{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Stadlbauer A"],"funding":["German Research Foundation","Forschungsimpulse","Deutsche Forschungsgemeinschaft","Lower Austrian Provincial Health Agency (NÖ LGA) and Karl Landsteiner University of Health Sciences"],"pagination":["1102"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10969299"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["16(6)"],"pubmed_abstract":["The mutational status of the isocitrate dehydrogenase (<i>IDH</i>) gene plays a key role in the treatment of glioma patients because it is known to affect energy metabolism pathways relevant to glioma. Physio-metabolic magnetic resonance imaging (MRI) enables the non-invasive analysis of oxygen metabolism and tissue hypoxia as well as associated neovascularization and microvascular architecture. However, evaluating such complex neuroimaging data requires computational support. Traditional machine learning algorithms and simple deep learning models were trained with radiomic features from clinical MRI (cMRI) or physio-metabolic MRI data. A total of 215 patients (first center: 166 participants + 16 participants for independent internal testing of the algorithms versus second site: 33 partici"],"journal":["Cancers"],"pubmed_title":["Machine Learning-Based Prediction of Glioma <i>IDH</i> Gene Mutation Status Using Physio-Metabolic MRI of Oxygen Metabolism and Neovascularization (A Bicenter Study)."],"pmcid":["PMC10969299"],"funding_grant_id":["Seed Funding Project (Forschungsimpulse) SF45","SF_0045","STA 1331/3-1","DO 721/9-1"],"pubmed_authors":["Marhold F","Doerfler A","Oberndorfer S","Bistrian DA","Meyer-Base A","Stadlbauer A","Kinfe TM","Schnell O","Nikolic K"],"additional_accession":[]},"is_claimable":false,"name":"Machine Learning-Based Prediction of Glioma <i>IDH</i> Gene Mutation Status Using Physio-Metabolic MRI of Oxygen Metabolism and Neovascularization (A Bicenter Study).","description":"The mutational status of the isocitrate dehydrogenase (<i>IDH</i>) gene plays a key role in the treatment of glioma patients because it is known to affect energy metabolism pathways relevant to glioma. Physio-metabolic magnetic resonance imaging (MRI) enables the non-invasive analysis of oxygen metabolism and tissue hypoxia as well as associated neovascularization and microvascular architecture. However, evaluating such complex neuroimaging data requires computational support. Traditional machine learning algorithms and simple deep learning models were trained with radiomic features from clinical MRI (cMRI) or physio-metabolic MRI data. A total of 215 patients (first center: 166 participants + 16 participants for independent internal testing of the algorithms versus second site: 33 partici","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2026-07-08T03:13:52.251Z","creation":"2024-11-12T00:07:49.262Z"},"accession":"S-EPMC10969299","cross_references":{"pubmed":["38539436"],"doi":["10.3390/cancers16061102"]}}