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Epidemiological trends, relative survival, and prognosis risk factors of WHO Grade III gliomas: A population-based
Jun-Hao Fang1, Dong-Dong Lin1, Xiang-Yang Deng1
1Department of Neurosurgery, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Background:
Population-based studies on grade III gliomas are still lacking. The purpose of our study was to investigate epidemiological characteristics, survival, and risk factors of these tumors.
Patients And Methods:
All data of patients with grade III gliomas were extracted from the Surveillance, Epidemiology, and End Results database. This database provides analysis to evaluate age-adjusted incidence, incidence-based mortality, and limited-duration prevalence. The trends of incidence and mortality were modeled using Joinpoint program. Relative survival was also available in this database. Univariate and multivariate analyses were used to access the prognostic significance of risk factors on cancer-specific survival. Nomogram was constructed to predict 3-, 5-, and 10-year survival.
Results:
Our study showed that during 2000-2013, the incidence was stable and the mortality rate dropped significantly with APC as -1.95% (95% CI: -3.35% to -0.54%). Patients aged 40-59 had the highest prevalent cases. The 1-, 3-, 5-, and 10-year relative survival rates for all patients were 74.7%, 52.8%, 44.4%, and 32.4%. And it varied by risk factors. Cox regression analysis showed older age, male, black race, divorced status, histology of AA, tumor size <3.5 cm and no surgery were associated with worse survival.
Conclusion:
Our study provides reasonable estimates of the incidence, mortality, and prevalence for patients with grade III gliomas during 2000-2013. The results of relative survival and Cox regression analysis revealed that age, race, sex, year of diagnosis, tumor site, histologic type, tumor size, and surgery were the identifiable prognostic indicators. The effects of radiotherapy still need further study. We integrated these risk factors to construct an effective clinical prediction model.
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