» Articles » PMID: 29218370

Differentiation of Residual/recurrent Gliomas from Postradiation Necrosis with Arterial Spin Labeling and Diffusion Tensor Magnetic Resonance Imaging-derived Metrics

Overview
Journal Neuroradiology
Specialties Neurology
Radiology
Date 2017 Dec 9
PMID 29218370
Citations 54
Authors
Affiliations
Soon will be listed here.
Abstract

Purpose: The aim of this study is to differentiate recurrent/residual gliomas from postradiation changes using arterial spin labeling (ASL) perfusion and diffusion tensor imaging (DTI)-derived metrics.

Methods: Prospective study was conducted upon 42 patients with high-grade gliomas after radiotherapy only or prior to other therapies that underwent routine MR imaging, ASL, and DTI. The tumor blood flow (TBF), fractional anisotropy (FA), and mean diffusivity (MD) of the enhanced lesion and related edema were calculated. The lesion was categorized as recurrence/residual or postradiation changes.

Results: There was significant differences between residual/recurrent gliomas and postradiation changes of TBF (P = 0.001), FA (P = 0.001 and 0.04), and MD (P = 0.001) of enhanced lesion and related edema respectively. The area under the curve (AUC) of TBF of enhanced lesion and related edema used to differentiate residual/recurrent gliomas from postradiation changes were 0.95 and 0.93 and of MD were 0.95 and 0.81 and of FA were 0.81 and 0.695, respectively. Combined ASL and DTI metrics of the enhanced lesion revealed AUC of 0.98, accuracy of 95%, sensitivity of 93.8%, specificity of 95.8%, positive predictive value (PPV) of 93.8%, and negative predictive value (NPV) of 95.8%. Combined metrics of ASL and DTI of related edema revealed AUC of 0.97, accuracy of 92.5%, sensitivity of 93.8%, specificity of 91.7%, PPV of 88.2%, and NPV of 95.7.

Conclusion: Combined ASL and DTI metrics of enhanced lesion and related edema are valuable noninvasive tools in differentiating residual/recurrent gliomas from postradiation changes.

Citing Articles

Impact of Sex Hormones on Glioblastoma: Sex-Related Differences and Neuroradiological Insights.

Rossi J, Zedde M, Napoli M, Pascarella R, Pisanello A, Biagini G Life (Basel). 2025; 14(12.

PMID: 39768232 PMC: 11677825. DOI: 10.3390/life14121523.


Machine Learning and Radiomics in Gliomas.

Cepeda S Adv Exp Med Biol. 2024; 1462:231-243.

PMID: 39523269 DOI: 10.1007/978-3-031-64892-2_14.


White matter correlates of impulsive behavior in healthy individuals: A diffusion magnetic resonance imaging study.

Rashidi F, Parsaei M, Kiani I, Sadri A, Aarabi M, Darijani S PCN Rep. 2024; 3(4):e70018.

PMID: 39420963 PMC: 11483545. DOI: 10.1002/pcn5.70018.


The complementary role of MRI and FET PET in high-grade gliomas to differentiate recurrence from radionecrosis.

Sahu A, Mathew R, Ashtekar R, Dasgupta A, Puranik A, Mahajan A Front Nucl Med. 2024; 3:1040998.

PMID: 39355021 PMC: 11440952. DOI: 10.3389/fnume.2023.1040998.


The Differentiation between Progressive Disease and Treatment-Induced Effects with Perfusion-Weighted Arterial Spin-Labeling in High-Grade Gliomas.

Flies C, Snijders T, De Leeuw B, van Maren E, Kersten B, Verhoeff J AJNR Am J Neuroradiol. 2024; 45(7):920-926.

PMID: 38871374 PMC: 11286024. DOI: 10.3174/ajnr.A8336.


References
1.
Hope T, Vardal J, Bjornerud A, Larsson C, Arnesen M, Salo R . Serial diffusion tensor imaging for early detection of radiation-induced injuries to normal-appearing white matter in high-grade glioma patients. J Magn Reson Imaging. 2014; 41(2):414-23. DOI: 10.1002/jmri.24533. View

2.
Xu J, Li Y, Lian J, Dou S, Yan F, Wu H . Distinction between postoperative recurrent glioma and radiation injury using MR diffusion tensor imaging. Neuroradiology. 2010; 52(12):1193-9. DOI: 10.1007/s00234-010-0731-4. View

3.
Telles B, DAmore F, Lerner A, Law M, Shiroishi M . Imaging of the Posttherapeutic Brain. Top Magn Reson Imaging. 2015; 24(3):147-54. DOI: 10.1097/RMR.0000000000000051. View

4.
Tensaouti F, Khalifa J, Lusque A, Plas B, Lotterie J, Berry I . Response Assessment in Neuro-Oncology criteria, contrast enhancement and perfusion MRI for assessing progression in glioblastoma. Neuroradiology. 2017; 59(10):1013-1020. DOI: 10.1007/s00234-017-1899-7. View

5.
Fink J, Carr R, Matsusue E, Iyer R, Rockhill J, Haynor D . Comparison of 3 Tesla proton MR spectroscopy, MR perfusion and MR diffusion for distinguishing glioma recurrence from posttreatment effects. J Magn Reson Imaging. 2011; 35(1):56-63. DOI: 10.1002/jmri.22801. View