PT - JOURNAL ARTICLE AU - Shuo Wang AU - Jingyun Shi AU - Zhaoxiang Ye AU - Di Dong AU - Dongdong Yu AU - Mu Zhou AU - Ying Liu AU - Olivier Gevaert AU - Kun Wang AU - Yongbei Zhu AU - Hongyu Zhou AU - Zhenyu Liu AU - Jie Tian TI - Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning AID - 10.1183/13993003.00986-2018 DP - 2019 Mar 01 TA - European Respiratory Journal PG - 1800986 VI - 53 IP - 3 4099 - http://erj.ersjournals.com/content/53/3/1800986.short 4100 - http://erj.ersjournals.com/content/53/3/1800986.full SO - Eur Respir J2019 Mar 01; 53 AB - Epidermal growth factor receptor (EGFR) genotyping is critical for treatment guidelines such as the use of tyrosine kinase inhibitors in lung adenocarcinoma. Conventional identification of EGFR genotype requires biopsy and sequence testing which is invasive and may suffer from the difficulty of accessing tissue samples. Here, we propose a deep learning model to predict EGFR mutation status in lung adenocarcinoma using non-invasive computed tomography (CT).We retrospectively collected data from 844 lung adenocarcinoma patients with pre-operative CT images, EGFR mutation and clinical information from two hospitals. An end-to-end deep learning model was proposed to predict the EGFR mutation status by CT scanning.By training in 14 926 CT images, the deep learning 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 over previous studies using hand-crafted CT features or clinical characteristics (p<0.001). The deep learning score demonstrated significant differences in EGFR-mutant and EGFR-wild type tumours (p<0.001).Since CT is routinely used in lung cancer diagnosis, the deep learning model provides a non-invasive and easy-to-use method for EGFR mutation status prediction.Deep learning provides a noninvasive method for EGFR mutation prediction (AUC 0.81) in lung adenocarcinoma, which shows significant improvement over using hand-crafted CT features or clinical characteristics http://ow.ly/LtDJ30nhc5Q