Multi-modal magnetic resonance imaging-based grading analysis for gliomas by integrating radiomics and deep features.

ABSTRACT:

Background

To investigate the feasibility of integrating global radiomics and local deep features based on multi-modal magnetic resonance imaging (MRI) for developing a noninvasive glioma grading model.

Methods

In this study, 567 patients [211 patients with glioblastomas (GBMs) and 356 patients with low-grade gliomas (LGGs)] between May 2006 and September 2018, were enrolled and divided into training (n=186), validation (n=47), and testing cohorts (n=334), respectively. All patients underwent postcontrast enhanced T1-weighted and T2 fluid-attenuated inversion recovery MRI scanning. Radiomics and deep features (trained by 8,510 3D patches) were extracted to quantify the global and local information of gliomas, respectively. A kernel fusion-based support vector machine (SVM) c

SUBMITTER: Ning Z 

PROVIDER: S-EPMC7944310 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature