The University of Texas Southwestern Glioma Dataset - MRI, Molecular Markers and Segmentations

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.