ArticleIET systems biology
A Neutrophil-Based Predictive Model for Axillary De-Escalation After Neoadjuvant Therapy in Node-Positive Breast Cancer.
Article in IET systems biology. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to develop a novel immunoscore system integrating peripheral blood immune signatures and clinical factors to predict axillary pathological complete response (apCR) in clinically node-positive (cN+) breast cancer patients after neoadjuvant treatment (NAT) and facilitate personalized axillary de-escalation strategies. A retrospective analysis was conducted on cN+ breast cancer patients who received NAT at Sichuan Cancer Hospital, with 437 cases (June 2018-June 2023) as the training set and 266 cases (July 2023-July 2024) as the validation set, where clinicopathological data and peripheral blood immune indices were collected, multivariate logistic regression was used to identify independent predictors of apCR, predictive models were compared via ROC analysis, and a nomogram was constructed based on the optimal model. The apCR rate was 48.7% (213/437), with multivariate analysis revealing HER2 positivity (OR = 6.32, 95% CI: 3.95-10.12, p < 0.001), clinical response (RECIST 1.1), and baseline neutrophil count (OR = 1.26 per unit increase, 95% CI: 1.08-1.48, p = 0.003) as independent predictors, while the combined clinical-hematologic model (AUC = 0.766) outperformed the clinical-only model (AUC = 0.757) with consistent performance in the validation cohort (AUC = 0.759) and baseline neutrophil count exhibiting a strong linear correlation with apCR rates (r = 0.97, p < 0.001). In conclusion, baseline neutrophil count, HER2 status, and clinical response jointly predict apCR post-NAT in cN+ breast cancer, and the proposed immunoscore nomogram offers a practical tool to guide axillary de-escalation and optimize surgical decision-making.
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