A new model to predict risk of nonsentinel lymph node status in Chinese sentinel lymph node‐positive patients after neoadjuvant chemotherapy. Issue 6 (20th May 2018)

• Record Type: Journal Article
• Authors: Yang, Yang; He, Yingjian; Fan, Zhaoqing; Liu, Yiqiang; Ouyang, Tao
• Is Part Of: Breast journal. Volume 24:Issue 6(2018)
• Page Start: 883
• Page End: 888
• Publication Date: 2018-05-20
• Subjects: breast cancer -- nonsentinel lymph node -- predictive model -- prospective validation -- sentinel lymph node; Breast -- Diseases -- Periodicals; Breast -- Cancer -- Periodicals; 618.19
• DOI: 10.1111/tbj.13065
• Languages: English
• ISSNs: 1075-122X

Abstract

Abstract: There is no previous predictive model to assess risk of nonsentinel lymph node metastases (NSLN) in sentinel lymph node (SLN)‐positive breast cancer patients after neoadjuvant chemotherapy (NAT). Our goal was to develop a new predictive model for SLN‐positive patients after NAT, and validate this new model. A series of 513 patients with metastases in SLN who received NAT were used to evaluate factors affecting NSLN status. Logistic regression analysis was performed to develop a predictive model, which was validated by a subsequent prospective 138 patients. There were 115 (22.4%) patients with metastases in NSLN followed by axillary lymph node dissection (ALND). Multivariate analysis indicated that tumor (T) stage, number of positive SLN, micrometastases, extracapsular extension (ECE), and clinical response of primary tumor after NAT were significant independent predictors for the NSLN metastases. Area under the curve (AUC) of the model was 0.795 (95% CI, 0.734‐0.861). When applied to the prospective series, the model accurately predicted the risk of NSLN disease, AUC was 0.772 (95% CI, 0.653‐0.845). We present a new predictive model to assess the risk of NSLN status in Chinese SLN‐positive breast cancer patients after NAT. The predictive model performed well in prospective validation but needs to be further studied in external center patients before application to clinical use.