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Artificial intelligence predictive system of individual survival rate for lung adenocarcinoma

BACKGROUND: The current research aimed to develop an artificial intelligence predictive system for individual survival rate of lung adenocarcinoma (LUAD). METHODS: Independent risk variables were identified by multivariate Cox regression. Artificial intelligence predictive system was constructed usi...

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Detalles Bibliográficos
Autores principales: He, Tingshan, Li, Jing, Wang, Peng, Zhang, Zhiqiao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9123088/
https://www.ncbi.nlm.nih.gov/pubmed/35615023
http://dx.doi.org/10.1016/j.csbj.2022.05.005
Descripción
Sumario:BACKGROUND: The current research aimed to develop an artificial intelligence predictive system for individual survival rate of lung adenocarcinoma (LUAD). METHODS: Independent risk variables were identified by multivariate Cox regression. Artificial intelligence predictive system was constructed using three different data mining algorithms. RESULTS: Stage, PM, chemotherapy, PN, age, PT, sex, and radiation_surgery were determined as risk factors for LUAD patients. For 12-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.852, 0.821, and 0.835, respectively. For 36-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.901, 0.864, and 0.862, respectively. For 60-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.899, 0.874, and 0.866, respectively. The concordance indexes in validation dataset were similar to those in model dataset. CONCLUSIONS: The current study designed an individualized survival predictive system, which could provide individual survival curves using three different artificial intelligence algorithms. This artificial intelligence predictive system could directly convey treatment benefits by comparing individual mortality risk curves under different treatments. This artificial intelligence predictive tool is available at https://zhangzhiqiao11.shinyapps.io/Artificial_Intelligence_Survival_Prediction_System_AI_E1001/.