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