Insights into the molecular underpinnings of primary central nervous system tumors have radically changed the approach to tumor diagnosis and classification. Diagnostic emphasis has shifted from the morphology of a tumor under the microscope to an integrated approach based on morphologic and molecular features, including gene mutations, chromosomal copy number alterations, and gene rearrangements. In 2016, the World Health Organization provided guidelines for making an integrated diagnosis that incorporates both morphologic and molecular features in a subset of brain tumors. The integrated diagnosis now applies to infiltrating gliomas, a category that includes diffusely infiltrating astrocytoma grades II, III, and IV, and oligodendroglioma, grades II and III, thereby encompassing the most common primary intra-axial central nervous system tumors. Other neoplasms such as medulloblastoma, embryonal tumor with multilayered rosettes, certain supratentorial ependymomas, and atypical teratoid/rhabdoid tumor are also eligible for integrated diagnosis, which can sometimes be aided by characteristic immunohistochemical markers. Since 2016, advances in molecular neuro-oncology have resulted in periodic updates and clarifications to the integrated diagnostic approach. These advances reflect expanding knowledge on the molecular pathology of brain tumors, but raise a challenge in rapidly incorporating new molecular findings into diagnostic practice. This review provides a background on the molecular characteristics of primary brain tumors, emphasizing the molecular basis for classification of infiltrating gliomas, the most common entities that are eligible for an integrated diagnosis. We then discuss entities within the diffuse gliomas that do not receive an integrated diagnosis by WHO 2016 criteria, but have distinctive molecular features that are important to recognize because their clinical behavior can influence clinical management and prognosis. Particular attention is given to the histone H3 G34R/G34V mutant astrocytomas, an entity to consider when faced with an infiltrating glioma in the cerebral hemisphere of children and young adults, and to the group of histologically lower grade diffuse astrocytic gliomas with molecular features of glioblastoma, an important category of tumors to recognize due to their aggressive clinical behavior.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6457044 | PMC |
http://dx.doi.org/10.1186/s13000-019-0802-8 | DOI Listing |
Nat Med
January 2025
Department of Neurology & Neurological Sciences, Stanford Movement Disorders Center, Stanford University, Stanford, CA, USA.
Cerebral accumulation of alpha-synuclein (αSyn) aggregates is the hallmark event in a group of neurodegenerative diseases-collectively called synucleinopathies-which include Parkinson's disease, dementia with Lewy bodies and multiple system atrophy. Currently, these are diagnosed by their clinical symptoms and definitively confirmed postmortem by the presence of αSyn deposits in the brain. Here, we summarize the drawbacks of the current clinical definition of synucleinopathies and outline the rationale for moving toward an earlier, biology-anchored definition of these disorders, with or without the presence of clinical symptoms.
View Article and Find Full Text PDFSci Rep
January 2025
Medical Genome Center, Research Institute, National Center for Geriatrics and Gerontology, 7-430 Morioka-cho, Obu, 474-8511, Aichi, Japan.
The prevalence of Alzheimer's disease (AD) is increasing as society ages. The details of AD pathogenesis have not been fully elucidated, and a comprehensive gene expression analysis of the process leading up to the onset of AD would be helpful for understanding the mechanism. We performed an RNA sequencing analysis on a cohort of 1227 Japanese blood samples, representing 424 AD patients, 543 individuals with mild cognitive impairment (MCI), and 260 cognitively normal (CN) individuals.
View Article and Find Full Text PDFThe classification of chronic diseases has long been a prominent research focus in the field of public health, with widespread application of machine learning algorithms. Diabetes is one of the chronic diseases with a high prevalence worldwide and is considered a disease in its own right. Given the widespread nature of this chronic condition, numerous researchers are striving to develop robust machine learning algorithms for accurate classification.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
LMFE, Faculty of Sciences Semlalia, Cadi Ayyad University, 40000, Marrakesh, Morocco.
In the last decades, natural and anthropogenic pressures have caused observable changes in the argan landscape despite its significance in Morocco. Remote sensing data can be used to monitor these changes over time and provide information on vegetation health and land cover changes. This study assesses the performance of supervised methods (support vector machine, maximum likelihood, and minimum distance) and unsupervised classification method (Isodata) for mapping the argan forest in the Smimou area of Essaouira province using remote sensing data from Landsat-5 and Landsat-8 (1985 and 2019).
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Charles Nicolle Hospital, Tunis El Manar University, Tunis, Tunisia.
Traumatic brain injuries present significant diagnostic challenges in emergency medicine, where the timely interpretation of medical images is crucial for patient outcomes. In this paper, we propose a novel AI-based approach for automatic radiology report generation tailored to cranial trauma cases. Our model integrates an AC-BiFPN with a Transformer architecture to capture and process complex medical imaging data such as CT and MRI scans.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!