Machine Learning-based Prognostic Subgrouping of Glioblastoma: A Multi-center Study.

Background: Glioblastoma is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification.

Methods: We developed a highly reproducible, personalized prognostication and clinical subgrouping system using machine learning (ML) on routine clinical data, MRI, and molecular measures from 2,838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]).

A Taxometric Analysis and External Validation of the Latent Structure of Student Risks and Needs.

The present study applied taxometric analyses to the Washington Assessment of Risks and Needs of Students (WARNS)-an instrument designed to assess multiple domains of functioning related to justice system involvement arising from school disengagement-a trajectory referred to as . Previous taxometric studies of constructs related to juvenile justice system involvement found dimensional rather than taxonic (dichotomous) latent structures. Participants were 5008 students from 89 Washington school districts who completed the WARNS as part of standard educational practices.

Clinical Performance of Semi-Automated Spectral-Domain Optical Coherence Tomography Angiography.

: To evaluate the clinical performance of two optical coherence tomography angiography (OCTA) devices, including a semi-automated device, with respect to image quality and pathology detection, with fluorescein angiography (FA) and indocyanine green angiography (ICGA) serving as the reference standards. : In this prospective cross-sectional study, normal eyes and those with various retinal and choroidal pathologies were enrolled and underwent OCTA scanning using semi-automated 3D OCT-1 Maestro2 and Cirrus™ HD-OCT 5000 devices, as well as FA/ICGA imaging. OCTA scans and FA/ICGA images were independently graded for image quality and the visibility of prespecified anatomic vascular features, along with the presence or absence of pathology on the OCTA scans and the FA/ICGA images (within regions corresponding to the OCTA scan areas).

Relationship between MRI brain-age heterogeneity, cognition, genetics and Alzheimer's disease neuropathology.

Background: Brain ageing is highly heterogeneous, as it is driven by a variety of normal and neuropathological processes. These processes may differentially affect structural and functional brain ageing across individuals, with more pronounced ageing (older brain age) during midlife being indicative of later development of dementia. Here, we examined whether brain-ageing heterogeneity in unimpaired older adults related to neurodegeneration, different cognitive trajectories, genetic and amyloid-beta (Aβ) profiles, and to predicted progression to Alzheimer's disease (AD).