Ye-Yu Zhao1, Si-Hai Chen2, Zheng Hao1, Hua-Xin Zhu1, Ze-Long Xing1, Mei-Hua Li3. 1. Department of Neurosurgery, First Affiliated Hospital of Nanchang University, Nanchang, China. 2. Department of Neurosurgery, First Affiliated Hospital of Nanchang University, Nanchang, China; Department of Gastroenterology, First Affiliated Hospital of Nanchang University, Nanchang, China. 3. Department of Neurosurgery, First Affiliated Hospital of Nanchang University, Nanchang, China. Electronic address: limeihua2000@sina.com.
Abstract
OBJECTIVE: The present study aimed to develop and evaluate a nomogram for predicting the overall survival (OS) of patients with low-grade glioma (LGG). METHODS: Patients 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. RESULTS: A 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. CONCLUSIONS: We 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.
OBJECTIVE: The present study aimed to develop and evaluate a nomogram for predicting the overall survival (OS) of patients with low-grade glioma (LGG). METHODS:Patients 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. RESULTS: A 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. CONCLUSIONS: We 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.
Authors: Danique E Bruil; Szabolcs David; Steven H J Nagtegaal; Sophia F A M de Sonnaville; Joost J C Verhoeff Journal: Neurooncol Adv Date: 2022-01-13