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Nomogram to predict prognosis of head and neck rhabdomyosarcoma patients in children and adolescents.
Wu, Jinwen; Zeng, Qi.
Affiliation
  • Wu J; Department of Ultrasound, The First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, China.
  • Zeng Q; Gannan Medical University, Ganzhou, Jiangxi, China.
Front Oncol ; 14: 1378251, 2024.
Article in En | MEDLINE | ID: mdl-38590659
ABSTRACT

Purpose:

This study aims to explore the prognostic factors of head and neck rhabdomyosarcoma (HNRMS) in children and adolescents and construct a simple but reliable nomogram model for estimating overall survival (OS) of patients.

Methods:

Data of all HNRMS patients during 2004-2018 were identified from the Surveillance, Epidemiology, and End Result database. Kaplan-Meier method was performed to calculate OS stratified by subgroups and comparison between subgroups was completed by log-rank test. Univariate and multivariate Cox regressions analysis were employed for identifying independent predictors, which subsequently were used for a predictive model by R software, and the efficacy of the model was evaluated by applying receiver operating curve (ROC), calibration and decision curve analysis (DCA).

Results:

A total of 446 patients were included in the study. The 1-, 3-, and 5-year OS rate of the whole cohort was 90.6%, 80.0%, and 75.5%, respectively. The results of univariate and multivariate Cox regression analysis indicated that the primary site in parameningeal region, alveolar RMS histology, M1 stage, IRS stage 4, surgery, and chemotherapy were significant prognostic factors (all P<0.05). The performance of nomogram model was validated by discrimination and calibration, with AUC values of 1, 3, and 5 years OS of 0.843, 0.851, and 0.890, respectively.

Conclusion:

We constructed a prognostic nomogram model for predicting the OS in HNRMS patients in children and adolescents and this model presented practical and applicable clinical value to predict survival when choosing treatment strategies.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Oncol Year: 2024 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Front Oncol Year: 2024 Document type: Article Affiliation country: China