Multiparametric Radiogenomic Model to Predict Survival in Patients with Glioblastoma.

ABSTRACT:

Background

Clinical, histopathological, and imaging variables have been associated with prognosis in patients with glioblastoma (GBM). We aimed to develop a multiparametric radiogenomic model incorporating MRI texture features, demographic data, and histopathological tumor biomarkers to predict prognosis in patients with GBM.

Methods

In this retrospective study, patients were included if they had confirmed diagnosis of GBM with histopathological biomarkers and pre-operative MRI. Tumor segmentation was performed, and texture features were extracted to develop a predictive radiomic model of survival (<18 months vs. ≥18 months) using multivariate analysis and Least Absolute Shrinkage and Selection Operator (LASSO) regularization to reduce the risk of overfitting. This radiomic

SUBMITTER: Mahmoudi K 

PROVIDER: S-EPMC10854536 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature