Multi-Institutional Annotated Multiparametric MRI Dataset of Pediatric High-Grade Gliomas.
Authors
Affiliations (26)
Affiliations (26)
- Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, Pa.
- Division of Neurosurgery, Department of Surgery, Children's Hospital of Philadelphia, Street Address, Philadelphia, PA ####.
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pa.
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC.
- Center for Data-Driven Discovery in Biomedicine (Db), Children's Hospital of Philadelphia, Philadelphia, Pa.
- College of Arts and Sciences, University of Pennsylvania, Philadelphia, Pa.
- Department of Radiology, University of Pennsylvania, Philadelphia, Pa.
- Brigham and Women's Hospital, Dana-Farber Brigham Cancer Center & Boston Children's Hospital, Boston, Mass.
- Department of Radiology, Nationwide Children's Hospital, Columbus, Ohio.
- Department of Radiology, Unidad de Patología Clínica, Guadalajara, Mexico.
- Department of Radiology, Weill Cornell Medicine, New York, NY.
- Department of Radiology, New York Presbyterian Hospital, New York, NY.
- Department of Diagnostic and Interventional Radiology, All India Institute of Medical Sciences, Rishikesh, India.
- Department of Radiology, Children's Health Orange County, Orange, Calif.
- Imaging Department, University Hospitals Plymouth NHS Trust, Plymouth, UK.
- Division of Neuroradiology, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Mass.
- Department of Neurosurgery, Thomas Jefferson University Hospital, Philadelphia, Pa.
- Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Mass.
- Department of Radiology, Duke University, Durham, NC.
- Weill Cornell Medicine-Qatar, Qatar Foundation, Education City, Doha, Qatar.
- Division of Radiology, The Children's Hospital of Philadelphia, Philadelphia, Pa.
- Brain Tumor Center, Cincinnati Children's Hospital, Cincinnati, Ohio.
- Brain Tumor Institute, Children's National Hospital, Washington, DC.
- Department of Pediatrics, University of Michigan Medical School, Ann Arbor, Mich.
- Division of Computational Pathology, Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Ind.
- Center for Cancer and Immunology Research, Brain Tumor Institute, Children's National Research Institute, Washington, DC.
Abstract
Pediatric brain tumors are rare and still represent the most common solid tumors in children and the leading cause of cancer-related mortality in the pediatric population. Compared to adult brain tumors, they exhibit distinct biology, anatomy, and clinical behavior, posing unique diagnostic and therapeutic challenges. Artificial intelligence (AI) methods have the potential to improve diagnosis, disease monitoring, and treatment response assessment, but progress in pediatric neuro-oncology has been hampered by the lack of large, standardized, and publicly accessible datasets. We introduce the Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) dataset, the first large-scale open-access benchmark data dedicated to pediatric brain tumor segmentation and analysis. The dataset comprises multiparametric MRI scans from 457 pediatric patients with high-grade gliomas, collected across multiple institutions and international consortia. Each case includes pre- and post-contrast T1-weighted, T2-weighted, and T2-FLAIR MRI sequences. Tumor subregions were annotated following the Response Assessment in Pediatric Neuro-Oncology (RAPNO) recommendations through a semi-automated process combining pediatric-specific auto-segmentation and expert manual refinement by neuroradiologists. The dataset is partitioned into training (n = 257), validation (n = 91), and hidden testing (n = 109) subsets to support reproducible benchmarking. BraTS-PEDs is the first large-scale, standardized resource for developing and evaluating AI algorithms in pediatric neuro-oncology. It provides a foundation for reproducible method comparison, model generalization across institutions, and future integration of imaging with molecular and clinical data for precision medicine applications.