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July 10, 2026Breast Cancer ResearchOpen Access

Predicting sentinel lymph node metastasis in breast cancer using an interpretable machine learning approach based on multi-domain clinical features

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Authors

RWRuoyan WangXLXiongwu LiHZHongni Zhu

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Overview

Randomized trial demonstrates predictive modeling of sentinel lymph node metastasis in breast cancer, suggesting clinical validation and utility.

Key Points

  • The study aims to develop an interpretable machine learning model to predict sentinel lymph node metastasis in breast cancer using multi-domain clinical features.
  • Retrospective cohort of 1,485 patients trained nine machine learning algorithms using clinical, imaging, and pathological features.
  • Independent prospective validation cohort of 103 patients was used to assess model performance.
  • Data reliability was enhanced through multiple imputation, and feature selection streamlined models while retaining essential information.
  • The support vector machine model achieved the highest performance, with an AUC of 0.892 (95% CI: 0.854-0.940), outperforming the clinical model (AUC 0.796).
  • In prospective validation, the model achieved an AUC of 0.775 (95% CI: 0.667-0.865), exceeding the baseline model (AUC 0.650).
  • The model could spare 58.1% of node-negative patients from unnecessary sentinel lymph node biopsy.

Cite This Study

Wang et al. (2026) studied this question.

synapsesocial.com/papers/6a508b536eeac72a4379ffd8https://doi.org/10.1186/s13058-026-02329-1
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