Development and validation of a machine learning model to evaluate survival in patients with newly diagnosed breast cancer with liver metastasis - Summary - MDSpire

Development and validation of a machine learning model to evaluate survival in patients with newly diagnosed breast cancer with liver metastasis

  • By

  • Yao Wang

  • Yu Yue

  • Xu-Chen Cao

  • June 16, 2026

  • 0 min

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Objective:

To develop a prognostic nomogram for patients with newly diagnosed breast cancer and liver metastases using data from the SEER database (2010-2021).

Approach:
    Key Findings:
    • C-indices for the training, internal validation, and external validation cohorts were 0.760, 0.740, and 0.787, respectively.
    • 1-, 3-, and 5-year AUCs were 0.777, 0.757, and 0.764 (training); 0.755, 0.769, and 0.754 (internal validation); and 0.727, 0.752, and 0.801 (external validation).
    • Calibration curves indicated good agreement between predicted and observed outcomes, and DCA confirmed the clinical utility of the model.
    Interpretation:

    Limitations:
    • The study relies on retrospective data from the SEER database, which may limit the generalizability of the findings.
    • Independent external validation was conducted but the external cohort was geographically and institutionally distinct.
    Conclusion:

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