AI team wins international competition to measure pediatric brain tumors
Children’s National Hospital scientists won first place in a global competition to use artificial intelligence (AI) to analyze pediatric brain tumor volumes, demonstrating the team’s ground-breaking advances in imaging and machine learning.
During the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), the Children’s National team demonstrated the most accurate algorithm to study the volume of brain tumors – the most common solid tumors affecting children and adolescents and a leading cause of disease-related death at this young age. The technology could someday help oncologists understand the extent of a patient’s disease, quantify the efficacy of treatments and predict patient outcomes.
“The Brain Tumor Segmentation Challenge inspires leaders in medical imaging and deep learning to try to solve some of the most vexing problems facing radiologists, oncologists, computer engineers and data scientists,” said Marius George Linguraru, D.Phil., M.A., M.Sc., the Connor Family Professor in Research and Innovation and principal investigator in the Sheikh Zayed Institute for Pediatric Surgical Innovation. “I am honored that our team won, and I’m even more thrilled for our clinicians and their patients, who need us to keep moving forward to find new ways to treat pediatric brain tumors.”
Why we’re excited
With roughly 4,000 children diagnosed yearly, pediatric brain tumors are consistently the most common type of pediatric solid tumor, second only to leukemia in pediatric malignancies. At the urging of Linguraru and one of his peers at the Children’s Hospital of Philadelphia, pediatric data was included in the international competition for the first time, helping to ensure that children are represented in medical and technological advances.
The contest required participants to use data from multiple institutions and consortia to test competing methods fairly. The Children’s National team created a method to tap into the power of two types of imaging and machine learning: 3D convolutional neural network and 3D Vision Transformer-based deep learning models. They identified regions of the brain affected by tumors, made shrewd data-processing decisions driven by the team’s experience in AI for pediatric healthcare and achieved state-of-the-art results.
The competition drew 18 teams who are leaders from across the AI and machine learning community. The runner-up teams were from NVIDIA and the University of Electronic Science and Technology of China.
The big picture
“Children’s National has an all-star lineup, and I am thrilled to see our scientists recognized on an international stage,” said interim Executive Vice President and Chief Academic Officer Catherine Bollard, M.D., M.B.Ch.B., director of the Center for Cancer for Immunology Research. “As we work to attack brain tumors from multiple angles, we continue to show our exceptional ability to create new and better tools for diagnosing, imaging and treating these devastating tumors.”
