Imaging-Based Algorithm for the Local Grading of Glioma.

ABSTRACT:

Background and purpose

Gliomas are highly heterogeneous tumors, and optimal treatment depends on identifying and locating the highest grade disease present. Imaging techniques for doing so are generally not validated against the histopathologic criterion standard. The purpose of this work was to estimate the local glioma grade using a machine learning model trained on preoperative image data and spatially specific tumor samples. The value of imaging in patients with brain tumor can be enhanced if pathologic data can be estimated from imaging input using predictive models.

Materials and methods

Patients with gliomas were enrolled in a prospective clinical imaging trial between 2013 and 2016. MR imaging was performed with anatomic, diffusion, permeability, and perfusion sequen

SUBMITTER: Gates EDH 

PROVIDER: S-EPMC7077885 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature