebral gliomas from single metastatic brain tumors. Acta Radiol. 2010; 51(3): 316–325.
  19. Crisi G, Orsingher L, Filice S. Lipid and macromolecules quantitation in differentiating glioblastoma from solitary metastasis: a short-echo time single-voxel magnetic resonance spectroscopy study at 3 T. J Comput Assist Tomogr. 2013; 37(2): 265–271.
  20. Wang Q, Zhang J, Xu W, et al. Role of magnetic resonance spectroscopy to differentiate high-grade gliomas from metastases. Tumour Biol. 2017; 39(6): 1010428317710030.
  21. Lehmann P, Saliou G, de Marco G, et al. Cerebral peritumoral oedema study: does a single dynamic MR sequence assessing perfusion and permeability can help to differentiate glioblastoma from metastasis? Eur J Radiol. 2012; 81(3): 522–527.
  22. Server A, Orheim TE, Graff BA, et al. Diagnostic examination performance by using microvascular leakage, cerebral blood volume, and blood flow derived from 3-T dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in the differentiation of glioblastoma multiforme and brain metastasis. Neuroradiology. 2011; 53(5): 319–330.
  23. Blasel S, Jurcoane A, Franz K, et al. Elevated peritumoural rCBV values as a mean to differentiate metastases from high-grade gliomas. Acta Neurochir (Wien). 2010; 152(11): 1893–1899.
  24. Hakyemez B, Erdogan C, Gokalp G, et al. Solitary metastases and high-grade gliomas: radiological differentiation by morphometric analysis and perfusion-weighted MRI. Clin Radiol. 2010; 65(1): 15–20.
  25. Halshtok Neiman O, Sadetzki S, Chetrit A, et al. Perfusion-weighted imaging of peritumoral edema can aid in the differential diagnosis of glioblastoma mulltiforme versus brain metastasis. Isr Med Assoc J. 2013; 15(2): 103–105.
  26. Suh CH, Kim HoS, Jung SC, et al. Perfusion MRI as a diagnostic biomarker for differentiating glioma from brain metastasis: a systematic review and meta-analysis. Eur Radiol. 2018; 28(9): 3819–3831.
  27. Law M, Cha S, Knopp EA, et al. High-grade gliomas and solitary metastases: differentiation by using perfusion and proton spectroscopic MR imaging. Radiology. 2002; 222(3): 715–721.
  28. Chiang IC, Kuo YT, Lu CY, et al. Distinction between high-grade gliomas and solitary metastases using peritumoral 3-T magnetic resonance spectroscopy, diffusion, and perfusion imagings. Neuroradiology. 2004; 46(8): 619–627.
  29. Fayed N, Modrego PJ. The contribution of magnetic resonance spectroscopy and echoplanar perfusion-weighted MRI in the initial assessment of brain tumours. J Neurooncol. 2005; 72(3): 261–265.
  30. Rollin N, Guyotat J, Streichenberger N, et al. Clinical relevance of diffusion and perfusion magnetic resonance imaging in assessing intra-axial brain tumors. Neuroradiology. 2006; 48(3): 150–159.
  31. Sparacia G, Gadde JA, Iaia A, et al. Usefulness of quantitative peritumoural perfusion and proton spectroscopic magnetic resonance imaging evaluation in differentiating brain gliomas from solitary brain metastases. Neuroradiol J. 2016; 29(3): 160–167.
  32. Svolos P, Tsolaki E, Kapsalaki E, et al. Investigating brain tumor differentiation with diffusion and perfusion metrics at 3T MRI using pattern recognition techniques. Magn Reson Imaging. 2013; 31(9): 1567–1577.
  33. Wang S, Kim S, Chawla S, et al. Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging. AJNR Am J Neuroradiol. 2011; 32(3): 507–514.
  34. Wang S, Kim SJ, Poptani H, et al. Diagnostic utility of diffusion tensor imaging in differentiating glioblastomas from brain metastases. AJNR Am J Neuroradiol. 2014; 35(5): 928–934.
  35. Bauer AH, Erly W, Moser FG, et al. Differentiation of solitary brain metastasis from glioblastoma multiforme: a predictive multiparametric approach using combined MR diffusion and perfusion. Neuroradiology. 2015; 57(7): 697–703.
  36. Tsolaki E, Svolos P, Kousi E, et al. Automated differentiation of glioblastomas from intracranial metastases using 3T MR spectroscopic and perfusion data. Int J Comput Assist Radiol Surg. 2013; 8(5): 751–761.
  37. Mouthuy N, Cosnard G, Abarca-Quinones J, et al. Multiparametric magnetic resonance imaging to differentiate high-grade gliomas and brain metastases. J Neuroradiol. 2012; 39(5): 301–307.
  38. Tsougos I, Svolos P, Kousi E, et al. Differentiation of glioblastoma multiforme from metastatic brain tumor using proton magnetic resonance spectroscopy, diffusion and perfusion metrics at 3 T. Cancer Imaging. 2012; 12: 423–436.
  39. Wu O, Østergaard L, Weisskoff RM, et al. Tracer arrival timing-insensitive technique for estimating flow in MR perfusion-weighted imaging using singular value decomposition with a block-circulant deconvolution matrix. Magn Reson Med. 2003; 50(1): 164–174.
  40. Rees JH, Smirniotopoulos JG, Jones RV, et al. Glioblastoma multiforme: radiologic-pathologic correlation. Radiographics. 1996; 16(6): 1413–38; quiz 1462.
  41. Long DM. Capillary ultrastructure in human metastatic brain tumors. J Neurosurg. 1979; 51(1): 53–58.
  42. Weber MA, Zoubaa S, Schlieter M, et al. Diagnostic performance of spectroscopic and perfusion MRI for distinction of brain tumors. Neurology. 2006; 66(12): 1899–1906.
  43. 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(4): 823–829.
  44. Wijnen JP, Idema AJS, Stawicki M, et al. Quantitative short echo time 1H MRSI of the peripheral edematous region of human brain tumors in the differentiation between glioblastoma, metastasis, and meningioma. J Magn Reson Imaging. 2012; 36(5): 1072–1082.
  45. Stadlbauer A, Gruber S, Nimsky C, et al. Preoperative grading of gliomas by using metabolite quantification with high-spatial-resolution proton MR spectroscopic imaging. Radiology. 2006; 238(3): 958–969.
  46. Guo J, Yao C, Chen H, et al. The relationship between Cho/NAA and glioma metabolism: implementation for margin delineation of cerebral gliomas. Acta Neurochir (Wien). 2012; 154(8): 1361–70; discussion 1370.
  47. Price SJ, Young AMH, Scotton WJ, et al. Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas. J Magn Reson Imaging. 2016; 43(2): 487–494.
  48. Nafe R, Herminghaus S, Raab P, et al. Preoperative proton-MR spectroscopy of gliomas--correlation with quantitative nuclear morphology in surgical specimen. J Neurooncol. 2003; 63(3): 233–245.

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