Predicting level 2 axillary lymph node metastasis in a Chinese breast cancer population post-neoadjuvant chemotherapy: development and assessment of a new predictive nomogram.

Abstract

BACKGROUND:

We aimed to develop a new nomogram to predict the probability of level 2 axillary lymph node metastasis (L-2-ALNM) in breast cancer (BC) patients treated with neoadjuvant chemotherapy (NAC).

METHODS:

Data were collected from 709 patients who received neoadjuvant chemotherapy and then underwent axillary lymph node (ALN) dissection between May 2009 and December 2015 at the Liaoning Cancer Hospital. The level 2 axillary lymph node metastasis (L-2-ALNM ) nomogram was created from the logistic regression model. An additional set of 141 consecutive patients treated at the same institution between January 2015 and December 2015 were enrolled as the validation group. The predictive accuracy of the L-2-ALNM nomogram was measured by calculating the area under the receiver operating characteristic curve (AUC).

RESULTS:

In multivariate analysis, age, tumor size, histological grade, skin invasion, and response to neoadjuvant chemotherapy were identified as independent predictors of L-2-ALNM. The new model was accurate and discriminating for both the modeling and validation groups (AUC: 0.819 vs 0.849). The false-negative rates of the L-2-ALNM nomogram were 4.44% and 7.69% for the predicted probability cut-off points of 10% and 20%.

CONCLUSION:

The L-2-ALNM nomogram shows reasonable accuracy for making clinical decisions. The omission of level 2 axillary lymph node dissection after neoadjuvant chemotherapy might be possible if the probability of level 2 lymph node involvement was < 10% or < 20% in accordance with the acceptable risk determined by medical staff and patients.

• Authors: Caigang Liu, Yanlin Jiang, Xin Gu, Zhen Xu, Liping Ai, Hao Zhang, Guanglei Chen, Lisha Sun, Yue Li, Hong Xu, Huizi Gu, Ying Yu, Yangyang Xu, Qiyong Guo
• Journal: Oncotarget (Oncotarget) Vol. 8 Issue 45 Pg. 79147-79156 (Oct 03 2017) ISSN: 1949-2553 [Electronic] United States
• PMID: 29108294 (Publication Type: Journal Article)