{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T04:55:22Z","timestamp":1781585722147,"version":"3.54.5"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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\u20132021).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Ten stable prognostic variables\u2014age; 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\u2014were 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).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed nomogram demonstrated robust predictive performance for patients with breast cancer and liver metastases and may provide valuable prognostic information, pending further validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1777850","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T04:29:17Z","timestamp":1781584157000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a machine learning model to evaluate survival in patients with newly diagnosed breast cancer with liver 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China"]},{"name":"Tianjin\u2019s Clinical Research Center for Cancer","place":["Tianjin, China"]},{"name":"Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education","place":["Tianjin, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu-Chen","family":"Cao","sequence":"additional","affiliation":[{"name":"The First Department of Breast Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer","place":["Tianjin, China"]},{"name":"Key Laboratory of Cancer Prevention and Therapy","place":["Tianjin, China"]},{"name":"Tianjin\u2019s Clinical Research Center for Cancer","place":["Tianjin, China"]},{"name":"Key Laboratory of Breast Cancer Prevention and Therapy, Tianjin Medical University, Ministry of Education","place":["Tianjin, 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