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An international study presenting a federated learning AI platform for pediatric brain tumors.
Lee, Edward H; Han, Michelle; Wright, Jason; Kuwabara, Michael; Mevorach, Jacob; Fu, Gang; Choudhury, Olivia; Ratan, Ujjwal; Zhang, Michael; Wagner, Matthias W; Goetti, Robert; Toescu, Sebastian; Perreault, Sebastien; Dogan, Hakan; Altinmakas, Emre; Mohammadzadeh, Maryam; Szymanski, Kathryn A; Campen, Cynthia J; Lai, Hollie; Eghbal, Azam; Radmanesh, Alireza; Mankad, Kshitij; Aquilina, Kristian; Said, Mourad; Vossough, Arastoo; Oztekin, Ozgur; Ertl-Wagner, Birgit; Poussaint, Tina; Thompson, Eric M; Ho, Chang Y; Jaju, Alok; Curran, John; Ramaswamy, Vijay; Cheshier, Samuel H; Grant, Gerald A; Wong, S Simon; Moseley, Michael E; Lober, Robert M; Wilms, Mattias; Forkert, Nils D; Vitanza, Nicholas A; Miller, Jeffrey H; Prolo, Laura M; Yeom, Kristen W.
Afiliación
  • Lee EH; Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA. edward.heesung.lee@gmail.com.
  • Han M; Department of Radiology, Lucas Center, Stanford University, Stanford, CA, USA. edward.heesung.lee@gmail.com.
  • Wright J; Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
  • Kuwabara M; Department of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
  • Mevorach J; Department of Radiology, Seattle Children's Hospital, Seattle, WA, USA.
  • Fu G; Department of Radiology, Phoenix Children's Hospital, Phoenix, AZ, USA.
  • Choudhury O; Amazon Web Services, Seattle, WA, USA.
  • Ratan U; Amazon Web Services, Seattle, WA, USA.
  • Zhang M; Amazon Web Services, Seattle, WA, USA.
  • Wagner MW; Amazon Web Services, Seattle, WA, USA.
  • Goetti R; Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
  • Toescu S; Department of Diagnostic and Interventional Neuroradiology, University Hospital Augsburg, Augsburg, Germany.
  • Perreault S; Department of Medical Imaging, The Children's Hospital at Westmead, Sydney, NSW, Australia.
  • Dogan H; Great Ormond Street Hospital for Children, London, UK.
  • Altinmakas E; Division of Child Neurology, Department of Pediatrics, Centre Hospitalier Universitaire Sainte-Justine, Université de Montréal, Montreal, QC, Canada.
  • Mohammadzadeh M; Department of Radiology, Koç University School of Medicine, Istanbul, Turkey.
  • Szymanski KA; Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Campen CJ; Department of Radiology, Tehran University of Medical Sciences, Tehran, Iran.
  • Lai H; Department of Radiology, Phoenix Children's Hospital, Phoenix, AZ, USA.
  • Eghbal A; Creighton University School of Medicine-Phoenix Regional Campus, Phoenix, AZ, USA.
  • Radmanesh A; Department of Neurology, Lucile Packard Children's Hospital, Stanford University Medical School, Palo Alto, CA, USA.
  • Mankad K; Department of Radiology, Children's Hospital of Orange County, Orange, CA, USA.
  • Aquilina K; Department of Radiology, Children's Hospital of Orange County, Orange, CA, USA.
  • Said M; Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA.
  • Vossough A; Kaiser Los Angeles, Los Angeles, CA, USA.
  • Oztekin O; Great Ormond Street Hospital for Children, London, UK.
  • Ertl-Wagner B; Great Ormond Street Hospital for Children, London, UK.
  • Poussaint T; Radiology Department, Centre International Carthage Médicale, Monastir, Tunisia.
  • Thompson EM; Department of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA.
  • Ho CY; Department of Neuroradiology, Tepecik Education and Research Hospital, Izmir, Turkey.
  • Jaju A; Hamad Medical Corporation, Doha, Qatar.
  • Curran J; Department of Diagnostic and Interventional Radiology, The Hospital for Sick Children, Toronto, ON, Canada.
  • Ramaswamy V; Department of Radiology, Boston Children's Hospital, Boston, MA, USA.
  • Cheshier SH; Department of Neurosurgery, Duke Children's Hospital & Health Center, Durham, NC, USA.
  • Grant GA; Department of Radiology & Imaging Sciences, Riley Children's Hospital, Indianapolis, IN, USA.
  • Wong SS; Department of Radiology, Phoenix Children's Hospital, Phoenix, AZ, USA.
  • Moseley ME; Department of Radiology, Phoenix Children's Hospital, Phoenix, AZ, USA.
  • Lober RM; Division of Haematology/Oncology, Department of Pediatrics, The Hospital for Sick Children, Toronto, ON, Canada.
  • Wilms M; Department of Neurosurgery, University of Utah School of Medicine, Salt Lake City, UT, USA.
  • Forkert ND; Department of Neurosurgery, Duke Children's Hospital & Health Center, Durham, NC, USA.
  • Vitanza NA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA.
  • Miller JH; Department of Radiology, Lucas Center, Stanford University, Stanford, CA, USA.
  • Prolo LM; Division of Neurosurgery, Dayton Children's Hospital, Dayton, OH, USA.
  • Yeom KW; Departments of Pediatrics, Community Health Sciences, and Radiology, University of Calgary, Calgary, AB, Canada.
Nat Commun ; 15(1): 7615, 2024 Sep 02.
Article en En | MEDLINE | ID: mdl-39223133
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Inteligencia Artificial Límite: Adolescent / Child / Child, preschool / Female / Humans / Male Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias Encefálicas / Inteligencia Artificial Límite: Adolescent / Child / Child, preschool / Female / Humans / Male Idioma: En Revista: Nat Commun Asunto de la revista: BIOLOGIA / CIENCIA Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido