AI Article Synopsis
- The study created a nomogram to predict overall survival (OS) for patients with low-grade glioma (LGG) using data from 3732 patients monitored from 1973 to 2013.
- Seven clinical variables were identified as significant for predicting 5- and 9-year OS rates, and the nomogram showed high accuracy in both internal and external validation, with C-index values around 0.777 to 0.776.
- This nomogram can aid healthcare providers in assessing risks and making treatment decisions for LGG patients based on their clinical characteristics.
Article 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.
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| http://dx.doi.org/10.1016/j.wneu.2019.06.169 | DOI Listing |
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