Academic Editor: Michael H. Dahan
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