Utilizing 2D UNet with Synthetic Attention for Enhanced Classification of IDH Mutations Based on Anatomical MRI in Gliomas

Motivation: There is a need to preoperatively assess the isocitrate dehydrogenase (IDH) mutational status in gliomas, which highly affects the treatment planning and patient prognosis. Goal(s): To develop a robust deep learning pipeline for noninvasively assessing the IDH mutational status of gliomas based on anatomical MRIApproach: Post-contrast T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) MRI of 501 adult diffuse gliomas (103 IDH-mutant, 308 IDH-wildtype) of the UCSF-PGDM dataset were evaluated with a 2D UNet architecture using synthetic attention. Results: The model utilizing all three anatomical modalities achieved an accuracy of 93.31% (sensitivity=93.33%, specificity=93.24%). Impact: IDH mutational status in gliomas was identified with over 93% accuracy utilizing a 2D UNet architecture with synthetic attention for the evaluation of whole tumor slices of three standard anatomical MRI modalities.