Machine Learning-Based Prognosis Prediction in Glioblastoma Multiforme Patients by Integrating Clinical Data with Multimodal Radiomics

<b>Objectives</b>: Glioblastoma multiforme (GBM) is considered the most aggressive primary brain tumor, which often exhibits tumor heterogeneity. Hypoxia is a key aspect of intratumoral heterogeneity that contributes to poor prognosis in GBM. In this study, we aimed to develop machine learning (ML)...

Olles dieđut

Bibliográfalaš dieđut

Váldodahkkit:	Mohan Huang, Man Kiu Chan, Ka Lung Cheng, Pak Yuen Hui, Shing Yau Tam
Materiálatiipa:	Artigo
Giella:	Inglês
Almmustuhtton:	MDPI AG 2026-02-01
Ráidu:	Diagnostics
Fáttát:	glioblastoma multiforme hypoxia machine learning multimodal imaging prognostic model radiomics
Liŋkkat:	https://www.mdpi.com/2075-4418/16/4/512
Fáddágilkorat:	Lasit fáddágilkoriid Eai fáddágilkorat, Lasit vuosttaš fáddágilkora!