Deep learning model to predict clinical outcomes in patients with advanced non-small cell lung cancer treated with immune checkpoint inhibitors

A. Elkrief, K. Phan, L. Di Jorio, R. Simpson, M. Chassé, J. Malo, C. Richard, M. Kosyakov, F. Chandelier, K. Kafi, B. Routy

Immune checkpoint inhibitors (ICI) represent a major change in non-small cell lung cancer (NSCLC) treatment, however robust biomarkers are needed. Emerging data suggest that features discovered by deep learning (DL) models from CT scan images using artificial intelligence (AI) algorithms can accurately predict outcomes. In this study, our objective was to explore the potential of AI-based DL radiomics models in patients with advanced NSCLC treated with ICI.