Literature DB >> 29028423

2016 Updates to the WHO Brain Tumor Classification System: What the Radiologist Needs to Know.

Derek R Johnson1, Julie B Guerin1, Caterina Giannini1, Jonathan M Morris1, Lawrence J Eckel1, Timothy J Kaufmann1.   

Abstract

Radiologists play a key role in brain tumor diagnosis and management and must stay abreast of developments in the field to advance patient care and communicate with other health care providers. In 2016, the World Health Organization (WHO) released an update to its brain tumor classification system that included numerous significant changes. Several previously recognized brain tumor diagnoses, such as oligoastrocytoma, primitive neuroectodermal tumor, and gliomatosis cerebri, were redefined or eliminated altogether. Conversely, multiple new entities were recognized, including diffuse leptomeningeal glioneuronal tumor and multinodular and vacuolating tumor of the cerebrum. The glioma category has been significantly reorganized, with several infiltrating gliomas in children and adults now defined by genetic features for the first time. These changes were driven by increased understanding of important genetic factors that directly impact tumorigenesis and influence patient care. The increased emphasis on genetic factors in brain tumor diagnosis has important implications for radiology, as we now have tools that allow us to evaluate some of these alterations directly, such as the identification of 2-hydroxyglutarate within infiltrating gliomas harboring mutations in the genes for the isocitrate dehydrogenases. For other tumors, such as medulloblastoma, imaging can demonstrate characteristic patterns that correlate with particular disease subtypes. The purpose of this article is to review the changes to the WHO brain tumor classification system that are most pertinent to radiologists. ©RSNA, 2017.

Entities:  

Mesh:

Year:  2017        PMID: 29028423     DOI: 10.1148/rg.2017170037

Source DB:  PubMed          Journal:  Radiographics        ISSN: 0271-5333            Impact factor:   5.333


  28 in total

1.  Unusual radiological and histological presentation of a diffuse leptomeningeal glioneuronal tumor (DLGNT) in a 13-year-old girl.

Authors:  Nishant Tiwari; Benita Tamrazi; Nathan Robison; Mark Krieger; Jianling Ji; Di Tian
Journal:  Childs Nerv Syst       Date:  2019-02-15       Impact factor: 1.475

Review 2.  Magnetic resonance imaging of the brainstem in children, part 2: acquired pathology of the pediatric brainstem.

Authors:  Asha Sarma; Josh M Heck; Aashim Bhatia; Rekha S Krishnasarma; Sumit Pruthi
Journal:  Pediatr Radiol       Date:  2021-01-19

Review 3.  Neuro-Oncology Practice Clinical Debate: Early treatment or observation for patients with newly diagnosed oligodendroglioma and small-volume residual disease.

Authors:  Shannon E Fogh; Lauren Boreta; Jean L Nakamura; Derek R Johnson; Andrew S Chi; Sylvia C Kurz
Journal:  Neurooncol Pract       Date:  2020-06-27

Review 4.  Central nervous system ependymoma: clinical implications of the new molecular classification, treatment guidelines and controversial issues.

Authors:  P D Delgado-López; E M Corrales-García; E Alonso-García; R García-Leal; R González-Rodrigálvarez; E Araus-Galdós; J Martín-Alonso
Journal:  Clin Transl Oncol       Date:  2019-03-13       Impact factor: 3.405

5.  Neuroimaging Appearance of Cerebral Malignant Epithelioid Glioneuronal Tumors in Children.

Authors:  G Orman; S Mohammed; H D B Tran; F Y Lin; A Meoded; N Desai; T A G M Huisman; S F Kralik
Journal:  AJNR Am J Neuroradiol       Date:  2020-07-16       Impact factor: 3.825

6.  Deep Convolutional Radiomic Features on Diffusion Tensor Images for Classification of Glioma Grades.

Authors:  Zhiwei Zhang; Jingjing Xiao; Shandong Wu; Fajin Lv; Junwei Gong; Lin Jiang; Renqiang Yu; Tianyou Luo
Journal:  J Digit Imaging       Date:  2020-08       Impact factor: 4.056

7.  Monoexponential, biexponential and stretched exponential models of diffusion weighted magnetic resonance imaging in glioma in relation to histopathologic grade and Ki-67 labeling index using high B values.

Authors:  Nabin Chaudhary; Guiling Zhang; Shihui Li; Wenzhen Zhu
Journal:  Am J Transl Res       Date:  2021-11-15       Impact factor: 4.060

Review 8.  Challenges and opportunities for artificial intelligence in oncological imaging.

Authors:  H M C Cheung; D Rubin
Journal:  Clin Radiol       Date:  2021-04-24       Impact factor: 3.389

9.  MRI predictors for brain invasion in meningiomas.

Authors:  Thomas Ong; Aditya Bharatha; Reema Alsufayan; Sunit Das; Amy Wei Lin
Journal:  Neuroradiol J       Date:  2020-09-14

10.  Exploring diagnostic performance of T2 mapping in diffuse glioma grading.

Authors:  Weibin Gu; Shiyuan Fang; Xinyi Hou; Ding Ma; Shaowu Li
Journal:  Quant Imaging Med Surg       Date:  2021-07
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.