1068P Predicting response to pembrolizumab in non-small cell lung cancer using spatial analysis of biopsy images by deep learning

Abstract

Over the past 5 years, immune checkpoint inhibitors (ICI) have become the standard treatment for metastatic non-small cell lung cancer (mNSCLC). However, only a small proportion of patients (pts) derive durable clinical benefit (DCB). The predictive value of PD-L1 score is limited and better predictive biomarkers are needed. Spatial arrangement of immune cells in the tumor microenvironment (TME) is a potential biomarker for ICI efficacy. In this work, we utilized deep-learning (DL) models to extract TME features from digitized H&E whole slide images (WSI) and evaluated their role in predicting outcomes in mNSCLC pts treated with pembrolizumab.