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

Abstract

Purpose: Axillary lymph node (ALN) pathologic complete remission (pCR) rate after neoadjuvant therapy (NAT) is high in triple negative (TN) or human epidermal growth factor receptor 2-positive (HER2+) breast cancer patients. We aimed to identify factors associated with pathological ALN status after NAT in these patients, and establish a nomogram model to avoid unnecessary ALND. Methods: TN or HER2+ breast cancer patients receiving NAT in the Shanghai Jiao Tong University Breast Cancer Database (SJTU-BCDB) were retrospectively included in training cohort and internal validation cohort. Patients at the International Peace Maternity & Child Health Hospital (IPMCH) of China Welfare Institute were retrospectively collected for external validation. Based on univariate and multivariate logistic regression, a nomogram model was constructed to predict the probability of pathologically node-positive disease after NAT (ypN+) in TN or HER2+ patients. Results: 1,686 patients were assigned to the training set, and 723 patients in the validation set. Five independent factors including clinical nodal (cN) stage (P < 0.001), molecular subtype (P < 0.001), Ki67 expression (P = 0.003), tumor grade (P < 0.001), and clinical complete response (P < 0.001) together with clinical tumor (cT) stage were selected to construct the nomogram. The nomogram indicated the areas under ROC curve (AUCs) were 0.782, 0.753 and 0.783 in training cohort, internal validation cohort and external validation cohort, respectively. Conclusion: We developed a nomogram model for predicting the risk of ypN+ in TN or HER2+ breast cancer patients, which may guide the de-escalating ALN surgery after NAT.