Development and Assessment of a Novel Core Biopsy-Based Prediction Model for Pathological Complete Response to Neoadjuvant Chemotherapy in Women with 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: This was a retrospective study involving 920 BC patients administered NAC between January 2012 and December 2018. The patients were divided into a primary cohort (769 patients from January 2012 to December 2017) and a validation cohort (151 patients from January 2017 to December 2018). After converting continuous variables to categorical variables, variables entering the model were sequentially identified via univariate analysis, a 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. Results: The optimal predictive threshold for estrogen receptor (ER), Ki67, and p53 were 22.5%, 32.5%, and 37.5%, respectively (all p < 0.001). Five variables were selected to develop the model: clinical T staging (cT), clinical nodal (cN) status, 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 and 0.774 for the primary and validation cohorts, respectively, and good calibration. Decision curve analysis (DCA) showed that the model had practical clinical value. Conclusions: 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.

A nomogram model for predicting the risk of axillary lymph node metastasis in patients with early breast cancer and cN0 status

“…A multicollinearity test performed on the four independent risk factors revealed that the tolerance values for LVI, US, MD and molecular subtype were 0.939, 0.942, 0.994 and 0.979, respectively, all of which were >0.1. Moreover, the tolerance values for VIF were 1.065, 1.061, 1.006 and 1.021, respectively, all of which were <5 ( 30 ) ( Table IV ). Hence, it was concluded that there was no multicollinearity.…”

Nomogram to predict pathological axillary lymph node status after neoadjuvant therapy in triple negative or HER2 positive breast cancer

“…Our model was established to predict the risk of ypN + after NAT in patients with TN or HER2 + diseases. Different from models for all molecule types in most previous literatures [27][28][29][30], ours was the rst model aimed at TN or HER2 + patients for reasons of the higher nodal pCR rate in those patients. In our study, the included variables of nomogram could be obtained during routine examination before NAT, which did not require additional examination and was more consistent with actual clinical operation, showing the feasibility of nomogram as a predictive tool.…”

“…The higher rates of nodal pCR in patients with TN and HER2 + diseases make this group more likely to be omitted with ALND. However, there was no accurate model to predict pathologically node-positive disease after NAT (ypN+) in patients with TN or HER2 + diseases [27][28][29][30][31][32]. In current study, we aimed to analyze factors associated with ypN + status and established a nomogram model based on these factors to predict the risk of ypN + after NAT in breast cancer patients with TN or HER2 + diseases, thus to identify the candidate patients to receive the de-escalating SLNB surgery instead of ALND.…”

Development of a multiparametric model for predicting the response to neoadjuvant chemotherapy in breast cancer

“…The histopathological features and genomic expression of tumors are used to monitor disease progression and responses to treatment, detect tumor reoccurrence, and evaluate the prognosis of breast cancer patients ( 6 , 7 ). Some studies have reported that the histopathologic features of tumor tissue, including programmed death-ligand 1 expression and Ki-67 status, are related to the efficacy of NAC in breast cancer patients ( 8 , 9 ). Moreover, the predictive effects of molecular markers and liquid biopsies in the NAC of breast cancer patients have received widespread attention ( 10 , 11 ).…”