Segmentation of pre- and posttreatment diffuse glioma tissue subregions including resection cavities.

ABSTRACT:

Background

Evaluating longitudinal changes in gliomas is a time-intensive process with significant interrater variability. Automated segmentation could reduce interrater variability and increase workflow efficiency for assessment of treatment response. We sought to evaluate whether neural networks would be comparable to expert assessment of pre- and posttreatment diffuse gliomas tissue subregions including resection cavities.

Methods

A retrospective cohort of 647 MRIs of patients with diffuse gliomas (average 55.1 years; 29%/36%/34% female/male/unknown; 396 pretreatment and 251 posttreatment, median 237 days post-surgery) from 7 publicly available repositories in The Cancer Imaging Archive were split into training (536) and test/generalization (111) samples. T1, T1-post-cont

SUBMITTER: Baig S 

PROVIDER: S-EPMC11407510 | biostudies-literature | 2024 Jan-Dec

REPOSITORIES: biostudies-literature