Artificial intelligence enables prediction of MET amplification & associated morphologic features from H&E-stained NSCLC specimens

Study Background

MET receptor tyrosine kinase, when upregulated by mutation, fusion, or gene amplification, acts as an oncogenic driver in many cancers, including non-small cell lung cancer (NSCLC).

Tumors with MET gene amplification (MET-amp) exhibit distinct clinical behavior. Thus, distinguishing MET-amp from non-MET-amp cases, typically determined by fluorescent in situ hybridization (FISH), is crucial.

Here, we introduce an artificial intelligence (AI) model to predict MET-amp status directly from whole slide images (WSIs) of routine hematoxylin and eosin (H&E)-stained histologic sections and reveal elevated densities of highly pleomorphic (HP) tumor cells in MET-amp NSCLC

Conference

AACR 2025

Partner

AbbVie