# Factors Influencing Pathological Complete Response After Neoadjuvant Chemotherapy in Breast Cancer: A Single‐Center Retrospective Study Focusing on ER and HER‐2 Status

## TL;DR

This study identifies ER negativity and HER-2 positivity as key predictors of a complete response to chemotherapy in breast cancer patients, helping guide treatment decisions.

## Contribution

The study introduces a three-dimensional evaluation model combining clinical, imaging, and molecular factors to predict chemotherapy response in breast cancer.

## Key findings

- HER-2–positive patients had the highest pCR rate at 73.0%.
- ER negativity and HER-2 positivity were confirmed as independent predictors of higher pCR rates.
- ER and HER-2 showed the strongest discriminative ability with AUC values above 0.8.

## Abstract

To identify predictive factors of pathological complete response (pCR) in breast cancer patients receiving neoadjuvant chemotherapy (NAC), and to establish a “clinical–imaging–molecular” three‐dimensional evaluation model to guide clinical decision‐making.

A retrospective study was conducted on 55 breast cancer patients who underwent NAC at Nanyang Second People′s Hospital from January 2023 to August 2024. Collected data included demographic variables (age, BMI, and menstrual status), tumor characteristics (tumor size, axillary lymph node [N] stage, histological grade, color Doppler ultrasound features including blood flow signal [CDFI], morphology, and aspect ratio), molecular markers (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor‐2 [HER‐2], and Ki‐67), and treatment‐related factors (chemotherapy regimen). Univariate analyses (Pearson′s chi‐square test or Fisher′s exact test) were initially conducted to screen variables potentially associated with pCR (p < 0.05). To address potential multicollinearity among clinically relevant factors, binomial LASSO regression with 10‐fold cross‐validation was applied to select parsimonious predictors (variables with nonzero coefficients were retained), which were then incorporated into multivariate logistic regression to determine independent predictors of pCR. The discriminative power of key factors was evaluated using receiver operating characteristic (ROC) curves, with the area under the curve (AUC) as the primary metric.

The overall pCR rate was 36.4% (20/55). Among molecular subtypes, HER‐2–positive patients (40.0% of the cohort) had the highest pCR rate (73.0%, 16/22), followed by triple‐negative breast cancer (TNBC) patients (15.0% of the cohort, 50.0%, 4/8). Univariate analysis showed that N stage, chemotherapy regimen, Ki‐67 index, ER status, PR status, and HER‐2 status were significantly correlated with pCR (all p < 0.05). ROC analysis demonstrated excellent discriminative performance for ER (AUC = 0.84), HER‐2 (AUC = 0.81), PR (AUC = 0.79), chemotherapy regimen (AUC = 0.71), and Ki‐67 (AUC = 0.68). After LASSO‐based dimension reduction, multivariate logistic regression confirmed that ER negativity (p = 0.039, OR = 15.079, 95% CI: 1.151–197.543) and HER‐2 positivity (p = 0.044, OR = 0.014, 95% CI: 0.000–0.896) were independent predictors of higher pCR rates.

pCR rates in breast cancer patients post‐NAC vary significantly by molecular subtype. ER negativity and HER‐2 positivity emerge as independent predictive factors for pCR, with ER and HER‐2 exhibiting the strongest discriminative ability (AUC > 0.8). Clinicians should integrate patients′ baseline clinical data, ultrasound features, and molecular markers to screen optimal NAC candidates and develop individualized strategies, thereby maximizing therapeutic benefits.