{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Khorrami M"],"funding":["BLRD VA","NCATS NIH HHS","NIBIB NIH HHS","Congressionally Directed Medical Research Programs","NCRR NIH HHS","NHLBI NIH HHS","National Cancer Institute","NCI NIH HHS","Department of Veterans Affairs"],"pagination":["146-158"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8087632"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["148"],"pubmed_abstract":["<h4>Objective</h4>To identify stable and discriminating radiomic features on non-contrast CT scans to develop more generalisable radiomic classifiers for distinguishing granulomas from adenocarcinomas.<h4>Methods</h4>In total, 412 patients with adenocarcinomas and granulomas from three institutions were retrospectively included. Segmentations of the lung nodules were performed manually by an expert radiologist in a 2D axial view. Radiomic features were extracted from intra- and perinodular regions. A total of 145 patients were used as part of the training set (S<sub>tr</sub>), whereas 205 patients were used as part of test set I (S<sub>te</sub><sup>1</sup>) and 62 patients were used as part of independent test set II (S<sub>te</sub><sup>2</sup>). To mitigate the variation of CT acquisition"],"journal":["European journal of cancer (Oxford, England : 1990)"],"pubmed_title":["Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans."],"pmcid":["PMC8087632"],"funding_grant_id":["R01 HL151277","UL1 TR002548","U54 CA254566","C06 RR012463","R43 EB028736","R01 CA216579","U01 CA248226","R01 CA249992","I01 BX004121","U24 CA199374","U01 CA239055","R01 CA220581","R01 CA202752","R01 CA208236"],"pubmed_authors":["Gupta A","Linden P","Thawani R","Pennell N","Khorrami M","Rajiah P","Bera K","Jacono F","Gilkeson RC","Madabhushi A","Fu P","Velcheti V"],"additional_accession":[]},"is_claimable":false,"name":"Distinguishing granulomas from adenocarcinomas by integrating stable and discriminating radiomic features on non-contrast computed tomography scans.","description":"<h4>Objective</h4>To identify stable and discriminating radiomic features on non-contrast CT scans to develop more generalisable radiomic classifiers for distinguishing granulomas from adenocarcinomas.<h4>Methods</h4>In total, 412 patients with adenocarcinomas and granulomas from three institutions were retrospectively included. Segmentations of the lung nodules were performed manually by an expert radiologist in a 2D axial view. Radiomic features were extracted from intra- and perinodular regions. A total of 145 patients were used as part of the training set (S<sub>tr</sub>), whereas 205 patients were used as part of test set I (S<sub>te</sub><sup>1</sup>) and 62 patients were used as part of independent test set II (S<sub>te</sub><sup>2</sup>). To mitigate the variation of CT acquisition","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 May","modification":"2025-04-04T10:17:57.397Z","creation":"2025-04-04T10:17:57.397Z"},"accession":"S-EPMC8087632","cross_references":{"pubmed":["33743483"],"doi":["10.1016/j.ejca.2021.02.008"]}}