Frontiers in oncology · 2026
Development and validation of a machine learning model to evaluate survival in patients with newly diagnosed breast cancer with liver metastasis.
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Wang Yao, Yue Yu, Cao Xu-Chen
Аннотация
BACKGROUND: Breast cancer liver metastasis (BCLM) is associated with substantial prognostic heterogeneity and poor survival outcomes. Existing staging systems and prognostic models have a limited capacity for individualized survival prediction for patients with newly diagnosed BCLM, highlighting the need for more accurate and clinically applicable predictive tools. This study aimed to develop a prognostic nomogram for patients with newly diagnosed breast cancer and liver metastases using Surveillance, Epidemiology, and End Results (SEER) database data (2010-2021).
METHODS: Ten stable prognostic variables-age; tumor size; brain, bone, and lung metastases; histological grade; chemotherapy; estrogen receptor (ER) status; progesterone receptor (PR) status; and human epidermal growth factor receptor-2 (HER2) status-were selected using four machine learning algorithms (Least Absolute Shrinkage And Selection Operator, Extreme Gradient Boosting, Decision Tree, and Random Forest). The SEER cohort was randomly divided into training (70%) and internal validation (30%) cohorts, with an independent external cohort for validation. These variables were incorporated into a multivariate Cox proportional hazards regression model to construct a nomogram. Model performance was evaluated using the concordance index (C-index), calibration curves, decision curve analysis (DCA), and area under the curve (AUC).
RESULTS: The C-indices for the training, internal validation, and external validation cohorts were 0.760, 0.740, and 0.787, respectively. The 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), respectively. The calibration curves indicated good agreement and DCA confirmed the clinical utility of the model.
CONCLUSION: The proposed nomogram demonstrated robust predictive performance for patients with breast cancer and liver metastases and may provide valuable prognostic information, pending further validation.
METHODS: Ten stable prognostic variables-age; tumor size; brain, bone, and lung metastases; histological grade; chemotherapy; estrogen receptor (ER) status; progesterone receptor (PR) status; and human epidermal growth factor receptor-2 (HER2) status-were selected using four machine learning algorithms (Least Absolute Shrinkage And Selection Operator, Extreme Gradient Boosting, Decision Tree, and Random Forest). The SEER cohort was randomly divided into training (70%) and internal validation (30%) cohorts, with an independent external cohort for validation. These variables were incorporated into a multivariate Cox proportional hazards regression model to construct a nomogram. Model performance was evaluated using the concordance index (C-index), calibration curves, decision curve analysis (DCA), and area under the curve (AUC).
RESULTS: The C-indices for the training, internal validation, and external validation cohorts were 0.760, 0.740, and 0.787, respectively. The 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), respectively. The calibration curves indicated good agreement and DCA confirmed the clinical utility of the model.
CONCLUSION: The proposed nomogram demonstrated robust predictive performance for patients with breast cancer and liver metastases and may provide valuable prognostic information, pending further validation.