The Value of Diffusion Tensor Imaging in Predicting the Histopathological Grade of Diffuse Gliomas in Adults

Nguyễn Quốc Hưng Lương1, , Vo Phuong Truc2, Do Hai Thanh Anh3
1 BV Đại học Y Dược TPHCM
2 University Medical Center Ho Chi Minh City
3 University of Medicine and Pharmacy Ho Chi Minh City

Main Article Content

Abstract

Objective: The purpose of this study was to determine the value of DTI in preoperative predicting the histological grade of diffuse gliomas in adults.


Subjects and methods: This cross-sectional study included 38 patients with histologically confirmed diffuse gliomas who were treated at the University Medical Center of Ho Chi Minh City between January 2020 and April 2024. Preoperative imaging included conventional MRI and Diffusion Tensor Imaging (DTI). Mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and fractional anisotropy (FA) values were calculated for both the tumor and the peritumor. DTI parameters and tumor grades were statistically analyzed, and receiver operating characteristic (ROC) curve analysis was performed to assess diagnostic performance.


Results: The values of tumor MD, AD, and RD parameters (tMD, tAD, tRD), as well as the peritumoral FA parameter (pFA), showed statistically significant differences between low-grade and high-grade diffuse gliomas.


Conclusion: The values of tMD, tAD, tRD, and pFA parameters demonstrate diagnostic potential in distinguishing between high-grade and low-grade diffuse gliomas.

Article Details

References

1. Louis DN, Perry A, Reifenberger G, et al. (2016). “The 2016 World Health Organization classification of tumors of
the central nervous system: a summary”. Acta neuropathologica.131:803-820. doi:10.1007/s00401-016-1545-1
2. Johnson DR, Giannini C, Vaubel RA, et al. (2022). “A radiologist’s guide to the 2021 WHO Central Nervous System
Tumor Classification: part I—key concepts and the spectrum of diffuse gliomas”. Radiology.304(3):494-508.
3. Alexander AL, Hurley SA, Samsonov AA, et al. (2011). “Characterization of cerebral white matter properties using
quantitative magnetic resonance imaging stains”. Brain Connect.1(6):423-46.
4. Tae W-S, Ham B-J, Pyun S-B, Kang S-H, Kim B-J. (2018). “Current clinical applications of diffusion-tensor imaging
in neurological disorders”. Journal of clinical neurology.14(2):129-140.
5. Vincentelli C, Hwang SN, Holder CA, Brat DJ. (2012). “The use of neuroimaging to guide the histologic diagnosis
of central nervous system lesions”. Advances in Anatomic Pathology.19(2):97-107.
6. Bulakbasi N. (2009). “Diffusion-tensor imaging in brain tumors”. Imaging in Medicine.1(2):155-171.
7. Nandu H, Wen PY, Huang RY. (2018). “Imaging in neuro-oncology”. Therapeutic advances in neurological
disorders.11:1-19. doi:10.1177/1756286418759865
8. Hiếu NĐ, Anh NN, Dũng LT, Hùng ND. (2024). “Giá trị cộng hưởng từ phổ và khuếch tán sức căng định lượng trong
phân bậc u thần kinh đệm trên lều”. Tạp chí Nghiên cứu Y học.175(2):85-95. doi:10.52852/tcncyh.v175i2.2236
9. El-Serougy L, Abdel Razek AAK, Ezzat A, Eldawoody H, El-Morsy A. (2016). “Assessment of diffusion tensor
imaging metrics in differentiating low-grade from high-grade gliomas”. The neuroradiology journal.29(5):400-407.
10. Hung ND, Duc NM, Van Anh N, Dung LT, He D. (2021). “Diagnostic performance of diffusion tensor imaging for
preoperative glioma grading”. La Clinica Terapeutica.172(4):315-321. doi:10.7417/CT.2021.2335
11. Mohamad N, Sayuti KA, Mustapha M. (2023). “Diffusion tensor imaging (DTI) studies of cerebral white matter
integrity in normal to moderate cardiovascular risk patients”. Neurology Asia.28(1):131-139.
12. Server A, Graff BA, Josefsen R, et al. (2014). “Analysis of diffusion tensor imaging metrics for gliomas grading at
3 T”. European journal of radiology.83(3):156-165. doi:10.1016/j.ejrad.2013.12.023
13. Shukla S, Kashikar R, Desai S. (2023). “Analysis of diffusion tensor imaging metrics for glioma grading at 3T:
Comparison with histopathology as gold standard”. IP Indian Journal of Neurosciences.7(1):52-66.
14. Jiang L, Xiao C-Y, Xu Q, et al. (2017). “Analysis of DTI-derived tensor metrics in differential diagnosis between
low-grade and high-grade gliomas”. Frontiers in Aging Neuroscience.9(1)271. doi:10.3389/fnagi.2017.00271