A comparison of machine learning methods for predicting recurrence and death after curative-intent radiotherapy for non-small cell lung cancer: Development and validation of multivariable clinical prediction models

BACKGROUND: Surveillance is universally recommended for non-small cell lung cancer (NSCLC) patients treated with curative-intent radiotherapy. High-quality evidence to inform optimal surveillance strategies is lacking. Machine learning demonstrates promise in accurate outcome prediction for a variet...

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Detalles Bibliográficos
Autores principales: Hindocha, Sumeet, Charlton, Thomas G., Linton-Reid, Kristofer, Hunter, Benjamin, Chan, Charleen, Ahmed, Merina, Robinson, Emily J., Orton, Matthew, Ahmad, Shahreen, McDonald, Fiona, Locke, Imogen, Power, Danielle, Blackledge, Matthew, Lee, Richard W., Aboagye, Eric O.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8897583/
https://www.ncbi.nlm.nih.gov/pubmed/35248997
http://dx.doi.org/10.1016/j.ebiom.2022.103911

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8897583/
https://www.ncbi.nlm.nih.gov/pubmed/35248997
http://dx.doi.org/10.1016/j.ebiom.2022.103911

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