Investigating distinct clinical features and constructing a nomogram model for survival probability in adults with cerebellar high-grade gliomas

Abstract

Background: The clinical features of cerebellar high-grade gliomas (cHGGs) in adults have not been thoroughly explored. This large-scale, population-based study aimed to comprehensively outline these traits and construct a predictive model. Methods: Patient records diagnosed with gliomas were collected from various cohorts and analyzed to compare the features of cHGGs and supratentorial HGGs (sHGGs). Cox regression analyses were employed to identify prognostic factors for overall survival and to develop a nomogram for predicting survival probabilities in patients with cHGGs. Multiple machine learning methods were applied to evaluate the efficacy of the predictive model. Results: There were significant differences in prognosis, with SEER-cHGGs showing a median survival of 7.5 months and sHGGs 14.9 months (p < 0.001). Multivariate Cox regression analyses revealed that race, WHO grade, surgical procedures, radiotherapy, and chemotherapy were independent prognostic factors for cHGGs. Based on these factors, a nomogram was developed to predict 1-, 3-, and 5-year survival probabilities, with AUC of 0.860, 0.837, and 0.810, respectively. The model’s accuracy was validated by machine learning approaches, demonstrating consistent predictive effectiveness. Conclusions: Adult cHGGs are distinguished by distinctive clinical features different from those of sHGGs and are associated with an inferior prognosis. Based on these risk factors affecting cHGGs prognosis, the nomogram prediction model serves as a crucial tool for clinical decision-making in patient care.