Pediatric brain and spinal cancers remain the leading cause of cancer-related
death in children. Advancements in clinical decision-support in pediatric
neuro-oncology utilizing the wealth of radiology imaging data collected through
standard care, however, has significantly lagged other domains. Such data is
ripe for use with predictive analytics such as artificial intelligence (AI)
methods, which require large datasets. To address this unmet need, we provide a
multi-institutional, large-scale pediatric dataset of 23,101 multi-parametric
MRI exams acquired through routine care for 1,526 brain tumor patients, as part
of the Children's Brain Tumor Network. This includes longitudinal MRIs across
various cancer diagnoses, with associated patient-level clinical information,
digital pathology slides, as well as tissue genotype and omics data. To
facilitate downstream analysis, treatment-na\"ive images for 370 subjects were
processed and released through the NCI Childhood Cancer Data Initiative via the
Cancer Data Service. Through ongoing efforts to continuously build these
imaging repositories, our aim is to accelerate discovery and translational AI
models with real-world data, to ultimately empower precision medicine for
children.

小儿脑和脊髓癌是儿童癌症相关死亡的主要原因。通过临床决策支持，在小儿神经肿瘤学中利用收集的丰富放射学成像数据，与其他领域相比，然而显著滞后。因此，我们提供了一种多机构、大规模的小儿数据集，包括 23,101 个经过例行护理收集的多参数 MRI 检查，适用于 1,526 名脑肿瘤患者，作为儿童脑肿瘤网络的一部分。这包括对各种癌症诊断的纵向 MRI，以及相关的临床患者信息、数字病理学切片以及组织基因型和组学数据。通过连续构建这些成像存储库的努力，我们的目标是加速发现和翻译 AI 模型，并利用实际数据赋予儿童精准医学能力。