Deep learning predicts EGFR mutation status from histology images in non-small cell lung cancer

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 (GP), a DL model trained and tuned using over 12,000 whole-slide images, that predicts EGFR mutation status from routine hematoxylin and eosin images. Using a diverse dataset (n=1,461) that captures histological subtypes, multiple whole-slide scanners, different scan magnifications and specimen types, we report an overall area under the ROC curve (AUROC) of 0.905. The model demonstrates robust performance across specimen types (biopsies and surgical resections, 0.804 and 0.912, respectively), histologic subtypes (adenocarcinoma and non-adenocarcinoma, 0.880 and 0.801, respectively), and EGFR mutation subtypes (AUROC 0.854-0.931). Additionally, across a second independent test set (n=599) sourced from 11 countries utilizing 5 different slide scanners, Lunit SCOPE GP achieved a robust AUROC of 0.860. Furthermore, across a multi-scanner test set (n=2,261), EGFR mutation predictions were concordant in 90.4% of cases among five of six frequently used slide scanners. This validation across diverse clinical settings represents a vital step towards the application of artificial intelligence (AI)-based digital pathology tools in routine clinical practice to augment molecular EGFR mutation screening.