An international study presenting a federated learning AI platform for pediatric brain tumors

Edward H. Lee (), Michelle Han, Jason Wright, Michael Kuwabara, Jacob Mevorach, Gang Fu, Olivia Choudhury, Ujjwal Ratan, Michael Zhang, Matthias W. Wagner, Robert Goetti, Sebastian Toescu, Sebastien Perreault, Hakan Dogan, Emre Altinmakas, Maryam Mohammadzadeh, Kathryn A. Szymanski, Cynthia J. Campen, Hollie Lai, Azam Eghbal, Alireza Radmanesh, Kshitij Mankad, Kristian Aquilina, Mourad Said, Arastoo Vossough, Ozgur Oztekin, Birgit Ertl-Wagner, Tina Poussaint, Eric M. Thompson, Chang Y. Ho, Alok Jaju, John Curran, Vijay Ramaswamy, Samuel H. Cheshier, Gerald A. Grant, S. Simon Wong, Michael E. Moseley, Robert M. Lober, Mattias Wilms, Nils D. Forkert, Nicholas A. Vitanza, Jeffrey H. Miller, Laura M. Prolo () and Kristen W. Yeom ()
Additional contact information
Edward H. Lee: Stanford University School of Medicine
Michelle Han: Stanford University School of Medicine
Jason Wright: Seattle Children’s Hospital
Michael Kuwabara: Phoenix Children’s Hospital
Jacob Mevorach: Amazon Web Services
Gang Fu: Amazon Web Services
Olivia Choudhury: Amazon Web Services
Ujjwal Ratan: Amazon Web Services
Michael Zhang: Stanford University School of Medicine
Matthias W. Wagner: University Hospital Augsburg
Robert Goetti: The Children’s Hospital at Westmead
Sebastian Toescu: Great Ormond Street Hospital for Children
Sebastien Perreault: Université de Montréal
Hakan Dogan: Koç University School of Medicine
Emre Altinmakas: Icahn School of Medicine at Mount Sinai
Maryam Mohammadzadeh: Tehran University of Medical Sciences
Kathryn A. Szymanski: Phoenix Children’s Hospital
Cynthia J. Campen: Stanford University Medical School
Hollie Lai: Children’s Hospital of Orange County
Azam Eghbal: Children’s Hospital of Orange County
Alireza Radmanesh: New York University Grossman School of Medicine
Kshitij Mankad: Great Ormond Street Hospital for Children
Kristian Aquilina: Great Ormond Street Hospital for Children
Mourad Said: Centre International Carthage Médicale
Arastoo Vossough: Children’s Hospital of Philadelphia
Ozgur Oztekin: Tepecik Education and Research Hospital
Birgit Ertl-Wagner: The Hospital for Sick Children
Tina Poussaint: Boston Children’s Hospital
Eric M. Thompson: Duke Children’s Hospital & Health Center
Chang Y. Ho: Riley Children’s Hospital
Alok Jaju: Phoenix Children’s Hospital
John Curran: Phoenix Children’s Hospital
Vijay Ramaswamy: The Hospital for Sick Children
Samuel H. Cheshier: University of Utah School of Medicine
Gerald A. Grant: Duke Children’s Hospital & Health Center
S. Simon Wong: Stanford University
Michael E. Moseley: Stanford University
Robert M. Lober: Dayton Children’s Hospital
Mattias Wilms: University of Calgary
Nils D. Forkert: University of Calgary
Nicholas A. Vitanza: Seattle Children’s Research Institute
Jeffrey H. Miller: Phoenix Children’s Hospital
Laura M. Prolo: Stanford University School of Medicine
Kristen W. Yeom: Stanford University School of Medicine

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract While multiple factors impact disease, artificial intelligence (AI) studies in medicine often use small, non-diverse patient cohorts due to data sharing and privacy issues. Federated learning (FL) has emerged as a solution, enabling training across hospitals without direct data sharing. Here, we present FL-PedBrain, an FL platform for pediatric posterior fossa brain tumors, and evaluate its performance on a diverse, realistic, multi-center cohort. Pediatric brain tumors were targeted due to the scarcity of such datasets, even in tertiary care hospitals. Our platform orchestrates federated training for joint tumor classification and segmentation across 19 international sites. FL-PedBrain exhibits less than a 1.5% decrease in classification and a 3% reduction in segmentation performance compared to centralized data training. FL boosts segmentation performance by 20 to 30% on three external, out-of-network sites. Finally, we explore the sources of data heterogeneity and examine FL robustness in real-world scenarios with data imbalances.

Date: 2024
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DOI: 10.1038/s41467-024-51172-5

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