Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients.

ABSTRACT: Gliomas can be classified into five molecular groups based on the status of IDH mutation, 1p/19q codeletion, and TERT promoter mutation, whereas they need to be obtained by biopsy or surgery. Thus, we aimed to use MRI-based radiomics to noninvasively predict the molecular groups and assess their prognostic value. We retrospectively identified 357 patients with gliomas and extracted radiomic features from their preoperative MRI images. Single-layered radiomic signatures were generated using a single MR sequence using Bayesian-regularization neural networks. Image fusion models were built by combing the significant radiomic signatures. By separately predicting the molecular markers, the predictive molecular groups were obtained. Prognostic nomograms were developed based on the predictive mol

SUBMITTER: Yan J 

PROVIDER: S-EPMC8313682 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature