Machine learning-based prognostic model for triple-negative breast cancer with axillary lymph node metastasis - Takeaways - MDSpire

Machine learning-based prognostic model for triple-negative breast cancer with axillary lymph node metastasis

  • By

  • Ruyi Huang

  • Tianlu Jiang

  • Xidong Lv

  • Na Yao

  • Yujiang Guo

  • July 10, 2026

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  • 1

    The study developed a machine learning-based prognostic model for triple-negative breast cancer patients with axillary lymph node involvement.

  • 2

    A total of 19,289 TNBC patients with axillary lymph node metastasis were analyzed using data from the SEER database.

  • 3

    Thirteen independent prognostic factors were identified, including demographic characteristics and tumor pathological features.

  • 4

    The Extremely Randomized Survival Trees model demonstrated the highest C-index and was selected for SHAP interpretation.

  • 5

    SHAP analysis identified tumor grade, N stage, and radiotherapy as key prognostic drivers associated with increased mortality risk.

Original Source(s)

Machine learning-based prognostic model for triple-negative breast cancer with axillary lymph node metastasis

  • by Ruyi Huang, Tianlu Jiang, Xidong Lv, Na Yao, Yujiang Guo

  • July 10, 2026

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