A model for predicting non-sentinel lymph node metastatic disease when the sentinel lymph node is positive

Background: Women with axillary sentinel lymph node (SLN)-positive breast cancer usually undergo completion axillary lymph node dissection (ALND). However, not all patients with positive SLNs have further axillary nodal disease. Therefore, in the patients with low risk of further disease, completion ALND could be avoided. The Memorial Sloan-Kettering Cancer Center (MSKCC) developed a nomogram to estimate the risk of non-SLN disease. This study critically appraised the nomogram and refined the model to improve predictive accuracy.

Methods: The MSKCC nomogram was applied to 118 patients with a positive axillary SLN biopsy who subsequently had completion ALND. Predictive accuracy was assessed by calculating the area under the receiver-operator characteristic (ROC) curve. A further predictive model was developed using more detailed pathological information. Backward stepwise multiple logistic regression was used to develop the predictive model for further axillary lymph node disease. This was then converted to a probability score. After k-fold cross-validation within the data, an inverse variance weighted mean ROC curve and area below the ROC curve was calculated.

Results: The MSKCC nomogram had an area under the ROC curve of 68 per cent. The revised predictive model showed the weighted mean area under the ROC curve to be 84 per cent.

Conclusion: The modified predictive model, which incorporated size of SLN metastasis, improved predictive accuracy, although further testing on an independent data set is desirable.