Turkish Neurosurgery
Nomograms for Predicting the Overall and Cancer-Specific Survival of Patients With High-Grade Glioma: A Surveillance, Epidemiology, and End Results Study.
Yuhan Xia2, Weixin Liao3, Shaozhuo Huang3, Zhicheng Liu3, Xiaowen Huang3, Chen Yang3, Chao Ye4, Yingjie Jiang4, Jun Wang1
1Nanfang Hospital, Southern Medical University, Department of neurosurgery, Guangzhou,
2Southern Medical University, Basic Medical College, Guangzhou,
3Southern Medical University, First Clinical Medical School, Guangzhou,
4Navy Medical University, Basic Medical College, Shanghai,
DOI: 10.5137/1019-5149.JTN.26131-19.2

Aim: We aimed to predict the overall survival (OS) and the cancer-specific survival (CSS) of patients with high-grade glioma (HGG) using nomograms and the Surveillance, Epidemiology, and End Results (SEER) database (2000-2013). Material and Methods:A total of 3706 patients with high-grade glioma were identified by the SEER database (2000-2013). Based on the relevant information of these patients, we divided the primary cohort into a training cohort (n = 3336) and a validation cohort (n = 370). The nomograms were constructed by the training cohort and corroborated by the validation cohort. Results: According to the multivariate analysis of the training cohort, the nomograms of OS and CSS indicated that patient age at diagnosis, laterality, radiation, and the extent of resection are significantly correlated with the survival rate. The c-indexes of the nomograms of OS and CSS of the training cohort are 0.682 [95% confidence interval (CI): 0.671-0.693] and 0.678 (95%CI: 0.666-0.690), respectively. The calibration curve plots of 1- and 3-year OS and CSS showed that the nomogram predictions are consistent with the observed outcomes for both the training and validation cohorts. Conclusion:Based on the data obtained, we established a scoring model to predict the OS and the CSS of patients with HGG. All calibration curves showed high consistency between the predicted and actual survival.

Corresponding author : Jun Wang