Machine learning models for identifying predictors of clinical outcomes with first-line immune checkpoint inhibitor therapy in advanced non-small cell lung cancer

Machine learning models for identifying predictors of clinical outcomes with first-line immune checkpoint inhibitor therapy in advanced non-small cell lung cancer

Abstract Immune checkpoint inhibitors (ICIs) are standard-of-care as first-line (1L) therapy for advanced non-small cell lung cancer (aNSCLC) without actionable oncogenic driver mutations. While clinical trials demonstrated benefits of ICIs over chemotherapy, variation in outcomes across patients ha...

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Bibliographic Details
Main Authors: Ying Li, Matthew Brendel, Ning Wu, Wenzhen Ge, Hao Zhang, Petra Rietschel, Ruben G. W. Quek, Jean-Francois Pouliot, Fei Wang, James Harnett
Format: Article
Language:English
Published: Nature Portfolio 2022-10-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-022-20061-6

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https://doi.org/10.1038/s41598-022-20061-6

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