{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T11:38:24Z","timestamp":1792150704610,"version":"4.4.0"},"reference-count":42,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2021,5,7]],"date-time":"2021-05-07T00:00:00Z","timestamp":1620345600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Medicine"],"published-print":{"date-parts":[[2021,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>Parotid\u2010gland carcinoma (PGC) is a relatively rare tumor that comprises a group of heterogeneous histologic subtypes. We used the Surveillance, Epidemiology, and End Results (SEER) program database to apply a competing\u2010risks analysis to PGC patients, and then established and validated predictive nomograms for PGC.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Specific screening criteria were applied to identify PGC patients and extract their clinical and other characteristics from the SEER database. We used the cumulative incidence function to estimate the cumulative incidence rates of PGC\u2010specific death (GCD) and other cause\u2010specific death (OCD), and tested for differences between groups using Gray's test. We then identified independent prognostic factors by applying the Fine\u2013Gray proportional subdistribution hazard approach, and constructed predictive nomograms based on the results. Calibration curves and the concordance index (C\u2010index) were employed to validate the nomograms.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>We finally identified 4,075 eligible PGC patients who had been added to the SEER database from 2004 to 2015. Their 1\u2010, 3\u2010, and 5\u2010year cumulative incidence rates of GCD were 10.1%, 21.6%, and 25.7%, respectively, while those of OCD were 2.9%, 6.6%, and 9.0%. Age, race, World Health Organization histologic risk classification, differentiation grade, American Joint Committee on Cancer (AJCC) T stage, AJCC N stage, AJCC M stage, and RS (radiotherapy and surgery status) were independent predictors of GCD, while those of OCD were age, sex, marital status, AJCC T stage, AJCC M stage, and RS. These factors were integrated for constructing predictive nomograms. The results for calibration curves and the C\u2010index suggested that the nomograms were well calibrated and had good discrimination ability.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>We have used the SEER database to establish\u2014to the best of our knowledge\u2014the first competing\u2010risks nomograms for predicting the 1\u2010, 3\u2010, and 5\u2010year cause\u2010specific mortality in PGC. The nomograms showed relatively good performance and can be used in clinical practice to assist clinicians in individualized treatment decision\u2010making.<\/jats:p><\/jats:sec>","DOI":"10.1002\/cam4.3919","type":"journal-article","created":{"date-parts":[[2021,5,8]],"date-time":"2021-05-08T23:03:02Z","timestamp":1620514982000},"page":"3756-3769","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Competing\u2010risks nomograms for predicting cause\u2010specific mortality in parotid\u2010gland carcinoma: A population\u2010based analysis"],"prefix":"10.1002","volume":"10","author":[{"given":"Fengshuo","family":"Xu","sequence":"first","affiliation":[{"name":"Department of Clinical Research The First Affiliated Hospital of Jinan University Guangzhou, Guangdong Province China"},{"name":"School of Public Health Xi'an Jiaotong University Health Science Center Xi'an, Shaanxi Province 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