Keywords: AI/ML Software, Brain, Deep Learning, Attention
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 MRI
Approach: 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.
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