Department of Neurosurgery and Neurosurgery Research Laboratory, West China Hospital, Sichuan University, Chengdu, China.
Clinical Medicine School, Traditional Chinese Medicine of Jiangxi University, Jiangxi, China.
BMC Cancer. 2024 Jul 13;24(1):836. doi: 10.1186/s12885-024-12580-4.
The clinical features of cerebellar high-grade gliomas (cHGGs) in adults have not been thoroughly explored. This large-scale, population-based study aimed to comprehensively outline these traits and construct a predictive model.
Patient records diagnosed with gliomas were collected from various cohorts and analyzed to compare the features of cHGGs and supratentorial HGGs (sHGGs). Cox regression analyses were employed to identify prognostic factors for overall survival and to develop a nomogram for predicting survival probabilities in patients with cHGGs. Multiple machine learning methods were applied to evaluate the efficacy of the predictive model.
There were significant differences in prognosis, with SEER-cHGGs showing a median survival of 7.5 months and sHGGs 14.9 months (p < 0.001). Multivariate Cox regression analyses revealed that race, WHO grade, surgical procedures, radiotherapy, and chemotherapy were independent prognostic factors for cHGGs. Based on these factors, a nomogram was developed to predict 1-, 3-, and 5-year survival probabilities, with AUC of 0.860, 0.837, and 0.810, respectively. The model's accuracy was validated by machine learning approaches, demonstrating consistent predictive effectiveness.
Adult cHGGs are distinguished by distinctive clinical features different from those of sHGGs and are associated with an inferior prognosis. Based on these risk factors affecting cHGGs prognosis, the nomogram prediction model serves as a crucial tool for clinical decision-making in patient care.
成人小脑高级别胶质瘤（cHGG）的临床特征尚未得到充分探索。本大规模、基于人群的研究旨在全面概述这些特征并构建预测模型。
从各种队列中收集诊断为胶质瘤的患者记录，并进行分析以比较 cHGG 和幕上高级别胶质瘤（sHGG）的特征。采用 Cox 回归分析确定总生存期的预后因素，并为 cHGG 患者的生存概率开发预测列线图。应用多种机器学习方法评估预测模型的疗效。
预后存在显著差异，SEER-cHGG 的中位生存期为 7.5 个月，sHGG 为 14.9 个月（p<0.001）。多变量 Cox 回归分析显示，种族、WHO 分级、手术方式、放疗和化疗是 cHGG 的独立预后因素。基于这些因素，开发了一个列线图来预测 1、3 和 5 年的生存率，AUC 分别为 0.860、0.837 和 0.810。通过机器学习方法验证了该模型的准确性，表明具有一致的预测效果。
成人 cHGG 具有与 sHGG 不同的独特临床特征，预后较差。基于这些影响 cHGG 预后的风险因素，该列线图预测模型是患者治疗中临床决策的重要工具。
