Application of machine learning for lung cancer survival prognostication-A systematic review and meta-analysis.

Autor:	Didier AJ; Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States., Nigro A; Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States., Noori Z; Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States.; Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States., Omballi MA; Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States.; Division of Pulmonary and Critical Care Medicine, Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States., Pappada SM; Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States.; Department of Anesthesiology, The University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States., Hamouda DM; Department of Medicine, University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States.; Division of Hematology and Oncology, Department of Medicine, The University of Toledo College of Medicine and Life Sciences, Toledo, OH, United States.
Jazyk:	angličtina
Zdroj:	Frontiers in artificial intelligence [Front Artif Intell] 2024 Apr 05; Vol. 7, pp. 1365777. Date of Electronic Publication: 2024 Apr 05 (Print Publication: 2024).
DOI:	10.3389/frai.2024.1365777
Abstrakt:	Introduction: Machine learning (ML) techniques have gained increasing attention in the field of healthcare, including predicting outcomes in patients with lung cancer. ML has the potential to enhance prognostication in lung cancer patients and improve clinical decision-making. In this systematic review and meta-analysis, we aimed to evaluate the performance of ML models compared to logistic regression (LR) models in predicting overall survival in patients with lung cancer. Methods: We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A comprehensive search was conducted in Medline, Embase, and Cochrane databases using a predefined search query. Two independent reviewers screened abstracts and conflicts were resolved by a third reviewer. Inclusion and exclusion criteria were applied to select eligible studies. Risk of bias assessment was performed using predefined criteria. Data extraction was conducted using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS) checklist. Meta-analytic analysis was performed to compare the discriminative ability of ML and LR models. Results: The literature search resulted in 3,635 studies, and 12 studies with a total of 211,068 patients were included in the analysis. Six studies reported confidence intervals and were included in the meta-analysis. The performance of ML models varied across studies, with C-statistics ranging from 0.60 to 0.85. The pooled analysis showed that ML models had higher discriminative ability compared to LR models, with a weighted average C-statistic of 0.78 for ML models compared to 0.70 for LR models. Conclusion: Machine learning models show promise in predicting overall survival in patients with lung cancer, with superior discriminative ability compared to logistic regression models. However, further validation and standardization of ML models are needed before their widespread implementation in clinical practice. Future research should focus on addressing the limitations of the current literature, such as potential bias and heterogeneity among studies, to improve the accuracy and generalizability of ML models for predicting outcomes in patients with lung cancer. Further research and development of ML models in this field may lead to improved patient outcomes and personalized treatment strategies. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Didier, Nigro, Noori, Omballi, Pappada and Hamouda.)
Databáze:	MEDLINE
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