Open Access
Open access
volume 13 issue 1 publication number 7346

Federated learning enables big data for rare cancer boundary detection

Sarthak Pati 1, 2, 3, 4
Ujjwal Baid 1, 2, 3
Micah Sheller 5
Shih-Han Wang 5
G. Anthony Reina 5
Alexey Gruzdev 5
Chiharu Sako 1, 2
Michel Bilello 1, 2
Suyash Mohan 1, 2
Felix Sahm 7, 8
Klaus Maier-Hein 9, 10
Martin Bendszus 6
Wolfgang Wick 7, 11
Javier Villanueva-Meyer 12
Soonmee Cha 12
Madhura Ingalhalikar 13
Manali Jadhav 13
Umang Pandey 13
Jitender Saini 14
John W. Garrett 15, 16
Matthew Larson 15
Robert Jeraj 15, 16
Stuart Currie 17
Russell Frood 17
Kavi Fatania 17
Raymond Y Huang 18
Ken Chang 19
J. Capellades 20
Jaume Capellades 21
Josep Puig 22
Josef Pichler 24
Georg Necker 23
S. Meckel 23, 25
GAURAV SHUKLA 1, 26
Spencer Liem 27
Joseph Lombardo 27, 29
Joshua Palmer 30
Adam E. Flanders 31
Haris I. Sair 32, 33
Meirui Jiang 35
Tiffany Y So 35
Cheng Chen 35
PHENG-ANN HENG 35
Qi Dou 35
Filip Lux 36
Jan Michálek 36
Petr Matula 36
Marek Dostál 37, 38
Michael A. Vogelbaum 40
J. Ross Mitchell 41, 42
Joseph A. Maldjian 44
Chandan Ganesh Bangalore Yogananda 44
Marco C. Pinho 44
Divya Reddy 44
James Holcomb 44
Benjamin C. Wagner 44
Benjamin M. Ellingson 45, 46
Tim Cloughesy 46
Catalina Raymond 45
Talia Oughourlian 45, 47
Akifumi Hagiwara 47
Chencai Wang 47
Minh-Son To 48, 49
Sargam Bhardwaj 48
Chee Chong 50
Marc Agzarian 50, 51
David Menotti 56
Diego R Lucio 56
Daniel Marcus 57
Benedikt Wiestler 58, 59
Florian Kofler 58, 59, 60
Ivan Ezhov 4, 59, 60
Marie Metz 58
Rajan Jain 61, 62
Matthew Lee 61
Y. Lui 61
Piotr Radojewski 63
Raphael Meier 63
R Wiest 63
Derrick Murcia 64
Eric Fu 64
Rourke Haas 64
John Thompson 64
Chaitra Badve 65
Andrew E. Sloan 66, 67, 68
Vachan Vadmal 68
Rivka R. Colen 70, 71
Linmin Pei 72
Murat Ak 70
Ashok Srinivasan 73
J. R. BAPURAJ 73
Arvind Rao 74
Nicholas Wang 74
Yoshiaki Ota 73
Toshio Moritani 73
Sevcan Turk 73
Joonsang Lee 74
Snehal Prabhudesai 74
Fanny Morón 75
J. Mandel 51
Ben Glocker 76
Luke Dixon 78
Matt Williams 79
SOTIRIS ALEXIOU 83
Dimitrios M Kardamakis 85
Seung-Koo Lee 86
Sung Yul Ahn 86
Bing Luo 87
Ning Wen 87, 89
Pallavi Tiwari 90
Ruchika Verma 42, 90
Rohan Bareja 90
Ipsa Yadav 90
Jonathan Chen 90
Neeraj Kumar 41, 42
Marion Smits 91
Sebastian R Van Der Voort 91
Ahmed Alafandi 91
Fatih Incekara 91, 92
Maarten M J Wijnenga 93
Joost W. Schouten 92
H. J. Dubbink 94
P J French 93
Stefan Klein 95
Yading Yuan 96
Sonam Sharma 96
Tzu Chi Tseng 96
Saba Adabi 96
Ann Christin Hau 97, 99
Martin Vallières 100, 101
David Fortin 101, 102
Martin Lepage 101, 103
Karthik Ramadass 104
Kaiwen Xu 105
Silky Chotai 106
Lola B. Chambless 106
Akshitkumar Mistry 106
Reid C. Thompson 106
Y. Gusev 107
Bhuvaneshwar K 107
Anousheh Sayah 108
Camelia Bencheqroun 107
Anas Belouali 107
Subha Madhavan 107
Thomas C. Booth 109, 110
Alysha Chelliah 109
Marc Modat 109
Haris Shuaib 111, 112
Carmen Dragos 111
Aly Abayazeed 113
Kenneth Kolodziej 113
Michael Hill 113
Ahmed Abbassy 114
Shady Gamal 114
Mahmoud Mekhaimar 114
Mohamed Qayati 114
Mauricio Reyes 115
Ji Eun Park 116
Jihye Yun 116
Ho Sung Kim 116
Mark Muzi 118
Sean Benson 119
Regina G.H. Beets-Tan 120, 121
Jonas Teuwen 119
MARIA TRUJILLO 123
William Escobar 122, 123
Ana Abello 123
JOSE M. BERNAL 123, 124
Jhon Gómez 123
Joseph Choi 125
Stephen Baek 126
Yusung Kim 127
Heba Ismael 127
Bryan G. Allen 127
John M. Buatti 127
Aikaterini Kotrotsou 128
Hongwei Li 129
Tobias Weiss 130
M Weller 130
Andrea Bink 131
Hassan F Shaykh 132
Joel H Saltz 133
Prateek Prasanna 133
Sampurna Shrestha 133
Kartik M Mani 133, 134
David Payne 135
Tahsin Kurc 133, 136
Enrique Pelaez 137
Heydy Franco Maldonado 138
Francis Loayza 137
Pamela Guevara 140
Esteban Torche 140
Franco Vera 140
Elvis Ríos 140
Eduardo López 140
Sergio A. Velastin 141
Godwin Ogbole 142
Mayowa Soneye 142
Dotun Oyekunle 142
Olubunmi Odafe Oyibotha 143
Babatunde Osobu 142
Mustapha Shuaibu 144
Adeleye Dorcas 145
Farouk Dako 2, 146
Amber L. Simpson 112, 147
Mohammad Hamghalam 147, 148
Jacob J Peoples 147
Ricky Hu 147
Anh Tran 147
Danielle Cutler 149
MICHAEL A. BOSS 151
James Gimpel 151
Kendall Schmidt 152
Brian Bialecki 152
Sailaja Marella 151
Cynthia Price 151
Lisa Cimino 151
Charles Apgar 151
Bjoern Menze 4, 129
Jason Martin 5
Spyridon Bakas 1, 2, 3
5
 
Intel Corporation, Santa Clara, USA
17
 
Leeds Teaching Hospitals Trust, Department of Radiology, Leeds, UK
21
 
Consorci MAR Parc de Salut de Barcelona, Catalonia, Spain
23
 
Institute of Neuroradiology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
24
 
Department of Neurooncology, Neuromed Campus (NMC), Kepler University Hospital Linz, Linz, Austria
25
 
Institute of Diagnostic and Interventional Neuroradiology, RKH Klinikum Ludwigsburg, Ludwigsburg, Germany
26
 
Department of Radiation Oncology, Christiana Care Health System, Philadelphia, USA
42
 
Alberta Machine Intelligence Institute, Edmonton, Canada
54
 
Instituto de Neurologia de Curitiba, Curitiba, Brazil
65
 
Department of Radiology, University Hospitals Cleveland, Cleveland, USA
66
 
Department of Neurological Surgery, University Hospitals-Seidman Cancer Center, Cleveland, USA
67
 
Case Comprehensive Cancer Center, Cleveland, USA
87
 
Department of Radiation Oncology, Henry Ford Health System, Detroit, USA
88
 
Public Health Sciences, Henry Ford Health System, Detroit, USA
99
 
Luxembourg Center of Neuropathology, Laboratoire National De Santé, Luxembourg, Luxembourg
101
 
Centre de Recherche du Centre Hospitalière Universitaire de Sherbrooke, Sherbrooke, Canada
110
 
Department of Neuroradiology, Ruskin Wing, King’s College Hospital NHS Foundation Trust, London, UK
111
 
Stoke Mandeville Hospital, Aylesbury, UK
113
 
Neosoma Inc., Groton, USA
114
 
University of Cairo School of Medicine, Giza, Egypt
117
 
The Clatterbridge Cancer Centre NHS Foundation Trust Pembroke Place, Liverpool, UK
121
 
GROW School of Oncology and Developmental Biology, Maastricht, Netherlands
122
 
Clínica Imbanaco Grupo Quirón Salud, Cali, Colombia
123
 
Universidad Del Valle, Cali, Colombia
138
 
Sociedad de Lucha Contral el Cancer - SOLCA, Guayaquil Ecuador, Guayaquil, Ecuador
143
 
Clinix Healthcare, Lagos, Lagos, Nigeria
144
 
Department of Radiology, Muhammad Abdullahi Wase Teaching Hospital, Kano, Nigeria
151
 
Center for Research and Innovation, American College of Radiology, Philadelphia, USA
152
 
Data Science Institute, American College of Radiology, Reston, USA
Publication typeJournal Article
Publication date2022-12-05
scimago Q1
wos Q1
SJR4.761
CiteScore23.4
Impact factor15.7
ISSN20411723
General Chemistry
General Biochemistry, Genetics and Molecular Biology
Multidisciplinary
General Physics and Astronomy
Abstract

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing.

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Pati S. et al. Federated learning enables big data for rare cancer boundary detection // Nature Communications. 2022. Vol. 13. No. 1. 7346
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Pati S. et al. Federated learning enables big data for rare cancer boundary detection // Nature Communications. 2022. Vol. 13. No. 1. 7346
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BibTex (up to 50 authors) Copy
@article{2022_Pati,
author = {Sarthak Pati and Ujjwal Baid and Brandon Edwards and Micah Sheller and Shih-Han Wang and G. Anthony Reina and Patrick Foley and Alexey Gruzdev and Deepthi Karkada and Christos Davatzikos and Chiharu Sako and Satyam Ghodasara and Michel Bilello and Suyash Mohan and Philipp Kickingereder and Gianluca Brugnara and Chandrakanth J Preetha and Felix Sahm and Klaus Maier-Hein and Maximilian Zenk and Martin Bendszus and Wolfgang Wick and Evan Calabrese and Jeffrey D. Rudie and Javier Villanueva-Meyer and Soonmee Cha and Madhura Ingalhalikar and Manali Jadhav and Umang Pandey and Jitender Saini and John W. Garrett and Matthew Larson and Robert Jeraj and Stuart Currie and Russell Frood and Kavi Fatania and Raymond Y Huang and Ken Chang and J. Capellades and Jaume Capellades and Josep Puig and Johannes Trenkler and Josef Pichler and Georg Necker and Andreas Haunschmidt and S. Meckel and GAURAV SHUKLA and Spencer Liem and Gregory S Alexander and Joseph Lombardo and others},
title = {Federated learning enables big data for rare cancer boundary detection},
journal = {Nature Communications},
year = {2022},
volume = {13},
publisher = {Springer Nature},
month = {dec},
url = {https://doi.org/10.1038/s41467-022-33407-5},
number = {1},
pages = {7346},
doi = {10.1038/s41467-022-33407-5}
}
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