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.