Abstract
Axillary lymph node (ALN) metastasis is a key prognostic factor in breast cancer (BC). Although neoadjuvant chemotherapy (NAC) is widely used to downstage tumors and facilitate surgical management, accurately predicting ALN status after NAC remains a clinical challenge. The present study aimed to develop a predictive model using clinical and pathological variables to assess the risk of ALN metastasis following NAC. A retrospective analysis was conducted on 156 female patients with BC who received NAC, of whom 131 met inclusion criteria and were analyzed. The patients were randomly divided into a training cohort (97 patients) and a validation cohort (34 patients). Clinical and pathological variables, including age, menopausal status, tumor stage before chemotherapy, lymph node stage, histological grade, molecular subtyping, estrogen and progesterone receptor expression, HER-2 status, Ki67 expression, post-chemotherapy tumor stage, and the proportion of tumor and Ki67 regression before and after chemotherapy were collected. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of ALN metastasis. A logistic regression-based nomogram was constructed using the multivariate analysis, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC). In the training cohort, age, pre-chemotherapy lymph node status (N stage), Ki67 reduction level, and pre-chemotherapy molecular subtyping were identified as independent predictors of ALN metastasis. The nomogram demonstrated favorable predictive accuracy, with an AUC of 0.877. The validation cohort showed an AUC of 0.842, with sensitivity, specificity and positive predictive value of 76, 82 and 81%, respectively. The false negative rate in the validation cohort was 24%. In conclusion, a predictive model based on age, pre-chemotherapy lymph node status, Ki67 reduction level and molecular subtyping was developed to assess ALN metastasis after NAC in patients with BC. While the model demonstrated favorable accuracy, further refinement is needed to reduce the false negative rate and improve clinical utility. The incorporation of molecular biomarkers and advanced imaging techniques may enhance the model's performance.