Intraoperative predictive model for the detection of metastasis in non-sentinel axillary lymph nodes

Abstract

Background: To design a software-applied predictive model relating patients clinical and pathological traits associated with sentinel lymph-node total tumor load to individually establish the need to perform an axillary lymph-node dissection. Methods: Retrospective observational study including 127 patients with breast cancer in which a sentinel lymph-node biopsy was performed with the one step nucleic acid amplification method and a subsequent axillary lymph-node dissection. We created various binary multivariate logistic regression models using non-automated methods to predict the presence of metastasis in non-sentinel lymph-nodes, including Log total tumor load, immunohistochemistry, multicentricity and progesterone receptors. These parameters were progressively added according to the simplicity of their evaluation and their predictive value to detect metastasis in non-sentinel lymph-nodes. Results: The final model was selected for having maximum discriminatory capability, good calibration, along with parsimony and interpretability. The binary logistic regression model chosen was the one which identified the variables Log total tumor load, immunohistochemistry, multicentricity and progesterone receptors as predictors of metastasis in non-sentinel lymph-nodes. Harrell’s C-index obtained from the area under the curve of the predicted probabilities by Model 4 was 0.77 (95% CI, 0.689–0.85; p < 0.0005). Conclusions: the combination of total tumor load, immunohistochemistry, multicentricity and progesterone receptors can predict 77% of patients with metastasis in non-sentinel lymph-nodes and said prediction may be made intraoperatively in a feasible manner.