{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13(1)"],"submitter":["Kinoshita F"],"pubmed_abstract":["There are great expectations for artificial intelligence (AI) in medicine. We aimed to develop an AI prognostic model for surgically resected non-small cell lung cancer (NSCLC). This study enrolled 1049 patients with pathological stage I-IIIA surgically resected NSCLC at Kyushu University. We set 17 clinicopathological factors and 30 preoperative and 22 postoperative blood test results as explanatory variables. Disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS) were set as objective variables. The eXtreme Gradient Boosting (XGBoost) was used as the machine learning algorithm. The median age was 69 (23-89) years, and 605 patients (57.7%) were male. The numbers of patients with pathological stage IA, IB, IIA, IIB, and IIIA were 553 (52.7%), 223 (21.4%), 100 (9.5%), 55 (5.3%), and 118 (11.2%), respectively. The 5-year DFS, OS, and CSS rates were 71.0%, 82.8%, and 88.7%, respectively. Our AI prognostic model showed that the areas under the curve of the receiver operating characteristic curves of DFS, OS, and CSS at 5 years were 0.890, 0.926, and 0.960, respectively. The AI prognostic model using XGBoost showed good prediction accuracy and provided accurate predictive probability of postoperative prognosis of NSCLC."],"journal":["Scientific reports"],"pagination":["15683"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10514331"],"repository":["biostudies-literature"],"pubmed_title":["Development of artificial intelligence prognostic model for surgically resected non-small cell lung cancer."],"pmcid":["PMC10514331"],"pubmed_authors":["Nakashima N","Mori M","Wakasu S","Kinoshita F","Yamashita T","Ono Y","Tagawa T","Matsumoto K","Oku Y","Takenaka T","Haratake N"],"additional_accession":[]},"is_claimable":false,"name":"Development of artificial intelligence prognostic model for surgically resected non-small cell lung cancer.","description":"There are great expectations for artificial intelligence (AI) in medicine. We aimed to develop an AI prognostic model for surgically resected non-small cell lung cancer (NSCLC). This study enrolled 1049 patients with pathological stage I-IIIA surgically resected NSCLC at Kyushu University. We set 17 clinicopathological factors and 30 preoperative and 22 postoperative blood test results as explanatory variables. Disease-free survival (DFS), overall survival (OS), and cancer-specific survival (CSS) were set as objective variables. The eXtreme Gradient Boosting (XGBoost) was used as the machine learning algorithm. The median age was 69 (23-89) years, and 605 patients (57.7%) were male. The numbers of patients with pathological stage IA, IB, IIA, IIB, and IIIA were 553 (52.7%), 223 (21.4%), 100 (9.5%), 55 (5.3%), and 118 (11.2%), respectively. The 5-year DFS, OS, and CSS rates were 71.0%, 82.8%, and 88.7%, respectively. Our AI prognostic model showed that the areas under the curve of the receiver operating characteristic curves of DFS, OS, and CSS at 5 years were 0.890, 0.926, and 0.960, respectively. The AI prognostic model using XGBoost showed good prediction accuracy and provided accurate predictive probability of postoperative prognosis of NSCLC.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Sep","modification":"2025-04-04T21:02:33.885Z","creation":"2025-04-04T21:02:33.885Z"},"accession":"S-EPMC10514331","cross_references":{"pubmed":["37735585"],"doi":["10.1038/s41598-023-42964-8"]}}