LinkedIn

 آمار

تعداد نشریات20
تعداد شماره‌ها1,149
تعداد مقالات10,518
تعداد مشاهده مقاله45,416,442
تعداد دریافت فایل اصل مقاله11,292,522
Davanian, F, Faeghi, F, Shahzadi, S, Farshidfar, Z. (1398). Evaluation of Diffusion Anisotropy and Diffusion Shape in Grading of Glial Tumors. سامانه مدیریت نشریات علمی, 9(4), 459-464. doi: 10.31661/jbpe.v0i0.513
F Davanian; F Faeghi; S Shahzadi; Z Farshidfar. "Evaluation of Diffusion Anisotropy and Diffusion Shape in Grading of Glial Tumors". سامانه مدیریت نشریات علمی, 9, 4, 1398, 459-464. doi: 10.31661/jbpe.v0i0.513
Davanian, F, Faeghi, F, Shahzadi, S, Farshidfar, Z. (1398). 'Evaluation of Diffusion Anisotropy and Diffusion Shape in Grading of Glial Tumors', سامانه مدیریت نشریات علمی, 9(4), pp. 459-464. doi: 10.31661/jbpe.v0i0.513
Davanian, F, Faeghi, F, Shahzadi, S, Farshidfar, Z. Evaluation of Diffusion Anisotropy and Diffusion Shape in Grading of Glial Tumors. سامانه مدیریت نشریات علمی, 1398; 9(4): 459-464. doi: 10.31661/jbpe.v0i0.513

Evaluation of Diffusion Anisotropy and Diffusion Shape in Grading of Glial Tumors

Journal of Biomedical Physics and Engineering
مقاله 9، دوره 9، شماره 4، آبان 2019، صفحه 459-464    اصل مقاله (562.95 K)
نوع مقاله: Original Research
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.513
نویسندگان
F Davanian1؛ F Faeghi* 1؛ S Shahzadi2؛ Z Farshidfar3
1Radiology Technology Department, School of Paramedicine, Shahid Beheshti University of Medical Sciences; Tehran, Iran
2Neurosurgery Department, Shohada Tajrish Hospital, Shahid Beheshti University of Medical Sciences; Tehran, Iran
3Shiraz University of Medical Sciences, Shiraz, Iran
چکیده
Background: The most common primary tumors of brain are gliomas. Grading of tumor is vital for designing proper treatment plans. The gold standard choice to determine the grade of glial tumor is biopsy which is an invasive method.
Objective: In this study, we try to investigate the role of fractional anisotropy (diffusion anisotropy) and linear anisotropy coefficient (its shape) with the aim of Diffusion Tensor imaging (as a non-invasive method) in the grading of gliomas.
Methods: A group of 20 patients with histologically glial approved was evaluated. In this study, we used a 1.5-Tesla MR system (AVANTO; Siemens, Germany) with a standard head coil for scanning. Multi-directional diffusion weighted imaging (measured in 12 non-collinear directions) and T1 weighted non-enhanced were performed for all patients. We defined two Regions of Interest (ROIs); white matter adjacent to the tumor and the homologous fiber tracts to the first ROI in the contralateral hemisphere.
Results: Linear anisotropy coefficient (CL), fractional anisotropy (FA) values and ratios of low-grade peri-tumoral fiber tracts were higher than high-grade gliomas (P-value CLt=0.014, P-value CLt/n=0.019 and P-value FAt=0.006, P-value FAt/n=0.024). In addition, we perform ROC curve for each parameter (CL ratio-AUC = 0.82 and FA ratio-AUC = 0.868).
Conclusion: Our findings prove significant difference between diffusion anisotropy (FA) and diffusion shape (Cl) between low grade and high grade glioma, based on which we find this evaluation helpful in the grading of glial tumors.
کلیدواژه‌ها
Diffusion Magnetic Resonance Imaging؛ Glioma؛ Anisotropy
مراجع
  1. Chen Y, Shi Y, Song Z. Differences in the architecture of low-grade and high-grade gliomas evaluated using fiber density index and fractional anisotropy. J Clin Neurosci. 2010;17:824-9. doi.org/10.1016/j.jocn.2009.11.022. PubMed PMID: 20427187.
  2. Min ZG, Niu C, Rana N, Ji HM, Zhang M. Differentiation of pure vasogenic edema and tumor-infiltrated edema in patients with peritumoral edema by analyzing the relationship of axial and radial diffusivities on 3.0T MRI. Clin Neurol Neurosurg. 2013;115:1366-70. doi.org/10.1016/j.clineuro.2012.12.031. PubMed PMID: 23351840.
  3. Kayama T, Kumabe T, Tominaga T, Yoshimoto T. Prognostic value of complete response after the initial treatment for malignant astrocytoma. Neurol Res. 1996;18:321-4. PubMed PMID: 8875449.
  4. Ma L, Song ZJ. Differentiation between low-grade and high-grade glioma using combined diffusion tensor imaging metrics. Clin Neurol Neurosurg. 2013;115:2489-95. doi.org/10.1016/j.clineuro.2013.10.003. PubMed PMID: 24183513.
  5. Brat DJ, Van Meir EG. Vaso-occlusive and prothrombotic mechanisms associated with tumor hypoxia, necrosis, and accelerated growth in glioblastoma. Lab Invest. 2004;84:397-405. doi.org/10.1038/labinvest.3700070. PubMed PMID: 14990981.
  6. Lee HY, Na DG, Song IC, Lee DH, Seo HS, Kim JH, et al. Diffusion-tensor imaging for glioma grading at 3-T magnetic resonance imaging: analysis of fractional anisotropy and mean diffusivity. J Comput Assist Tomogr. 2008;32:298-303. doi.org/10.1097/RCT.0b013e318076b44d. PubMed PMID: 18379322.
  7. Sawin PD, Hitchon PW, Follett KA, Torner JC. Computed imaging-assisted stereotactic brain biopsy: a risk analysis of 225 consecutive cases. Surg Neurol. 1998;49:640-9. doi.org/10.1016/S0090-3019(97)00435-7. PubMed PMID: 9637625.
  8. Malone H, Yang J, Hershman DL, Wright JD, Bruce JN, Neugut AI. Complications Following Stereotactic Needle Biopsy of Intracranial Tumors. World Neurosurg. 2015;84:1084-9. doi.org/10.1016/j.wneu.2015.05.025. PubMed PMID: 26008141.
  9. Johnson PC, Hunt SJ, Drayer BP. Human cerebral gliomas: correlation of postmortem MR imaging and neuropathologic findings. Radiology. 1989;170:211-7. doi.org/10.1148/radiology.170.1.2535765. PubMed PMID: 2535765.
  10. Watanabe M, Tanaka R, Takeda N. Magnetic resonance imaging and histopathology of cerebral gliomas. Neuroradiology. 1992;34:463-9. doi.org/10.1007/BF00598951. PubMed PMID: 1436452.
  11. 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:S205-23. doi.org/10.1148/rg.26si065510. PubMed PMID: 17050517.
  12. Goebell E, Paustenbach S, Vaeterlein O, Ding XQ, Heese O, Fiehler J, et al. Low-grade and anaplastic gliomas: differences in architecture evaluated with diffusion-tensor MR imaging. Radiology. 2006;239:217-22. doi.org/10.1148/radiol.2383050059. PubMed PMID: 16484348.
  13. 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;107:174-80. doi.org/10.1016/j.clineuro.2004.06.011. PubMed PMID: 15823671.
  14. Liu X, Tian W, Kolar B, Yeaney GA, Qiu X, Johnson MD, et al. MR diffusion tensor and perfusion-weighted imaging in preoperative grading of supratentorial nonenhancing gliomas. Neuro Oncol. 2011;13:447-55. doi.org/10.1093/neuonc/noq197. PubMed PMID: 21297125. PubMed PMCID: 3064693.
  15. Server A, Graff BA, Josefsen R, Orheim TE, Schellhorn T, Nordhoy W, et al. Analysis of diffusion tensor imaging metrics for gliomas grading at 3 T. Eur J Radiol. 2014;83:e156-65. doi.org/10.1016/j.ejrad.2013.12.023. PubMed PMID: 24457139.
  16. Smitha KA, Gupta AK, Jayasree RS. Total magnitude of diffusion tensor imaging as an effective tool for the differentiation of glioma. Eur J Radiol. 2013;82:857-61. doi.org/10.1016/j.ejrad.2012.12.027. PubMed PMID: 23394764.
  17. Wang S, Kim S, Melhem ER. Diffusion Tensor Imaging: Introduction and Applications to Brain Tumor Characterization. Functional Brain Tumor Imaging: Springer; 2014. p. 27-38.
  18. Mori S. New image contrasts from diffusion tensor imaging: Theory, meaning, and usefulness of DTI-based image contrast. In: Mori S, editor. Introduction to Diffusion Tensor Imaging. Amsterdam: Elsevier Science BV; 2007. p. 69-84.
  19. Seunarine KK, Alexander DC. Multiple Fibers: Beyond the Diffusion Tensor. In: Johansen-Berg H, Behrens TEJ, editors. Diffusion MRI (2nd edition). San Diego: Academic Press; 2014. p. 105-23.
آمار
تعداد مشاهده مقاله: 1,693
تعداد دریافت فایل اصل مقاله: 266


سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب