ArticleComputational and structural biotechnology journal2022
Artificial intelligence predictive system of individual survival rate for lung adenocarcinoma.
Article in Computational and structural biotechnology journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Prediction of overall survival in primary lung adenocarcinoma using machine learning and deep learning: a cohort study in Latin America country cancer center.Frontiers in oncology · 2026Article
- Identification of CAV1 and CDH5 as potential diagnostic and prognostic biomarkers in lung adenocarcinoma.Experimental and therapeutic medicine · 2025Article
- Article
- Development and application of a risk nomogram for the prediction of risk of carbapenem-resistant Acinetobacter baumannii infections in neuro-intensive care unit: a mixed method study.Antimicrobial resistance and infection control · 2024Article
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Authors and funding
4 authors.
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Abstract
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/.
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