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Related Experiment Video

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Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using a Three-tier MRI Radiomics Model

Hong Li1, Weiqing Huang1, Jiefeng Liang1

  • 1Department of Radiology, The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China (H.L., W.H., J.L., S.H., H.C.).

Academic Radiology
|March 5, 2026
PubMed
Summary

A new three-tier model combining clinical, MRI, and radiomics features significantly improves the prediction of breast cancer axillary lymph node metastasis. This approach offers a more accurate preoperative assessment for patient management.

Keywords:
Axillary lymph node metastasisBreast cancerCross-validation preoperative predictionMagnetic resonance imagingRadiomics

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Area of Science:

  • Oncology
  • Radiology
  • Medical Imaging

Background:

  • Accurate preoperative prediction of axillary lymph node (ALN) metastasis is crucial for breast cancer staging and treatment planning.
  • Current methods often have limitations in sensitivity and specificity, necessitating improved predictive tools.

Purpose of the Study:

  • To develop and validate a novel three-tier model for predicting ALN metastasis in breast cancer.
  • The model integrates clinical, qualitative magnetic resonance imaging (MRI), and radiomics features.
  • Nested cross-validation was employed to ensure unbiased performance estimation.

Main Methods:

  • A retrospective study of 494 breast cancer patients who underwent preoperative MRI (DCE and T2FS-STIR).
  • Three models were progressively built: clinical variables, clinical + qualitative MRI features, and clinical + qualitative MRI + radiomics features.
  • Radiomics features were extracted and selected using established filtering techniques, with nested cross-validation for feature selection and model training.

Main Results:

  • The comprehensive three-tier model (Model 3) demonstrated superior predictive performance with a significantly higher Area Under the Curve (AUC) of 0.769 compared to models with fewer features.
  • SHAP analysis identified T2FS-derived radiomics as the most influential predictors.
  • The model exhibited good calibration and demonstrated clinical utility through decision curve analysis.

Conclusions:

  • The developed three-tier MRI radiomics model significantly enhances the preoperative prediction of axillary lymph node metastasis in breast cancer.
  • The robust validation methodology ensures reliable performance estimates, supporting potential clinical implementation.