Magnetic resonance imaging-based deep learning for predicting subtypes of glioma.

ABSTRACT:

Purpose

To explore the value of deep learning based on magnetic resonance imaging (MRI) in the classification of glioma subtypes.

Methods

This study retrospectively included 747 adult patients with surgically pathologically confirmed gliomas from a public database and 64 patients from our hospital. Patients were classified into IDH-wildtype (IDHwt) (490 cases), IDH-mutant/1p19q-noncodeleted (IDHmut-intact) (105 cases), and IDH-mutant/1p19q-codeleted (IDHmut-codel) (216 cases) based on their pathological findings, with the public database of patients were divided into training and validation sets, and patients from our hospital were used as an independent test set. The models were developed based on five categories of preoperative T1-weighted, T1-weighted gadolinium contrast-

SUBMITTER: Yang Z 

PROVIDER: S-EPMC11813795 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature