A multi-institutional pediatric dataset of clinical radiology MRIs by
  the Children's Brain Tumor Network
Ariana M. Familiar Anahita Fathi Kazerooni Hannah Anderson Aliaksandr Lubneuski Karthik Viswanathan Rocky Breslow Nastaran Khalili Sina Bagheri Debanjan Haldar Meen Chul Kim Sherjeel Arif Rachel Madhogarhia Thinh Q. Nguyen Elizabeth A. Frenkel Zeinab Helili Jessica Harrison Keyvan Farahani Marius George Linguraru Ulas Bagci Yury Velichko Jeffrey Stevens Sarah Leary Robert M. Lober Stephani Campion Amy A. Smith Denise Morinigo Brian Rood Kimberly Diamond Ian F. Pollack Melissa Williams Arastoo Vossough Jeffrey B. Ware Sabine Mueller Phillip B. Storm Allison P. Heath Angela J. Waanders Jena V. Lilly Jennifer L. Mason Adam C. Resnick Ali Nabavizadeh
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.