A multi-omics-based serial deep learning approach to predict clinical outcomes of single-agent anti-PD-1/PD-L1 immunotherapy in advanced stage non-small-cell lung cancer.

Abstract

=0.01) than high-risk patients. In conclusion, the SimTA-based multi-omics serial deep learning provides a promising methodology for predicting response of advanced NSCLC patients to anti-PD-1/PD-L1 monotherapy. Moreover, our model could better differentiate survival benefit among SD patients than the traditional RECIST evaluation method.