Ghaith Abdul Karim, Yang Xinlan, Al-Mistarehi Abdel-Hameed, Khalilullah Taha, Ruchika Fnu, Weinberg Joshua, Bhimreddy Meghana, Menta Arjun K, Zeitoun Khaled, Foster Chase, Xu David, Theodore Nicholas, Lubelski Daniel
Johns Hopkins University, School of Medicine, Department of Neurosurgery.
Ohio State University, School of Medicine, Department of Neurosurgery.
Spine J. 2025 Aug 28. doi: 10.1016/j.spinee.2025.08.335.
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.
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.
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.
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%).
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.
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.
Pediatric patients presented with larger tumors on average but had significantly better OS than adults (long-term mortality: 8.2% vs. 36.8%, p<0.001). Surgical resection, including gross total resection (GTR) and subtotal resection (STR), was associated with improved OS in both age groups (p=0.0015). Adults were more likely to receive radiation therapy (47.8% vs. 19.1%, p<0.001), while pediatric patients more frequently received chemotherapy (18.4% vs. 11.7%, p=0.007); however, both treatments were associated with poorer OS (p<0.0001). Multivariate Cox regression identified pediatric age (HR=0.26, p<0.001) and surgery alone (HR=0.43, p<0.001) 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.
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.
脊髓低度恶性胶质瘤(SLGGs)是罕见的、生长缓慢的中枢神经系统肿瘤,影响儿童和成人。由于其罕见性,其预后和最佳治疗策略仍不明确,需要进一步研究结果和危险因素的年龄相关差异。
本研究旨在评估儿童和成人SLGG患者治疗方式和临床结果的差异。此外,它试图通过预测模型确定长期生存的危险因素。
使用来自国家癌症数据库(NCDB)的数据进行回顾性队列研究。将患者分为儿童(≤21岁)和成人(>21岁)组进行比较分析。
共纳入884例诊断为SLGGs(I级和II级)的患者:儿童患者(≤21岁):294例(33.3%),成人患者(>21岁):590例(66.7%)。
主要结果是总生存期(OS),使用Kaplan-Meier生存曲线和对数秩检验进行分析。预测模型用于识别与死亡率相关的重要危险因素。
从国家癌症数据库(NCDB)中识别出SLGGs(I级和II级)患者,并分为儿童(≤21岁)和成人(>21岁)组。使用单因素分析比较人口统计学、肿瘤和治疗特征。使用Kaplan-Meier生存曲线和对数秩检验评估总生存期(OS)。进行多变量Cox比例风险建模以识别死亡率的独立预测因素。应用三种机器学习模型预测死亡风险,使用曲线下面积(AUC)和一致性指数(C-index)评估性能。使用SHapley加性解释(SHAP)分析来解释最佳表现模型中的特征重要性。
儿童患者平均肿瘤较大,但总生存期明显优于成人(长期死亡率:8.2%对36.8%,p<0.001)。手术切除,包括全切除(GTR)和次全切除(STR),在两个年龄组中均与总生存期改善相关(p=0.0015)。成人更有可能接受放射治疗(47.8%对19.1%,p<0.001),而儿童患者更频繁接受化疗(18.4%对11.7%,p=0.007);然而,两种治疗均与较差的总生存期相关(p<0.0001)。多变量Cox回归确定儿童年龄(HR=0.26,p<0.001)和单纯手术(HR=0.43,p<0.001)是死亡率的保护因素。随机生存森林模型表现出最高的预测性能(AUC=0.74,C-index=0.71),确定高合并症评分、单纯放疗和更长的就诊距离是死亡的关键预测因素。
SLGGs儿童患者的生存期明显优于成人,即使肿瘤较大。手术切除,特别是全切除,与生存期改善相关。相比之下,放疗或化疗与死亡率增加之间的关联可能反映了患者选择和疾病严重程度,强调了个体化治疗决策的必要性。高合并症负担、非手术放疗和更长的就诊距离等关键危险因素突出了结果预测的多方面性质。在未来的研究中整合分子谱分析、治疗顺序和长期功能结果对于推进SLGGs患者的精准治疗至关重要。