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

By D. Louis, A. Perry, Guido Reifenberger, A. Deimling, D. Figarella-Branger, W. Cavenee, H. Ohgaki, O. Wiestler, P. Kleihues, D. Ellison. Acta Neuropathologica, 2016

14,109 citations recorded by the source corpus.

Subfields: Epidemiology, Genetics, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/9a6db7898bea7fe7847c1cff0faaff7dc529878e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

1. Pierre Baldi
2. Arvid Lundervold

In Genetics

1. Pierre Baldi
2. Suzanne J. Baker
3. Daniel Rueckert
4. Christos Davatzikos
5. Marco A. Marra
6. Thomas Schaffter
7. Steven J. M. Jones
8. Arvid Lundervold

In Pulmonary and Respiratory Medicine

1. Daniel Rueckert
2. Christos Davatzikos
3. Russell T. Shinohara

Citations in the corpus

• Residual Convolutional Neural Network for Determination of IDH Status in Low- and High-grade Gliomas from MR Imaging (2017, methodology): “Increasing research and the updated 2016 WHO classification of CNS tumors further emphasize molecular phenotype as a critical determinant of glioma behavior even before the assignment of histopathologic grade (13).”
• The whole-genome landscape of medulloblastoma subtypes (2017, positive): “The relatively recent recognition of consensus MB subgroups has rapidly changed the way MB is studied in the research setting and how it is diagnosed and treated in the clinic 39 .”
• Chinese Glioma Genome Atlas (CGGA): A Comprehensive Resource with Functional Genomic Data from Chinese Glioma Patients (2021, background): “, IDH (IDH1 and IDH2) mutation and chromosome 1p/19q codeletion status [6].”
• Glioma Subclassifications and Their Clinical Significance (2017, methodology): “Reprinted with permission from theWorld Health Organization [10] Glioma Subclassifications and Their Clinical Significance”
• MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers (2021, background): “In the human body, the brain is an enormous and complex organ that controls the whole nervous system, and it contains around 100-billion nerve cells [1].”
• Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network (2020, background): “Pituitary tumors, even if benign, can cause other medical damage, unlike meningiomas, which are slow-growing tumors [5,6].”
• Accurate brain tumor detection using deep convolutional neural network (2022, background): “Among these three, meningioma tumors are perhaps the most prominent primary brain tumor in the meninges and affect the brain and spinal cord [2].”
• Multimodal Co-Attention Transformer for Survival Prediction in Gigapixel Whole Slide Images (2021, background): “In the current state-of-the-art, the manual assessment of histology and genomics by pathologists is the gold standard for patient triage, risk assessment, stratification into treatment groups [33].”
• Algorithm fairness in artificial intelligence for medicine and healthcare (2023, background): “…of diffuse gliomas to include molecular subtyping, the ongoing refinement of American College of Cardiology/American Heart Association guidelines for defining hypertension severity and the 17 varying diagnostic criteria for Behcet’s disease that have been proposed around the world 97,190–192 .”
• An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics (2018, methodology): “Indeed, the clinical and molecular work of this paper factored into evidence used by the World Health Organization (WHO) to support their 2016 diagnostic update for glioma (Louis et al., 2016).”
• Mining TCGA database for genes of prognostic value in glioblastoma microenvironment (2018, neutral): “divided GBM into three subtypes: (1) IDH-wildtype, (2) IDH-mutant, and (3) NOS (not otherwise specified) [9].”
• The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification (2021, background): “The release of the current revised World Health Organization (WHO) clas-sification of CNS tumors [9] highlighted the appreciation of integrated diagnostics, and transitioned the clinical tumor diagnosis from a purely morphologic-histopathologic classification to integrating molecular-cytogenetic…”
• Artificial Intelligence and Digital Pathology: Challenges and Opportunities (2018, neutral): “This extreme polymorphism makes recognizing tissues by image algorithms exceptionally challenging.[16,17] Thus, the inherent architecture of deep AI requires many training cases for each variation.”