A Logistic Regression Model for Predicting Axillary Lymph Node Metastases in Early Breast Carcinoma Patients

Abstract

Nodal staging in breast cancer is a key predictor of prognosis. This paper presents the results of potential clinicopathological predictors of axillary lymph node involvement and develops an efficient prediction model to assist in predicting axillary lymph node metastases. Seventy patients with primary early breast cancer who underwent axillary dissection were evaluated. Univariate and multivariate logistic regression were performed to evaluate the association between clinicopathological factors and lymph node metastatic status. A logistic regression predictive model was built from 50 randomly selected patients; the model was also applied to the remaining 20 patients to assess its validity. Univariate analysis showed a significant relationship between lymph node involvement and absence of nm-23 (<em>p = </em>0.010) and Kiss-1 (<em>p = </em>0.001) expression. Absence of Kiss-1 remained significantly associated with positive axillary node status in the multivariate analysis (<em>p = </em>0.018). Seven clinicopathological factors were involved in the multivariate logistic regression model: menopausal status, tumor size, ER, PR, HER2, nm-23 and Kiss-1. The model was accurate and discriminating, with an area under the receiver operating characteristic curve of 0.702 when applied to the validation group. Moreover, there is a need discover more specific candidate proteins and molecular biology tools to select more variables which should improve predictive accuracy.

Keywords

• breast cancer
• axillary metastases
• predictive model
• logistic regression
• lymph node staging