Background
Epidermal growth factor receptor (EGFR) mutations represent one of the most clinically significant molecular alterations in non-small cell lung cancer, particularly in lung adenocarcinoma (LUAD). These mutations serve as predictive biomarkers for targeted therapy with EGFR tyrosine kinase inhibitors, which have transformed treatment outcomes for affected patients. The identification of EGFR mutations has traditionally relied on molecular testing methods such as next-generation sequencing (NGS), polymerase chain reaction-based assays, or Sanger sequencing—all requiring tissue sampling and laboratory processing time that may delay treatment initiation.
In recent years, artificial intelligence (AI) models have emerged as promising tools for extracting genomic information directly from routine hematoxylin-eosin (H&E) stained pathology slides. These computational pathology approaches aim to democratize access to molecular profiling by leveraging the morphological patterns that correlate with underlying genetic alterations. However, the generalizability of these models across diverse patient populations and clinical contexts remains inadequately characterized. Given the known interpopulation differences in EGFR mutation frequencies and potential confounding factors such as tissue composition and staining variability, rigorous evaluation of AI model performance across ancestry groups is essential before clinical implementation.
