Li Hongjian, He Yingya, Huang Lianfang, Luo Hui, Zhu Xiao
Southern Marine Science and Engineering Guangdong Laboratory (Zhanjiang), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang, China.
Cancer Center, The Affiliated Hospital, Guangdong Medical University, Zhanjiang, China.
Front Oncol. 2020 Jun 26;10:1051. doi: 10.3389/fonc.2020.01051. eCollection 2020.
Patients with glioblastoma have a poor prognosis. We want to develop and validate nomograms for predicting overall survival in patients with glioblastoma. Data of patients with glioblastoma diagnosed pathologically in the SEER database from 2007 to 2016 were collected by SEERStat software. After eliminating invalid and missing clinical information, 3,635 patients (total group) were finally identified and randomly divided into the training group (2,183 cases) and the verification group (1,452 cases). Cox proportional risk regression model was used in the training group, the verification group and the total group to analyze the prognostic factors of patients in the training group, and then the nomogram was constructed. C-indexes and calibration curves were used to evaluate the predictive value of nomogram by internal (training group data) and external validation (verification group data). Cox proportional risk regression model in the training group showed that age, year of diagnosis, laterality, radiation, chemotherapy were all influential factors for prognosis of patients with glioblastoma ( < 0.05) and were all used to construct nomogram as well. The internal and external validation results of nomogram showed that the C-index of the training group was 0.729 [95% CI was (0.715, 0.743)], and the verification group was 0.734 [95% CI was (0.718, 0.750)]. The calibration curves of both groups showed good consistency. The proposed nomogram resulted in accurate prognostic prediction for patients with glioblastoma.
胶质母细胞瘤患者预后较差。我们希望开发并验证用于预测胶质母细胞瘤患者总生存期的列线图。通过SEERStat软件收集了2007年至2016年SEER数据库中经病理诊断的胶质母细胞瘤患者的数据。在剔除无效和缺失的临床信息后，最终确定了3635例患者（总组），并将其随机分为训练组（2183例）和验证组（1452例）。在训练组、验证组和总组中使用Cox比例风险回归模型分析训练组患者的预后因素，然后构建列线图。使用C指数和校准曲线通过内部验证（训练组数据）和外部验证（验证组数据）来评估列线图的预测价值。训练组的Cox比例风险回归模型显示，年龄、诊断年份、肿瘤位置、放疗、化疗均为胶质母细胞瘤患者预后的影响因素（<0.05），且均用于构建列线图。列线图的内部和外部验证结果显示，训练组的C指数为0.729 [95%CI为（0.715，0.743）]，验证组为0.734 [95%CI为（0.718，0.750）]。两组的校准曲线显示出良好的一致性。所提出的列线图对胶质母细胞瘤患者的预后预测准确。
