Development and validation of an interpretable machine learning model for predicting 5-year recurrence in breast cancer - Takeaways - MDSpire

Development and validation of an interpretable machine learning model for predicting 5-year recurrence in breast cancer

  • By

  • Shaoda Meng

  • Sicheng Liu

  • Minghua Lai

  • Li Li

  • June 10, 2026

  • 0 min

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  • 1

    A transparent machine learning model was developed to predict 5-year breast cancer recurrence, improving risk stratification beyond traditional TNM staging.

  • 2

    The XGBoost model utilized 15 clinicopathological features and demonstrated an AUC of 0.877, outperforming logistic regression and the TNM system.

  • 3

    SHAP analysis identified the Ki-67 index and positive lymph nodes as key predictors, revealing non-linear associations in recurrence risk.

  • 4

    The model effectively stratified patients within TNM Stages II and III into distinct high- and low-risk groups, addressing intra-stage heterogeneity.

  • 5

    This study highlights the potential of machine learning to enhance personalized clinical decision-making in breast cancer management.

Original Source(s)

Development and validation of an interpretable machine learning model for predicting 5-year recurrence in breast cancer

  • by Shaoda Meng, Sicheng Liu, Minghua Lai, Li Li

  • June 10, 2026

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