Introduction: Prediction models are useful to guide decision making. Our goal was to compare three published nomograms predicting axillary response to neoadjuvant chemotherapy (NAC), clinically node-positive breast cancer. Methods: Patients with cT1–T4, cN1–N3 breast cancer treated with NAC and surgery from 2008 to 2019 were reviewed. The predicted probability of pathologic node-negative (ypN0) status was estimated for each nomogram. Area under the curve (AUC) was compared across models, overall and by biologic subtype. Results: Of 581 patients, 253 (43.5%) were ypN0. ypN0 status varied by subtype: 23.9% for estrogen receptor-positive (ER+)/human epidermal growth factor receptor 2-negative (HER2−), 68.9% for HER2-positive (HER2+), and 47.2% for ER-negative (ER−)/HER2−. The three nomograms had similar AUC values (0.761–0.769; p = 0.80). The Mayo model-predicted probability was significantly lower (p < 0.001) than the observed probability of ypN0 status, while the MD Anderson Cancer Center (MDACC) 1- and 2-predicted probabilities were similar to the observed probability. At a predicted probability threshold of 50%, the Mayo model had the highest sensitivity (89.6%) for detecting ypN+ patients compared with MDACC models 1 and 2 (76.5%; p < 0.001). However, both MDACC models had higher specificity in identifying ypN0 status among HER2+ (81.7%) and ER−/HER2− (75.9–77.6%) patients compared with the Mayo model (59.5% and 43.1%; each p < 0.001). None of the models identified the ER+/HER2− patients with ypN0 status well at the ≥ 50% threshold (specificity 0–9.4%). Conclusion: All three models predicting nodal response to NAC performed well overall with respect to discrimination, but differed with respect to calibration and performance at a 50% probability threshold. However, none of the models performed well at the 50% threshold for ER+/HER2− patients.
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