We found that the developed machine learning models utilized IMPRESS and clinical features can accurately predict the response to NAC in breast cancer patients (HER2+ AUC = 0.8975; TNBC AUC = 0.7674), and outperformed the results learned by features which were manually generated by pathologists. Here, ERBB2 is linked to breast carcinoma.