Novel risk calculator predicting long-term survival in pediatric versus adult patients diagnosed with spinal low-grade glioma: a nationwide analysis.

Abstract

Background context: Spinal low-grade gliomas (SLGGs) are rare, slow-growing central nervous system tumors affecting both pediatric and adult populations. Due to their rarity, their prognosis and optimal treatment strategies remain poorly defined, necessitating further investigation into age-related differences in outcomes and risk factors.

Purpose: This study aims to evaluate differences in treatment modalities and clinical outcomes between pediatric and adult SLGG patients. Additionally, it seeks to identify risk factors for long-term survival using predictive modeling.

Design: A retrospective cohort study using data from the National Cancer Database (NCDB) was conducted. Patients were stratified into pediatric (≤21 years) and adult (>21 years) groups for comparative analysis.

Patient sample: A total of 884 patients diagnosed with SLGGs (grades I and II) were included: Pediatric patients (≤21 years): 294 (33.3%) and adult patients (>21 years): 590 (66.7%).

Outcome measures: The primary outcome was overall survival (OS), analyzed using Kaplan-Meier survival curves and the Log-rank test. Predictive modeling was used to identify significant risk factors associated with mortality.

Methods: Patients with SLGGs (grades I and II) were identified from the National Cancer Database (NCDB) and categorized into pediatric (≤21 years) and adult (>21 years) groups. Demographic, tumor, and treatment characteristics were compared using univariate analysis. Overall survival (OS) was assessed using Kaplan-Meier survival curves and the Log-rank test. Multivariate Cox proportional hazards modeling was performed to identify independent predictors of mortality. Three machine learning models were applied to predict mortality risk, with performance evaluated using the Area Under the Curve (AUC) and Concordance index (C-index). SHapley Additive exPlanations (SHAP) analysis was used to interpret feature importance in the best-performing model.

Results: Pediatric patients presented with larger tumors on average but had significantly better OS than adults (long-term mortality: 8.2% vs. 36.8%, p<.001). Surgical resection, including gross total resection (GTR) and subtotal resection (STR), was associated with improved OS in both age groups (p=.0015). Adults were more likely to receive radiation therapy (47.8% vs. 19.1%, p<.001), while pediatric patients more frequently received chemotherapy (18.4% vs. 11.7%, p=.007); however, both treatments were associated with poorer OS (p<.0001). Multivariate Cox regression identified pediatric age (HR=0.26, p<.001) and surgery alone (HR=0.43, p<.001) as protective factors against mortality. The Random Survival Forest model demonstrated the highest predictive performance (AUC=0.74, C-index=0.71), identifying high comorbidity scores, radiation alone, and greater travel distance as key predictors of mortality.

Conclusions: Pediatric patients with SLGGs have significantly better survival than adults, even when presenting with larger tumors. Surgical resection, particularly GTR, was associated with improved survival. In contrast, the associations between radiation or chemotherapy and increased mortality are likely to reflect patient selection and disease severity, emphasizing the need for individualized treatment decisions. Key risk factors such as high comorbidity burden, radiation without surgery, and increased travel distance highlight the multifaceted nature of outcome prediction. Integrating molecular profiling, treatment sequencing, and long-term functional outcomes in future studies will be essential to advance precision care for patients with SLGGs.