Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study.
Publication/Presentation Date
5-15-2025
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 with HRs between subgroups I-II and I-III of 1.62 (95% CI: 1.43-1.84, P < .001) and 3.48 (95% CI: 2.94-4.11, P < .001), respectively. Analysis of imaging features revealed several tumor properties contributing unique prognostic value, supporting the feasibility of a generalizable prognostic classification system in a diverse cohort.
CONCLUSIONS: Our ML model demonstrates extensive reproducibility and online accessibility, utilizing routine imaging data rather than complex imaging protocols. This platform offers a unique approach to personalized patient management and clinical trial stratification in GBM.
Volume
27
Issue
4
First Page
1102
Last Page
1115
ISSN
1523-5866
Published In/Presented At
Akbari, H., Bakas, S., Sako, C., Fathi Kazerooni, A., Villanueva-Meyer, J., Garcia, J. A., Mamourian, E., Liu, F., Cao, Q., Shinohara, R. T., Baid, U., Getka, A., Pati, S., Singh, A., Calabrese, E., Chang, S., Rudie, J., Sotiras, A., LaMontagne, P., Marcus, D. S., … ReSPOND consortium (2025). Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study. Neuro-oncology, 27(4), 1102–1115. https://doi.org/10.1093/neuonc/noae260
Disciplines
Business Administration, Management, and Operations | Health and Medical Administration | Management Sciences and Quantitative Methods
PubMedID
39665363
Department(s)
Administration and Leadership
Document Type
Article