Advanced Imaging in Adult Diffusely Infiltrating Low-grade Gliomas

Overview

Journal Insights Imaging

Publisher Springer

Specialty Radiology

Date 2019 Dec 20

PMID 31853670

Citations 9

Authors

Nail Bulakbasi

Yahya Paksoy

Affiliations

Soon will be listed here.

Abstract

The adult diffusely infiltrating low-grade gliomas (LGGs) are typically IDH mutant and slow-growing gliomas having moderately increased cellularity generally without mitosis, necrosis, and microvascular proliferation. Supra-total resection of LGG significantly increases the overall survival by delaying malignant transformation compared with a simple debulking so accurate MR diagnosis is crucial for treatment planning. Data from meta-analysis support the addition of diffusion and perfusion-weighted MR imaging and MR spectroscopy in the diagnosis of suspected LGG. Typically, LGG has lower cellularity (ADC), angiogenesis (rCBV), capillary permeability (K), and mitotic activity (Cho/Cr ratio) compared to high-grade glioma. The identification of 2-hydroxyglutarate by MR spectroscopy can reflect the IDH status of the tumor. The initial low ADC, high rCBV, and K values are consistent with the poor prognosis. The gradual increase in intratumoral Cho/Cr ratio and rCBV values are well correlated with tumor progression. Besides MR-based technical artifacts, which are minimized by the voxel-based assessment of data obtained by histogram analysis, the problems derived from the diversity and the analysis of imaging data should be solved by using artificial intelligence techniques. The quantitative multiparametric MR imaging of LGG can either improve the diagnostic accuracy of their differential diagnosis or assess their prognosis.

Citing Articles

Disclosing transcriptomics network-based signatures of glioma heterogeneity using sparse methods.

Martins S, Coletti R, Lopes M BioData Min. 2023; 16(1):26.

PMID: 37752578 PMC: 10523751. DOI: 10.1186/s13040-023-00341-1.

Quantitative susceptibility mapping evaluation of glioma.

Zeng S, Ma H, Xie D, Huang Y, Wang M, Zeng W Eur Radiol. 2023; 33(10):6636-6647.

PMID: 37095360 DOI: 10.1007/s00330-023-09647-4.

Sex-specific radiomic features of L-[S-methyl-C] methionine PET in patients with newly-diagnosed gliomas in relation to IDH1 predictability.

Papp L, Rasul S, Spielvogel C, Krajnc D, Poetsch N, Woehrer A Front Oncol. 2023; 13:986788.

PMID: 36816966 PMC: 9936222. DOI: 10.3389/fonc.2023.986788.

Prognostic Factors of Low-Grade Gliomas in Adults.

Deacu M, Popescu S, Docu Axelerad A, Topliceanu T, Aschie M, Bosoteanu M Curr Oncol. 2022; 29(10):7327-7342.

PMID: 36290853 PMC: 9600247. DOI: 10.3390/curroncol29100576.

Elevated expression of mitochondrial transcription elongation factor (TEFM) predicts poor prognosis in low grade glioma-an analysis of the Cancer Genome Atlas (TCGA) dataset.

Li S, Wang W, Zi J, Sun M, Mei W, Yang N Transl Cancer Res. 2022; 9(5):3610-3622.

PMID: 35117724 PMC: 8798344. DOI: 10.21037/tcr.2020.04.16.

References

Hlaihel C, Guilloton L, Guyotat J, Streichenberger N, Honnorat J, Cotton F . Predictive value of multimodality MRI using conventional, perfusion, and spectroscopy MR in anaplastic transformation of low-grade oligodendrogliomas. J Neurooncol. 2009; 97(1):73-80. DOI: 10.1007/s11060-009-9991-4. View

Stadlbauer A, Zimmermann M, Kitzwogerer M, Oberndorfer S, Rossler K, Dorfler A . MR Imaging-derived Oxygen Metabolism and Neovascularization Characterization for Grading and IDH Gene Mutation Detection of Gliomas. Radiology. 2016; 283(3):799-809. DOI: 10.1148/radiol.2016161422. View

Kong L, Chen H, Yang Y, Chen L . A meta-analysis of arterial spin labelling perfusion values for the prediction of glioma grade. Clin Radiol. 2016; 72(3):255-261. DOI: 10.1016/j.crad.2016.10.016. View

Hasselblatt M, Jaber M, Reuss D, Grauer O, Bibo A, Terwey S . Diffuse Astrocytoma, IDH-Wildtype: A Dissolving Diagnosis. J Neuropathol Exp Neurol. 2018; 77(6):422-425. DOI: 10.1093/jnen/nly012. View

Delgado A, Delgado A . Discrimination between Glioma Grades II and III Using Dynamic Susceptibility Perfusion MRI: A Meta-Analysis. AJNR Am J Neuroradiol. 2017; 38(7):1348-1355. PMC: 7959917. DOI: 10.3174/ajnr.A5218. View

Louis D, Ohgaki H, Wiestler O, Cavenee W, Burger P, Jouvet A . The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol. 2007; 114(2):97-109. PMC: 1929165. DOI: 10.1007/s00401-007-0243-4. View

Pepin K, McGee K, Arani A, Lake D, Glaser K, Manduca A . MR Elastography Analysis of Glioma Stiffness and -Mutation Status. AJNR Am J Neuroradiol. 2017; 39(1):31-36. PMC: 5766369. DOI: 10.3174/ajnr.A5415. View

Chawla S, Krejza J, Vossough A, Zhang Y, Kapoor G, Wang S . Differentiation between oligodendroglioma genotypes using dynamic susceptibility contrast perfusion-weighted imaging and proton MR spectroscopy. AJNR Am J Neuroradiol. 2013; 34(8):1542-9. PMC: 8051455. DOI: 10.3174/ajnr.A3384. View

Nguyen T, Cron G, Mercier J, Foottit C, Torres C, Chakraborty S . Preoperative prognostic value of dynamic contrast-enhanced MRI-derived contrast transfer coefficient and plasma volume in patients with cerebral gliomas. AJNR Am J Neuroradiol. 2014; 36(1):63-9. PMC: 7965903. DOI: 10.3174/ajnr.A4006. View

10.

Law M, Oh S, Babb J, Wang E, Inglese M, Zagzag D . Low-grade gliomas: dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging--prediction of patient clinical response. Radiology. 2006; 238(2):658-67. DOI: 10.1148/radiol.2382042180. View

11.

Cuccarini V, Erbetta A, Farinotti M, Cuppini L, Ghielmetti F, Pollo B . Advanced MRI may complement histological diagnosis of lower grade gliomas and help in predicting survival. J Neurooncol. 2015; 126(2):279-88. DOI: 10.1007/s11060-015-1960-5. View

12.

Usinskiene J, Ulyte A, Bjornerud A, Venius J, Katsaros V, Rynkeviciene R . Optimal differentiation of high- and low-grade glioma and metastasis: a meta-analysis of perfusion, diffusion, and spectroscopy metrics. Neuroradiology. 2016; 58(4):339-50. DOI: 10.1007/s00234-016-1642-9. View

13.

Wu C, Jain R, Radmanesh A, Poisson L, Guo W, Zagzag D . Predicting Genotype and Survival in Glioma Using Standard Clinical MR Imaging Apparent Diffusion Coefficient Images: A Pilot Study from The Cancer Genome Atlas. AJNR Am J Neuroradiol. 2018; 39(10):1814-1820. PMC: 7410731. DOI: 10.3174/ajnr.A5794. View

14.

Bulakbasi N, Kocaoglu M, Farzaliyev A, Tayfun C, Ucoz T, Somuncu I . Assessment of diagnostic accuracy of perfusion MR imaging in primary and metastatic solitary malignant brain tumors. AJNR Am J Neuroradiol. 2005; 26(9):2187-99. PMC: 7976133. View

15.

Caulo M, Panara V, Tortora D, Mattei P, Briganti C, Pravata E . Data-driven grading of brain gliomas: a multiparametric MR imaging study. Radiology. 2014; 272(2):494-503. DOI: 10.1148/radiol.14132040. View

16.

Tietze A, Choi C, Mickey B, Maher E, Ulhoi B, Sangill R . Noninvasive assessment of isocitrate dehydrogenase mutation status in cerebral gliomas by magnetic resonance spectroscopy in a clinical setting. J Neurosurg. 2017; 128(2):391-398. DOI: 10.3171/2016.10.JNS161793. View

17.

Smits M, van den Bent M . Imaging Correlates of Adult Glioma Genotypes. Radiology. 2017; 284(2):316-331. DOI: 10.1148/radiol.2017151930. View

18.

Zhang L, Min Z, Tang M, Chen S, Lei X, Zhang X . The utility of diffusion MRI with quantitative ADC measurements for differentiating high-grade from low-grade cerebral gliomas: Evidence from a meta-analysis. J Neurol Sci. 2017; 373:9-15. DOI: 10.1016/j.jns.2016.12.008. View

19.

Castillo M, Smith J, Kwock L . Correlation of myo-inositol levels and grading of cerebral astrocytomas. AJNR Am J Neuroradiol. 2000; 21(9):1645-9. PMC: 8174883. View

20.

Miloushev V, Chow D, Filippi C . Meta-analysis of diffusion metrics for the prediction of tumor grade in gliomas. AJNR Am J Neuroradiol. 2014; 36(2):302-8. PMC: 7965672. DOI: 10.3174/ajnr.A4097. View