Clinical Nomogram Model for Predicting the Prognosis of Patients with Brainstem Glioma : A Population-Based Study
Year: 2025
Type: article
Study design: Observational Study
Abstract: OBJECTIVE: The current understanding and clinical prediction of brainstem glioma (BSG) are still limited. This study aimed to conduct a large-scale population-based study to construct a clinical predictive model. METHODS: Patients with BSG diagnosed histologically from 1973 to 2016 were identified using the SEER (Surveillance Epidemiology and End Results) database. According to World Health Organization grade, the whole population was divided into the low-grade BSG (LGBSG) cohort and the high-grade BSG (HGBSG) cohort. Univariate and multivariate cox regression analyses were employed to determine prognostic factors of overall survival (OS). All independently prognostic variables were further used to construct nomograms to predict the 1- and 2-year OS probability. The precision and reliability of the nomogram were evaluated by C-index and calibration plots. RESULTS: Cox regression analysis showed that four independent prognostic factors, were identified in the LGBSG cohort and two independent prognostic factors were identified in the HGBSG cohort. These independently prognostic factors and the main demographic data were further used to construct clinical nomograms for the LGBSG and HGBSG cohorts, respectively. The C-index for the internal validation was 0.89 (95% confidence interval [CI], 0.83-0.95) and 0.64 (95% CI, 0.60-0.68) in the LGBSG and HGBSG cohorts, respectively. The results of the calibration plots showed that the actual observation and prediction values obtained by the nomogram had good consistency in the LGBSG and HGBSG cohorts. CONCLUSION: This study identified several independent prognostic variables and further constructed the clinical nomogram model. The nomogram model can provide valuable clinical reference and risk assessments for clinicians to further manage these patients with BSG.
Authors: Rui Zhang, Gaoyue Jiang, Yanming Ren, Yuekang Zhang, Xiaodong Niu
Language: English
Subfield: Genetics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $300
OpenAlex ID: W4410528123