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

Abstract

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.

Nomogram based on immune-inflammatory indicators and age-adjusted Charlson comorbidity index score to predict prognosis of postoperative parotid gland carcinoma patients

“…PGC is a rare head and neck tumor with remarkable heterogeneity, which results highly variable in treatment outcomes. Generally, the AJCC stage system was the widely-used PGC has been studied previously by various researchers from different aspects [4,6,14,15]. In this study, we obtained the total cases from two tertiary hospitals in China and focused on the role of immune-inflammatory-nutrition indicators and ACCI in PGC patients which had not been noticed before.…”

Establishment and Validation of Prognostic Nomograms for Patients With Parotid Gland Adenocarcinoma Not Otherwise Specified: A SEER Analysis From 2004 to 2016

“…In our study, age was an independent prognostic factor of OS and other cause-specific death, but it had no effect on CSS. This may be explained by the fact that PANOS has a relatively longer survival time compared with other cancers ( 3 ), and that older patients may suffer from death caused by competing events like cardiovascular events, which may hinder the occurrence of cancer-specific death.…”

“…As a rare malignant tumor, parotid gland carcinoma accounts for 1–3% of all head and neck malignancies ( 1 ). Most head and neck malignancies are squamous cell carcinoma, but parotid gland carcinomas are more diverse, with 24 different histologic subtypes according to the 2017 World Health Organization (WHO) classification ( 2 , 3 ). The most common pathological types of parotid gland carcinomas are mucoepidermoid carcinoma and adenoid cystic carcinoma, which has been systematically studied ( 4 – 6 ).…”

A nomogram for predicting survival in patients with gastrointestinal stromal tumor: a study based on the surveillance, epidemiology, and end results database

“…One widely used tool in tumor prediction models is the nomogram, which provides a straightforward and accurate means of estimating a patient's chances of survival based on various clinical and demographic factors (9). While nomograms have been established for several cancer types, including tonsil, parotid-gland, and breast cancer (10)(11)(12), the development of a nomogram specifically designed for GIST is yet to be reported. Consequently, there exists a need to construct a comprehensive nomogram for GIST patients, utilizing pertinent data from reliable sources such as the Surveillance, Epidemiology, and End Results (SEER) database.…”