Development and validation of nomograms for predicting axillary non-SLN metastases in breast cancer patients with 1–2 positive sentinel lymph node macro-metastases: a retrospective analysis of two independent cohorts

Abstract

Background It is reported that appropriately 50% of early breast cancer patients with 1–2 positive sentinel lymph node (SLN) micro-metastases could not benefit from axillary lymph node dissection (ALND) or breast-conserving surgery with whole breast irradiation. However, whether patients with 1–2 positive SLN macro-metastases could benefit from ALND remains unknown. The aim of our study was to develop and validate nomograms for assessing axillary non-SLN metastases in patients with 1–2 positive SLN macro-metastases, using their pathological features alone or in combination with STMs. Methods We retrospectively reviewed pathological features and STMs of 1150 early breast cancer patients from two independent cohorts. Best subset regression was used for feature selection and signature building. The risk score of axillary non-SLN metastases was calculated for each patient as a linear combination of selected predictors that were weighted by their respective coefficients. Results The pathology-based nomogram possessed a strong discrimination ability for axillary non-SLN metastases, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.727 (95% CI: 0.682–0.771) in the primary cohort and 0.722 (95% CI: 0.653–0.792) in the validation cohort. The addition of CA 15–3 and CEA can significantly improve the performance of pathology-based nomogram in the primary cohort (AUC: 0.773 (0.732–0.815) vs. 0.727 (0.682–0.771), P \u2009<\u20090.001) and validation cohort (AUC: (0.777 (0.713–0.840) vs. 0.722 (0.653–0.792), P \u2009<\u20090.001). Decision curve analysis demonstrated that the nomograms were clinically useful. Conclusion The nomograms based on pathological features can be used to identify axillary non-SLN metastases in breast cancer patients with 1–2 positive SLN. In addition, the combination of STMs and pathological features can identify patients with patients with axillary non-SLN metastases more accurately than pathological characteristics alone.