Clinical application of machine-based deep learning in patients with radiologically presumed adult-type diffuse glioma grades 2 or 3.

Background: Radiologically presumed diffuse lower-grade glioma (dLGG) are typically non or minimal enhancing tumors, with hyperintensity in T2w-images. The aim of this study was to test the clinical usefulness of deep learning (DL) in mutation prediction in patients with radiologically presumed dLGG.

Methods: Three hundred and fourteen patients were retrospectively recruited from 6 neurosurgical departments in Sweden, Norway, France, Austria, and the United States.

Stemness and clinical features in relation to the subventricular zone in diffuse lower-grade glioma: an exploratory study.

Background: The subventricular zone (SVZ) of the human brain is a site of adult stem cell proliferation and a microenvironment for neural stem cells (NSCs). It has been suggested that NSCs in the SVZ are potential cells of origin containing driver mutations of glioblastoma, but their role in the origin of diffuse lower-grade gliomas (dLGGs) is not much studied.

Methods: We included 188 patients ≥18 years with -mutated dLGG (WHO grades 2-3) histologically diagnosed between 2007 and 2020.

Lower-Grade Gliomas: An Epidemiological Voxel-Based Analysis of Location and Proximity to Eloquent Regions.

Background: Glioma is the most common intra-axial tumor, and its location relative to critical areas of the brain is important for treatment decision-making. Studies often report tumor location based on anatomical taxonomy alone since the estimation of eloquent regions requires considerable knowledge of functional neuroanatomy and is, to some degree, a subjective measure. An unbiased and reproducible method to determine tumor location and eloquence is desirable, both for clinical use and for research purposes.