Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study.

ABSTRACT:

Background

Glioblastoma (GBM) 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, magnetic resonance imaging (MRI), and molecular measures from 2838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, and III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]).

Results

The ML model stratified patients into distinct prognostic subgroups

SUBMITTER: Akbari H 

PROVIDER: S-EPMC12083074 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature