RT Journal Article
SR Electronic
T1 Predicting EGFR Mutation Status in Lung Adenocarcinoma on CT Image Using Deep Learning
JF European Respiratory Journal
JO Eur Respir J
FD European Respiratory Society
SP 1800986
DO 10.1183/13993003.00986-2018
A1 Shuo Wang
A1 Jingyun Shi
A1 Zhaoxiang Ye
A1 Di Dong
A1 Dongdong Yu
A1 Mu Zhou
A1 Ying Liu
A1 Olivier Gevaert
A1 Kun Wang
A1 Yongbei Zhu
A1 Hongyu Zhou
A1 Zhenyu Liu
A1 Jie Tian
YR 2019
UL http://erj.ersjournals.com/content/early/2019/01/02/13993003.00986-2018.abstract
AB 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<0.001). The deep learning score demonstrated significant difference in EGFR-mutant and EGFR-wild type tumours (p<0.001).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.FootnotesThis manuscript has recently been accepted for publication in the European Respiratory Journal. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJ online. Please open or download the PDF to view this article.Conflict of interest: Dr. Wang has nothing to disclose.Conflict of interest: Dr. Shi has nothing to disclose.Conflict of interest: Dr. Ye has nothing to disclose.Conflict of interest: Dr. Dong has nothing to disclose.Conflict of interest: Dr. Yu has nothing to disclose.Conflict of interest: Dr. Zhou has nothing to disclose.Conflict of interest: Dr. Liu has nothing to disclose.Conflict of interest: Dr. Gevaert has nothing to disclose.Conflict of interest: Dr. Wang has nothing to disclose.Conflict of interest: Dr. Zhu has nothing to disclose.Conflict of interest: Dr. Zhou has nothing to disclose.Conflict of interest: Dr. Liu has nothing to disclose.Conflict of interest: Dr. Tian has nothing to disclose.