Glioblastoma Differential Diagnosis [Neurosurgery Wiki]

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====== Glioblastoma Differential Diagnosis ======

[[Tumor]]s are classically distinguished based on [[biopsy]] of the tumor itself, as well as a radiological interpretation using diverse [[MRI]] modalities. 
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As its historical name glioblastoma multiforme implies, [[glioblastoma]] is a histologically diverse, World Health Organization grade IV astrocytic neoplasm. In spite of its simple definition of presence of vascular proliferation and/or necrosis in a [[diffuse astrocytoma]], the wide variety of cytohistomorphologic appearances overlap with many other neoplastic or non-neoplastic lesions
((Gokden M. If it is Not a Glioblastoma, Then What is it? A Differential Diagnostic Review. Adv Anat Pathol. 2017 Nov;24(6):379-391. doi: 10.1097/PAP.0000000000000170. PMID: 28885262.)).
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General imaging differential considerations include:

[[Intracranial metastases]]

may look identical

both may appear multifocal

[[metastases]] usually are centered on grey-white matter junction and spare the overlying cortex
rCBV in the '[[edema]]' will be reduced 


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[[Cerebral abscess]]
central restricted [[diffusion]] is helpful, however, if Glioblastoma is hemorrhagic then the assessment may be difficult 
presence of smooth and complete SWI low-intensity rim presence of dual rim sign 
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[[Anaplastic astrocytoma]]
should not have central necrosis
consider histology sampling bias 
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[[Tumefactive demyelination lesion]]
can appear similar
often has an open ring pattern of enhancement
usually younger patients
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Subacute [[cerebral infarction]]
history is essential in suggesting the diagnosis
should not have elevated [[choline]]
should not have elevated [[rCBV]]
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[[Cerebral toxoplasmosis]]
especially in patients with [[AIDS]]
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In a [[study]], Samani et al. of the overarching goal are to demonstrate that [[primary glioblastoma]]s and secondary ([[brain metastases]]) malignancies can be differentiated based on the microstructure of the [[peritumoral region]]. This is achieved by exploiting the extracellular water differences between [[vasogenic edema]] and infiltrative tissue and training a [[convolutional neural network]] (CNN) on the [[Diffusion Tensor Imaging]] (DTI)-derived free water volume fraction. They obtained 85% accuracy in discriminating [[extracellular]] water differences between local patches in the peritumoral area of 66 glioblastomas and 40 metastatic patients in a cross-validation setting. On an independent test cohort consisting of 20 glioblastomas and 10 metastases, we got 93% accuracy in discriminating metastases from glioblastomas using majority voting on patches. This level of accuracy surpasses CNNs trained on other conventional DTI-based measures such as [[fractional anisotropy]] (FA) and [[mean diffusivity]] (MD), which have been used in other studies. Additionally, the CNN captures the peritumoral heterogeneity better than conventional texture features, including [[Gabor filter]] and [[radiomic]] features. The results demonstrate that the extracellular water content of the [[peritumoral]] [[tissue]], as captured by the free water volume fraction, is best able to characterize the differences between infiltrative and vasogenic peritumoral regions, paving the way for its use in classifying and benchmarking peritumoral tissue with varying degrees of infiltration
((Samani ZR, Parker D, Wolf R, Hodges W, Brem S, Verma R. Distinct tumor signatures using deep learning-based characterization of the peritumoral microenvironment in glioblastomas and brain metastases. Sci Rep. 2021 Jul 14;11(1):14469. doi: 10.1038/s41598-021-93804-6. PMID: 34262079.)).




===== Immunodeficiency-associated CNS lymphoma =====


[[Immunodeficiency-associated CNS lymphoma]]