Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning.

ABSTRACT:

Background

Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is time-consuming. Previously, deep learning methods have been developed that can either non-invasively predict the genetic or histological features of glioma, or that can automatically delineate the tumor, but not both tasks at the same time. Here, we present our method that can predict the molecular subtype and grade, while simultaneously providing a delineation of the tumor.

Methods

We developed a single multi-task convolutional neural network that uses the full 3D, structural, preoperative MRI scans to predict the IDH mutation status, the 1p/19q co-deletion status, and the grade of a tumor, while simultane

SUBMITTER: van der Voort SR 

PROVIDER: S-EPMC9925710 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature