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Date Published:
09/07/2024
Online Published:
31/08/2023
Abstract Views: 55
Views pdf: 19
Views pdf: 19
Issue
Section
Scientific research
How to Cite
Ngo, D. Y., Vu, D. L., Dam, T. T., & Nguyen, C. T. (2023). Spectroscopy, DCE-MRI, glioma grading. Vietnamese Journal of Radiology and Nuclear Medicine, 52, 47-55. https://doi.org/10.55046/vjrnm.52.727.2023
Spectroscopy, DCE-MRI, glioma grading
Main Article Content
Abstract
Purpose: To evaluate the diagnostic accuracy of magnetic resonance imaging (MRI) spectroscopy and dynamic contrast-enhanced (DCE) for glioma grading.
Materials and Methods: Fifteen patient confirmed pathological glioma who underwent MR spectroscopy and DCE in 3 Tesla MRI machine. The following parameters were used: Ktrans, Ve, Cho/NAA, Cho/Cre. The diagnostic accuracy for glioma grading was determined by ROC analysis.
Results: There were 10 patients in the high-grade group and 5 patients in the low-grade group. Ktrans, Ve, Cho/NAA and Cho/Cre measures differed significantly between high and low-grade tumor. The AUC was 0.956 for Ktrans.
Conclusion: Ktrans, Ve parameters demonstrated to be useful for glioma grading.
Article Details
Keywords
Spectroscopy, DCE-MRI, glioma grading
References
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15. Mariko Toyooka, Hirohiko Kimura, Hidemasa Uematsu, et al. (2008). Tissue characterization of glioma by proton magnetic resonance spectroscopy and perfusion-weighted magnetic resonance imaging: glioma grading and histological correlation. doi: 10.1016/j.clinimag.2007.12.006
2. Jitender Saini ,Rakesh Kumar Gupta,Manoj Kumar,Anup Singh, et al (2019). Comparative evaluation of cerebral gliomas using rCBV measurements during sequential acquisition of T1-perfusion and T2*-perfusion MRI. doi: 10.1371/journal.pone.0215400
3. John G. Webster, E. Russell Ritenour, Slavik Tabakov, & and Kwan-Hoong Ng (2018). (Series in Medical Physics and Biomedical Engineering) Ioannis Tsougos - Advanced MR Neuroimaging_ From Theory to Clinical PracticeCRC Press. (1st Edition). CRC Press
4. DCE Parameters. What quantitative parameters can be extracted from the DCE data? (2022). https://mriquestions. com/dce-tissue-parmeters.html.
5. G. Parenti, F. Albarello, P. Campioni, et al. (2014). Role of MR Spectroscopy (H1-MRS) of the Testis in Men with Semen Analysis Altered. doi:10.1594/ecr2014/C-0534
6. Alena Horská, Ph.D.1 and Peter B. Barker, D. Phil, et al. (2010). Imaging of Brain Tumors: MR Spectroscopy and Metabolic Imaging. doi: 10.1016/j.nic.2010.04.003
7. Ming zhao, Li-Li Luo, Ning Huang, Qiong Wu, Li Zhou, et al. (2017). Quantitative analysis of permeability for glioma grading using dynamic contrast-enhanced magnetic resonance imaging. doi:10.3892/ol.2017.6895
8. Nguyễn Duy Hùng, et al. (2018). Nghiên cứu giá trị của cộng hưởng từ tưới máu và cộng hưởng từ phổ trong chẩn đoán một số u thần kinh đệm trên lều ở người lớn. URI: http://dulieuso.hmu.edu.vn//handle/hmu/1775
9. J Magn Reson Imaging, et al (2010), pp. 39-45. Short echo time MR spectroscopy of brain tumors: grading of cerebral gliomas by correlation analysis of normalized spectral amplitudes. https://doi.org/10.1002/jmri.21991
10. Xiaoguang Li, Yongshan Zhu, Houyi Kang, Yulong Zhang, et al. (2015). Glioma grading by microvascular permeability parameters derived from dynamic contrast-enhanced MRI and intratumoral susceptibility signal on susceptibility weighted imaging. doi: 10.1186/s40644-015-0039-z
11. Thomas Nielsen,* Thomas Wittenborn, and Michael R. Horsman. (2012). Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in Preclinical Studies of Antivascular Treatments.
12. Corrado Santarosaa, Antonella Castellanoa, et al. Dynamic contrast-enhanced and dynamic susceptibility contrast perfusion MR imaging for glioma grading: Preliminary comparison of vessel compartment and permeability parameters using hotspot and histogram analysis. doi: 10.3390/pharmaceutics4040563
13. Cao M, Suo S, Han X, Jin K, Sun Y1, Wang Y1, et al. (2018). Application of a Simplified Method for Estimating Perfusion Derived from Diffusion-Weighted MR Imaging in Glioma Grading. DOI: 10.3389/fnagi.2017.00432
14. Zhongzheng Jia a, Daoying Geng, Tianwen Xie, et al. (2012). Quantitative analysis of neovascular permeability in glioma by dynamic contrast-enhanced MR imaging. doi: 10.1016/j.jocn.2011.08.030
15. Mariko Toyooka, Hirohiko Kimura, Hidemasa Uematsu, et al. (2008). Tissue characterization of glioma by proton magnetic resonance spectroscopy and perfusion-weighted magnetic resonance imaging: glioma grading and histological correlation. doi: 10.1016/j.clinimag.2007.12.006