Artificial intelligence-powered spatial analysis of tumor microenvironment in patients with non-small cell lung cancer with acquired resistance to EGFR tyrosine kinase inhibitor.

ABSTRACT:

Purpose

This study evaluated the dynamic changes in the tumor microenvironment (TME) in patients with non-small cell lung cancer (NSCLC) and acquired resistance to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) using an artificial intelligence (AI)-powered spatial TME analyzer. We then assessed the predictive efficacy of immune-checkpoint inhibitors (ICIs)-based treatment.

Experimental design

An AI-powered whole-slide image analyzer was used to segment cancer areas (CAs) and cancer stroma and to identify tumor-infiltrating lymphocytes (TILs), tertiary lymphoid structures, fibroblasts, and endothelial cells (ECs) in the tumor tissue. We analyzed 143 NSCLC samples after resistance to EGFR-TKIs from two cohorts: (1) 89 patients treated with ICI monoth

SUBMITTER: Bang YH 

PROVIDER: S-EPMC12581073 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature