Generalization optimizing machine learning to improve CT scan radiomics and assess immune checkpoint inhibitors’ response in non-small cell lung cancer: a multicenter cohort study

BACKGROUND: Recent developments in artificial intelligence suggest that radiomics may represent a promising non-invasive biomarker to predict response to immune checkpoint inhibitors (ICIs). Nevertheless, validation of radiomics algorithms in independent cohorts remains a challenge due to variations...

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Detalles Bibliográficos
Autores principales: Tonneau, Marion, Phan, Kim, Manem, Venkata S. K., Low-Kam, Cecile, Dutil, Francis, Kazandjian, Suzanne, Vanderweyen, Davy, Panasci, Justin, Malo, Julie, Coulombe, François, Gagné, Andréanne, Elkrief, Arielle, Belkaïd, Wiam, Di Jorio, Lisa, Orain, Michele, Bouchard, Nicole, Muanza, Thierry, Rybicki, Frank J., Kafi, Kam, Huntsman, David, Joubert, Philippe, Chandelier, Florent, Routy, Bertrand
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10400292/
https://www.ncbi.nlm.nih.gov/pubmed/37546399
http://dx.doi.org/10.3389/fonc.2023.1196414

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10400292/
https://www.ncbi.nlm.nih.gov/pubmed/37546399
http://dx.doi.org/10.3389/fonc.2023.1196414

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