Identifying epidermal growth factor receptor mutation status in patients with lung adenocarcinoma by three-dimensional convolutional neural networks

Objective: Genetic phenotype plays a central role in making treatment decisions of lung adenocarcinoma, especially the tyrosine-kinase-inhibitors-sensitive mutations of the epidermal growth factor receptor (EGFR) gene. We constructed three-dimensional convolutional neural networks (CNN) to analyze underlying patterns in CT images that could indicate that EGFR gene mutation status but are invisible to human eyes. Methods: From 2012 to 2015, 503 Chinese patients with lung adenocarcinoma that had underwent surgery were included. Pathological types and EGFR mutation status were tested from surgical resections. EGFR mutations (exon 19 deletion or exon 21 L858R) were found in 215/345 (62.3%) and 91/158 (57.6%) patients in the training and independent validation set, respectively. CT images were taken before any invasive operation. The patients were randomly chosen to train the CNNs or validate the CNNs' performance. The performance was quantified using area under receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results: The CNNs showed an AUC of 0.776 (range: 0.702-0.849, p< 0.0001) in the independent validation set and a fusion model of CNNs and clinical features (sex and smoking history) showed an AUC of 0.838 (range: 0.778-0.899, p< 0.0001), accuracy of 77.2%, sensitivity of 75.8% and specificity of 79.1% at the best diagnostic decision point. Conclusion: The CNN exhibits potential ability to identify EGFR mutation status in patients with lung adenocarcinoma which might help make clinical decisions. Advances in knowledge: The CNN showed some diagnostic power and its performance could be further improved by increasing the training set, optimizing the network structure and training strategy. Medical image based CNN has the potential to reflect spatial heterogeneity.