Prediction of EGFR Mutation Status Based on (18)F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma

OBJECTIVE: The purpose of this study was to develop a deep learning-based system to automatically predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in (18)F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT). METHODS: Three hundred and one lung adenocarcinoma patients with EGFR mutation status were enrolled in this study. Two deep learning models (SE(CT) and SE(PET)) were developed with Squeeze-and-Excitation Residual Network (SE-ResNet) module for the prediction of EGFR mutation with CT and PET images, respectively. The deep learning models were trained with a training data set of 198 patients and tested with a testing data set of 103 patients. Stacked generalization was used to integrate the results of SE(CT) and SE(PET). RESULTS: The AUCs of the SE(CT) and SE(PET) were 0.72 (95% CI, 0.62–0.80) and 0.74 (95% CI, 0.65–0.82) in the testing data set, respectively. After integrating SE(CT) and SE(PET) with stacked generalization, the AUC was further improved to 0.84 (95% CI, 0.75–0.90), significantly higher than SE(CT) (p<0.05). CONCLUSION: The stacking model based on (18)F-FDG PET/CT images is capable to predict EGFR mutation status of patients with lung adenocarcinoma automatically and non-invasively. The proposed model in this study showed the potential to help clinicians identify suitable advanced patients with lung adenocarcinoma for EGFR‐targeted therapy.