SavedAdd your journal

Journals / Marmara Medical Journal / 2026 / Volume: 39 Issue: 2

Diagnostic utility of fractional anisotropy in differentiating glioma grades

Zeynep Fırat(YEDITEPE UNIVERSITY)0000-0001-6810-6360
Cumhur Kaan Yaltırık(Maltepe University)0000-0002-4312-5685
Gazanfer Ekinci(Yeditepe University)0000-0002-1589-1833
Pages
—
Publication Date
2026-05-21
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Conclusion: Raw and NAWM-normalized FA metrics, particularly in the tumor core and peritumoral region, differentiated grade 1–2 from grade 3–4 gliomas and may serve as supportive noninvasive markers for preoperative grading.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Objective: This study investigates the diagnostic potential of fractional anisotropy (FA), derived from diffusion tensor imaging (DTI), for distinguishing low-grade (grade 1–2) from high-grade (grade 3–4) gliomas by evaluating the tumor core, tumor margin, and peritumoral region.
📊
Key Findings & Data:Patients and Methods: Sixty patients with histopathologically confirmed gliomas (grade 1–2, n=30; grade 3–4, n=30) underwent preoperative 3T Magnetic Resonance Imaging (MRI) including DTI.
💡
Conclusion & Impact:Conclusion: Raw and NAWM-normalized FA metrics, particularly in the tumor core and peritumoral region, differentiated grade 1–2 from grade 3–4 gliomas and may serve as supportive noninvasive markers for preoperative grading.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Diffusion tensor imaging · Fractional anisotropy · Glioma

Abstract

Objective: This study investigates the diagnostic potential of fractional anisotropy (FA), derived from diffusion tensor imaging (DTI), for distinguishing low-grade (grade 1–2) from high-grade (grade 3–4) gliomas by evaluating the tumor core, tumor margin, and peritumoral region. Patients and Methods: Sixty patients with histopathologically confirmed gliomas (grade 1–2, n=30; grade 3–4, n=30) underwent preoperative 3T Magnetic Resonance Imaging (MRI) including DTI. FA was measured in the tumor core (solid component), tumor margin, peritumoral region, and contralateral normal-appearing white matter (NAWM). NAWM-normalized FA ratios were also evaluated. Groups were compared using independent-samples t tests, and diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis including AUC, optimal cut-off, sensitivity, and specificity. Results: Fractional anisotropy in tumor-related regions was lower than contralateral NAWM in both groups. Grade 3–4 gliomas showed significantly lower raw FA in the tumor core, tumor margin, and peritumoral region, whereas NAWM FA did not differ between groups. NAWM-normalized FA ratios were also significantly reduced in grade 3–4 gliomas (all p < 0.05). Tumor core raw FA showed the highest discrimination (AUC = 0.842, 95% CI: 0.734–0.932, cut-off = 0.150, sensitivity = 0.80, specificity = 0.73). Conclusion: Raw and NAWM-normalized FA metrics, particularly in the tumor core and peritumoral region, differentiated grade 1–2 from grade 3–4 gliomas and may serve as supportive noninvasive markers for preoperative grading.

🔖

Cite This Article

Fırat, Z., Yaltırık, C. K., Ekinci, G., *, Z. F. (2026). Diagnostic utility of fractional anisotropy in differentiating glioma grades. Marmara Medical Journal, Volume: 39 Issue: 2. https://doi.org/10.5472/marumj.1953942

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (27)

  1. Ostrom QT, Bauchet L, Davis FG, et al. The epidemiology of glioma in adults: A state of the science review. Neuro Oncol 2014;16:896–913. doi: 10.1093/neuonc/nou087.
  2. Ostrom QT, Price M, Neff C, et al. CBTRUS Statistical Report: Primary brain and other central nervous system tumors diagnosed in the united states in 2015-2019. Neuro Oncol 2022;24:V1-95. doi: 10.1093/neuonc/noac202.
  3. Louis DN, Perry A, Wesseling P, et al. The 2021 WHO classification of tumors of the central nervous system: A summary. Neuro Oncol.2021;23:1231-51. doi: 10.1093/ neuonc/noab106.
  4. Weller M, van den Bent M, Preusser M, et al. EANO guidelines on the diagnosis and treatment of diffuse gliomas of adulthood. Nat Rev Clin Oncol 2021;18:170-86. doi: 10.1038/s41571-020- 00447-z
  5. Villanueva-Meyer JE, Mabray MC, Cha S. Current clinical brain tumor imaging. Clin Neurosurg. 2017;81:397-415. doi: 10.1093/neuros/nyx103.
  6. Law M, Young R, Babb JS, et al. Comparing perfusion metrics obtained from a single compartment versus pharmacokinetic modeling methods using dynamic susceptibility contrastenhanced perfusion MR imaging with glioma grade. Am J Neuroradiol 2006;27:1975-82.
  7. Usinskiene J, Ulyte A, Bjørnerud A, et al. Optimal differentiation of high – and low-grade glioma and metastasis: a meta-analysis of perfusion, diffusion, and spectroscopy metrics. Neuroradiology 2016;58:339-50. doi: 10.1007/ s00234-016-1642-9.
  8. Maximov II, Tonoyan AS, Pronin IN. Differentiation of glioma malignancy grade using diffusion MRI. Phys Medica [Internet]. 2017;40:24-32. doi: 10.1016/j.ejmp.2017.07.002.
  9. Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusiontensor MRI. J Magn Reson 1996;111:209-19. doi: 10.1006/ jmrb.1996.0086.
  10. Pierpaoli C, Basser PJ. Toward a quantitative assessment of diffusion anisotropy. Magn Reson Med 1996;36:893-906. doi: 10.1002/mrm.1910360612.
  11. Kinoshita M, Hashimoto N, Goto T, et al. Fractional anisotropy and tumor cell density of the tumor core show positive correlation in diffusion tensor magnetic resonance imaging of malignant brain tumors. Neuroimage 2008;43:29- 35. doi: 10.1016/j.neuroimage.2008.06.041.
  12. Beppu T, Inoue T, Shibata Y, et al. Measurement of fractional anisotropy using diffusion tensor MRI in supratentorial astrocytic tumors. J Neurooncol 2003;63:109-16. doi: 10.1023/a:1023977520909.
  13. Miloushev VZ, Chow DS, Filippi CG. Meta-analysis of diffusion metrics for the prediction of tumor grade in gliomas. Am J Neuroradiol 2015;36:302-8. doi: 10.3174/ajnr.A4097.
  14. Flores-Alvarez E, Durand-Muñoz C, Cortes-Hernandez F, et al. Clinical significance of fractional anisotropy measured in peritumoral edema as a biomarker of overall survival in glioblastoma: Evidence using correspondence analysis. Neurol India 2019;67:1074-81. doi:10.4103/0028-3886.266284.
  15. White ML, Zhang Y, Yu F, Jaffar Kazmi SA. Diffusion tensor MR imaging of cerebral gliomas: Evaluating fractional anisotropy characteristics. Am J Neuroradiol 2011;32:374-81. doi: 10.3174/ajnr.A2267.
  16. Server A, Graff BA, Josefsen R, et al. Analysis of diffusion tensor imaging metrics for gliomas grading at 3 T. Eur J Radiol 2014;83:e156-65. doi: 10.1016/j.ejrad.2013.12.023.
  17. Sinha S, Bastin ME, Whittle IR, Wardlaw JM. Diffusion tensor MR imaging of high-grade cerebral gliomas. Am J Neuroradiol 2002;23:520-7.
  18. Price SJ, Gillard JH. Imaging biomarkers of brain tumour margin and tumour invasion. Br J Radiol 2011;84(SPEC. ISSUE 2):159-67. doi: 10.1259/bjr/26838774.
  19. Price SJ, Burnet NG, Donovan T, et al. Diffusion tensor imaging of brain tumours at 3T: A potential tool for assessing white matter tract invasion? Clin Radiol [Internet] 2003 [cited 2014 Nov 22];58:455-62. doi: 10.1016/S0009-9260(03)00115-6.
  20. Akbari H, Bakas S, Sako C, et al. Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study. Neuro Oncol 2025;27:1102-15. doi: 0.1093/neuonc/noae260.
  21. Alexander AL, Lee JE, Lazar M, Field AS. Diffusion tensor imaging of the brain. Neurotherapeutics 2007;4:316-29. doi: 10.1016/j.nurt.2007.05.011.
  22. Akbari H, Macyszyn L, Da X, et al. Imaging surrogates of infiltration obtained via multiparametric imaging pattern analysis predict subsequent location of recurrence of glioblastoma. Neurosurgery 2016;78:572-80. doi: 10.1227/ NEU.0000000000001202.
  23. Sundgren PC, Dong Q, Gómez-Hassan D, Mukherji SK, Maly P, Welsh R. Diffusion tensor imaging of the brain: Review of clinical applications. Neuroradiology 2004;46:339-50. doi: 10.1007/s00234-003-1114-x.
  24. Ma L, Song ZJ. Differentiation between low-grade and highgrade glioma using combined diffusion tensor imaging metrics. Clin Neurol Neurosurg [Internet]. 2013;115:2489-95. doi: 10.1016/j.clineuro.2013.10.003.
  25. Price SJ, Jena R, Burnet NG, et al. Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: An image-guided biopsy study. Am J Neuroradiol 2006;27:1969-74.
  26. Er FÇ, Firat Z, Kovanlikaya I, Ture U, Ozturk-Isik E. Multivariate discriminant analysis of multiparametric brain MRI to differentiate high grade and low grade gliomas – A computer-aided diagnosis development study. 13th IEEE International Conference on BioInformatics and BioEngineering, Chania, Greece, 2013;1-5, ddoi: 10.1109/ BIBE.2013.6701649.
  27. Jain R, Poisson LM, Gutman D, et al. Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: Focus on the nonenhancing component of the tumor. Radiology 2014;272:484-93. doi: 10.1148/ radiol.14131691.