Predicting EGFR Mutation Status in Lung Adenocarcinoma on CT Image Using Deep Learning

Epidermal Growth Factor Receptor (EGFR) genotyping is critical for treatment guideline such as the use of tyrosine kinase inhibitors in lung adenocarcinoma (LA). Conventional identification of EGFR genotype requires biopsy and sequence testing that is invasive and may suffer from the difficulty in accessing tissue samples. Here, we proposed a deep learning (DL) model to predict the EGFR mutation status in LA by non-invasive computed tomography (CT). We retrospectively collected 844 LA patients with preoperative CT image, EGFR mutation and clinical information from two hospitals. An end-to-end DL model was proposed to predict the EGFR mutation status by CT scanning. By training in 14926 CT images, the DL model achieved encouraging predictive performance in both the primary cohort (n=603; AUC=0.85, 95% CI 0.83–0.88) and the independent validation cohort (n=241; AUC=0.81, 95% CI 0.79–0.83), which showed significant improvement than previous studies using hand-crafted CT features or clinical characteristics (p Since CT is routinely used in lung cancer diagnosis, the DL model provides a non-invasive and easy-to-use method for EGFR mutation status prediction.