Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00023884%3A_____%2F25%3A00010164" target="_blank" >RIV/00023884:_____/25:00010164 - isvavai.cz</a>
Result on the web
<a href="https://academic.oup.com/neuro-oncology/article/27/4/1102/7922273?login=true" target="_blank" >https://academic.oup.com/neuro-oncology/article/27/4/1102/7922273?login=true</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1093/neuonc/noae260" target="_blank" >10.1093/neuonc/noae260</a>
Alternative languages
Result language
angličtina
Original language name
Machine learning-based prognostic subgrouping of glioblastoma: A multicenter study
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30103 - Neurosciences (including psychophysiology)
Result continuities
Project
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Continuities
N - Vyzkumna aktivita podporovana z neverejnych zdroju
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neuro-oncology
ISSN
1522-8517
e-ISSN
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Volume of the periodical
27
Issue of the periodical within the volume
4
Country of publishing house
GB - UNITED KINGDOM
Number of pages
14
Pages from-to
1102-1115
UT code for WoS article
001439562400001
EID of the result in the Scopus database
2-s2.0-105005266476