Development and assessment of a novel core biopsy-based prediction model for pathological complete response to neoadjuvant chemotherapy in breast cancer

Abstract

Purpose Pathological complete response (pCR), the goal of NAC, is considered a surrogate for favorable outcomes in breast cancer (BC) patients administrated neoadjuvant chemotherapy (NAC). This study aimed to develop and assess a novel nomogram model for predicting the probability of pCR based on the core biopsy. Methods It was a retrospective study involving 769 BC patients administrated NAC between January 2012 and December 2017. After converting continuous variables to categorical variables, variables entering the model were sequentially identified by univariate analysis, multicollinearity test, and binary logistic regression analysis, and then a nomogram model was developed. The performance of the model was assessed concerning its discrimination, accuracy, and clinical utility. Internal validation was performed by the Bootstrap method. Results The optimal predictive threshold for estrogen receptor (ER), progesterone receptor (PR), Ki67, and p53 were 22.5% (95%CI: 0.603-0.718), 6.5% (95%CI: 0.578-0.693), 32.5% (95%CI: 0.610-0.731), and 37.5% (95%CI: 0.586-0.716), respectively (all P <0.001). Five variables were selected to develop the model, clinical T staging (cT), clinical nodal status (cN), ER status, Ki67 status, and p53 status (all P ≤0.001). The nomogram showed good discrimination with the area under the curve (AUC) of 0.804 (95% CI: 0.756-0.853; P <0.001) and good calibration. Decision curve analysis (DCA) showed that the model had practical clinical value. Conclusion This study constructed a novel nomogram model based on cT, cN, ER status, Ki67 status, and p53 status, which could be applied to personalize the prediction of pCR in BC patients treated with NAC.