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Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Prognostic models for 1-year survival of NPC after radiotherapy in different ages
Qu-Xia Zhang1, Lv-Ping Zhuang2, Zhong-Yang Lin3
1Fujian Medical University Cancer Hospital, Fujian Cancer Hospital, No. 420 Fu-ma Road, Fuzhou, 350014, China. 473618399@qq.com.
Purpose:
Previous studies have shown that approximately 10% of nasopharyngeal cancer (NPC) patients die within a year of disease onset, and that age is an independent predictor. However, no predictive model has been developed. We aimed to establish novel prognostic models to predict the 1-year cancer-specific survival (CSS) of young, middle-aged, and older patients with NPC after radiotherapy.
Methods:
The data of 2822 NPC patients who underwent radiotherapy between 2004 and 2015 were reviewed from the surveillance, epidemiology, and end results database. We divided them into young, middle-aged, and older people groups according to age (< 44 years, 45-59 years, and ≥ 60 years, respectively). Multivariate analyses were performed, and prognostic models were constructed.
Results:
Multivariate analyses indicated that age, ethnicity, histological subtype, T, and M stage were independent predictors of 1-year CSS in the older people group. In contrast, ethnicity and age were not found to have predictive value in the young and middle-aged groups, respectively. Accordingly, three prognostic models with excellent predictive values were established for the three groups (C-indices: 0.791 [95% CI 0.722-0.859], 0.763 [95% CI 0.721-0.806] and 0.723 [95% CI 0.683-0.763], respectively). These predictive values are higher than those of the eighth edition American joint committee cancer tumor-node-metastasis (TNM) classification system.
Conclusion:
Three prognostic models for predicting the 1-year CSS of young, middle-aged, and older NPC patients after radiotherapy showed better predictive power than the TNM classification system. These models may guide treatment strategies and clinical decision-making in different cohorts.
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