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Diffusion tensor imaging in brain tumors: The role of fractional anisotropy values

Year 2019, Volume: 58 Issue: 3, 257 - 264, 20.09.2019
https://doi.org/10.19161/etd.453779

Abstract

Aim: To evaluate
the role of Fractional anisotropy (FA) values obtained from diffusion tensor
magnetic resonance imaging (DTI) in the differentiation and grading of brain
tumors.

Materials and Methods: This study examined the conventional and diffusion
tensor MR imaging findings of twenty-seven patients diagnosed with brain tumors
between 2008 and 2010. Patients were divided into four groups based on tumor
types; meningiomas, low-grade gliomas, high-grade gliomas, and metastases.
Fractional anisotropy (FA) values were then obtained from the solid components
and (if present) peritumoral vasogenic edema of the tumors for each patient by
using the region of interest (ROI) method. Finally, the patient groups were
analyzed in terms of any statistically significant differences.

Results: The FA values obtained from the solid portions and
peritumoral edema of meningiomas were found to be higher than those of all
other groups (p<0.015). Moreover, the FA values of high-grade gliomas were
found to be higher than those of low-grade
gliomas (p=0.042). Finally, no statistically significant difference was
observed between high-grade gliomas and metastases in terms of the FA values of
solid components and peritumoral edema.







Conclusion: The determination of FA values among DTI results can
be a useful
method for differentiating brain tumors such as
meningioma, low-grade glioma, high-grade glioma, and metastasis, as the
treatment protocols and prognoses of each may differ. Moreover, FA values may
contribute preoperatively to the differentiation of brain tumors in multimodal
brain tumor imaging. It would be useful to use diffusion tensor imaging in
conjunction with conventional MRI in the imaging of brain tumors.

References

  • Butowski NA. Epidemiology and diagnosis of brain tumors. Continuum (Minneap Minn) 2015; 21 (2): 301-13.
  • Louis David N, Ohgaki H, Wiestler Otmar D, et al. The 2007 WHO classification of tumors of the central nervous system. Acta Neuropathology 2007; 114 (2): 97-109.
  • Law M, Stanley Yang, James S, et al. Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast enhanced perfusion MR. Imaging with glioma grade. AJNR Am J Neuroradiol 2004; 25 (5): 746-55.
  • Hagmann P, Jonasson L, Maeder P, Thiran JP, Wedeen VJ, Meuli R. Understanding diffusion MR imaging techniques: From scalar diffusion weighted imaging to diffusion tensor imaging and beyond. Radiographics 2006;26 (Suppl 1): 205-23.
  • Tóth E, Szabó N, Csete G, et al. Gray matter atrophy is primarily related to demyelination of lesions in multiple sclerosis: A diffusion tensor imaging MRI study. Front Neuroanat 2017; 11 (1): 23.
  • Tievsky AL, Ptak T, Farkas J. Investigation of apparent diffusion coefficient and diffusion tensor anisotropy in acute and chronic multiple sclerosis lesions. AJNR Am J Neuroradiology 1999; 20 (8): 1491-9.
  • Jones DK, Lythgoe D, Horsfield MA, et al. Characterization of white matter damage in ischemic leukoaraiosis with diffusion tensor MRI. Stroke1999; 30 (2): 393-7.
  • Hajnal JV, Doran M, Hall AS, et al. MR imaging of anisotropically restricted diffusion of water in the nervous system: Technical, anatomic, and pathologic considerations. J Comput Assist Tomogr 1991; 15 (1): 1-18.
  • Toh CH, Castillo M, Wong AM, et al. Differentiation between classic and atypical meningiomas with use of diffusion tensor imaging. Am J Neuroradiology 2008; 29 (9): 1630-5.
  • Inoue T, Ogasawara K, Beppu T, Ogawa A, Kabasawa H. Diffusion tensor imaging for preoperative evaluation of tumor grade in gliomas. Clin Neurol Neurosurg 2005; 107 (3): 174-80.
  • Ferda J, Kastner J, Mukensnabl P, et al. Diffusion tensor magnetic resonance imaging of glial brain tumors. Eur J Radiol 2010; 74 (3): 428-36.
  • Tsuchiya K, Fujikawa A, Nakajima M, Honya K. Differentiation between solitary brain metastasis and high-grade glioma by diffusion tensor imaging. Br J Radiol 2005; 78 (930): 533-7.
  • Deng Z, Yan Y, Zhong D, et al. Quantitative analysis of glioma cell invasion by diffusion tensor imaging. J Clin Neuroscience 2010; 17 (12): 1530-6.
  • Lu S, Ahn D, Johnson G, Cha S. Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors. AJNR Am J Neuroradiol 2003; 24 (5): 937-41.
  • Toh CH, Wong AMC, Wei KC, Ng SH, Wong HF, Wan YL. Peritumoral edema of meningiomas and metastatic brain tumors: Differences in diffusion characteristics evaluated with diffusion-tensor MR imaging. Neuroradiology 2007; 49 (6): 489-94.

Beyin tümörlerinde difüzyon tensör görüntüleme: Fraksiyonel anizotropi değerlerinin rolü

Year 2019, Volume: 58 Issue: 3, 257 - 264, 20.09.2019
https://doi.org/10.19161/etd.453779

Abstract

Amaç: Beyin tümörlerinin evrelenmesi ve ayrımında
difüzyon tensör manyetik rezonans görüntüleme ile elde edilen fraksiyonel
anizotropi değerlerinin katkısını araştırmak.



Gereç ve Yöntem: 2008 ve 2010 yılları arasında beyin tümörü
tanısı almış ve bölümümüzde konvansiyonel MRG ve difüzyon tensör görüntüleme
yapılmış 27 olgu retrospektif olarak tarandı. Olgular menenjiom, düşük dereceli
ve yüksek dereceli gliomlar ile metastazlar olmak üzere 4 gruba ayrıldı. Her
olguda tümörün solid komponentinden ve peritümöral ödem barındıranlarda
vazojenik ödem sahasından ROI (region of interest) yöntemi kullanılarak FA
değerleri ölçüldü. Gruplar arasında anlamlı farklılık olup olmadığı
istatistiksel yöntemlerle analiz edildi.

Bulgular: Tüm tümör grupları karşılaştırıldığında;
menenjiomların solid komponentinin ve peritümöral ödeminin FA değeri diğer
gruplara oranla istatistiksel anlamlı olarak yüksek bulundu (p<0.015). Düşük
ve yüksek dereceli gliomlar kıyaslandığında ise yüksek derecelilerin FA değeri
düşük derecelilere göre anlamlı yüksek bulundu (p=0.042). Yüksek dereceli gliom
grubu ile metastazların karşılaştırılmasında ise gerek solid kısımlar arasında
gerekse peritümöral ödem sahasında FA değerlerinde anlamlı farklılık
saptanmadı.



Sonuç: Difüzyon tensör manyetik rezonans
görüntüleme ve kantitatif FA ölçümü tedavi ve prognozu farklılıklar gösteren
menenjiom, metastaz, düşük ve yüksek dereceli glial tümörlerin ayrımında
faydalı bir yöntem olabilir. Beyin tümörlerinin görüntülenmesinde konvansiyonel
MR görüntüleri ile birlikte difüzyon tensor görüntülemenin kullanılması faydalı
olacaktır.


References

  • Butowski NA. Epidemiology and diagnosis of brain tumors. Continuum (Minneap Minn) 2015; 21 (2): 301-13.
  • Louis David N, Ohgaki H, Wiestler Otmar D, et al. The 2007 WHO classification of tumors of the central nervous system. Acta Neuropathology 2007; 114 (2): 97-109.
  • Law M, Stanley Yang, James S, et al. Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast enhanced perfusion MR. Imaging with glioma grade. AJNR Am J Neuroradiol 2004; 25 (5): 746-55.
  • Hagmann P, Jonasson L, Maeder P, Thiran JP, Wedeen VJ, Meuli R. Understanding diffusion MR imaging techniques: From scalar diffusion weighted imaging to diffusion tensor imaging and beyond. Radiographics 2006;26 (Suppl 1): 205-23.
  • Tóth E, Szabó N, Csete G, et al. Gray matter atrophy is primarily related to demyelination of lesions in multiple sclerosis: A diffusion tensor imaging MRI study. Front Neuroanat 2017; 11 (1): 23.
  • Tievsky AL, Ptak T, Farkas J. Investigation of apparent diffusion coefficient and diffusion tensor anisotropy in acute and chronic multiple sclerosis lesions. AJNR Am J Neuroradiology 1999; 20 (8): 1491-9.
  • Jones DK, Lythgoe D, Horsfield MA, et al. Characterization of white matter damage in ischemic leukoaraiosis with diffusion tensor MRI. Stroke1999; 30 (2): 393-7.
  • Hajnal JV, Doran M, Hall AS, et al. MR imaging of anisotropically restricted diffusion of water in the nervous system: Technical, anatomic, and pathologic considerations. J Comput Assist Tomogr 1991; 15 (1): 1-18.
  • Toh CH, Castillo M, Wong AM, et al. Differentiation between classic and atypical meningiomas with use of diffusion tensor imaging. Am J Neuroradiology 2008; 29 (9): 1630-5.
  • Inoue T, Ogasawara K, Beppu T, Ogawa A, Kabasawa H. Diffusion tensor imaging for preoperative evaluation of tumor grade in gliomas. Clin Neurol Neurosurg 2005; 107 (3): 174-80.
  • Ferda J, Kastner J, Mukensnabl P, et al. Diffusion tensor magnetic resonance imaging of glial brain tumors. Eur J Radiol 2010; 74 (3): 428-36.
  • Tsuchiya K, Fujikawa A, Nakajima M, Honya K. Differentiation between solitary brain metastasis and high-grade glioma by diffusion tensor imaging. Br J Radiol 2005; 78 (930): 533-7.
  • Deng Z, Yan Y, Zhong D, et al. Quantitative analysis of glioma cell invasion by diffusion tensor imaging. J Clin Neuroscience 2010; 17 (12): 1530-6.
  • Lu S, Ahn D, Johnson G, Cha S. Peritumoral diffusion tensor imaging of high-grade gliomas and metastatic brain tumors. AJNR Am J Neuroradiol 2003; 24 (5): 937-41.
  • Toh CH, Wong AMC, Wei KC, Ng SH, Wong HF, Wan YL. Peritumoral edema of meningiomas and metastatic brain tumors: Differences in diffusion characteristics evaluated with diffusion-tensor MR imaging. Neuroradiology 2007; 49 (6): 489-94.
There are 15 citations in total.

Details

Primary Language English
Subjects Health Care Administration
Journal Section Research Articles
Authors

Dinçer Aydın Akyılmaz 0000-0002-2305-893X

Mehmet Cem Çallı 0000-0001-9295-2866

Erkin Özgiray 0000-0002-1156-9468

Yeşim Ertan 0000-0002-4522-0774

Emine Serra Kamer 0000-0001-8316-9976

Ömer Kitiş 0000-0003-4201-5449

Publication Date September 20, 2019
Submission Date June 28, 2018
Published in Issue Year 2019Volume: 58 Issue: 3

Cite

Vancouver Akyılmaz DA, Çallı MC, Özgiray E, Ertan Y, Kamer ES, Kitiş Ö. Diffusion tensor imaging in brain tumors: The role of fractional anisotropy values. EJM. 2019;58(3):257-64.