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Background <p>Accurate identification of sentinel lymph node (SLN) status is essential for surgical and adjuvant therapy decisions in breast cancer. Although sentinel lymph node biopsy (SLNB) is less invasive than axillary lymph node dissection, it still exposes node-negative patients to unnecessary surgical risks. Most existing models predicting lymph node status in breast cancer rely on a limited set of features or variables that are not routinely accessible, and their interpretability remains limited. Therefore, developing a comprehensive and explainable model to evaluate the feasibility of omitting SLNB has important clinical significance.</p> Methods <p>A retrospective cohort of 1,485 patients trained nine machine learning (ML) algorithms using clinical, imaging, and pathological features to predict sentinel lymph node metastasis (SLNM). An independent prospective cohort of 103 patients served for prospective validation. Multiple imputation enhanced data reliability, and multi-dimensional feature selection simplified models while retaining key information. Model performance was evaluated using AUC, precision, recall, and calibration. SHapley Additive exPlanations (SHAP) were applied to interpret model outputs and quantify feature contributions.</p> Results <p>Ten-fold cross-validation showed that the support vector machine (SVM) model achieved the best performance among nine ML algorithms. After optimizing to 26 variables, the internal test achieved an AUC of 0.892 (95% CI: 0.854–0.940), outperforming the clinical model (AUC 0.796, 95% CI: 0.791–0.811). In the prospective cohort, the AUC was 0.775 (95% CI: 0.667–0.865), superior to the baseline radionuclide- and imaging-based model (AUC 0.65, 95% CI: 0.564–0.729). When applying a safety-optimized threshold, the model calibrated well and could spare 58.1% of node-negative patients from SLNB. SHAP analysis identified tumor margin smoothness, AR expression, and nuclear pleomorphism as top predictors. Decision curve analysis supported clinical utility. A Shiny-based web tool enables real-time prediction.</p> Conclusions <p>This study developed an SLNM prediction model based on multi-dimensional features available from routine diagnostic examinations. Prospective and exploratory external validation confirmed its clinical applicability. The model’s performance in assessing axillary status was close to that of surgical SLNB, providing preliminary evidence to inform potential de-escalation or stratification of surgical approaches in breast cancer management.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月7日

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  • Ruoyan Wang,
  • Xiongwu Li,
  • Hongni Zhu,
  • Linjun Li,
  • Tingting Xiong,
  • Fangdan Li,
  • Longke Ran,
  • Yaying Yang

摘要

Background

Accurate identification of sentinel lymph node (SLN) status is essential for surgical and adjuvant therapy decisions in breast cancer. Although sentinel lymph node biopsy (SLNB) is less invasive than axillary lymph node dissection, it still exposes node-negative patients to unnecessary surgical risks. Most existing models predicting lymph node status in breast cancer rely on a limited set of features or variables that are not routinely accessible, and their interpretability remains limited. Therefore, developing a comprehensive and explainable model to evaluate the feasibility of omitting SLNB has important clinical significance.

Methods

A retrospective cohort of 1,485 patients trained nine machine learning (ML) algorithms using clinical, imaging, and pathological features to predict sentinel lymph node metastasis (SLNM). An independent prospective cohort of 103 patients served for prospective validation. Multiple imputation enhanced data reliability, and multi-dimensional feature selection simplified models while retaining key information. Model performance was evaluated using AUC, precision, recall, and calibration. SHapley Additive exPlanations (SHAP) were applied to interpret model outputs and quantify feature contributions.

Results

Ten-fold cross-validation showed that the support vector machine (SVM) model achieved the best performance among nine ML algorithms. After optimizing to 26 variables, the internal test achieved an AUC of 0.892 (95% CI: 0.854–0.940), outperforming the clinical model (AUC 0.796, 95% CI: 0.791–0.811). In the prospective cohort, the AUC was 0.775 (95% CI: 0.667–0.865), superior to the baseline radionuclide- and imaging-based model (AUC 0.65, 95% CI: 0.564–0.729). When applying a safety-optimized threshold, the model calibrated well and could spare 58.1% of node-negative patients from SLNB. SHAP analysis identified tumor margin smoothness, AR expression, and nuclear pleomorphism as top predictors. Decision curve analysis supported clinical utility. A Shiny-based web tool enables real-time prediction.

Conclusions

This study developed an SLNM prediction model based on multi-dimensional features available from routine diagnostic examinations. Prospective and exploratory external validation confirmed its clinical applicability. The model’s performance in assessing axillary status was close to that of surgical SLNB, providing preliminary evidence to inform potential de-escalation or stratification of surgical approaches in breast cancer management.