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The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary.

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TLDR
The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor and is hoped that it will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.
Abstract
The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor. For the first time, the WHO classification of CNS tumors uses molecular parameters in addition to histology to define many tumor entities, thus formulating a concept for how CNS tumor diagnoses should be structured in the molecular era. As such, the 2016 CNS WHO presents major restructuring of the diffuse gliomas, medulloblastomas and other embryonal tumors, and incorporates new entities that are defined by both histology and molecular features, including glioblastoma, IDH-wildtype and glioblastoma, IDH-mutant; diffuse midline glioma, H3 K27M-mutant; RELA fusion-positive ependymoma; medulloblastoma, WNT-activated and medulloblastoma, SHH-activated; and embryonal tumour with multilayered rosettes, C19MC-altered. The 2016 edition has added newly recognized neoplasms, and has deleted some entities, variants and patterns that no longer have diagnostic and/or biological relevance. Other notable changes include the addition of brain invasion as a criterion for atypical meningioma and the introduction of a soft tissue-type grading system for the now combined entity of solitary fibrous tumor / hemangiopericytoma-a departure from the manner by which other CNS tumors are graded. Overall, it is hoped that the 2016 CNS WHO will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.

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Multigene signature for predicting prognosis of patients with 1p19q co-deletion diffuse glioma

TL;DR: A 35-gene signature was identified that identifies high-risk and low-risk categories of 1p/19q positive glioma patients and provided a stepping stone towards risk stratification.
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A Review on Tumor-Treating Fields (TTFields): Clinical Implications Inferred From Computational Modeling

TL;DR: Computational approaches used to characterize TTFields are reviewed, outlining the potential clinical value inferred from computational modeling in the human head and of the microscopic field distribution in tumor cells.
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State of the Art: Machine Learning Applications in Glioma Imaging.

TL;DR: Recent work using machine learning in brain tumor imaging, specifically segmentation and MRI radiomics of gliomas is reviewed, highlighting the challenges of these techniques as well as the future potential in clinical diagnostics, prognostics, and decision making.
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Computer-aided grading of gliomas based on local and global MRI features

TL;DR: A computer-aided diagnosis (CAD) system based on quantitative magnetic resonance imaging (MRI) features was developed to evaluate the malignancy of diffuse gliomas, which are central nervous system tumors.
Journal ArticleDOI

The classification of gliomas based on a Pyramid dilated convolution resnet model

TL;DR: A deep learning convolutional neural network ResNet based on the pyramid dilated convolution for Gliomas classification is proposed, integrated into the bottom of Resnet to increase the receptive field of the original network and improve the classification accuracy.
References
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Journal ArticleDOI

Comprehensive, Integrative Genomic Analysis of Diffuse Lower-Grade Gliomas.

Daniel J. Brat, +306 more
TL;DR: The integration of genomewide data from multiple platforms delineated three molecular classes of lower-grade gliomas that were more concordant with IDH, 1p/19q, and TP53 status than with histologic class.
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Somatic histone H3 alterations in pediatric diffuse intrinsic pontine gliomas and non-brainstem glioblastomas

TL;DR: To identify somatic mutations in pediatric diffuse intrinsic pontine glioma (DIPG), whole-genome sequencing of DNA from seven DIPGs and matched germline tissue and targeted sequencing of an additional 43 DIPG and 36 non-brainstem pediatric glioblastomas (non-BS-PGs) were performed.
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Population-based studies on incidence, survival rates, and genetic alterations in astrocytic and oligodendroglial gliomas.

TL;DR: Data is summarized on incidence rates, survival, and genetic alterations from population-based studies of astrocytic and oligodendrogliomas that were carried out in the Canton of Zurich, Switzerland to suggest that the acquisition of TP53 mutations in these glioblastoma subtypes may occur through different mechanisms.
Journal ArticleDOI

The Definition of Primary and Secondary Glioblastoma

TL;DR: IDH1 mutations are the earliest detectable genetic alteration in precursor low-grade diffuse astrocytomas and in oligodendrogliomas, indicating that these tumors are derived from neural precursor cells that differ from those of primary glioblastomas.
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