Glioma subtype prediction based on radiomics of tumor and peritumoral edema under automatic segmentation.

ABSTRACT: Comprehensive and non-invasive preoperative molecular diagnosis is important for prognostic and therapy decision-making in adult-type diffuse gliomas. We employed a deep learning method for automatic segmentation of brain gliomas directly from conventional magnetic resonance imaging (MRI) scans of the tumor core and peritumoral edema regions based on available glioma MRI data provided in the BraTS2021. Three-dimensional volumes of interest were segmented from 424 cases of glioma imaging data retrospectively obtained from two medical centers using the segmentation method and radiomic features were extracted. We developed a subtype prediction model based on extracted radiomic features and analyzed significance and correlations between glioma morphological characteristics and pathological fea

SUBMITTER: Sun X 

PROVIDER: S-EPMC11551193 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature