Abstract
Background The clinical features of cerebellar high-grade gliomas (cHGGs) in adults have not been well 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 accuracy of this model was validated by machine learning approaches, and it exhibited good consistency in predicting effectiveness.
Conclusions Adult cHGGs are characterized by distinctive clinical features different from those of sHGGs and have an inferior prognosis. The nomogram prediction model, which is based on the risk factors affecting cHGGs prognosis, serves as a crucial tool for clinical decision-making in patient care.