{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Guiot J"],"funding":["European Research Council","Stichting Euregio Maas-Rijn","FP7 People: Marie-Curie Actions"],"pagination":["E41"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7823620"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["11(1)"],"pubmed_abstract":["The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.88"],"journal":["Diagnostics (Basel, Switzerland)"],"pubmed_title":["Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19."],"pmcid":["PMC7823620"],"funding_grant_id":["694812","EMR4","ERC-ADG-2015, n° 694812 -  Hypoximmuno","PREDICT - ITN - n° 766276","ERC-ADG-2015, n° 694812 -   Hypoximmuno"],"pubmed_authors":["Zerka F","Walsh S","Vos W","Henket M","Meunier P","Danthine D","Frix AN","Eftaxia E","Miraglio B","Louis R","Mathieu S","Canivet G","Thys M","Lambin P","Vaidyanathan A","Guiot J","Lovinfosse P","Leijenaar RTH","Tsoutzidis N","Moutschen M","Deprez L"],"additional_accession":[]},"is_claimable":false,"name":"Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19.","description":"The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.88","dates":{"release":"2020-01-01T00:00:00Z","publication":"2020 Dec","modification":"2026-07-16T10:16:54.925Z","creation":"2021-02-21T01:52:42Z"},"accession":"S-EPMC7823620","cross_references":{"pubmed":["33396587"],"doi":["10.3390/diagnostics11010041"]}}