ArticleCurrent medical science2024
Risk Factors and Predictive Nomogram for Survival in Elderly Patients with Brain Glioma.
Article in Current medical science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Construction and external validation of a prognostic model for high-grade glioma based on IDH1 mutation status: implications for concurrent chemoradiotherapy efficacy.American journal of cancer research · 2026Article
- Mapping the global research landscape and innovations on elderly glioma: a bibliometric analysis.Frontiers in oncology · 2026Article
- Comprehensive analysis of clinical, pathological, and molecular risk factors affecting the prognosis of patients with glioma.Medicine · 2025Observational
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo determine the factors that contribute to the survival of elderly individuals diagnosed with brain glioma and develop a prognostic nomogram.
methodsData from elderly individuals (age ≥65 years) histologically diagnosed with brain glioma were sourced from the Surveillance, Epidemiology, and End Results (SEER) database. The dataset was randomly divided into a training cohort and an internal validation cohort at a 6:4 ratio. Additionally, data obtained from Tangdu Hospital constituted an external validation cohort for the study. The identification of independent prognostic factors was achieved through the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis, enabling the construction of a nomogram. Model performance was evaluated using C-index, ROC curves, calibration plot and decision curve analysis (DCA).
resultsA cohort of 20 483 elderly glioma patients was selected from the SEER database. Five prognostic factors (age, marital status, histological type, stage, and treatment) were found to significantly impact overall survival (OS) and cancer-specific survival (CSS), with tumor location emerging as a sixth variable independently linked to CSS. Subsequently, nomogram models were developed to predict the probabilities of survival at 6, 12, and 24 months. The assessment findings from the validation queue indicate a that the model exhibited strong performance.
conclusionOur nomograms serve as valuable prognostic tools for assessing the survival probability of elderly glioma patients. They can potentially assist in risk stratification and clinical decision-making.
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