Machine learning highlights the deficiency of conventional dosimetric constraints for prevention of high-grade radiation esophagitis in non-small cell lung cancer treated with chemoradiation

BACKGROUND AND PURPOSE: Radiation esophagitis is a clinically important toxicity seen with treatment for locally-advanced non-small cell lung cancer. There is considerable disagreement among prior studies in identifying predictors of radiation esophagitis. We apply machine learning algorithms to ide...

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Detalles Bibliográficos
Autores principales: Luna, José Marcio, Chao, Hann-Hsiang, Shinohara, Russel T., Ungar, Lyle H., Cengel, Keith A., Pryma, Daniel A., Chinniah, Chidambaram, Berman, Abigail T., Katz, Sharyn I., Kontos, Despina, Simone, Charles B., Diffenderfer, Eric S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7132156/
https://www.ncbi.nlm.nih.gov/pubmed/32274426
http://dx.doi.org/10.1016/j.ctro.2020.03.007

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7132156/
https://www.ncbi.nlm.nih.gov/pubmed/32274426
http://dx.doi.org/10.1016/j.ctro.2020.03.007

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