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Background <p>The purpose of this study was to establish two preoperative nomogram models to evaluate the status of axillary lymph nodes (ALNs) in early breast cancer patients based on ultrasound data, PET/CT data, and clinicopathological features. Accordingly, more appropriate treatment strategies for breast cancer patients could be selected.</p> Methods <p>From April 2017 to April 2023, a total of 254 patients from the Clinical Data Research (CDR) database of Peking Union Medical College Hospital were included in this study and randomly assigned to the training and internal testing groups at a ratio of 8:2, with outcome variables being axillary lymph nodes metastasis (ALNM) and high nodal tumour burden (HNTB). The predictive factors determined by Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression analyses were used to construct the nomograms. Receiver operating characteristic (ROC) curves and calibration plots were used to assess the prediction models’ discrimination and calibration.</p> Results <p>LASSO regression and multivariate logistic regression analyses showed that Plnsuv (PET/CT lymph node SUVmax), unclear cortico-medullary delineation and Progesterone Receptor (PR) positive were independent predictors of ALNM. Moreover, Plnsuv, tumour size and Her-2 positive were independent predictors of HNTB, respectively. Integrating these independent predictors, two nomograms were successfully developed to accurately predict the status of ALN. For nomogram 1 (prediction of ALNM), the area under the ROC curve in the testing group was 0.889. For nomogram 2 (prediction of HNTB), the area under the ROC curve in the testing group was 0.784. The above results showed a satisfactory performance.</p> Conclusions <p>We developed two preoperative nomograms that can be used to predict ALN metastasis (LN − vs. LN+) and the number of metastatic ALNs (≤ 2 vs. &gt;2) in early breast cancer patients. They were well verified in internal testing groups. The nomograms can help clinicians predict the status of ALNs and guide discussions regarding axillary management.</p>

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Nomogram models for prediction of axillary lymph node metastasis in breast cancer patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月26日

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  • Zixi Deng,
  • Yuechong Li,
  • Yongchao Luo,
  • Xi Cao,
  • Songjie Shen

摘要

Background

The purpose of this study was to establish two preoperative nomogram models to evaluate the status of axillary lymph nodes (ALNs) in early breast cancer patients based on ultrasound data, PET/CT data, and clinicopathological features. Accordingly, more appropriate treatment strategies for breast cancer patients could be selected.

Methods

From April 2017 to April 2023, a total of 254 patients from the Clinical Data Research (CDR) database of Peking Union Medical College Hospital were included in this study and randomly assigned to the training and internal testing groups at a ratio of 8:2, with outcome variables being axillary lymph nodes metastasis (ALNM) and high nodal tumour burden (HNTB). The predictive factors determined by Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression analyses were used to construct the nomograms. Receiver operating characteristic (ROC) curves and calibration plots were used to assess the prediction models’ discrimination and calibration.

Results

LASSO regression and multivariate logistic regression analyses showed that Plnsuv (PET/CT lymph node SUVmax), unclear cortico-medullary delineation and Progesterone Receptor (PR) positive were independent predictors of ALNM. Moreover, Plnsuv, tumour size and Her-2 positive were independent predictors of HNTB, respectively. Integrating these independent predictors, two nomograms were successfully developed to accurately predict the status of ALN. For nomogram 1 (prediction of ALNM), the area under the ROC curve in the testing group was 0.889. For nomogram 2 (prediction of HNTB), the area under the ROC curve in the testing group was 0.784. The above results showed a satisfactory performance.

Conclusions

We developed two preoperative nomograms that can be used to predict ALN metastasis (LN − vs. LN+) and the number of metastatic ALNs (≤ 2 vs. >2) in early breast cancer patients. They were well verified in internal testing groups. The nomograms can help clinicians predict the status of ALNs and guide discussions regarding axillary management.