Machine-learning based radiogenomics analysis of MRI features and metagenes in glioblastoma multiforme patients with different survival time.

ABSTRACT:

Background

This study aimed to examine multi-dimensional MRI features' predictability on survival outcome and associations with differentially expressed Genes (RNA Sequencing) in groups of glioblastoma multiforme (GBM) patients.

Methods

Radiomics features were extracted from segmented lesions of T2-FLAIR MRI data of 137 GBM patients. Radiomics features include intensity, shape and textural features in seven classes were included in the analysis. Patients were divided into two groups depending on their survival time (shorter or longer than 1-year survival). Four different machine learning algorithms were implemented to construct the prediction models. Features with top importance (importance >0.04) were selected to construct the prediction model using the model with the best

SUBMITTER: Liao X 

PROVIDER: S-EPMC6533509 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature