BackgroundPediatric lower-grade gliomas (pLGGs) are a rare intracranial tumor that lacks a prognostic prediction model for clinical application. The present study aimed to construct a novel nomogram to predict overall survival for pLGGs.MethodsBased on data from the Surveillance, Epidemiology, and End Results (SEER) database, 746 pediatrics diagnosed with cerebral hemispheres lower-grade gliomas from 1998 to 2016 were enrolled for the research. All patients were randomly divided into training and validation datasets at a ratio of 7:3. The Cox and stepwise regression analysis was used to screen the independent prognosticators for developing the nomogram. The discriminating abilities and calibration of the nomogram were assessed by concordance index (C-index), receiver operating characteristic curves (ROCs), area under the ROCs (AUCs), and calibration curves. The accuracy and net benefits of the nomogram were evaluated by comparing it to the traditional prediction method by the net reclassification improvement (NRI), the integrated discrimination improvement (IDI), and the decision curve analysis (DCA). Finally, we employed risk stratifications for pLGGs. ResultsFive independent predicted indicators were associated with OS rates. The constructed nomogram showed reliable discrimination by the C-indexes of the novel nomogram for OS, which were 0.830 and 0.871, much higher than that in the traditional prediction method (0.749 and 0.728, respectively). The plotted calibration curves showed good consistency between the prediction survival rates and actual observed survival rates in both the training and validation dataset. IDI, NRI, and DCA showed the nomogram had a comparable clinical application to the traditional prediction method. Kaplan-Meier survival curves showed a significant difference among the three risk classifications.ConclusionIn conclusion, we developed a novel prognostic nomogram with improved accuracy, better clinical utility, and a more precise prediction of OS rates for hemisphere pLGGs than the traditional prediction method.