Noninvasive Classification of Glioma Subtypes Using Multiparametric MRI to Improve Deep Learning.

ABSTRACT: Background: Deep learning (DL) methods can noninvasively predict glioma subtypes; however, there is no set paradigm for the selection of network structures and input data, including the image combination method, image processing strategy, type of numeric data, and others. Purpose: To compare different combinations of DL frameworks (ResNet, ConvNext, and vision transformer (VIT)), image preprocessing strategies, magnetic resonance imaging (MRI) sequences, and numerical data for increasing the accuracy of DL models for differentiating glioma subtypes prior to surgery. Methods: Our dataset consisted of 211 patients with newly diagnosed gliomas who underwent preoperative MRI with standard and diffusion-weighted imaging methods. Different data combinations were used as inpu

SUBMITTER: Xiong D 

PROVIDER: S-EPMC9776470 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature