Prognostic Factors for Recurrent Glioma: A Population-Based Analysis.

ABSTRACT

Background: The overall survival (OS) for patients with recurrent glioma is meager. Also, the effect of radionecrosis and prognostic factors for recurrent glioma remains controversial. In this regard, developing effective predictive models and guiding clinical care is crucial for these patients. Methods: We screened patients with recurrent glioma after radiotherapy and those who received surgery between August 1, 2013, and December 31, 2020. Univariate and multivariate Cox regression analyses determined the independent prognostic factors affecting the prognosis of recurrent glioma. Moreover, nomograms were constructed to predict recurrent glioma risk and prognosis. Statistical methods were used to determine the prediction accuracy and discriminability of the nomogram prediction model based on the area under the curve (AUC), the C-index, the decision curve analysis (DCA), and the calibration curve. In order to distinguish high-risk and low-risk groups for OS, the X-Tile and Kaplan-Meier (K-M) survival curves were employed, and the nomogram prediction model was further validated by the X-Tile and K-M survival curves. Results: According to a Cox regression analysis, independent prognostic factors of recurrent glioma after radiotherapy with radionecrosis were World Health Organization (WHO) grade and gliosis percentage. We utilized a nomogram prediction model to analyze results visually. The C-index was 0.682 (95% CI 0.616-0.748). According to receiver operating characteristic (ROC) analysis, calibration plots, and DCA, the nomogram prediction model was found to have a high-performance ability, and all patients were divided into low-risk and high-risk groups based on OS (P < .001). Conclusion: WHO grade and gliosis percentage are prognostic factors for recurrent glioma with radionecrosis, and a nomogram prediction model was established based on these two variables. Patients could be divided into high- and low-risk groups with different OS by this model, and it will provide individualized clinical decisions for future treatment.