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Copyright © 2024 Javier De La Hoz-M et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

[7] on breast cancer have delved into the use of deep learning (DL) for histopathological image classification, demonstrating how this technology increases diagnostic precision and can be applied to the analysis of other types of cancer. [...]several recent studies have conducted comprehensive reviews of the literature on AI in LC, primarily using bibliometric analyses. [...]a recent analysis by Corral, Borras, and Lievens [11] reviewed the utilization of radiotherapy in LC treatment in Europe, revealing considerable variability in the application of treatments such as stereotactic body radiotherapy (SBRT) and chemoradiotherapy (CRT) across different countries and patient populations. Data Collection and Search Strategy For data collection, we applied the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) protocol [17]. [...]our dataset consists of 7109 articles.

Details

Title
Research Trends of Artificial Intelligence in Lung Cancer: A Combined Approach of Analysis With Latent Dirichlet Allocation and HJ-Biplot Statistical Methods
Author
De La Hoz-M, Javier 1   VIAFID ORCID Logo  ; Montes-Escobar, Karime 2   VIAFID ORCID Logo  ; Pérez-Ortiz, Viorkis 3   VIAFID ORCID Logo 

 Faculty of Engineering Universidad del Magdalena Santa Marta Colombia 
 Departamento de Matemáticas y Estadística Facultad de Ciencias Básicas Universidad Técnica de Manabí Portoviejo 130105 Ecuador 
 Facultad Ciencias de la Salud Carrera de Medicina Universidad Técnica de Manabí Portoviejo 130105 Ecuador 
Editor
Laura Pini
Publication year
2024
Publication date
2024
Publisher
John Wiley & Sons, Inc.
ISSN
20901836
e-ISSN
20901844
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3144715190
Copyright
Copyright © 2024 Javier De La Hoz-M et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.