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.
翻译:胶质母细胞瘤是一种高度侵袭性的原发性脑肿瘤,其患者预后不良。尽管磁共振成像 (MRI) 在诊断、表征和预测胶质母细胞瘤进展中发挥了关键作用,但公共MRI库存在实质性缺陷,包括术后和随访研究不足以及专家脑肿瘤分割数据不足。为解决这些问题,我们提供了“瑞奥霍特加大学医院胶质母细胞瘤数据集 (RHUH-GBM)”,其中包括多参数MRI图像、体积评估、分子数据和接受总体或近总体增强肿瘤切除手术的胶质母细胞瘤患者的生存详细信息。该数据集提供专家校正的肿瘤亚区域分割,为开发术后和随访MRI扫描的算法提供了有价值的基础数据。RHUH-GBM数据集的公开发布对胶质母细胞瘤研究做出了重要贡献,使科学界能够研究复发模式,开发新的诊断和预测模型。这可能导致更个性化、更有效的治疗,最终改善患者预后。