A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas.

ABSTRACT:

Background

Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a highly accurate, MRI-based, voxelwise deep-learning IDH classification network using T2-weighted (T2w) MR images and compare its performance to a multicontrast network.

Methods

Multiparametric brain MRI data and corresponding genomic information were obtained for 214 subjects (94 IDH-mutated, 120 IDH wild-type) from The Cancer Imaging Archive and The Cancer Genome Atlas. Two separate networks were developed, including a T2w image-only network (T2-net) and a multicontrast (T2w, fluid attenuated inversion recovery, and T1 postcontrast)

SUBMITTER: Bangalore Yogananda CG 

PROVIDER: S-EPMC7442388 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature