Mortality patterns of patients with tonsillar squamous cell carcinoma: a population-based study.

competitive risk model nomogram risk factor second primary malignancies tonsillar squamous cell carcinoma

Journal

Frontiers in endocrinology
ISSN: 1664-2392
Titre abrégé: Front Endocrinol (Lausanne)
Pays: Switzerland
ID NLM: 101555782

Informations de publication

Date de publication:
2023
Historique:
received: 04 02 2023
accepted: 13 11 2023
medline: 22 12 2023
pubmed: 22 12 2023
entrez: 22 12 2023
Statut: epublish

Résumé

Tonsillar squamous cell carcinoma (TSCC) and second primary malignancies (SPMs) are the most common causes of mortality in patients with primary TSCC. However, the competing data on TSCC-specific death (TSD) or SPM-related death in patients with TSCC have not been evaluated. This study aimed to analyze the mortality patterns and formulate prediction models of mortality risk caused by TSCC and SPMs. Data on patients with a first diagnosis of TSCC were extracted as the training cohort from the 18 registries comprising the Surveillance, Epidemiology, and End Results (SEER) database. A competing risk approach of cumulation incidence function was used to estimate cumulative incidence curves. Fine and gray proportional sub-distributed hazard model analyses were performed to investigate the risk factors of TSD and SPMs. A nomogram was developed to predict the 5- and 10-year risk probabilities of death caused by TSCC and SPMs. Moreover, data from the 22 registries of the SEER database were also extracted to validate the nomograms. In the training cohort, we identified 14,530 patients with primary TSCC, with TSCC (46.84%) as the leading cause of death, followed by SPMs (26.86%) among all causes of death. In the proportion of SPMs, the lungs and bronchus (22.64%) were the most common sites for SPM-related deaths, followed by the larynx (9.99%), esophagus (8.46%), and Non-Melanoma skin (6.82%). Multivariate competing risk model showed that age, ethnicity, marital status, primary site, summary stage, radiotherapy, and surgery were independently associated with mortality caused by TSCC and SPMs. Such risk factors were selected to formulate prognostic nomograms. The nomograms showed preferable discrimination and calibration in both the training and validation cohorts. Patients with primary TSCC have a high mortality risk of SPMs, and the competing risk nomogram has an ideal performance for predicting TSD and SPMs-related mortality. Routine follow-up care for TSCC survivors should be expanded to monitor SPMs.

Identifiants

pubmed: 38130394
doi: 10.3389/fendo.2023.1158593
pmc: PMC10733501
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1158593

Informations de copyright

Copyright © 2023 Wang, Li, Niu, Huang, Ye, Zhao, Yue, Hou and Wu.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Auteurs

Jia Wang (J)

Clinical Research Service Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Guangdong Engineering Research Center of Collaborative Innovation of Clinical Medical Big Data Cloud Service in Western Guangdong Medical Union, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.

Xiaolin Li (X)

Clinical Research Service Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Guangdong Engineering Research Center of Collaborative Innovation of Clinical Medical Big Data Cloud Service in Western Guangdong Medical Union, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.

Dongdong Niu (D)

Clinical Research Service Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Guangdong Engineering Research Center of Collaborative Innovation of Clinical Medical Big Data Cloud Service in Western Guangdong Medical Union, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.

Jiasheng Huang (J)

Clinical Research Service Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.

Enlin Ye (E)

Clinical Research Service Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.

Yumei Zhao (Y)

Clinical Research Service Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.

Suru Yue (S)

Clinical Research Service Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.

Xuefei Hou (X)

Clinical Research Service Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Guangdong Engineering Research Center of Collaborative Innovation of Clinical Medical Big Data Cloud Service in Western Guangdong Medical Union, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.

Jiayuan Wu (J)

Clinical Research Service Center, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.
Guangdong Engineering Research Center of Collaborative Innovation of Clinical Medical Big Data Cloud Service in Western Guangdong Medical Union, Affiliated Hospital of Guangdong Medical University, Zhanjiang, Guangdong, China.

Classifications MeSH