loading page

A Risk Stratification Model Based on a Population Analysis for Predicting Cancer Specific Survival in Pediatric Brain Stem Glioma.
  • +2
  • Kai Sun,
  • Xiaowei Fei,
  • Mingwei Xu,
  • Wenjin Chen,
  • Ruxiang Xu
Kai Sun
Sichuan Academy of Medical Sciences and Sichuan People's Hospital
Author Profile
Xiaowei Fei
Fourth Military Medical University
Author Profile
Mingwei Xu
Third Military Medical University Daping Hospital and Research Institute of Surgery
Author Profile
Wenjin Chen
University of Electronic Science and Technology of China Sichuan Provincial People's Hospital
Author Profile
Ruxiang Xu
Sichuan Academy of Medical Sciences and Sichuan People's Hospital
Author Profile

Abstract

Introduction: The aim of this study was to construct and validate a nomogram and risk stratification model for predicting cancer-specific survival (CSS) of pediatric brainstem glioma patients. Methods: Cases of pediatric brainstem glioma patients (<12 years) from 1998 to 2016 were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and demographic, clinicopathologic characteristics, treatments, and survival outcomes were analyzed. The total cohort was randomly divided into training and validation sets, followed by univariate and multivariate Cox regression analyses. A nomogram was constructed and risk stratification analysis incorporated using the selected variables from the multivariate analysis. The accuracy of the model was assessed using C-index and calibration curves. Results: A total of 806 pediatric cases with histologically confirmed diagnosis of brainstem glioma were selected and analyzed. Multivariate analysis showed that age, race, tumor size, grade and radiotherapy (P<0.05) were independent prognostic indicators of pediatric gliomas. For prediction of CSS, the C-index of the nomogram was 0.75, which shows a good predictive probability. Conclusion: The nomogram developed in this study for predicting survival of pediatric patients with histologically confirmed stem gliomas is the first to incorporate risk stratification. Combining nomogram and risk stratification system is a convenient tool to aid clinicians in the identification of high-risk patients and to perform targeted adjuvant treatment.