ArticleFrontiers in endocrinology2025
Machine learning algorithms for individualized prediction of prognosis in breast cancer liver metastases and the prognostic impact of primary tumor surgery: a multicenter study.
Article in Frontiers in endocrinology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Machine learning-based mapping of multimodal treatment allocations and prognostic implications of clinical concordance in non-small cell lung cancer.Scientific reports · 2026Article
- From Feasibility to Individualization: Surgery for Breast Cancer Liver and Lung Metastases.Cancers · 2026Review
- A machine learning-based prognostic model forFrontiers in oncology · 2026Article
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13 authors.
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Abstract
Background: The prognosis of patients with breast cancer liver metastasis (BCLM) is generally poor, and there are no specific treatment guidelines. Accurate prognostic tools are needed to estimate survival and support individualized management. Methods: The study cohort consisted of 4,817 patients diagnosed with BCLM from the SEER database spanning 2010 to 2020. Candidate predictors were screened using univariate and multivariate Cox regression. Five machine-learning algorithms-Random Forest (RF), Logistic Regression, XGBoost, Decision Tree, and Gradient Boosting-were trained to predict 6-month, 1-, 3-, and 5-year overall survival (OS) and breast cancer-specific survival (BCSS). Labels at each horizon were handled with inverse probability-of-censoring weighting, and performance was assessed with IPCW-AUC, accuracy, F1 score, calibration, and decision curve analysis. External validation included 124 BCLM patients from two Chinese hospitals. To evaluate the effect of primary tumor surgery (PTS), we modeled PTS as a time-dependent exposure and performed time-dependent Cox analyses with time-varying effects, Simon-Makuch curves, piecewise Cox modeling, 2-month landmark analysis, and E-value calculations. Results: RF was the top performer for both OS and BCSS across horizons (training AUCs = 0.840-0.899; internal test AUCs = 0.763-0.787), with good calibration and net benefit. External validation showed consistent discrimination (AUCs 0.779-0.815). SHAP analyses highlighted chemotherapy, age, subtype, and surgery as dominant contributors. In time-dependent analyses, PTS was associated with reduced risks of death (OS: HR 0.80, 95% CI 0.72-0.88) and breast cancer-specific mortality (BCSS: HR 0.77, 95% CI 0.69-0.86); findings were directionally consistent in piecewise and landmark analyses, and E-values (≥1.81) supported robustness to moderate unmeasured confounding. Conclusion: We developed and externally validated robust RF-based models for predicting OS and BCSS in BCLM. Our results indicate that PTS is associated with longer survival and lower breast cancer-specific mortality in carefully selected patients, supporting consideration within individualized, multidisciplinary decision-making.
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