Diffusion tensor imaging derived metrics in high grade glioma and brain metastasis differentiation
Abstract
Background: Pretreatment differentiation between glioblastoma and metastasis is a frequently encountered dilemma in neurosurgical practice. Distinction is required for precise planning of resection or radiotherapy, and also for defining further diagnostic procedures. Morphology and spectroscopy imaging features are not specific and frequently overlap. This limitation of magnetic resonance imaging and magnetic resonance spectroscopy was the reason to initiate this study. The aim of the present study was to determine whether the dataset of diffusion tensor imaging metrics contains information which may be used for the distinction between primary and secondary intra-axial neoplasms. Methods: Two diffusion tensor imaging parameters were measured in 81 patients with an expansive, ring-enhancing, intra-axial lesion on standard magnetic resonance imaging (1.5 T system). All tumors were histologically verified glioblastoma or secondary deposit. For qualitative analysis, two regions of interest were defined: intratumoral and immediate peritumoral region (locations 1 and 2, respectively). Fractional anisotropy and mean difusivity values of both groups were compared. Additional test was performed to determine if there was a significant difference in mean values between two locations. Results: A statistically significant difference was found in fractional anisotropy values among two locations, with decreasing values in the direction of neoplastic infiltration, although such difference was not observed in
fractional anisotropy values in the group with secondary tumors. Mean difusivity values did not appear helpful in differentiation between these two entities. In both groups there was no significant difference in mean difusivity values, neither in intratumoral nor in peritumoral location. Conclusion: The results of our study justify associating the diffusion tensor imaging technique to conventional morphologic magnetic resonance imaging as an additional diagnostic tool for the distinction between primary and secondary intra-axial lesions. Quantitative analysis of diffusion tensor imaging metric, in particular measurement of fractional anisotropy in peritumoral edema facilitates accurate diagnosis.
References
Min Z, Niu C, Zhang Q, Zhang M, Qian Y. Optimal Factors of Diffusion Tensor Imaging Predicting Corticospinal Tract Injury in Patients with Brain Tumors. 2017;18(5):844–51.
Zhao J, Yang ZY, Luo BN, Yang JY, Chu JP. Quantitative evaluation of diffusion and dynamic contrast-enhanced MR in tumor parenchyma and peritumoral area for distinction of brain tumors. PLoS One [Internet]. 2015;10(9):1–15. Available from: http://dx.doi.org/10.1371/journal.pone.0138573
Neska-matuszewska M, Bladowska J, Marek S, 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. 2018;1–18.
Pope WB, Brandal G. Conventional and advanced magnetic resonance imaging in patients with high-grade glioma. Q J Nucl Med Mol Imaging. 2018;62(3):239–53.
Munoz-Bendix C, Rapp M, Mijderwijk HJ, von Sass C, Dibué-Adjei M, Cornelius JF, et al. Risk factors for in-brain local progression in elderly patients after resection of cerebral metastases. Sci Rep. 2019;9(1):1–9.
Jin Y, Randall JW, Elhalawani H, Al Feghali KA, Elliott AM, Anderson BM, et al. Detection of glioblastoma subclinical recurrence using serial diffusion tensor imaging. Cancers (Basel). 2020;12(3):1–11.
Lee EJ, Ahn KJ, Lee EK, Lee YS, Kim DB. Potential role of advanced MRI techniques for the peritumoural region in differentiating glioblastoma multiforme and solitary metastatic lesions. Clin Radiol [Internet]. 2013;68(12):e689–97. http://dx.doi. org/10.1016/j.crad.2013.06.021
Jolapara M, Kesavadas C, Radhakrishnan V V, Thomas B, Gupta AK, Bodhey N, et al. Role of diffusion tensor imaging in differentiating subtypes of meningiomas Place de l ’ imagerie du tenseur de diffusion pour la différenciation des. J Neuroradiol [Internet]. 2010;37(5):277–83. http://dx.doi.org/10.1016/j.neurad.2010.03.001
Chen L, Liu M, Bao J, Xia Y, Zhang J, Zhang L, et al. The correlation between apparent diffusion coefficient and tumor cellularity in patients: A meta-analysis. PLoS One. 2013;8(11).
Sternberg E.J, Lipton M.L. B. Utility of Diffusion Tensor Imaging in Evaluation of the Peritumoral Region in Patients with Primary and Metastatic. Am J Neuroradiol. 2014;35:439–44.
Jiang R, Du F, He C, Gu M, Ke Z, Li J. The Value of Diffusion Tensor Imaging in Differentiating High-Grade Gliomas from Brain Metastases : A Systematic Review and Meta-Analysis. 2014;9(11):1–8.
Jiang L, Xiao C, Xu Q, Sun J, Chen H, Chen Y, et al. Analysis of DTI-Derived Tensor Metrics in Differential Diagnosis between Low-grade and High-grade Gliomas. 2017;9(August):1–8.
Lemercier P, Maya SP, Patrie JT, Flors L, Leiva-Salinas C. Gradient of apparent diffusion coefficient values in peritumoral edema helps in differentiation of glioblastoma from solitary metastatic lesions. Am J Roentgenol. 2014;203(1):163–9.
Holly KS, Fitz-gerald JS, Barker BJ, Murcia D, Daggett R, Ledbetter C, et al. Differentiation of High-Grade Glioma and Intracranial Metastasis Using Volumetric Diffusion Tensor Imaging Tractography. World Neurosurg [Internet]. 2018;1–11. https://doi.org/10.1016/j.wneu.2018.07.230
Lu S, Ahn D, Johnson G, Law M, Zagzag D, Grossman RI. Radiology Diffusion-Tensor MR Imaging of Intracranial Neoplasia and Associated Peritumoral Edema: Introduction of the Tumor Infiltration Index 1. 2004;(4):221–8.
Byrnes TJD, Barrick TR, Bell BA, Clark CA. Diffusion tensor imaging discriminates between glioblastoma and cerebral metastases in vivo. 2011;(March 2010):54–60
Chiang IC, Lu C, Lin W, Sheu F. Distinction between high-grade gliomas and solitary metastases using peritumoral 3-T magnetic resonance spectroscopy, diffusion , and perfusion imagings. 2004;619–27.
Surov A, Meyer HJ, Wienke A. Correlation between minimum apparent diffusion coefficient (ADCmin) and tumor cellularity: A meta-analysis. Anticancer Res 37(7). 2017;3810(September 2016):3807–10.
Server, Andres & Andreassen, Bettina & Maehlen, J & Josefsen, Roger & Schellhorn, Till & Kumar, T & Langberg, C.W. & Nakstad, P.H.. (2009). Quantitative Apparent Diffusion Coefficients in the Characterization of Brain
Tumors and Associated Peritumoral Edema. Acta radiologica (Stockholm, Sweden : 1987). 50. 682-9. 10.1080/02841850902933123.
Holly KS, Barker BJ, Murcia D, Bennett R, Kalakoti P, Clusmann H. High-grade Gliomas Exhibit Higher Peritumoral Fractional Anisotropy and Lower Mean Diffusivity than Intracranial Metastases. 2017;4(April):1–10.
Bauer AH, Erly W, Moser FG, Maya M, Nael K. Differentiation of solitary brain metastasis from glioblastoma multiforme : a predictive multiparametric approach using combined MR diffusion and perfusion. 2015;
Wang S, Kim S, Chawla S, Wolf RL, Knipp DE, Vossough A, et al. Differentiation between Glioblastomas , Solitary Brain Metastases, and Primary Cerebral Lymphomas Using Diffusion Tensor and Dynamic. 2011;
Pavlisa G, Rados M, Pavlisa G, Pavic L, Potocki K, Mayer D. The differences of water diffusion between brain tissue infiltrated by tumor and peritumoral vasogenic edema. J Clin Imaging [Internet]. 2009;33(2):96–101. http://dx.doi.org/10.1016/j.clinimag.2008.06.035
Stefanie B, Alexsandar T, Benedikt H, Jens TB, Florian G, Bernhard R, et al. Analysis of Fractional Anisotropy Facilitates Differentiation of Glioblastoma and Brain Metastases in a Clinical Setting. Eur J Radiol [Internet]. 2016; Available from: http://dx.doi.org/10.1016/j.ejrad.2016.10.002
Deng Z, Yan Y, Zhong D, Yang G, Tang W, Lü F, et al. Quantitative analysis of glioma cell invasion by diffusion tensor imaging. J Clin Neurosci [Internet]. 2010;17(12):1530–6. http://dx.doi.org/10.1016/j.jocn.2010.03.060
Tsuchiya K, Fujikawa A, Nakajima M, Honya K. Differentiation between solitary brain metastasis and high- grade glioma by diffusion tensor imaging. 2005;78(July 2004):533–7.
Papageorgiou TS, Chourmouzi D, Drevelengas A, Kouskouras K, Siountas A. Physica Medica Diffusion Tensor Imaging in brain tumors : A study on gliomas and metastases. Phys Medica [Internet]. 2015;1–7. Available from: http://dx.doi.org/10.1016/j.ejmp.2015.03.010
Hoefnagels FW, De Witt Hamer P, Sanz-Arigita E, Idema S, Kuijer JP, Pouwels PJ, Barkhof F, Vandertop WP. Differentiation of edema and glioma infiltration: proposal of a DTI-based probability map. J Neurooncol. 2014 Oct;120(1):187-98. doi: 10.1007/s11060-014-1544-9. Epub 2014 Jul 31. PMID: 25079117.
Bette S, Huber T, Wiestler B, Boeckh-Behrens T, Gempt J, Ringel F, et al. Analysis of fractional anisotropy facilitates differentiation of glioblastoma and brain metastases in a clinical setting. Eur J Radiol (2016) 85(12):2182–7. doi:10.1016/j.ejrad.2016.10.002
Gonçalves FG, Chawla S, Mohan S. Emerging MRI Techniques to Redefine Treatment Response in Patients With Glioblastoma. J Magn Reson Imaging. 2020;1–20.
Server AS, Kulle B, Mehlen J, Josefsen R, Schellhorn T, Kumar T, et al. Quantitative Apparent Diffusion Coefficients in the Characterization of Brain Tumors and Associated Peritumoral Edema. Acta Radioligica. 2009;50:682(0407).
Oh J, Cha S, Aiken AH, Han ET, Crane JC, Stainsby JA, et al. Quantitative Apparent Diffusion Coefficients and T2 Relaxation Times in Characterizing Contrast Enhancing Brain Tumors and Regions of Peritumoral Edema. 2005;708:701–8.
Sinha S, Bastin ME, Whittle IR, Wardlaw JM. Diffusion Tensor MR Imaging of High-Grade Cerebral Gliomas. 2002;(April):520–7.
Varga I, Hutóczki G, Petrás M, Scholtz B, Mikó E, Kenyeres A, et al. Expression of invasion-related extracellular matrix molecules in human glioblastoma versus intracerebral lung adenocarcinoma metastasis. Zentralbl Neurochir. 2010;71(4):173–80.
Pekmezci M, Perry A. Neuropathology of brain metastases. Surg Neurol Int. 2013;4(SUPPL4):245–55.
Nilsson M, Englund E, Szczepankiewicz F, Westen D Van, Sundgren PC. Imaging brain tumour microstructure. Neuroimage 2018;182 :232-250. Available from: https://doi.org/10.1016/j.neuroimage.2018.04.075
El-Serougy LG, Razek AAKA, Mousa AE, Eldawoody HAF, El-Morsy AE-ME. Differentiation between high-grade gliomas and metastatic brain tumors using diffusion tensor imaging metrics. Egypt J Radiol Nucl Med (2015) 46(4):1099–104. doi:10.1016/j.ejrnm.2015.08.005
El-serougy L, Abdel A, Abdel K, Ezzat A, Eldawoody H, El-morsy A. Assessment of diffusion tensor imaging metrics in differentiating low-grade from high-grade gliomas. 2016;1–8.
Lope-piedrafita S, Garcia-martin ML, Galons J, Gillies RJ, Trouard TP. Longitudinal diffusion tensor imaging in a rat brain glioma model. 2008;(May):799–808.
Byrnes TJD, Barrick TR, Bell BA, Clark CA. Diffusion tensor imaging discriminates between glioblastoma and cerebral metastases in vivo. 2011;(March 2010):54–60
Malayeri AA, Hospital W, Khouli R El, Jacobs MA. Principles and Applications of Diffusion-weighted Imaging in Cancer Detection, Staging and treatment follow-up. Radiogr. 2011;31(6)(October):1773–1791
Wang W, Steward CE, Desmond PM. Diffusion Tensor Imaging in Glioblastoma Multiforme and Brain Metastases : The Role of p , q , L , and Fractional Anisotropy American Journal of Neuroradiology Jan 2009, 30 (1) 203-208; DOI:10.3174/ajnr. A1303
Hoefnagels FW, De Witt Hamer P, Sanz-Arigita E, Idema S, Kuijer JP, Pouwels PJ, Barkhof F, Vandertop WP. Differentiation of edema and glioma infiltration: proposal of a DTI-based probability map. J Neurooncol. 2014 Oct;120(1):187-98. doi:10.1007/s11060-014-1544-9. Epub 2014 Jul 31. PMID: 25079117.
Alger JR. The diffusion tensor imaging toolbox. J Neurosci. 2012;32(22):7418–28.
Stadlbauer A, Nimsky C, Buslei R, Salomonowitz E, Hammen T, Buchfelder M, et al. Diffusion tensor imaging and optimized fiber tracking in glioma patients : Histopathologic evaluation of tumor-invaded white matter structures. 2007;34:949–56.
Kinoshita, Manabu & Goto, Tetsu & Okita, Yoshiko & Kagawa, Naoki & Haruhiko, Kishima & Hashimoto, Naoya & Yoshimine, Toshiki. (2009). Diffusion tensor-based tumor infiltration index cannot discriminate vasogenic edema from tumor-infiltrated edema. Journal of neuro-oncology. 96. 409-15. 10.1007/s11060-009-9979-0.
