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The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Anahita Fathi Kazerooni1,2,3, Nastaran Khalili1, Xinyang Liu4
1Center for Data-Driven Discovery in Biomedicine (D3b), Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Abstract:
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than 20%. Due to their rarity, the diagnosis of these entities is often delayed, their treatment is mainly based on historic treatment concepts, and clinical trials require multi-institutional collaborations. The MICCAI Brain Tumor Segmentation (BraTS) Challenge is a landmark community benchmark event with a successful history of 12 years of resource creation for the segmentation and analysis of adult glioma. Here we present the CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge, which represents the first BraTS challenge focused on pediatric brain tumors with data acquired across multiple international consortia dedicated to pediatric neuro-oncology and clinical trials. The BraTS-PEDs 2023 challenge focuses on benchmarking the development of volumentric segmentation algorithms for pediatric brain glioma through standardized quantitative performance evaluation metrics utilized across the BraTS 2023 cluster of challenges. Models gaining knowledge from the BraTS-PEDs multi-parametric structural MRI (mpMRI) training data will be evaluated on separate validation and unseen test mpMRI dataof high-grade pediatric glioma. The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge brings together clinicians and AI/imaging scientists to lead to faster development of automated segmentation techniques that could benefit clinical trials, and ultimately the care of children with brain tumors.
Insights
The CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs 2023 challenge benchmarks AI for segmenting pediatric brain gliomas. This aims to accelerate diagnosis and improve treatment for children with these rare, aggressive tumors.
Area of Science:
- Neuro-oncology
- Artificial Intelligence
- Medical Imaging
Background:
- Pediatric central nervous system tumors are the leading cause of cancer-related death in children, with high-grade gliomas having a poor prognosis.
- Rarity of pediatric brain tumors leads to diagnostic delays and reliance on outdated treatment strategies, hindering clinical trial progress.
Purpose of the Study:
- To introduce the first Brain Tumor Segmentation (BraTS) challenge focused on pediatric brain tumors.
- To benchmark volumetric segmentation algorithms for pediatric brain gliomas using standardized metrics.
Main Methods:
- Utilizing multi-parametric structural MRI (mpMRI) data from international pediatric neuro-oncology consortia.
- Evaluating AI models trained on BraTS-PEDs data using validation and unseen test datasets of high-grade pediatric gliomas.
Main Results:
- The challenge facilitates the development of automated segmentation techniques for pediatric brain gliomas.
- Standardized quantitative performance evaluation metrics are employed across the BraTS 2023 cluster of challenges.
Conclusions:
- The BraTS-PEDs 2023 challenge fosters collaboration between clinicians and AI/imaging scientists.
- Accelerated development of automated segmentation can significantly benefit clinical trials and improve care for pediatric brain tumor patients.
