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. 2019 Mar 28;53(3):1800986.
doi: 10.1183/13993003.00986-2018. Print 2019 Mar.

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

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Free PMC article

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

Shuo Wang et al. Eur Respir J. .
Free PMC article

Abstract

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.

Conflict of interest statement

Conflict of interest: S. Wang has nothing to disclose. Conflict of interest: J. Shi has nothing to disclose. Conflict of interest: Z. Ye has nothing to disclose. Conflict of interest: D. Dong has nothing to disclose. Conflict of interest: D. Yu has nothing to disclose. Conflict of interest: M. Zhou has nothing to disclose. Conflict of interest: Y. Liu has nothing to disclose. Conflict of interest: O. Gevaert has nothing to disclose. Conflict of interest: K. Wang has nothing to disclose. Conflict of interest: Y. Zhu has nothing to disclose. Conflict of interest: H. Zhou has nothing to disclose. Conflict of interest: Z. Liu has nothing to disclose. Conflict of interest: J. Tian has nothing to disclose.

Figures

FIGURE 1
FIGURE 1
Illustration of the deep learning model. This model is composed of convolutional layers with kernel size 3×3 and 1×1, batch normalisation and pooling layers. Sub-network 1 shares the same structure with the first 20 layers in DenseNet [31], which was pre-trained using 1.28 million natural images. Sub-network 2 was trained in the epidermal growth factor receptor (EGFR) mutation dataset, aiming at capturing the association between image features to EGFR mutation labels. When we feed a tumour into the deep learning model, it predicts the probability of the tumour being EGFR-mutant. CT: computed tomography.
FIGURE 2
FIGURE 2
Predictive performance of the deep learning model. a) Receiver operating characteristic curves of the deep learning (DL) model, radiomics model, semantic model and clinical model in the primary/validation cohorts. b) DL score between epidermal growth factor receptor (EGFR)-mutant and EGFR-wild type groups in the primary and validation cohorts. c) Decision curve of the DL model. The green line represents the benefit of treating all the patients as EGFR-wild type, and the blue line represents the benefit of treating all the patients as EGFR-mutant. The red line shows the benefit of using the DL model.
FIGURE 3
FIGURE 3
Suspicious tumour area discovery. We used 0.5 as cut-off value to acquire the suspicious areas according to the attention map of the deep learning (DL) model. EGFR: epidermal growth factor receptor.
FIGURE 4
FIGURE 4
Deep learning feature analysis. a) Convolutional filters (Conv_) from the 2nd, 13th, 20th and 24th layers of the deep learning model. Each convolutional layer includes hundreds of filters, and only the first three filters are illustrated in each layer. b) Response of the negative filter and the positive filter in epidermal growth factor receptor (EGFR)-mutant/-wild type tumours. The positive filter has strong response to EGFR-mutant tumours and the negative filter has strong response to EGFR-wild type tumours. All the tumour images are from the validation cohort. c) Response value of the positive and the negative filters in the two cohorts. d) Unsupervised clustering of lung adenocarcinoma patients (n=844) on the vertical axis and deep learning feature expression (feature dimension=32, the Conv_24 layer) on the horizontal axis.

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