Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
Key Laboratory of Post-trauma Neuro-repair and Regeneration in Central Nervous System, Ministry of Education & Key Laboratory of Injuries, Variations and Regeneration of Nervous System, Tianjin Neurological Institute, Tianjin, China.
J Investig Med. 2023 Apr;71(4):439-447. doi: 10.1177/10815589221147153. Epub 2023 Jan 10.
Predicting the prognosis of glioblastoma (GBM) has always been important for improving survival. An understanding of the prognostic factors for patients with GBM can help guide treatment. Herein, we aimed to construct a prognostic model for predicting overall survival (OS) for patients with GBM. We identified 11,375 patients with pathologically confirmed GBM from the Surveillance, Epidemiology, and End Results database between 2004 and 2015. The 1-, 2-, and 3-year survival probabilities were 48.8%, 22.5%, and 13.1%, respectively. The patients were randomly divided into the training cohort (n = 8531) and the validation cohort (n = 2844). A Cox proportional risk regression model was used to analyze the prognostic factors of patients in the training cohort, and a nomogram was constructed. Then concordance indexes (C-indexes), calibration curves, and receiver operating characteristic (ROC) curves were used to assess the performance of the nomograms by internal (training cohort) and external validation (validation cohort). Log-rank test and univariate analysis showed that age, race, marital status, extent of surgical resection, chemotherapy, and radiation were the prognostic factors for patients with GBM (p < 0.05), which were used to construct nomogram. The C-index of the nomogram was 0.717 (95% confidence interval (CI), 0.710-0.724) in the training cohort, and 0.724 (95% CI, 0.713-0.735) in the validation cohort. The nomogram had a higher areas under the ROC curve value. The nomogram was well validated, which can effectively predict the OS of patients with GBM. Thus, this nomogram could be applied in clinical practice.
预测胶质母细胞瘤（GBM）的预后一直对提高生存率很重要。了解 GBM 患者的预后因素有助于指导治疗。在此，我们旨在构建一个预测 GBM 患者总生存期（OS）的预后模型。我们从 2004 年至 2015 年的监测、流行病学和最终结果数据库中确定了 11375 名经病理证实的 GBM 患者。1、2 和 3 年的生存率分别为 48.8%、22.5%和 13.1%。患者被随机分为训练队列（n=8531）和验证队列（n=2844）。使用 Cox 比例风险回归模型分析训练队列中患者的预后因素，并构建列线图。然后，通过内部（训练队列）和外部验证（验证队列）评估列线图的一致性指数（C 指数）、校准曲线和接受者操作特征（ROC）曲线。对数秩检验和单因素分析显示，年龄、种族、婚姻状况、手术切除程度、化疗和放疗是 GBM 患者的预后因素（p<0.05），这些因素用于构建列线图。列线图在训练队列中的 C 指数为 0.717（95%置信区间（CI），0.710-0.724），在验证队列中的 C 指数为 0.724（95%CI，0.713-0.735）。列线图的 ROC 曲线下面积值较高。该列线图得到了很好的验证，能够有效预测 GBM 患者的 OS。因此，该列线图可应用于临床实践。
