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Case Reports
. 2017 Sep 7:8:223.
doi: 10.4103/sni.sni_166_17. eCollection 2017.

Impact of the revised WHO classification of diffuse low-grade glioma on clinical decision making: A case report

Tim A M Bouwens van der Vlis  1 , Ann Hoeben  2 , Jan C Beckervordersandforth  3 , Linda Ackermans  1 , Daniëlle B P Eekers  4 , Rianne M J Wennekes  5 , Olaf E M G Schijns  1
Affiliations
Case Reports

Impact of the revised WHO classification of diffuse low-grade glioma on clinical decision making: A case report

Tim A M Bouwens van der Vlis et al. Surg Neurol Int. .

Abstract

Background: In the 2016 update of the World Health Organization Classification of Tumors of the central nervous system, phenotypic and genotypic parameters are integrated in diffuse low-grade glioma (LGG) tumor classification. Implementation of this combined phenotypic-genotypic characterization identifies prognostic relevant subgroups.

Case description: We report a case of a 67-year-old patient with an LGG that showed molecular characteristics similar to glioblastoma multiforme (GBM). After gross total tumor resection, the patient received combination therapy (radiotherapy and chemotherapy) according to high-grade glioma treatment protocol.

Conclusion: The introduction of molecular parameters to the classification of LGG will add a level of objectivity, which will yield biological homogeneous subclasses. Consequently, this will influence patient counseling and clinical decision making regarding treatment protocols.

Keywords: Low-grade glioma; WHO classification; molecular characteristics; treatment.

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Conflict of interest statement

There are no conflicts of interest.

Figures

Figure 1
Figure 1
Preoperative MRI. (a and b) T1-weighted image without and with gadolinium. (c) T2-weighted image. (d) T2-FLAIR
Figure 2
Figure 2
Post-operative MRI. (a and b) T1-weighted image without and with gadolinium. (c) T2-weighted image, (d) T2-FLAIR, (e) Diffusion-weighted imaging
Figure 3
Figure 3
Histology staining. (a) Hematoxylin and eosin, ×100. (b) Glial fibrillary acidic protein (GFAP), ×100. (c) Ki-67 (mib1), ×100. (d) Neuronal nuclei (NeuN), ×100
Figure 4
Figure 4
WHO 2016 simplified algorithm for classification of LGG, adapted to Brat et al. (2015)
See this image and copyright information in PMC

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