{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:11:04Z","timestamp":1787026264040,"version":"build-2736575974"},"reference-count":24,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T00:00:00Z","timestamp":1670371200000},"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. Cell Dev. Biol."],"abstract":"<jats:p>\n                    <jats:bold>Purpose:<\/jats:bold>\n                    This study aims to develop a prediction model to categorize the risk of early death among breast cancer patients with bone metastases using machine learning models.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    This study examined 16,189 bone metastatic breast cancer patients between 2010 and 2019 from a large oncological database in the United States. The patients were divided into two groups at random in a 90:10 ratio. The majority of patients (\n                    <jats:italic>n<\/jats:italic>\n                    = 14,582, 90%) were served as the training group to train and optimize prediction models, whereas patients in the validation group (\n                    <jats:italic>n<\/jats:italic>\n                    = 1,607, 10%) were utilized to validate the prediction models. Four models were introduced in the study: the logistic regression model, gradient boosting tree model, decision tree model, and random forest model.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    Early death accounted for 17.4% of all included patients. Multivariate analysis demonstrated that older age; a separated, divorced, or widowed marital status; nonmetropolitan counties; brain metastasis; liver metastasis; lung metastasis; and histologic type of unspecified neoplasms were significantly associated with more early death, whereas a lower grade, a positive estrogen receptor (ER) status, cancer-directed surgery, radiation, and chemotherapy were significantly the protective factors. For the purpose of developing prediction models, the 12 variables were used. Among all the four models, the gradient boosting tree had the greatest AUC [0.829, 95% confident interval (CI): 0.802\u20130.856], and the random forest (0.828, 95% CI: 0.801\u20130.855) and logistic regression (0.819, 95% CI: 0.791\u20130.847) models came in second and third, respectively. The discrimination slopes for the three models were 0.258, 0.223, and 0.240, respectively, and the corresponding accuracy rates were 0.801, 0.770, and 0.762, respectively. The Brier score of gradient boosting tree was the lowest (0.109), followed by the random forest (0.111) and logistic regression (0.112) models. Risk stratification showed that patients in the high-risk group (46.31%) had a greater six-fold chance of early death than those in the low-risk group (7.50%).\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusion:<\/jats:bold>\n                    The gradient boosting tree model demonstrates promising performance with favorable discrimination and calibration in the study, and this model can stratify the risk probability of early death among bone metastatic breast cancer patients.\n                  <\/jats:p>","DOI":"10.3389\/fcell.2022.1059597","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T01:50:42Z","timestamp":1670377842000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":18,"title":["A machine learning\u2013Based model to predict early death among bone metastatic breast cancer patients: A large cohort of 16,189 patients"],"prefix":"10.3389","volume":"10","author":[{"given":"Fan","family":"Xiong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuyong","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaolin","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ze","family":"Long","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaosheng","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingxing","family":"Lei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2022,12,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1097\/CORR.0000000000001081","article-title":"External validation of PATHFx version 3.0 in patients treated surgically and nonsurgically for symptomatic skeletal metastases","volume":"478","author":"Anderson","year":"2020","journal-title":"Clin. Orthop. Relat. Res."},{"key":"B2","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.critrevonc.2017.04.008","article-title":"Systematic review and meta-analysis on the proportion of patients with breast cancer who develop bone metastases","volume":"115","author":"Body","year":"2017","journal-title":"Crit. Rev. Oncol. Hematol."},{"key":"B3","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.biocel.2018.01.003","article-title":"Breast cancer bone metastases: Pathogenesis and therapeutic targets","volume":"96","author":"Brook","year":"2018","journal-title":"Int. J. Biochem. Cell Biol."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1337","DOI":"10.1007\/s00264-020-04921-2","article-title":"C-reactive protein and tumour diagnosis predict survival in patients treated surgically for long bone metastases","volume":"45","author":"Errani","year":"2021","journal-title":"Int. Orthop."},{"key":"B5","doi-asserted-by":"publisher","first-page":"e19956","DOI":"10.1371\/journal.pone.0019956","article-title":"Estimating survival in patients with operable skeletal metastases: An application of a bayesian belief network","volume":"6","author":"Forsberg","year":"2011","journal-title":"PLoS One"},{"key":"B6","doi-asserted-by":"publisher","first-page":"e497","DOI":"10.1016\/j.clbc.2020.11.006","article-title":"Prognostic model and nomogram for estimating survival of small breast cancer: A SEER-based analysis","volume":"21","author":"Han","year":"2021","journal-title":"Clin. Breast Cancer"},{"key":"B7","doi-asserted-by":"publisher","first-page":"100363","DOI":"10.1016\/j.jbo.2021.100363","article-title":"Prognostic factors and survival of patients undergoing surgical intervention for breast cancer bone metastases","volume":"29","author":"Hankins","year":"2021","journal-title":"J. Bone Oncol."},{"key":"B8","doi-asserted-by":"publisher","first-page":"621","DOI":"10.1093\/jamia\/ocz228","article-title":"A tutorial on calibration measurements and calibration models for clinical prediction models","volume":"27","author":"Huang","year":"2020","journal-title":"J. Am. Med. Inf. Assoc."},{"key":"B9","doi-asserted-by":"crossref","first-page":"212","DOI":"10.14712\/fb2019065050212","article-title":"Machine learning and deep learning approaches in breast cancer survival prediction using clinical data","volume":"65","author":"Kalafi","year":"2019","journal-title":"Folia Biol."},{"key":"B10","doi-asserted-by":"publisher","first-page":"9605439","DOI":"10.1155\/2022\/9605439","article-title":"Global increase in breast cancer incidence: Risk factors and preventive measures","volume":"2022","author":"Kashyap","year":"2022","journal-title":"Biomed. Res. Int."},{"key":"B11","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1016\/j.ejso.2016.03.036","article-title":"Metastatic bone disease: A review of survival, prognostic factors and outcomes following surgical treatment of the appendicular skeleton","volume":"42","author":"Kirkinis","year":"2016","journal-title":"Eur. J. Surg. Oncol."},{"key":"B12","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.jbo.2013.09.001","article-title":"Incidence, consequences and treatment of bone metastases in breast cancer patients-Experience from a single cancer centre","volume":"2","author":"Kuchuk","year":"2013","journal-title":"J. Bone Oncol."},{"key":"B13","doi-asserted-by":"publisher","first-page":"693689","DOI":"10.3389\/fonc.2021.693689","article-title":"Clinical features and serological markers risk model predicts overall survival in patients undergoing breast cancer and bone metastasis surgeries","volume":"11","author":"Mou","year":"2021","journal-title":"Front. Oncol."},{"key":"B14","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.21037\/atm-21-4052","article-title":"Clinicopathological characteristics and prognostic risk factors of breast cancer patients with bone metastasis","volume":"9","author":"Pan","year":"2021","journal-title":"Ann. Transl. Med."},{"key":"B15","doi-asserted-by":"publisher","first-page":"4482","DOI":"10.1002\/sim.7139","article-title":"Discrimination slope and integrated discrimination improvement - properties, relationships and impact of calibration","volume":"36","author":"Pencina","year":"2017","journal-title":"Stat. Med."},{"key":"B16","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.suronc.2013.02.008","article-title":"Insight opinion to surgically treated metastatic bone disease: Scandinavian Sarcoma Group Skeletal Metastasis Registry report of 1195 operated skeletal metastasis","volume":"22","author":"Ratasvuori","year":"2013","journal-title":"Surg. Oncol."},{"key":"B17","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1016\/j.jclinepi.2009.11.009","article-title":"Use of Brier score to assess binary predictions","volume":"63","author":"Rufibach","year":"2010","journal-title":"J. Clin. Epidemiol."},{"key":"B18","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21332","article-title":"Cancer statistics","volume":"72","author":"Siegel","year":"2022","journal-title":"Ca. Cancer J. Clin."},{"key":"B19","doi-asserted-by":"publisher","first-page":"1925","DOI":"10.1093\/eurheartj\/ehu207","article-title":"Towards better clinical prediction models: Seven steps for development and an ABCD for validation","volume":"35","author":"Steyerberg","year":"2014","journal-title":"Eur. Heart J."},{"key":"B20","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"Ca. Cancer J. Clin."},{"key":"B21","doi-asserted-by":"publisher","first-page":"322","DOI":"10.1097\/CORR.0000000000000997","article-title":"Development and internal validation of machine learning algorithms for preoperative survival prediction of extremity metastatic disease","volume":"478","author":"Thio","year":"2020","journal-title":"Clin. Orthop. Relat. Res."},{"key":"B22","doi-asserted-by":"publisher","first-page":"1547","DOI":"10.1007\/s00590-021-03136-4","article-title":"What's new in the management of metastatic bone disease","volume":"31","author":"Tsukamoto","year":"2021","journal-title":"Eur. J. Orthop. Surg. Traumatol."},{"key":"B23","doi-asserted-by":"publisher","first-page":"20211033","DOI":"10.1259\/bjr.20211033","article-title":"Understanding breast cancer as a global health concern","volume":"95","author":"Wilkinson","year":"2022","journal-title":"Br. J. Radiol."},{"key":"B24","doi-asserted-by":"publisher","first-page":"196","DOI":"10.2106\/JBJS.16.01514","article-title":"An easy-to-use prognostic model for survival estimation for patients with symptomatic long bone metastases","volume":"100","author":"Willeumier","year":"2018","journal-title":"J. Bone Jt. Surg. Am."}],"container-title":["Frontiers in Cell and Developmental Biology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcell.2022.1059597\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,2]],"date-time":"2023-12-02T10:55:30Z","timestamp":1701514530000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fcell.2022.1059597\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,7]]},"references-count":24,"alternative-id":["10.3389\/fcell.2022.1059597"],"URL":"https:\/\/doi.org\/10.3389\/fcell.2022.1059597","relation":{},"ISSN":["2296-634X"],"issn-type":[{"value":"2296-634X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,7]]},"article-number":"1059597"}}