AUTHOR=Li Xiancai , Hu Mingbin , Gu Weiguo , Liu Dewu , Mei Jinhong , Chen Shaoqing 

TITLE=Nomogram Predicting Cancer-Specific Death in Parotid Carcinoma: a Competing Risk Analysis

JOURNAL=Frontiers in Oncology

VOLUME=Volume 11 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2021.698870

DOI=10.3389/fonc.2021.698870

ISSN=2234-943X

ABSTRACT=Purpose: 
Multiple factors have been shown to be tied to the prognosis of individuals with parotid cancer (PC), however there are limited number of reliable as well as straightforward tools available for clinical estimation of individualized mortality. Here a competing risk nomogram was established to assess the risk of cancer-specific deaths (CSD) in individuals with PC. 
Methods: 
Data of PC patients analyzed in this work was retrieved from the SEER data repository (Surveillance, Epidemiology, and End Results) and the First Affiliated Hospital of Nanchang University (China). Univariate, Lasso regression coupled with multivariate Cox assessments were adopted to explore the predictive factors influencing CSD. The CIF (cumulative incidence function) coupled with the Fine-Gray proportional hazards model was employed to determine the risk indicators tied to CSD as per the univariate, as well as multivariate analyses conducted in the R software. Finally, we created and validated nomogram to forecast the 3- and 5-year CSD likelihood.
Results:
   Overall, 1467 PC patients were identified from the SEER data repository, with the three- and five-years CSD CIF after diagnosis being 21.4% and 24.1%, respectively. The univariate along with the Lasso regression data revealed that nine independent risk factors were tied to CSD in the test data set (n=1035) retrieved from the SEER data repository. Additionally, multivariate data of Fine-Gray proportional sub-distribution hazards model illustrated that N stage, Age, T stage, Histologic, M stage, grade, surgery and radiation were independent risk factors influencing CSD in individual with PC in the test data set (P<.05). Based on optimization performed using Bayesian information criterion (BIC), six variables were incorporated in the prognostic nomogram. In the internal SEER data repository verification data set (n=432) and the external medical center verification data set (n=473), our nomogram was well calibrated and exhibited considerable estimation efficiency.
Conclusion:
The competing risk nomogram presented here can be used for assessing cancer-specific mortality in PC patients.