Journal of Basic Research in Medical Sciences
J Basic Res Med Sci
Volume 13, Issue 1 (1-2026)
jbrms 2026, 13(1): 34-42 |
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Alikhani S, Zakariaee S S. Performance of Magnetic Resonance Imaging-based Permeability Indices for Preoperative Glioma Grading. jbrms 2026; 13 (1) :34-42
URL: http://jbrms.medilam.ac.ir/article-1-1016-en.html
URL: http://jbrms.medilam.ac.ir/article-1-1016-en.html
Performance of Magnetic Resonance Imaging-based Permeability Indices for Preoperative Glioma Grading
Department of Medical Physics, Faculty of Paramedical Sciences, Ilam University of Medical Sciences, Ilam, Iran , salman_zakariaee@yahoo.com
Abstract: (40 Views)
Introduction: Due to the inherent limitations of histopathology as the gold standard method for glioma grading, in recent years, alternative methods, including methods based on imaging data, have been proposed for better glioma grading. This study aimed to determine the performance of permeability parameters (Ktrans, Kep, Ve, and Vp) quantified using the dynamic contrast-enhanced MRI (DCE-MRI) method for preoperative glioma grading.
Materials & Methods: The radiological data of 31 patients with pathologically confirmed gliomas were retrospectively reviewed. The permeability parameters, including Ktrans, Kep, Ve, and Vp, were quantified using DCE-MRI data. The Mann-Whitney U test was used to assess the significance of differences in these parameters between different grades of glioma. The performance of the parameters for glioma grading was evaluated using receiver operating characteristic (ROC) curve analysis.
Results: The mean age of the patients was 39.2 ± 14.1 years, and 18 of the participants were males (58.06%). Ktrans, Kep, and Vp parameters demonstrated a significant difference between different grades of glioma. The results showed that Ktrans yielded the best grading performance compared to other studied parameters (AUC>71%). Vp, Kep, and Ve parameters ranked next.
Conclusion: DCE-MRI provides valuable quantitative parameters that can reliably differentiate between glioma grades. These noninvasive imaging biomarkers can serve as a powerful complement to the standard histopathological grading system, guiding better treatment planning and preventing unnecessary interventions.
Materials & Methods: The radiological data of 31 patients with pathologically confirmed gliomas were retrospectively reviewed. The permeability parameters, including Ktrans, Kep, Ve, and Vp, were quantified using DCE-MRI data. The Mann-Whitney U test was used to assess the significance of differences in these parameters between different grades of glioma. The performance of the parameters for glioma grading was evaluated using receiver operating characteristic (ROC) curve analysis.
Results: The mean age of the patients was 39.2 ± 14.1 years, and 18 of the participants were males (58.06%). Ktrans, Kep, and Vp parameters demonstrated a significant difference between different grades of glioma. The results showed that Ktrans yielded the best grading performance compared to other studied parameters (AUC>71%). Vp, Kep, and Ve parameters ranked next.
Conclusion: DCE-MRI provides valuable quantitative parameters that can reliably differentiate between glioma grades. These noninvasive imaging biomarkers can serve as a powerful complement to the standard histopathological grading system, guiding better treatment planning and preventing unnecessary interventions.
Keywords: Glioma, Neoplasm Grading, Dynamic Contrast-Enhanced Magnetic Resonance Imaging, Permeability, Pharmacokinetics
Type of Study: Research |
Subject:
Medical physics
Received: 2025/08/2 | Accepted: 2025/09/24 | Published: 2026/01/4
Received: 2025/08/2 | Accepted: 2025/09/24 | Published: 2026/01/4
References
1. Mesfin FB, Karsonovich T, Al-Dhahir MA. Gliomas. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024.https://www.ncbi.nlm.nih.gov/books/NBK441874/
2. Schwartzbaum JA, Fisher JL, Aldape KD, Wrensch M. Epidemiology and molecular pathology of glioma. Nat Clin Pract Neurol. 2006;2(9):494–503. [DOI:10.1038/ncpneuro0289.]
3. Larjavaara S, Mäntylä R, Salminen T, Haapasalo H, Raitanen J, Jääskeläinen J, et al. Incidence of gliomas by anatomic location. Neuro Oncol. 2007;9(3):319–25. [DOI:10.1215/15228517-2007-016.]
4. Pellerino A, Caccese M, Padovan M, Cerretti G, Lombardi G. Epidemiology, risk factors, and prognostic factors of gliomas. Clin Transl Imaging. 2022;10(5):467–75. [DOI:10.1007/s40336-022-00489-6.]
5. Muragaki Y, Chernov M, Maruyama T, Ochiai T, Taira T, Kubo O, et al. Low-grade glioma on stereotactic biopsy: how often is the diagnosis accurate? Minim Invasive Neurosurg. 2008;51(5):275–9. [DOI:10.1055/s-0028-1082322.]
6. Jackson RJ, Fuller GN, Abi-Said D, Lang FF, Gokaslan ZL, Shi WM, et al. Limitations of stereotactic biopsy in the initial management of gliomas. Neuro Oncol. 2001;3(3):193–200. [DOI:10.1093/neuonc/3.3.193.]
7. Law M, Yang S, Wang H, Babb JS, Johnson G, Cha S, et al. Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol. 2003;24(10):1989–98.
8. Lüdemann L, Grieger W, Wurm R, Budzisch M, Hamm B, Zimmer C. Comparison of dynamic contrast-enhanced MRI with WHO tumor grading for gliomas. Eur Radiol. 2001;11(7):1231–41. [DOI:10.1007/s003300000748.]
9. Pauliah M, Saxena V, Haris M, Husain N, Rathore RK, Gupta RK. Improved T(1)-weighted dynamic contrast-enhanced MRI to probe microvascularity and heterogeneity of human glioma. Magn Reson Imaging. 2007;25(9):1292–9. [DOI:10.1016/j.mri.2007.03.027.]
10. Tofts PS, Brix G, Buckley DL, Evelhoch JL, Henderson E, Knopp MV, et al. Estimating kinetic parameters from dynamic contrast-enhanced T(1)-weighted MRI of a diffusable tracer: standardized quantities and symbols. J Magn Reson Imaging. 1999;10(3):223–32. [DOI:10.1002/(SICI)1522-2586(199909)10:3.]
11. Association WM. World Medical Association Declaration of Helsinki: Ethical Principles for Medical Research Involving Human Subjects. JAMA. 2013;310(20):2191–4. [DOI:10.1001/jama.2013.281053.]
12. Aydin S, Fatihoğlu E, Koşar PN, Ergün E. Perfusion and permeability MRI in glioma grading. Egypt J Radiol Nucl Med. 2020;51(1):2. [DOI:10.1186/s43055-019-0127-3.]
13. Li X, Zhu Y, Kang H, Zhang Y, Liang H, Wang S, et al. Glioma grading by microvascular permeability parameters derived from dynamic contrast-enhanced MRI and intratumoral susceptibility signal on susceptibility weighted imaging. Cancer Imaging. 2015;15(1):4. [DOI:10.1186/s40644-015-0039-z.]
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