Automatic differentiation of Grade I and II meningiomas on magnetic resonance image using an asymmetric convolutional neural network.

ABSTRACT: The Grade of meningioma has significant implications for selecting treatment regimens ranging from observation to surgical resection with adjuvant radiation. For most patients, meningiomas are diagnosed radiologically, and Grade is not determined unless a surgical procedure is performed. The goal of this study is to train a novel auto-classification network to determine Grade I and II meningiomas using T1-contrast enhancing (T1-CE) and T2-Fluid attenuated inversion recovery (FLAIR) magnetic resonance (MR) images. Ninety-six consecutive treatment naïve patients with pre-operative T1-CE and T2-FLAIR MR images and subsequent pathologically diagnosed intracranial meningiomas were evaluated. Delineation of meningiomas was completed on both MR images. A novel asymmetric 3D convolutional neural n

SUBMITTER: Vassantachart A 

PROVIDER: S-EPMC8907289 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature