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