{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["10(2)"],"submitter":["Lin G"],"pubmed_abstract":["<h4>Background</h4>Currently, there is no reliable method for predicting the prognosis of patients with large cell lung cancer (LCLC). The aim of this study was to develop and validate a nomogram model for accurately predicting the prognosis of patients with LCLC.<h4>Methods</h4>LCLC patients, diagnosed from 2007 to 2009, were identified from the Surveillance, Epidemiology and End Results (SEER) database and used as the training dataset. Significant clinicopathologic variables (P<0.05) in a multivariate Cox regression were selected to build the nomogram. The performance of the nomogram model was evaluated by the concordance index (C-index), the area under the curve (AUC), and internal calibration. LCLC patients diagnosed from 2010 to 2016 in the SEER database were selected as a testing dat"],"journal":["Translational lung cancer research"],"pagination":["622-635"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7947411"],"repository":["biostudies-literature"],"pubmed_title":["A nomogram prognostic model for large cell lung cancer: analysis from the Surveillance, Epidemiology and End Results Database."],"pmcid":["PMC7947411"],"pubmed_authors":["Liu H","Li J","Qi K","Lin G","Liu B"],"additional_accession":[]},"is_claimable":false,"name":"A nomogram prognostic model for large cell lung cancer: analysis from the Surveillance, Epidemiology and End Results Database.","description":"<h4>Background</h4>Currently, there is no reliable method for predicting the prognosis of patients with large cell lung cancer (LCLC). The aim of this study was to develop and validate a nomogram model for accurately predicting the prognosis of patients with LCLC.<h4>Methods</h4>LCLC patients, diagnosed from 2007 to 2009, were identified from the Surveillance, Epidemiology and End Results (SEER) database and used as the training dataset. Significant clinicopathologic variables (P<0.05) in a multivariate Cox regression were selected to build the nomogram. The performance of the nomogram model was evaluated by the concordance index (C-index), the area under the curve (AUC), and internal calibration. LCLC patients diagnosed from 2010 to 2016 in the SEER database were selected as a testing dat","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Feb","modification":"2025-04-26T18:31:41.063Z","creation":"2025-02-19T01:41:08.476Z"},"accession":"S-EPMC7947411","cross_references":{"pubmed":["33718009"],"doi":["10.21037/tlcr-19-517b"]}}