Competing‐risks nomograms for predicting cause‐specific mortality in parotid‐gland carcinoma: A population‐based analysis

Autor: Fanfan Zhao, Fengshuo Xu, Qiao Huang, Chengzhuo Li, Xiaojie Feng, Jun Lyu, Shuai Zheng, Didi Han
Rok vydání: 2021
Předmět:
Male
0301 basic medicine
Oncology
Cancer Research
Time Factors
medicine.medical_treatment
cause‐specific mortality
0302 clinical medicine
Cause of Death
Epidemiology
Cumulative incidence
Stage (cooking)
RC254-282
Original Research
Incidence
Neoplasms. Tumors. Oncology. Including cancer and carcinogens
Middle Aged
Parotid Neoplasms
parotid‐gland carcinoma
030220 oncology & carcinogenesis
Calibration
Female
Cancer Prevention
Adult
competing‐risks analysis
medicine.medical_specialty
Risk Assessment
nomogram
03 medical and health sciences
Age Distribution
Internal medicine
Carcinoma
medicine
Humans
Radiology
Nuclear Medicine and imaging
Sex Distribution
Aged
Analysis of Variance
business.industry
Cancer
Nomogram
medicine.disease
SEER
Radiation therapy
Nomograms
030104 developmental biology
T-stage
business
SEER Program
Zdroj: Cancer Medicine, Vol 10, Iss 11, Pp 3756-3769 (2021)
Cancer Medicine
ISSN: 2045-7634
Popis: Introduction Parotid‐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. Methods 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‐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. Results We 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. Conclusion We 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.
Parotid‐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.
Databáze: OpenAIRE
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