A clinical decision support system to predict the efficacy for EGFR-TKIs based on artificial neural network

Background: The efficacy of epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) was affected by numerous factors. In the study, we developed and validated an artificial neural network (ANN) system based on clinical characteristics and next-generation sequencing (NGS) to support clinical decisions. Methods: A multicenter retrospective non-interventional study was conducted. 240 patients from three hospitals with advanced non-small cell lung cancer (NSCLC) and EGFR mutation were tested by NGS before the first treatment. All patients received formal EGFR-TKIs treatment. Five different models were individually trained to predict the efficacy of EGFR-TKIs based on one medical center with 188 patients. Two independent cohorts from other medical centers were collected for external validation. Results: Compared with logistic regression, four machine learning methods showed better predicting abilities for EGFR-TKIs. The inclusion of NGS tests improved the predictive power of models. ANN performed best on the dataset with mutations TP53, RB1, PIK3CA, EGFR mutation sites, and tumor mutation burden (TMB). The prediction accuracy, recall and AUC were 0.82, 0.82, and 0.82, respectively in our final model. In the external validation set, ANN still showed good performance and differentiated patients with poor outcomes. Finally, a clinical decision support software based on ANN was developed and provided a visualization interface for clinicians. Conclusion: This study provides an approach to assess the efficacy of NSCLC patients with first-line EGFR-TKI treatment. Software is developed to support clinical decisions.