Advances in digital pathology and machine learning have the potential to revolutionize diagnostic neuropathology. Current brain tumor models are typically trained and validated using morphologic features from a single hematoxylin and eosin (H&E)-stained slide per patient. Yet, brain tumors such as diffuse glioma are known for their epigenetic, genetic, and transcriptional heterogeneity within an individual patient. The impact of this heterogeneity on model accuracy and development is unknown. To quantitatively investigate morphologic intratumoral heterogeneity in glioblastoma (GBM), we acquired 92 regionally distinct samples representing maximal tumor sampling across 10 patients with isocitrate dehydrogenase-wildtype GBM and quantified cell density, nucleus area, and nucleus circularity from whole-slide scanned images of H&E-stained slides. All 3 parameters exhibited significant morphologic variation between tumors from different patients and within a given tumor. To identify potential drivers of this variation, tumor-level and sample-level mutation profiling was performed. Mutations in tumor protein 53 both at the tumor level and the sample level had larger nuclear area and decreased nuclear circularity. Morphological features were not associated with regional location within the tumor. Accurate and robust H&E-based models to improve diagnosis and disease prognostication may require training sets that incorporate multiple spatially distinct samples per patient.