<HashMap><database>biostudies-literature</database><scores/><additional><submitter>Alwalid O</submitter><funding>Natural Science Foundation of Hubei Province</funding><pagination>619864</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC7937935</full_dataset_link><repository>biostudies-literature</repository><omics_type>Unknown</omics_type><volume>12</volume><pubmed_abstract>&lt;b>Background:&lt;/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. &lt;b>Methods:&lt;/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 (&lt;i>n&lt;/i> = 274) and prospective test (&lt;i>n&lt;/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</pubmed_abstract><journal>Frontiers in neurology</journal><pubmed_title>CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture.</pubmed_title><pmcid>PMC7937935</pmcid><funding_grant_id>2019CFB297</funding_grant_id><pubmed_authors>Cen C</pubmed_authors><pubmed_authors>Liu H</pubmed_authors><pubmed_authors>Han P</pubmed_authors><pubmed_authors>Yang J</pubmed_authors><pubmed_authors>Long X</pubmed_authors><pubmed_authors>Xie M</pubmed_authors><pubmed_authors>Alwalid O</pubmed_authors></additional><is_claimable>false</is_claimable><name>CT Angiography-Based Radiomics for Classification of Intracranial Aneurysm Rupture.</name><description>&lt;b>Background:&lt;/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. &lt;b>Methods:&lt;/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 (&lt;i>n&lt;/i> = 274) and prospective test (&lt;i>n&lt;/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</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021</publication><modification>2025-04-04T08:31:34.514Z</modification><creation>2021-03-12T08:17:23Z</creation></dates><accession>S-EPMC7937935</accession><cross_references><pubmed>33692741</pubmed><doi>10.3389/fneur.2021.619864</doi></cross_references></HashMap>