ArticleScientific data2026
The University of Texas Southwestern Glioma Dataset - MRI, Molecular Markers and Segmentations.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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15 authors.
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Abstract
Gliomas are the most common type of primary brain tumors. Their management options and outcomes depend significantly on the underlying molecular-marker profile. Traditionally, molecular markers are determined through pathological testing on a tissue specimen acquired through biopsy. Several Magnetic Resonance Imaging (MRI) based Deep Learning (DL) methods offer a promising, non-invasive approach to predict these markers. However, they often require high-quality, well-annotated datasets. To support this need, we present a well-curated brain tumor dataset developed at The University of Texas Southwestern (UTSW) Medical Center. This dataset includes multi-contrast-MRI, demographics, molecular-markers, and multi-label tumor segmentations for 625 patients treated at UTSW between 2006 and 2023. Each patient record contains four MRI contrasts: pre-contrast-T1w, post-contrast-T1w, T2w, and T2-weighted fluid-attenuated inversion recovery (T2w-FLAIR) images. The dataset also provides comprehensive genetic information, including IDH mutation-status, 1p19q co-deletion, MGMT promoter methylation, tumor-type, and tumor-grade. This dataset offers a valuable resource for exploring the relationship between MRI characteristics and tumor genetics. It also serves as a robust benchmark for developing and validating DL models for various downstream tasks.
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