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]).

Building consensus on clinical outcome assessments for BCI devices. A summary of the 10th BCI society meeting 2023 workshop.

: The 10th International brain computer interface (BCI) Society Meeting, 'Balancing Innovation and Translation', was held from the 6th to 9th of June 2023 in Brussels, Belgium. This report provides a summary of the workshop ''. This workshop was intended to give participants an overview of the current state of BCI, future opportunities, and how different countries and regions provide regulatory oversight to support the BCI community to develop safe and effective devices for patients.

Sex Difference in Disease-Related Adverse Events Post-Diagnosis of Lung Cancer Brain Metastases in Medicare Individuals ≥ 66 Years of Age.

Sex differences are evident in adverse events (AEs) related to brain tumors, yet sex differences in AEs specific to brain metastases (BrMs) are underexplored. Lung cancer BrMs dominate among BrM, comprising over half of cases. This study examined sex differences in AEs associated with lung cancer BrMs in individuals aged 66 or older using the SEER-Medicare dataset.