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Updated: Jul 8, 2025
Making MR Imaging Child's Play - Pediatric Neuroimaging Protocol, Guidelines and Procedure
Published on: July 30, 2009
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network
Ariana M Familiar1,2, Anahita Fathi Kazerooni1,2,3, Hannah Anderson1,4
1Center for Data-Driven Discovery in Biomedicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Insights
Pediatric brain tumor research now has a large dataset of 23,101 multi-parametric MRI scans from 1,526 children. This real-world data aims to accelerate AI-driven precision medicine for pediatric neuro-oncology.
Area of Science:
- Pediatric Neuro-oncology
- Medical Imaging
- Artificial Intelligence
Background:
- Pediatric brain and spinal cancers are leading causes of childhood cancer mortality.
- Clinical decision-support in pediatric neuro-oncology has lagged behind other fields, despite abundant radiology imaging data.
- Artificial intelligence (AI) methods require large datasets for effective predictive analytics.
Approach:
- A multi-institutional, large-scale pediatric dataset comprising 23,101 multi-parametric MRI exams from 1,526 brain tumor patients was created.
- The dataset includes longitudinal MRIs, clinical information, digital pathology slides, and omics data for diverse cancer diagnoses.
- Treatment-naïve images for 370 subjects were processed and released via the NCI Childhood Cancer Data Initiative.
Key Points:
- This dataset represents a significant resource for pediatric neuro-oncology research.
- It facilitates the development and validation of AI models using real-world data.
- The data supports translational research aimed at improving precision medicine for children.
Conclusions:
- The established pediatric imaging repository aims to accelerate AI-driven discovery in pediatric neuro-oncology.
- By leveraging this comprehensive dataset, the goal is to advance precision medicine for children with brain tumors.
- Continued efforts in building imaging repositories are crucial for future breakthroughs.
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-naïve 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.
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