The Nomogram Model Predicting Overall Survival and Guiding Clinical Decision in Patients With Glioblastoma Based on the SEER Database - Li, Hongjian; He, Yingya; Huang, Lianfang; Luo, Hui; Zhu, Xiao (Frontiers in Oncology, 2020)
Background: Patients with glioblastoma have a poor prognosis. We want to develop and validate nomograms for predicting overall survival in patients with glioblastoma. Methods: Data of patients with glioblastoma diagnosed pathologically in the SEER database from 2007 to 2016 were collected by SEER*Stat software. After eliminating invalid and missing clinical information, 3635 patients (total group) were finally identified and randomly divided into the training group (2183 cases) and the verification group (1452 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). Results: 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 (P<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. Conclusions: The proposed nomogram resulted in accurate prognostic prediction for patients with glioblastoma.