Prediction of EGFR Mutation Status Based on 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 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 and SE) 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 and SE.

RESULTS

The AUCs of the SE and SE 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 and SE with stacked generalization, the AUC was further improved to 0.84 (95% CI, 0.75-0.90), significantly higher than SE (p<0.05).

CONCLUSION

The stacking model based on 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.