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Prognostic Factors and Survival Prediction of Pediatric Glioblastomas: A Population-Based Study
Wang DA1, Cai Xueli, Peng Xiao
1Hwa Mei Hospital, University of Chinese Academy of Sciences, Department of Emergency Medicine, Zhejiang Province, China.
Insights
Older age and infratentorial tumors worsen survival in pediatric glioblastoma (GBM). Gross total resection and chemotherapy improve outcomes, aiding survival prediction with a new nomogram model.
Area of Science:
- Pediatric Oncology
- Neuro-oncology
- Cancer Epidemiology
Background:
- Pediatric glioblastomas (GBMs) are aggressive brain tumors with complex risk factors.
- Effective prediction models are crucial for estimating survival rates in young GBM patients.
Purpose of the Study:
- To identify key risk factors associated with pediatric GBM.
- To develop a predictive model for estimating survival in pediatric GBM patients.
Main Methods:
- Utilized data from 378 pediatric GBM patients from the Surveillance, Epidemiology, and End Results (SEER) database.
- Employed Kaplan-Meier analysis, univariate and multivariate Cox regression for prognostic factor identification.
- Constructed a nomogram model for survival prediction.
Main Results:
- Older age at diagnosis and infratentorial tumor location were linked to poorer survival.
- Gross total resection (GTR) and chemotherapy significantly improved overall survival.
- Radiotherapy showed no significant association with survival outcomes.
Conclusions:
- Older pediatric GBM patients and those with infratentorial tumors face poorer prognoses.
- Surgical resection (GTR) and chemotherapy are vital for enhancing survival.
- A validated nomogram model demonstrates good performance in predicting pediatric GBM survival.
Aim:
To identify the risk factors for pediatric glioblastomas (GBMs), and to develop an effective prediction model to estimate the survival rate for these patients.
Material And Methods:
Pediatric patients with GBM were extracted from the Surveillance, Epidemiology, and End Results database. Kaplan-Meier analyses were performed for overall survival. Significant prognostic factors were identified using univariate and multivariate Cox regression analyses. A nomogram model was also established.
Results:
A total of 378 pediatric patients with GBM were included in our study. The multivariate Cox analysis revealed that age at diagnosis (HR, 1.67; 95% CI, 1.19-2.35; p=0.003), tumor site (infratentorial vs. supratentorial: HR, 1.44; 95% CI, 1.03-2.03; p=0.035), surgery (gross total resection [GTR] vs. no surgery: HR, 0.53; 95% CI, 0.36-0.77, p < 0.001), and chemotherapy (HR, 0.56; 95% CI, 0.42-0.74; p < 0.001) were independent prognostic factors of overall survival for pediatric GBMs. Additionally, we found that patients with tumors located in the infratentorial region (p < 0.001) tended to receive conservative treatments. Moreover, our nomogram model showed favorable discriminative ability.
Conclusion:
At the population level, we found that older children and tumors located in the infratentorial region were associated with poor survival, while both GTR and chemotherapy were associated with improved survival. There was no association between radiotherapy and survival outcomes. Moreover, a nomogram with good performance was constructed to predict the overall survival of these patients.
