Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas.

ABSTRACT: Background and purpose: The World Health Organization has recently placed new emphasis on the integration of genetic information for gliomas. While tissue sampling remains the criterion standard, noninvasive imaging techniques may provide complimentary insight into clinically relevant genetic mutations. Our aim was to train a convolutional neural network to independently predict underlying molecular genetic mutation status in gliomas with high accuracy and identify the most predictive imaging features for each mutation.

Materials and methods: MR imaging data and molecular information were retrospectively obtained from The Cancer Imaging Archives for 259 patients with either low- or high-grade gliomas. A convolutional neural network was trained to classify isocitrate

SUBMITTER: Chang P 

PROVIDER: S-EPMC6880932 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature