Nomogram predicting clinical outcomes in breast cancer patients treated with neoadjuvant chemotherapy

Purpose The aim of this study was to combine clinical pathologic variables that are associated with pathologic completer response (pCR) and relapse-free survival (RFS) after neoadjuvant chemotherapy into prediction nomograms. Methods A total of 370 stage II or III breast cancer patients who received neoadjuvant docetaxel/doxorubicin chemotherapy were enrolled in this study. We developed the nomograms using logistic regression model for pCR and Cox proportional hazard regression model for RFS. Results The nomogram for pCR based on initial tumor size, estrogen receptor (ER), human epidermal growth factor receptor 2, and Ki67 had good discrimination performance (AUROC = 0.830). Multivariate Cox model identified age less than 35, initial clinical stage, pathologic stage, ER, Ki67 as prognostic factors, and the nomogram for RFS was developed based on these covariates. The concordance index for the second nomogram was 0.781, and calibration was also good. Conclusions We developed nomograms based on clinical and pathologic characteristics to predict the probability of pCR and RFS for patients receiving neoadjuvant docetaxel/doxorubicin chemotherapy.