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A New Model Incorporating Axillary Ultrasound After Neoadjuvant Chemotherapy to Predict Non-Sentinel Lymph Node Metastasis in Invasive Breast Cancer

Authors Zhang K, Zhu Q, Sheng D, Li J, Chang C

Received 25 November 2019

Accepted for publication 29 January 2020

Published 10 February 2020 Volume 2020:12 Pages 965—972

DOI https://doi.org/10.2147/CMAR.S239921

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Antonella D'Anneo


Kai Zhang,* Qian Zhu,* Danli Sheng, Jiawei Li, Cai Chang

Department of Medical Ultrasound, Fudan University Shanghai Cancer Center, Shanghai, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Cai Chang
Fudan University Shanghai Cancer Center, No. 270, Dong’an Road, Xuhui District, Shanghai 200032, People’s Republic of China
Tel +86 180 1731 2605
Fax +86 21 6417 4774
Email changc62@126.com

Purpose: Few models with good discriminative power have been introduced to predict the risk of non-sentinel lymph node (non-SLN) metastasis in breast cancer after neoadjuvant chemotherapy (NAC). We aimed to develop a new and simple model for predicting the probability of non-SLN metastasis in breast cancer and facilitate the selection of patients who could avoid unnecessary axillary lymph node dissection following NAC.
Patients and Methods: A total of 298 patients diagnosed with invasive breast cancer, who underwent SLN biopsy after completing NAC and subsequently breast surgery, were included and classified into the training set (n=228) and testing set (n=70). Univariate and multivariate analyses were used to select factors that could be determined prior to breast surgery and significantly correlated with non-SLN metastasis in the training set. A logistic regression model was developed based on these factors and validated in the testing set.
Results: Nodal status before NAC, post-NAC axillary ultrasound status, SLN number, and SLN metastasis number were independent predictors of non-SLN metastases in breast cancer after NAC. A predictive model based on these factors yielded an area under the curve of 0.838 (95% confidence interval: 0.774– 0.902, p< 0.001) in the training set. When applied to the testing set, this model yielded an area under the curve of 0.808 (95% confidence interval: 0.609– 1.000, p= 0.003).
Conclusion: A new and simple model, which incorporated factors that could be determined prior to breast surgery, was developed to predict non-SLN metastasis in invasive breast cancer following NAC. Although this model performed excellently in internal validation, it requires external validation before it can be widely utilized in the clinical setting.

Keywords: breast carcinoma, neoadjuvant chemotherapy, sentinel lymph node, ultrasonography

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