Department of Surgery, Division of Neurosurgery, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand.
School of Medicine, Mae Fah Luang University, Chiang Rai, Thailand.
J Cancer Res Ther. 2021 Jul-Sep;17(4):1052-1058. doi: 10.4103/jcrt.JCRT_233_19.
Genomic-based tools have been used to predict poor prognosis high-grade glioma (HGG). As genetic technologies are not generally available in countries with limited resources, clinical parameters may be still necessary to use in predicting the prognosis of the disease. This study aimed to identify prognostic factors associated with survival of patients with HGG. We also proposed a validated nomogram using clinical parameters to predict the survival of patients with HGG.
A multicenter retrospective study was conducted in patients who were diagnosed with anaplastic astrocytoma (WHO III) or glioblastoma (WHO IV). Collected data included clinical characteristics, neuroimaging findings, treatment, and outcomes. Prognostic factor analysis was conducted using Cox proportional hazard regression analysis. Then, we used the significant prognostic factors to develop a nomogram. A split validation of nomogram was performed. Twenty percent of the dataset was used to test the performance of the developed nomogram.
Data from 171 patients with HGG were analyzed. Overall median survival was 12 months (interquartile range: 5). Significant independent predictors included frontal HGG (hazard ratio [HR]: 0.62; 95% confidence interval [CI]: 0.40-0.60), cerebellar HGG (HR: 4.67; 95% CI: 0.93-23.5), (HR: 1.55; 95% CI: 1.03-2.32; reference = total resection), and postoperative radiotherapy (HR: 0.18; 95% CI: 0.10-0.32). The proposed nomogram was validated using nomogram's predicted 1-year mortality rate. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the curve of our nomogram were 1.0, 0.50, 0.45, 1.0, 0.64, and 0.75, respectively.
We developed a nomogram for individually predicting the prognosis of HGG. This nomogram had acceptable performances with high sensitivity for predicting 1-year mortality.
基于基因组的工具已被用于预测预后不良的高级别胶质瘤(HGG)。由于遗传技术在资源有限的国家通常不可用,因此临床参数可能仍然需要用于预测疾病的预后。本研究旨在确定与 HGG 患者生存相关的预后因素。我们还提出了一种使用临床参数验证的列线图,以预测 HGG 患者的生存。
进行了一项多中心回顾性研究,纳入诊断为间变性星形细胞瘤(WHO III)或胶质母细胞瘤(WHO IV)的患者。收集的数据包括临床特征、神经影像学表现、治疗和结局。使用 Cox 比例风险回归分析进行预后因素分析。然后,我们使用有意义的预后因素来开发列线图。对列线图进行了分割验证。数据集的 20%用于测试开发的列线图的性能。
分析了 171 例 HGG 患者的数据。总体中位生存期为 12 个月(四分位间距:5)。显著的独立预测因素包括额叶 HGG(风险比 [HR]:0.62;95%置信区间 [CI]:0.40-0.60)、小脑 HGG(HR:4.67;95% CI:0.93-23.5)、(HR:1.55;95% CI:1.03-2.32;参考=全切除)和术后放疗(HR:0.18;95% CI:0.10-0.32)。使用列线图预测的 1 年死亡率验证了提出的列线图。我们的列线图的敏感性、特异性、阳性预测值、阴性预测值、准确性和曲线下面积分别为 1.0、0.50、0.45、1.0、0.64 和 0.75。
我们开发了一种用于个体预测 HGG 预后的列线图。该列线图具有较高的敏感性,可用于预测 1 年死亡率,性能可接受。