• Ye-Yu Zhao, Si-Hai Chen, Zheng Hao, Hua-Xin Zhu, Ze-Long Xing, and Mei-Hua Li.
• Department of Neurosurgery, First Affiliated Hospital of Nanchang University, Nanchang, China.
• World Neurosurg. 2019 Oct 1; 130: e605-e612.

ObjectiveThe present study aimed to develop and evaluate a nomogram for predicting the overall survival (OS) of patients with low-grade glioma (LGG).MethodsPatients with LGG diagnosed from 1973 to 2013 were identified using the Surveillance, Epidemiology, and End Results (SEER) database. A total of 3732 patients were randomly divided into a training set (n = 2612) and a validation set (n = 1120). Univariate and multivariate Cox regression analyses of the clinical variables were performed to screen for significant prognostic factors. Next, a nomogram that included significant prognostic variables was formulated to predict for LGG. Harrell's concordance index (C-index) and calibration plots were formulated to evaluate the reliability and accuracy of the nomogram using bootstrapping according to the internal (training set) and external (validation set) validity.ResultsA nomogram was developed to predict the 5- and 9-year OS rates using 7 variables in the training set: age, tumor site, sex, marital status, histological type, tumor size, and surgery (P < 0.05). The C-index for internal validation, which the nomogram used to predict OS according to the training set, was 0.777 (range, 0.763-0.791), and the C-index for external validation (validation set) was 0.776 (range, 0.754-0.797). The results of the calibration plots showed that the actual observation and prediction values obtained by the nomogram had good consistency between the 2 sets.ConclusionsWe have developed a ready-to-use nomogram model that includes clinical characteristics to predict OS. The nomogram might provide consultation and risk assessments for subsequent treatment of patients with LGG.Copyright © 2019. Published by Elsevier Inc.

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