Development of artificial intelligence prognostic model for surgically resected non-small cell lung cancer.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
21 09 2023
21 09 2023
Historique:
received:
10
11
2022
accepted:
17
09
2023
medline:
25
9
2023
pubmed:
22
9
2023
entrez:
22
9
2023
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 37735585
doi: 10.1038/s41598-023-42964-8
pii: 10.1038/s41598-023-42964-8
pmc: PMC10514331
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
15683Informations de copyright
© 2023. Springer Nature Limited.
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