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Modeling the risk of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer: A retrospective study

  • Authors:
    • Wenxin Chen
    • Rihua Hu
    • Changming Chen
    • Maoquan Zhang
    • Xinghang Fu
    • Yanmei Wen
  • View Affiliations / Copyright

    Affiliations: Department of Breast Surgery, Affiliated Sanming First Hospital of Fujian Medical University, Sanming, Fujian 365001, P.R. China, Department of Pathology, Affiliated Sanming First Hospital of Fujian Medical University, Sanming, Fujian 365001, P.R. China
    Copyright: © Chen et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 89
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    Published online on: July 23, 2025
       https://doi.org/10.3892/mco.2025.2884
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Abstract

Axillary lymph node (ALN) metastasis is a key prognostic factor in breast cancer (BC). Although neoadjuvant chemotherapy (NAC) is widely used to downstage tumors and facilitate surgical management, accurately predicting ALN status after NAC remains a clinical challenge. The present study aimed to develop a predictive model using clinical and pathological variables to assess the risk of ALN metastasis following NAC. A retrospective analysis was conducted on 156 female patients with BC who received NAC, of whom 131 met inclusion criteria and were analyzed. The patients were randomly divided into a training cohort (97 patients) and a validation cohort (34 patients). Clinical and pathological variables, including age, menopausal status, tumor stage before chemotherapy, lymph node stage, histological grade, molecular subtyping, estrogen and progesterone receptor expression, HER‑2 status, Ki67 expression, post‑chemotherapy tumor stage, and the proportion of tumor and Ki67 regression before and after chemotherapy were collected. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of ALN metastasis. A logistic regression‑based nomogram was constructed using the multivariate analysis, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC). In the training cohort, age, pre‑chemotherapy lymph node status (N stage), Ki67 reduction level, and pre‑chemotherapy molecular subtyping were identified as independent predictors of ALN metastasis. The nomogram demonstrated favorable predictive accuracy, with an AUC of 0.877. The validation cohort showed an AUC of 0.842, with sensitivity, specificity and positive predictive value of 76, 82 and 81%, respectively. The false negative rate in the validation cohort was 24%. In conclusion, a predictive model based on age, pre‑chemotherapy lymph node status, Ki67 reduction level and molecular subtyping was developed to assess ALN metastasis after NAC in patients with BC. While the model demonstrated favorable accuracy, further refinement is needed to reduce the false negative rate and improve clinical utility. The incorporation of molecular biomarkers and advanced imaging techniques may enhance the model's performance.
View Figures

Figure 1

Study design.

Figure 2

Nomogram of the predictive model in
the validation cohort.

Figure 3

ROC and calibration curves of the
predictive model in the training cohort. (A) ROC curve showing an
AUC of 0.877 (95% CI: 0.856-0.899), indicating strong
discriminatory power. (B) Calibration plot demonstrating favorable
agreement between predicted and observed probabilities, suggesting
the model is well calibrated and accurate for clinical risk
prediction. ROC, receiver operating characteristic; AUC, area under
the curve.

Figure 4

ROC curve of the predictive model in
the validation cohort. ROC, receiver operating characteristic; AUC,
area under the curve.

Figure 5

Predictive confusion matrices for the
(A) training and (B) validation groups.
View References

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Copy and paste a formatted citation
Spandidos Publications style
Chen W, Hu R, Chen C, Zhang M, Fu X and Wen Y: Modeling the risk of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer: A retrospective study. Mol Clin Oncol 23: 89, 2025.
APA
Chen, W., Hu, R., Chen, C., Zhang, M., Fu, X., & Wen, Y. (2025). Modeling the risk of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer: A retrospective study. Molecular and Clinical Oncology, 23, 89. https://doi.org/10.3892/mco.2025.2884
MLA
Chen, W., Hu, R., Chen, C., Zhang, M., Fu, X., Wen, Y."Modeling the risk of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer: A retrospective study". Molecular and Clinical Oncology 23.4 (2025): 89.
Chicago
Chen, W., Hu, R., Chen, C., Zhang, M., Fu, X., Wen, Y."Modeling the risk of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer: A retrospective study". Molecular and Clinical Oncology 23, no. 4 (2025): 89. https://doi.org/10.3892/mco.2025.2884
Copy and paste a formatted citation
x
Spandidos Publications style
Chen W, Hu R, Chen C, Zhang M, Fu X and Wen Y: Modeling the risk of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer: A retrospective study. Mol Clin Oncol 23: 89, 2025.
APA
Chen, W., Hu, R., Chen, C., Zhang, M., Fu, X., & Wen, Y. (2025). Modeling the risk of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer: A retrospective study. Molecular and Clinical Oncology, 23, 89. https://doi.org/10.3892/mco.2025.2884
MLA
Chen, W., Hu, R., Chen, C., Zhang, M., Fu, X., Wen, Y."Modeling the risk of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer: A retrospective study". Molecular and Clinical Oncology 23.4 (2025): 89.
Chicago
Chen, W., Hu, R., Chen, C., Zhang, M., Fu, X., Wen, Y."Modeling the risk of axillary lymph node metastasis after neoadjuvant chemotherapy in breast cancer: A retrospective study". Molecular and Clinical Oncology 23, no. 4 (2025): 89. https://doi.org/10.3892/mco.2025.2884
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