Deep Learning Predicts EGFR Mutation Status from Histology Images in Non-Small Cell Lung Cancer.

ABSTRACT: EGFR mutation screening in non-small cell lung cancer (NSCLC) remains variable globally and represents a significant care gap despite international recommendations and molecular testing guidelines. Recently, the use of deep learning (DL) methods to extract clinically actionable features from routine histology images has gained regulatory approval for multiple clinical applications. Therefore, the integration of predictive DL to complement molecular EGFR mutation screening may improve biomarker testing rates in NSCLC. To address this unmet need, we developed and validated Lunit SCOPE Genotype Predictor, a DL model trained and tuned using more than 12,000 whole-slide images, that predicts EGFR mutation status from routine hematoxylin and eosin images. Using a diverse dataset (n = 1,461) that

SUBMITTER: Park J 

PROVIDER: S-EPMC12682618 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature