{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Alwalid O"],"funding":["Natural Science Foundation of Hubei Province"],"pagination":["619864"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7937935"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12"],"pubmed_abstract":["<b>Background:</b> Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture. <b>Methods:</b> Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (<i>n</i> = 274) and prospective test (<i>n</i> = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selecte"],"journal":["Frontiers in neurology"],"pubmed_title":["CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture."],"pmcid":["PMC7937935"],"funding_grant_id":["2019CFB297"],"pubmed_authors":["Cen C","Liu H","Han P","Yang J","Long X","Xie M","Alwalid O"],"additional_accession":[]},"is_claimable":false,"name":"CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture.","description":"<b>Background:</b> Intracranial aneurysm rupture is a devastating medical event with a high morbidity and mortality rate. Thus, timely detection and management are critical. The present study aimed to identify the aneurysm radiomics features associated with rupture and to build and evaluate a radiomics classification model of aneurysm rupture. <b>Methods:</b> Radiomics analysis was applied to CT angiography (CTA) images of 393 patients [152 (38.7%) with ruptured aneurysms]. Patients were divided at a ratio of 7:3 into retrospective training (<i>n</i> = 274) and prospective test (<i>n</i> = 119) cohorts. A total of 1,229 radiomics features were automatically calculated from each aneurysm. The feature number was systematically reduced, and the most important classifying features were selecte","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021","modification":"2025-04-04T08:31:34.514Z","creation":"2021-03-12T08:17:23Z"},"accession":"S-EPMC7937935","cross_references":{"pubmed":["33692741"],"doi":["10.3389/fneur.2021.619864"]}}