{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Liu X"],"funding":["Shanghai Shen Kang Hospital Development Center","National Natural Science Foundation of China"],"pagination":["1420213"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11215045"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14"],"pubmed_abstract":["Purpose
To construct and validate a computed tomography (CT) radiomics model for differentiating lung neuroendocrine neoplasm (LNEN) from lung adenocarcinoma (LADC) manifesting as a peripheral solid nodule (PSN) to aid in early clinical decision-making.Methods
A total of 445 patients with pathologically confirmed LNEN and LADC from June 2016 to July 2023 were retrospectively included from five medical centers. Those patients were split into the training set (n = 316; 158 LNEN) and external test set (n = 129; 43 LNEN), the former including the cross-validation (CV) training set and CV test set using ten-fold CV. The support vector machine (SVM) classifier was used to develop the semantic, radiomics and merged models. The diagnostic performances were evaluated by the area und"],"journal":["Frontiers in oncology"],"pubmed_title":["CT radiomics to differentiate neuroendocrine neoplasm from adenocarcinoma in patients with a peripheral solid pulmonary nodule: a multicenter study."],"pmcid":["PMC11215045"],"funding_grant_id":["SHDC2020CR3080B","82172030"],"pubmed_authors":["Liu X","Zhang G","Yang H","Li H","Shan F","Wang S","Yang S","Xu Y"],"additional_accession":[]},"is_claimable":false,"name":"CT radiomics to differentiate neuroendocrine neoplasm from adenocarcinoma in patients with a peripheral solid pulmonary nodule: a multicenter study.","description":"Purpose
To construct and validate a computed tomography (CT) radiomics model for differentiating lung neuroendocrine neoplasm (LNEN) from lung adenocarcinoma (LADC) manifesting as a peripheral solid nodule (PSN) to aid in early clinical decision-making.Methods
A total of 445 patients with pathologically confirmed LNEN and LADC from June 2016 to July 2023 were retrospectively included from five medical centers. Those patients were split into the training set (n = 316; 158 LNEN) and external test set (n = 129; 43 LNEN), the former including the cross-validation (CV) training set and CV test set using ten-fold CV. The support vector machine (SVM) classifier was used to develop the semantic, radiomics and merged models. The diagnostic performances were evaluated by the area und","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024","modification":"2025-04-04T12:53:54.663Z","creation":"2025-04-04T12:53:54.663Z"},"accession":"S-EPMC11215045","cross_references":{"pubmed":["38952551"],"doi":["10.3389/fonc.2024.1420213"]}}