{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Ma H"],"funding":["the Fundamental Research Program of Shanxi Province","Scientific Research Project Plan of Shanxi Provincial Health Commission","Agreement on Horizontal Scientific Research Cooperation Project of the First Hospital of Shanxi Medical University"],"pagination":["1597"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12532899"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(1)"],"pubmed_abstract":["<h4>Background</h4>Accurate prediction of prognosis and risk stratification in patients with laryngeal cancer can inform appropriate treatment decision-making. This study aims to develop a multi-channel deep learning radiomics model based on contrast-enhanced computed tomography (CECT) for predicting postoperative overall survival (OS) in patients.<h4>Methods</h4>A total of 272 patients with laryngeal cancer were retrospectively recruited from two hospitals between January 2016 and July 2021. Specifically, 156 patients were enrolled from Center 1 as the training cohort, and 116 patients from Center 2 as the external test cohort. Two imaging signatures, reflecting phenotypes of the radiomics and multi-channel deep learning features, were constructed using pretreatment venous-phase CECT imag"],"journal":["BMC cancer"],"pubmed_title":["Multi-channel deep learning radiomics model based on contrast-enhanced CT for predicting postoperative prognosis in laryngeal carcinoma."],"pmcid":["PMC12532899"],"funding_grant_id":["Nos 2024056","Nos. 20210302123253","Nos. 20210302123256","Nos. 08983"],"pubmed_authors":["Li Z","Zhang J","Zhang H","Ma H","Liang L","Wang W","Wei W","Zhang L","Hao Y","Zhang Q","Wang L"],"additional_accession":[]},"is_claimable":false,"name":"Multi-channel deep learning radiomics model based on contrast-enhanced CT for predicting postoperative prognosis in laryngeal carcinoma.","description":"<h4>Background</h4>Accurate prediction of prognosis and risk stratification in patients with laryngeal cancer can inform appropriate treatment decision-making. This study aims to develop a multi-channel deep learning radiomics model based on contrast-enhanced computed tomography (CECT) for predicting postoperative overall survival (OS) in patients.<h4>Methods</h4>A total of 272 patients with laryngeal cancer were retrospectively recruited from two hospitals between January 2016 and July 2021. Specifically, 156 patients were enrolled from Center 1 as the training cohort, and 116 patients from Center 2 as the external test cohort. Two imaging signatures, reflecting phenotypes of the radiomics and multi-channel deep learning features, were constructed using pretreatment venous-phase CECT imag","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Oct","modification":"2026-06-04T13:44:15.456Z","creation":"2026-05-09T03:12:23.834Z"},"accession":"S-EPMC12532899","cross_references":{"pubmed":["41102694"],"doi":["10.1186/s12885-025-14912-4"]}}