{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T05:04:49Z","timestamp":1783919089425,"version":"3.55.0"},"reference-count":54,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T00:00:00Z","timestamp":1783900800000},"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. Endocrinol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Primary Tumor Resection (PTR) remains controversial among women with stage IV breast cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Using a machine learning (ML) approach, this study investigates how PTR is associated with survival outcomes in stage IV breast cancer. We aim to develop a model capable of identifying patient characteristics linked to better prognosis following PTR, ultimately providing a data-informed tool to support prognostic assessment and clinical evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A propensity-score matched analysis of stage IV breast cancer patients in the SEER registry (2000-2020) was conducted, using Cox regression and Kaplan-Meier methods to estimate overall survival (OS) and cancer-specific survival (CSS). Among five ML models, an internally and externally validated ML model was crafted for predicting survival outcomes, with a user-friendly web platform based on the shiny platform for clinical use.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In a cohort of 10,194 stage IV breast cancer patients, with 5,732 matched subjects, Cox regression analysis showed PTR\u2019s positive associations with OS (HR, 0.61; 95% CI, 0.57 to 0.66) and CSS (HR, 0.64; 95% CI, 0.59 to 0.67). Subgroup analysis indicated better survival for patients with tumors \u22645 cm, N2 status (OS, HR, 0.52; 95% CI, 0.44 - 0.63;CSS,HR, 0.52; 95% CI, 0.43 - 0.63), and HER2 overexpression (OS, HR, 0.56; 95% CI, 0.52 - 0.61;CSS,HR, 0.56; 95% CI, 0.46 - 0.67), especially those without systemic treatment. The best outcomes were seen with trimodality therapy combining PTR, chemotherapy, and radiotherapy (OS, HR, 0.40; 95% CI, 0.37 - 0.44; CSS, HR, 0.03; 95% CI, 0.01 - 0.10). The Support Vector Machine (SVM) model (6-month: AUC\u00a0=\u00a00.935; 1-year: AUC\u00a0=\u00a00.945; 2-year: AUC\u00a0=\u00a00.941; 3-year: AUC\u00a0=\u00a00.921) was identified as the most precise tool for survival prediction, demonstrating high accuracy and consistency across external datasets. Furthermore, a user-friendly web application was created to make the prognostic model more accessible.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Patients with tumors \u22645 cm, N2 status, or HER2 overexpression were associated with better survival after PTR. Our ML model, which is based on eight clinical indicators, predicts survival and assists in identifying surgical candidates for stage IV cancer.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fendo.2026.1871537","type":"journal-article","created":{"date-parts":[[2026,7,13]],"date-time":"2026-07-13T04:23:21Z","timestamp":1783916601000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and validation of a prognostic model for stage IV breast cancer based on primary tumor resection with machine learning methods: retrospective cohort study"],"prefix":"10.3389","volume":"17","author":[{"given":"Yaoling","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Thyroid and Breast Surgery, The Affiliated Guangdong Second Provincial General Hospital of Jinan University","place":["Guangzhou, Guangdong, China"]},{"name":"Department of Breast, Guangdong Provincial People\u2019s Hospital 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China"]},{"name":"School of Medicine, South China University of Technology","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongyi","family":"Zhu","sequence":"additional","affiliation":[{"name":"Guangdong Lung Cancer Institute, Guangdong Provincial People\u2019s Hospital, Guangdong Academy of Medical Sciences, Southern Medical University","place":["Guangzhou, Guangdong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Thyroid and Breast Surgery, The Affiliated Guangdong Second Provincial General Hospital of Jinan University","place":["Guangzhou, Guangdong, China"]},{"name":"Department of Breast, Guangdong Provincial People\u2019s Hospital and Guangdong Academy of Medical Sciences, Southern Medical University","place":["Guangzhou, China"]},{"name":"School of Medicine, South China University of Technology","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjing","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Thyroid and Breast Surgery, The Affiliated Guangdong Second Provincial General Hospital of Jinan University","place":["Guangzhou, Guangdong, China"]},{"name":"Department of Breast, Guangdong Provincial People\u2019s Hospital and Guangdong Academy of Medical Sciences, Southern Medical University","place":["Guangzhou, China"]},{"name":"School of Medicine, South China University of Technology","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuangwei","family":"Mo","sequence":"additional","affiliation":[{"name":"Department of Medical Oncology, State Key Laboratory of Oncology in South China, Sun Yat-Sen University Cancer Center","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenchong","family":"Xiong","sequence":"additional","affiliation":[{"name":"Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anli","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Breast Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer","place":["Guangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Breast Surgery, Guangzhou Red Cross Hospital of Jinan University","place":["Guangzhou, Guangdong, 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