{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["9(4)"],"submitter":["Ning Z"],"pubmed_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"],"journal":["Annals of translational medicine"],"pagination":["298"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7944310"],"repository":["biostudies-literature"],"pubmed_title":["Multi-modal magnetic resonance imaging-based grading analysis for gliomas by integrating radiomics and deep features."],"pmcid":["PMC7944310"],"pubmed_authors":["Luo J","Zhang Y","Cai L","Chen Y","Ning Z","Wang J","Yu X","Xiao Q"],"additional_accession":[]},"is_claimable":false,"name":"Multi-modal magnetic resonance imaging-based grading analysis for gliomas by integrating radiomics and deep features.","description":"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","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Feb","modification":"2025-04-04T10:13:02.415Z","creation":"2025-02-19T01:40:08.702Z"},"accession":"S-EPMC7944310","cross_references":{"pubmed":["33708925"],"doi":["10.21037/atm-20-4076"]}}