  Glioblastoma, a highly aggressive primary brain tumor, is associated with
poor patient outcomes. Although magnetic resonance imaging (MRI) plays a
critical role in diagnosing, characterizing, and forecasting glioblastoma
progression, public MRI repositories present significant drawbacks, including
insufficient postoperative and follow-up studies as well as expert tumor
segmentations. To address these issues, we present the "R\'io Hortega
University Hospital Glioblastoma Dataset (RHUH-GBM)," a collection of
multiparametric MRI images, volumetric assessments, molecular data, and
survival details for glioblastoma patients who underwent total or near-total
enhancing tumor resection. The dataset features expert-corrected segmentations
of tumor subregions, offering valuable ground truth data for developing
algorithms for postoperative and follow-up MRI scans. The public release of the
RHUH-GBM dataset significantly contributes to glioblastoma research, enabling
the scientific community to study recurrence patterns and develop new
diagnostic and prognostic models. This may result in more personalized,
effective treatments and ultimately improved patient outcomes.
