Time-to-event overall survival prediction in glioblastoma multiforme patients using magnetic resonance imaging radiomics.

ABSTRACT:

Purpose

Glioblastoma Multiforme (GBM) represents the predominant aggressive primary tumor of the brain with short overall survival (OS) time. We aim to assess the potential of radiomic features in predicting the time-to-event OS of patients with GBM using machine learning (ML) algorithms.

Materials and methods

One hundred nineteen patients with GBM, who had T1-weighted contrast-enhanced and T2-FLAIR MRI sequences, along with clinical data and survival time, were enrolled. Image preprocessing methods included 64 bin discretization, Laplacian of Gaussian (LOG) filters with three Sigma values and eight variations of Wavelet Transform. Images were then segmented, followed by the extraction of 1212 radiomic features. Seven feature selection (FS) methods and six time-to-event ML a

SUBMITTER: Hajianfar G 

PROVIDER: S-EPMC10700216 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature