Integrating quantitative DCE-MRI parameters and radiomic features for improved IDH mutation prediction in gliomas.

ABSTRACT:

Objectives

To develop and validate a multiparametric prognostic model, incorporating dynamic contrast-enhanced (DCE) quantitative parameters and multi-modality radiomic features, for the accurate identification of isocitrate dehydrogenase 1 (IDH1) mutation status from glioma patients.

Methods

A total of 152 glioma patient data with confirmed IDH1 mutation status were retrospectively collected. A segmentation neural network was used to measure MRI quantitative parameters compared with the empirically oriented ROI selection. Radiomic features, extracted from conventional MR images (T1CE, T2W, and ADC), and DCE quantitative parameter images were combined with MRI quantitative parameters in our research to predict IDH1 mutation status. We constructed and analyzed Clinical Models

SUBMITTER: Ye M 

PROVIDER: S-EPMC11932857 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature