Improving the noninvasive classification of glioma genetic subtype with deep learning and diffusion-weighted imaging.

ABSTRACT:

Background

Diagnostic classification of diffuse gliomas now requires an assessment of molecular features, often including IDH-mutation and 1p19q-codeletion status. Because genetic testing requires an invasive process, an alternative noninvasive approach is attractive, particularly if resection is not recommended. The goal of this study was to evaluate the effects of training strategy and incorporation of biologically relevant images on predicting genetic subtypes with deep learning.

Methods

Our dataset consisted of 384 patients with newly diagnosed gliomas who underwent preoperative MRI with standard anatomical and diffusion-weighted imaging, and 147 patients from an external cohort with anatomical imaging. Using tissue samples acquired during surgery, each glioma was classi

SUBMITTER: Cluceru J 

PROVIDER: S-EPMC8972294 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature