Development of an Artificial Neural Network Model and Comparison with Nomogram for Prediction of Pathological Complete Response After Neoadjuvant Chemotherapy in Breast Cancer

Identifying breast cancer patients who may benefit from neoadjuvant chemotherapy will facilitate personalized treatment regarding chemotherapy and surgery. In our work, we developed two predictive models, nomogram and a machine learning model based on artificial neural network (ANN), to anticipate pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer. We demonstrated that high level of estrogen receptor (ER) positivity, positive human epidermal growth factor receptor 2 (HER2) status, complete response on magnetic resonance imaging (MRI), abnormal CEA level after NAC, and abnormal CA15-3 level after NAC were significant predictors of pCR. A nomogram and ANN model trained to predict pCR were developed using these five predictors. The performance of the two models were tested using a fully independent test set. Validation test showed the area under the receiver operating characteristic curve (AUC) of 0.789 (95% confidence interval (CI), 0.707-0.871) for the nomogram and 0.876 (95% CI, 0.808-0.943) for the ANN model. Both models showed excellent performance, but the ANN model performed better in terms of accuracy and discrimination. Machine-learning algorithms hold promise in medical application and provide better prediction than nomogram.