domingo, 20 de enero de 2019

Predicting EGFR Mutation Status in Lung Adenocarcinoma on CT Image Using Deep Learning. - PubMed - NCBI

2019 Jan 11. pii: 1800986. doi: 10.1183/13993003.00986-2018. [Epub ahead of print]

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

Wang S1,2, Shi J3, Ye Z4, Dong D1,2, Yu D1,2, Zhou M5, Liu Y4, Gevaert O5, Wang K1, Zhu Y1, Zhou H6, Liu Z1, Tian J1,2,7.

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Abstract

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.