Development and validation of a nomogram incorporating axillary lymph node ratio to predict survival in node-positive breast cancer patients after neoadjuvant chemotherapy

摘要

Objective: Over the past decade, several studies have highlighted that axillary lymph node ratio (ratio of involved over excised axillary lymph nodes) was a superior predictor for survival outcomes compared with ypN staging. Thus, this study aimed to integrate the prognostic value of axillary lymph node ratio to improve individualized prediction of survival in node-positive breast cancer patients after neoadjuvant chemotherapy. Methods: A clinical data of 339 node-positive breast cancer patients after neoadjuvant chemotherapy from two independent centers were retrospectively reviewed. A nomogram incorporating axillary lymph node ratio was constructed to predict disease-free survival based on Cox proportional hazards model. The discrimination, calibration ability, and clinical usefulness of the axillary lymph node ratio-based model were evaluated using C-index, calibration curve, risk group stratification and decision curve analysis and were compared with the TNM staging system. Results: Independent prognostic factors for disease-free survival were age, pathological T stage, axillary lymph node ratio, histological grade, estrogen receptor status, Ki67 and lymphovascular invasion, which were entered into the nomogram. The C-index of the axillary lymph node ratio-based nomogram was higher than that of the TNM staging system (0.773 vs 0.610). The calibration plot indicated close agreement between model predictions and actual observations. Based on the risk group stratification of the nomogram, Kaplan-Meier curves demonstrated significant differences between the low-risk and high-risk groups (P < 0.0001). Conclusions: The axillary lymph node ratio-based nomogram provided more accurate individualized risk prediction of disease-free survival in node-positive breast cancer patients after neoadjuvant chemotherapy. This practical tool may assist oncologists in selecting the high-risk patients who are in need of a specific treatment strategy.