scholarly journals Predictive factors of pathologic complete response in HER2-positive and axillary lymph node positive breast cancer after neoadjuvant paclitaxel, carboplatin plus with trastuzumab

Oncotarget ◽  
2017 ◽  
Vol 8 (34) ◽  
pp. 56626-56634 ◽  
Author(s):  
Jinhua Ding ◽  
Yinlong Yang ◽  
Li Jiang ◽  
Weizhu Wu ◽  
Zhiming Shao
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Predictive Factors ◽  
Axillary Lymph Node ◽  
Pathologic Complete Response ◽  
Complete Response ◽  
Axillary Lymph ◽  
Her2 Positive ◽  
Lymph Node Positive ◽  
Positive Breast Cancer

Nomogram for predicting axillary lymph node pathological complete response in node-positive breast cancer patients after neoadjuvant chemotherapy.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e12090-e12090
Author(s):  
Wenyan Wang ◽  
Xin Wang ◽  
Xiang Wang ◽  
Jiaqi Liu ◽  
Pin Zhang
Keyword(s):  
Breast Cancer ◽  
Lymph Node ◽  
Cancer Patients ◽  
Axillary Lymph Node ◽  
Complete Response ◽  
Axillary Lymph ◽  
Breast Cancer Patients ◽  
Internal Validation ◽  
Node Positive ◽  
Positive Breast Cancer

e12090 Background: Pathological complete response (pCR) of axillary lymph nodes (ALNs) is frequently achieved in patients with clinically node-positive breast cancer after neoadjuvant chemotherapy (NAC), and ALN status is an important prognostic factor for breast cancer patients. Our goal is to develop a new predictive clinical model to assess the axillary lymph node pCR rate after NAC. Methods: A retrospective series of 547 patients who had biopsy-proven positive ALNs at diagnosis and undergoing axillary lymph node dissection from 2007 to 2014 in National Cancer Center/Cancer Hospital of Chinese Academy of Medical Sciences. We analyzed the clinicopathologic features and developed a nomogram to predict the probability of ALN pCR. Univariate assessment was performed using a logistic regression model. A multivariate logistic regression stepwise model was used to generate a nomogram to predict ALN pCR in node positive patients Variables with P < 0.05 on multivariable analysis were included in the nomogram. The adjusted area under the receiver operating characteristic curve (AUC) was calculated to quantify the ability to rank patients by risk. Internal validation was estimated using 50-50 hold out validation method. Nomogram was validated externally with the prospective cohort of 167 patients from 2016 to 2018. Results: In retrospective study, there were 172 (31.4%) patients achieved axillary pCR after NAC. Multivariate analysis indicated that clinical nodal (N) stage, hormone receptor (HR) status and clinical response of primary tumor after NAC were significant independent predictors for axillary pCR ( P< 0.05). The NAC nomogram was based on these three variables. In the internal validation of performance, the AUCs for the training and test sets were 0.719 and 0.753, respectively. The nomogram was validated in an external cohort with an AUC of 0.734. Conclusions: We developed a nomogram to predict the likelihood of axillary pCR in node positive breast cancer patients after NAC. The predictive model performed well in prospective external validation. This practical tool could provide information to surgeons regarding whether to perform additional ALND after NAC.

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