The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)

• URL: http://arxiv.org/abs/2305.00005v2
• Date: Tue, 2 May 2023 08:27:55 GMT
• Title: The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)
• Authors: Santiago Cepeda, Sergio Garcia-Garcia, Ignacio Arrese, Francisco Herrero, Trinidad Escudero, Tomas Zamora, Rosario Sarabia
• Abstract summary: "R'io Hortega University Hospital Glioblastoma dataset" is a collection of multiparametric MRI images, volumetric assessments, molecular data, and survival details. The dataset features expert-corrected segmentations of tumor subregions, offering valuable ground truth data for developing algorithms for postoperative and follow-up MRI scans.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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