Interpretable multimodal transformer for prediction of molecular subtypes and grades in adult-type diffuse gliomas.

ABSTRACT: Molecular subtyping and grading of adult-type diffuse gliomas are essential for treatment decisions and patient prognosis. We introduce GlioMT, an interpretable multimodal transformer that integrates imaging and clinical data to predict the molecular subtype and grade of adult-type diffuse gliomas according to the 2021 WHO classification. GlioMT is trained on multiparametric MRI data from an institutional set of 1053 patients with adult-type diffuse gliomas to predict the IDH mutation status, 1p/19q codeletion status, and tumor grade. External validation on the TCGA (200 patients) and UCSF (477 patients) shows that GlioMT outperforms conventional CNNs and visual transformers, achieving AUCs of 0.915 (TCGA) and 0.981 (UCSF) for IDH mutation, 0.854 (TCGA) and 0.806 (UCSF) for 1p/19q codeleti

SUBMITTER: Byeon Y 

PROVIDER: S-EPMC11883078 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature