Survival prediction models for patients with anal carcinoma receiving definitive chemoradiation: A population‑based study
- Yinhang Wu
- Xiaoyang Han
- Yan Li
- Kunli Zhu
- Jinming Yu
Affiliations: Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan, Shandong 250000, P.R. China, Department of Oncology, Shandong Provincial Hospital Affiliated to Shandong University, Jinan, Shandong 250021, P.R. China, Clinical Laboratory, Huaiyin District Center for Disease Control and Prevention, Jinan, Shandong 250022, P.R. China
- Published online on: December 23, 2019 https://doi.org/10.3892/ol.2019.11238
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The present study aimed to develop two nomograms in order to predict cancer‑specific survival (CSS) and overall survival (OS) of patients with anal carcinoma receiving definitive chemoradiotherapy. Data from studies including patients with anal carcinoma, who were determined to be positive histologically and diagnosed between 2004 and 2010, were obtained from the Surveillance, Epidemiology, and End Results database. Significant prognostic factors for CSS and OS of patients were screened to develop nomograms through univariate and multivariate analyses. Nomograms were validated using internal and external data. The predictive abilities of the generated models were evaluated by concordance index (C‑index) and calibration curves. Risk stratification was performed for patients with the same TNM stage. A total of 1,473 patients and six independent prognostic factors for CSS and OS, namely age, sex, ethnicity, marital status at diagnosis, T stage and N stage, were included in the nomogram calculations. Calibration curves demonstrated that nomogram prediction was in high accordance with actual observation. The C‑indices of nomograms were greater than those of models based on the sixth edition of the American Joint Committee on Cancer TNM staging system for CSS prediction (training cohort, 0.72 vs. 0.70; validation cohort, 0.68 vs. 0.62) and OS (training cohort, 0.70 vs. 0.66; validation cohort, 0.68 vs. 0.62). Survival curves demonstrated significant survival differences among the different risk groups. Nomograms were more accurate than the conventional TNM staging system in prognosis prediction. In addition, survival performances of patients with the same TNM stage could be further distinguished by risk stratification, which provided individualized prediction for patients. These survival prediction methods may aid clinicians in patient counseling and in selecting more individualized therapeutic strategies.