Huang Hua, Tunje Regina Chizi, Xia Jiajie, Yang Zhihao
The Central Hospital Affiliated to Shaoxing University, Shaoxing City, Zhejiang Province, China.
Moi County Referral Hospital, Voi Kikambala, Kilifi, Kenya.
Sci Rep. 2025 Jul 29;15(1):27703. doi: 10.1038/s41598-025-12428-2.
Brainstem ependymomas (EPNs) are rare and aggressive central nervous system tumors with unique anatomical challenges and poor prognosis. Traditional survival estimates offer limited clinical guidance due to their static nature. This study aimed to investigate the dynamic survival patterns of brainstem EPNs using conditional survival (CS) analysis and to develop a web-based nomogram model for individualized, real-time prognostication. Patients diagnosed with primary brainstem EPNs between 2000 and 2021 were identified from the SEER database. CS analysis was performed to assess changes in survival probability over time. Annual hazard rates were calculated to identify high-risk periods. Prognostic variables were selected using best subset regression (BSR) and least absolute shrinkage and selection operator (LASSO) methods. A CS-based nomogram was constructed using multivariable Cox regression and validated through calibration plots, ROC curves, and decision curve analysis (DCA). A risk stratification system and an interactive web calculator were also developed. A total of 697 patients were included and randomly assigned to training (n = 487) and validation (n = 210) cohorts. CS analysis showed that as patients survive longer after diagnosis, their probability of surviving additional years increases steadily. And six variables (age, sex, race, histology, surgery, and radiotherapy) were identified via the LASSO model for nomogram construction. The CS-nomogram demonstrated good calibration and acceptable discrimination, with 1-, 3-, and 5-year AUCs of 0.626, 0.649, and 0.656 in the training cohort, and 0.688, 0.692, and 0.687 in the validation cohort, respectively. DCA confirmed the clinical utility of the model. A risk classification system effectively stratified patients into high- and low-risk groups with significantly different survival outcomes. A web-based calculator was created to facilitate real-world application. This study presents a novel CS-based nomogram that dynamically predicts survival in patients with brainstem EPNs. By capturing time-dependent survival probabilities and integrating key clinical factors, the model offers a practical tool to support individualized prognosis, patient counseling, and follow-up planning in clinical practice. While it offers individualized prognostic insights, its clinical use requires further external validation.
脑干室管膜瘤（EPNs）是罕见的侵袭性中枢神经系统肿瘤，具有独特的解剖学挑战且预后较差。传统的生存估计因其静态性质，提供的临床指导有限。本研究旨在使用条件生存（CS）分析来研究脑干EPNs的动态生存模式，并开发一个基于网络的列线图模型用于个体化的实时预后预测。从监测、流行病学和最终结果（SEER）数据库中识别出2000年至2021年间诊断为原发性脑干EPNs的患者。进行CS分析以评估生存概率随时间的变化。计算年度风险率以确定高危期。使用最佳子集回归（BSR）和最小绝对收缩和选择算子（LASSO）方法选择预后变量。使用多变量Cox回归构建基于CS的列线图，并通过校准图、ROC曲线和决策曲线分析（DCA）进行验证。还开发了一个风险分层系统和一个交互式网络计算器。总共纳入697例患者，并随机分配到训练队列（n = 487）和验证队列（n = 210）。CS分析表明，随着患者诊断后存活时间延长，其额外存活数年的概率稳步增加。通过LASSO模型确定了六个用于构建列线图的变量（年龄、性别、种族、组织学、手术和放疗）。CS列线图显示出良好的校准和可接受的区分度，训练队列中1年、3年和5年的曲线下面积（AUC）分别为0.626、0.649和0.656，验证队列中分别为0.688、0.692和0.687。DCA证实了该模型的临床实用性。一个风险分类系统有效地将患者分为生存结果显著不同的高风险和低风险组。创建了一个基于网络的计算器以促进实际应用。本研究提出了一种基于CS的新型列线图，可动态预测脑干EPNs患者的生存情况。通过捕捉时间依赖性生存概率并整合关键临床因素，该模型提供了一个实用工具，以支持临床实践中的个体化预后、患者咨询和随访计划。虽然它提供了个体化的预后见解，但其临床应用需要进一步的外部验证。
