Real-world data to build explainable trustworthy artificial intelligence models for prediction of immunotherapy efficacy in NSCLC patients

INTRODUCTION: Artificial Intelligence (AI) methods are being increasingly investigated as a means to generate predictive models applicable in the clinical practice. In this study, we developed a model to predict the efficacy of immunotherapy (IO) in patients with advanced non-small cell lung cancer...

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Detalles Bibliográficos
Autores principales: Prelaj, Arsela, Galli, Edoardo Gregorio, Miskovic, Vanja, Pesenti, Mattia, Viscardi, Giuseppe, Pedica, Benedetta, Mazzeo, Laura, Bottiglieri, Achille, Provenzano, Leonardo, Spagnoletti, Andrea, Marinacci, Roberto, De Toma, Alessandro, Proto, Claudia, Ferrara, Roberto, Brambilla, Marta, Occhipinti, Mario, Manglaviti, Sara, Galli, Giulia, Signorelli, Diego, Giani, Claudia, Beninato, Teresa, Pircher, Chiara Carlotta, Rametta, Alessandro, Kosta, Sokol, Zanitti, Michele, Di Mauro, Maria Rosa, Rinaldi, Arturo, Di Gregorio, Settimio, Antonia, Martinetti, Garassino, Marina Chiara, de Braud, Filippo G. M., Restelli, Marcello, Lo Russo, Giuseppe, Ganzinelli, Monica, Trovò, Francesco, Pedrocchi, Alessandra Laura Giulia
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/PMC9899835/
https://www.ncbi.nlm.nih.gov/pubmed/36755856
http://dx.doi.org/10.3389/fonc.2022.1078822

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9899835/
https://www.ncbi.nlm.nih.gov/pubmed/36755856
http://dx.doi.org/10.3389/fonc.2022.1078822

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