MRI radiomics based on machine learning in high-grade gliomas as a promising tool for prediction of CD44 expression and overall survival.

ABSTRACT: We aimed to predict CD44 expression and assess its prognostic significance in patients with high-grade gliomas (HGG) using non-invasive radiomics models based on machine learning. Enhanced magnetic resonance imaging, along with the corresponding gene expression and clinicopathological data, was downloaded from online database. Kaplan-Meier survival curves, univariate and multivariate COX analyses, and time-dependent receiver operating characteristic were used to assess the prognostic value of CD44. Following the screening of radiomic features using repeat least absolute shrinkage and selection operator, two radiomics models were constructed utilizing logistic regression and support vector machine for validation purposes. The results indicated that CD44 protein levels were higher in HGG com

SUBMITTER: Yu M 

PROVIDER: S-EPMC11876340 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature