ArticleCancer medicine2021
Competing-risks nomograms for predicting cause-specific mortality in parotid-gland carcinoma: A population-based analysis.
Article in Cancer medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 21 citations in OpenAlex.
- Prognostic Factors in Salivary Gland Malignancies: A Multicenter Study of 229 Patients from the Polish Salivary Network Database.Journal of clinical medicine · 2025Article
- The prognostic significance of the geriatric nutritional risk index in postoperative parotid gland carcinoma.Scientific reports · 2025Article
- Nomograms predicting cancer-specific survival and overall survival of advanced salivary gland malignancy patients: a study based on the SEER database.Discover oncology · 2025Article
- Nomogram based on immune-inflammatory indicators and age-adjusted charlson comorbidity index score to predict prognosis of postoperative parotid gland carcinoma patients.BMC oral health · 2024Article
- A nomogram for predicting survival in patients with gastrointestinal stromal tumor: a study based on the surveillance, epidemiology, and end results database.Frontiers in medicine · 2024Article
- Construction and validation of a prognostic nomogram for predicting cancer-specific survival in patients with intermediate and advanced colon cancer after receiving surgery and chemotherapy.Journal of cancer research and clinical oncology · 2023Article
- A nomogram for predicting survival in patients with skin non-keratinizing large cell squamous cell carcinoma: A study based on the Surveillance, Epidemiology, and End Results database.Frontiers in medicine · 2023Article
- Establishment and Validation of Prognostic Nomograms for Patients With Parotid Gland Adenocarcinoma Not Otherwise Specified: A SEER Analysis From 2004 to 2016.Frontiers in surgery · 2021Article
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8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionParotid-gland 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-risks analysis to PGC patients, and then established and validated predictive nomograms for PGC.
methodsSpecific 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-specific death (GCD) and other cause-specific death (OCD), and tested for differences between groups using Gray's test. We then identified independent prognostic factors by applying the Fine-Gray proportional subdistribution hazard approach, and constructed predictive nomograms based on the results. Calibration curves and the concordance index (C-index) were employed to validate the nomograms.
resultsWe finally identified 4,075 eligible PGC patients who had been added to the SEER database from 2004 to 2015. Their 1-, 3-, and 5-year 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-index suggested that the nomograms were well calibrated and had good discrimination ability.
conclusionWe have used the SEER database to establish-to the best of our knowledge-the first competing-risks nomograms for predicting the 1-, 3-, and 5-year cause-specific mortality in PGC. The nomograms showed relatively good performance and can be used in clinical practice to assist clinicians in individualized treatment decision-making.
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