Abstract
BACKGROUND: The aim of this study is to develop deep learning models based on (18)F-fluorodeoxyglucose positron emission tomography/computed tomographic ((18)F-FDG PET/CT) images for predicting individual epidermal growth factor receptor (EGFR) mutation status in lung adenocarcinoma (LUAD). METHODS: We enrolled 430 patients with non-small-cell lung cancer from two institutions in this study. The advanced Inception V3 model to predict EGFR mutations based on PET/CT images and developed CT, PET, and PET + CT models was used. Additionally, each patient's clinical characteristics (age, sex, and smoking history) and 18 CT features were recorded and analyzed. Univariate and multivariate regression analyses identified the independent risk factors for EGFR mutations, and a clinical model was established. The performance using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, recall, and F1-value was evaluated. The DeLong test was used to compare the predictive performance across various models. RESULTS: Among these four models, deep learning models based on CT and PET + CT exhibit the same predictive performance, followed by PET and the clinical model. The AUC values for CT, PET, PET + CT, and clinical models in the training set are 0.933 (95% CI, 0.922-0.943), 0.895 (95% CI, 0.882-0.907), 0.931 (95% CI, 0.921-0.942), and 0.740 (95% CI, 0.685-0.796), respectively; whereas those in the testing set are:0.921 (95% CI, 0.904-0.938), 0.876 (95% CI, 0.855-0.897), 0.921 (95% CI, 0.904-0.937), and 0.721 (95% CI, 0.629-0.814), respectively. The DeLong test results confirm that all deep learning models are superior to clinical one. CONCLUSION: The PET/CT images based on trained CNNs effectively predict EGFR and non-EGFR mutations in LUAD. The deep learning predictive models could guide treatment options.