Abstract:BackgroundImmunotherapy has become increasingly important in the perioperative period of non-small-cell lung cancer (NSCLC). In this study, we intended to develop a mutation-based model to predict the therapeutic effificacy of immune checkpoint inhibitors (ICIs) in patients with NSCLC.MethodsRandom Forest (RF) classifiers were generated to identify tumor gene mutated features associated with immunotherapy outcomes. Then the best classifier with the highest accuracy served for the development of the predictive … Show more
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