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A Neutrophil-Based Predictive Model for Axillary De-Escalation After Neoadjuvant Therapy in Node-Positive Breast

Exian Mou¹, Rui Guo¹, Huaichao Luo²

¹Department of Plastic and Reconstructive Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China.

IET Systems Biology

|November 28, 2025

Summary

A novel immunoscore combining neutrophil count, HER2 status, and clinical response predicts axillary pathological complete response (apCR) in breast cancer patients receiving neoadjuvant treatment (NAT). This tool aids personalized surgical decisions for node-positive breast cancer.

Keywords:

Pharmacology blood cancer toxicology

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

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Area of Science:

Oncology
Immunology
Clinical Medicine

Background:

Accurate prediction of axillary pathological complete response (apCR) is crucial for guiding surgical de-escalation in clinically node-positive (cN+) breast cancer patients undergoing neoadjuvant treatment (NAT).
Current predictive models often lack integration of peripheral immune signatures, limiting personalized treatment strategies.

Purpose of the Study:

To develop and validate a novel immunoscore system integrating peripheral blood immune markers and clinical factors to predict apCR in cN+ breast cancer patients post-NAT.
To establish a practical tool for optimizing axillary de-escalation strategies and surgical decision-making.

Main Methods:

Retrospective analysis of 437 (training) and 266 (validation) cN+ breast cancer patients treated with NAT.
Multivariate logistic regression to identify independent predictors of apCR, including clinicopathological data and peripheral blood immune indices.
Development and validation of a predictive nomogram based on identified predictors, with performance evaluated using ROC analysis.

Main Results:

The overall apCR rate was 48.7%.
Independent predictors of apCR included HER2 positivity, clinical response (RECIST 1.1), and baseline neutrophil count.
The combined clinical-hematologic model demonstrated superior predictive performance (AUC = 0.766) compared to the clinical-only model (AUC = 0.757), with consistent validation (AUC = 0.759).

Conclusions:

Baseline neutrophil count, HER2 status, and clinical response are significant joint predictors of apCR following NAT in cN+ breast cancer.
The developed immunoscore nomogram provides a valuable and practical tool for guiding axillary de-escalation and refining surgical management in this patient population.