Summary
Meningiomas exhibit considerable clinical and biological heterogeneity. We previously identified 4 distinct molecular groups (immunogenic, NF2-wildtype, hypermetabolic, and proliferative) that address much of this heterogeneity. Despite the utility of these groups, the stochasticity of clustering methods and the use of multi-omics data for discovery limits the potential for classifying prospective cases. We sought to address this with a dedicated classifier. Using an international cohort of 1698 meningiomas, we constructed and rigorously validated a machine learning-based molecular classifier
