LAUSR

Purpose Epidermal growth factor receptor (EGFR) mutation testing has several limitations. Therefore, we built predictive models to determine the EGFR mutation status of patients and guide therapeutic decision-making. Methods We collected data from 320 patients with lung carcinoma, including sex, age, smoking history, serum tumour marker levels, maximum standardized uptake value, pathological results, computed tomography images, and EGFR mutation status. Artificial neural network (ANN) models based on multiple clinical characteristics were proposed to predict EGFR mutation status. Results A training set ( n  = 200) was used to develop predictive models of the EGFR mutation status (Model 1: area under the receiver operating characteristic curve [AUROC] = 0.910, 95% CI 0.861–0.945; Model 2: AUROC = 0.859, 95% CI 0.803–0.904; Model 3: AUROC = 0.711, 95% CI 0.643–0.773). A testing set ( n  = 50) and temporal validation data set ( n  = 70) were used to evaluate the generalisation performance of the established models (testing set: Model 1, AUROC = 0.845, 95% CI 0.715–0.932; Model 2, AUROC = 0.882, 95% CI 0.759–0.956; Model 3, AUROC = 0.817, 95% CI 0.682–0.912; temporal validation dataset: Model 1, AUROC = 0.909, 95% CI 0.816–0.964; Model 2, AUROC = 0.855, 95% CI 0.751–0.928; Model 3, AUROC = 0.831, 95% CI 0.723–0.910). The predictive abilities of the three ANN models were superior to that of a previous logistic regression model ( P  < 0.001, 0.027, and 0.050, respectively). Conclusions ANN models provide a non-invasive and readily available method for EGFR mutation status prediction.