Nomogram Model for Predicting the Overall Survival of Patients With Meningiomas: a Retrospective Cohort Study

Abstract

To identify the significant prognostic factors of overall survival (OS) for patients living with meningiomas (MMs), and establish a novel graphical nomogram and an online dynamic nomogram. Patients diagnosed with MMs were identified retrospectively from the SEER database. The cohort was split into training (70%) and test (30%) groups randomly. Univariable and multivariable Cox models were successively used to screen the significant prognostic factors. Subsequently, the independent predictors were used as items to establish the graphic and dynamic nomogram model. To assess the accuracy of the model, a calibration curve was plotted. To assess the discrimination performance, C-index and time-dependent area under the receiver operator characteristic curve (AUC) were selected. Additionally, the decision curve was generated to evaluate the clinical net benefit of the model. A total of 899 patients were involved, of which 629 and 270 were split into training group and test group, respectively. Age, sex, radiotherapy, tumor size, and tumor histology were identified as the significant prognostic factors. Based on these factors, a graphical nomogram and online nomogram (Web site: https://helloshinyweb.shinyapps.io/dynamic_nomogram/) were developed. The calibration curve showed favorable consistence between predicted and actual survival rate. C-index and time-dependent AUC showed good discrimination ability, and the decision curve analysis showed positive net benefit of the model in clinical practice. Age of diagnosis, sex, tumor size, tumor histology, and radiotherapy were independent predictors for OS, while extent of resection had a borderline significant. A nomogram model was successfully developed and validated to dynamically predict the long-term OS for MM patients, expecting to help neurosurgeons optimize clinical management and treatment strategies.