ArticleGland surgery2026
Relative change rate of neutrophil-to-albumin ratio (ΔNAR) during neoadjuvant chemotherapy predicts axillary pathological complete response in locally advanced breast cancer.
Article in Gland surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Routine blood inflammatory-nutritional markers can predict neoadjuvant chemotherapy (NAC) response in locally advanced breast cancer. Neutrophil-to-albumin ratio (NAR) reflects inflammation and nutrition, but prior studies only use static baseline NAR without tracking its dynamic changes during NAC. Axillary pathological complete response (pCR) strongly guides surgery and prognosis. This study proposed ΔNAR, the relative change rate of NAR pre- Methods: We retrospectively enrolled 169 node-positive locally advanced breast cancer patients who finished standard NAC and radical surgery. Blood neutrophil and albumin were tested 7 days pre-NAC and preoperatively post-NAC to calculate ΔNAR. Receiver operating characteristic (ROC) curves determined the optimal ΔNAR cutoff. Chi-squared tests and univariate/multivariate logistic regression identified predictors of axillary pCR, defined as ypN0 (no invasive axillary tumor cells). Results: ROC analysis set the optimal ΔNAR cutoff at -19.29% (area under curve =0.663). The low ΔNAR subgroup (ΔNAR ≤-19.29%) had a markedly higher axillary pCR rate than the high ΔNAR group (P=0.001). Univariate analysis linked hormone receptor (HR) status, human epidermal growth factor receptor 2 (HER2) status, anti-HER2 chemotherapy and ΔNAR to axillary pCR. Multivariate regression confirmed that ΔNAR >-19.29% was independently associated with lower odds of axillary pCR (P=0.049). Conclusions: ΔNAR acts as an independent biomarker for axillary pCR in locally advanced breast cancer. Patients with ΔNAR above -19.29% have lower odds of axillary pathological remission. Derived from routine blood tests at no extra cost, ΔNAR assists clinicians in preoperative prediction of axillary tumor regression and aids therapeuticplan optimization. Its utility for tailoring surgical plans remains to be further validated.
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