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June 28, 2026Future Oncology

Machine learning prediction of axillary lymph node metastasis using multimodal ultrasound in breast cancer

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Authors

YSYan ShenWYWeiquan YuGree (China)QCQingqing Chen

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Overview

Randomized trial demonstrates improved prediction of axillary lymph node metastasis in breast cancer, indicating potential for better treatment planning.

Key Points

  • The study aims to create a machine-learning model to predict axillary lymph node metastasis (ALNM) in breast cancer patients using multimodal ultrasound features.
  • Included 696 breast cancer patients from January 2016 to December 2022.
  • Used 9 machine learning models, including XGBoost, evaluated by AUC-ROC.
  • Split data into training (606 patients) and validation (90 patients) sets for model performance assessment.
  • The XGBoost model achieved a ROC-AUC of 0.936, sensitivity of 0.951, and specificity of 0.949 in the training set.
  • External validation for XGBoost showed ROC-AUC of 0.944, sensitivity of 1.000, and specificity of 0.850.
  • Findings suggest that these multimodal ultrasound features are crucial for clinical decision-making.

Cite This Study

Shen et al. (2026) studied this question.

synapsesocial.com/papers/6a40b9c461bb0a67205c5e63https://doi.org/10.1080/14796694.2026.2691683
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A non-invasive preoperative prediction model for predicting axillary lymph node metastasis in breast cancer based on a machine learning approach: combining ultrasonographic parameters and breast gamma specific imaging features2024 · 7 citations
  2. 2Predicting axillary lymph node metastasis in breast cancer based on ultrasound radiofrequency time-series analysis2024
  3. 3An explainable predictive machine learning model for axillary lymph node metastasis in breast cancer based on multimodal data: A retrospective single-center study2025
  4. 4Prediction of axillary lymph node metastasis in breast cancer based on radiomics: a multicenter study2026
  5. 5 Prediction of axillary lymph node metastasis with the ultrasound and Magnetic Resonance Imaging features in breast cancer2024