Big data in U.S. neuro-oncology: trends and translational priorities.

PURPOSE: Neuro-oncology generates complex clinical, imaging, and molecular data, yet datasets remain relatively small and fragmented across modalities and institutions. While "big data" is traditionally defined by large sample size, neuro-oncology datasets are often characterized instead by high dimensionality. This study aims to provide an overview of the landscape of major U.S. neuro-oncology data resources and evaluate how these datasets are used in contemporary research. METHODS: A selection of neuro-oncology datasets was evaluated, including population registries, clinical data networks, federal and consortium research cohorts, institutional datasets, specialized resources, and artificial intelligence benchmarking resources. Analytical use was assessed through a large language model-assisted review of PubMed-indexed studies published over the past ten years referencing these datasets. Titles and abstracts were screened using a predefined classification schema, and structured data extraction identified study characteristics, analytical tasks, outcomes, modalities, validation strategies, and longitudinal modeling approaches. RESULTS: Of 11,651 screened publications, 3,608 met inclusion criteria. Analytical use was concentrated in a small number of datasets, particularly TCGA (~ 65%), SEER (~ 14%), and BraTS (~ 13%). Most studies modeled survival or tumor characteristics, whereas fewer than 1% examined functional or quality-of-life outcomes. Approximately 90% relied on a single dataset, and external validation and longitudinal modeling were rare. CONCLUSION: Big data in neuro-oncology is characterized by rich diversity. Expanding multimodal data capture, improving coverage of underrepresented populations and tumor types, strengthening longitudinal data collection, and enabling cross-dataset integration will be essential for translating high-dimensional datasets into clinically actionable insights.