1. A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network
- Author
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Familiar, Ariana M., Kazerooni, Anahita Fathi, Anderson, Hannah, Lubneuski, Aliaksandr, Viswanathan, Karthik, Breslow, Rocky, Khalili, Nastaran, Bagheri, Sina, Haldar, Debanjan, Kim, Meen Chul, Arif, Sherjeel, Madhogarhia, Rachel, Nguyen, Thinh Q., Frenkel, Elizabeth A., Helili, Zeinab, Harrison, Jessica, Farahani, Keyvan, Linguraru, Marius George, Bagci, Ulas, Velichko, Yury, Stevens, Jeffrey, Leary, Sarah, Lober, Robert M., Campion, Stephani, Smith, Amy A., Morinigo, Denise, Rood, Brian, Diamond, Kimberly, Pollack, Ian F., Williams, Melissa, Vossough, Arastoo, Ware, Jeffrey B., Mueller, Sabine, Storm, Phillip B., Heath, Allison P., Waanders, Angela J., Lilly, Jena V., Mason, Jennifer L., Resnick, Adam C., and Nabavizadeh, Ali
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Artificial Intelligence ,Computer Science - Computer Vision and Pattern Recognition - Abstract
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.
- Published
- 2023