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Federated learning enables big data for rare cancer boundary detection

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Sarthak Pati, Ujjwal Baid, Brandon Edwards, Micah Sheller, Shih-han Wang, G. Anthony Reina, Patrick Foley, Alexey Gruzdev, Deepthi Karkada, Christos Davatzikos, Chiharu Sako, Satyam Ghodasara, Michel Bilello, Suyash Mohan, Philipp Vollmuth, Gianluca Brugnara, Chandrakanth J. Preetha, Felix Sahm, Klaus Maier-Hein, Maximilian Zenk & 259 more Martin Bendszus, Wolfgang Wick, Evan Calabrese, Jeffrey Rudie, Javier Villanueva-Meyer, Soonmee Cha, Madhura Ingalhalikar, Manali Jadhav, Umang Pandey, Jitender Saini, John Garrett, Matthew Larson, Robert Jeraj, Stuart Currie, Russell Frood, Kavi Fatania, Raymond Y. Huang, Ken Chang, Carmen Balaña Quintero, Jaume Capellades, Josep Puig, Johannes Trenkler, Josef Pichler, Georg Necker, Andreas Haunschmidt, Stephan Meckel, Gaurav Shukla, Spencer Liem, Gregory S. Alexander, Joseph Lombardo, Joshua D. Palmer, Adam E. Flanders, Adam P. Dicker, Haris I. Sair, Craig K. Jones, Archana Venkataraman, Meirui Jiang, Tiffany Y. So, Cheng Chen, Pheng Ann Heng, Qi Dou, Michal Kozubek, Filip Lux, Jan Michálek, Petr Matula, Miloš Keřkovský, Tereza Kopřivová, Marek Dostál, Václav Vybíhal, Michael A. Vogelbaum, J. Ross Mitchell, Joaquim Farinhas, Joseph A. Maldjian, Chandan Ganesh Bangalore Yogananda, Marco C. Pinho, Divya Reddy, James Holcomb, Benjamin C. Wagner, Benjamin M. Ellingson, Timothy F. Cloughesy, Catalina Raymond, Talia Oughourlian, Akifumi Hagiwara, Chencai Wang, Minh-son To, Sargam Bhardwaj, Chee Chong, Marc Agzarian, Alexandre Xavier Falcão, Samuel B. Martins, Bernardo C. A. Teixeira, Flávia Sprenger, David Menotti, Diego R. Lucio, Pamela Lamontagne, Daniel Marcus, Benedikt Wiestler, Florian Kofler, Ivan Ezhov, Marie Metz, Rajan Jain, Matthew Lee, Yvonne W. Lui, Richard Mckinley, Johannes Slotboom, Piotr Radojewski, Raphael Meier, Roland Wiest, Derrick Murcia, Eric Fu, Rourke Haas, John Thompson, David Ryan Ormond, Chaitra Badve, Andrew E. Sloan, Vachan Vadmal, Kristin Waite, Rivka R. Colen, Linmin Pei, Murat Ak, Ashok Srinivasan, J. Rajiv Bapuraj, Arvind Rao, Nicholas Wang, Ota Yoshiaki, Toshio Moritani, Sevcan Turk, Joonsang Lee, Snehal Prabhudesai, Fanny Morón, Jacob Mandel, Konstantinos Kamnitsas, Ben Glocker, Luke V. M. Dixon, Matthew Williams, Peter Zampakis, Vasileios Panagiotopoulos, Panagiotis Tsiganos, Sotiris Alexiou, Ilias Haliassos, Evangelia I. Zacharaki, Konstantinos Moustakas, Christina Kalogeropoulou, Dimitrios M. Kardamakis, Yoon Seong Choi, Seung-koo Lee, Jong Hee Chang, Sung Soo Ahn, Bing Luo, Laila Poisson, Ning Wen, Pallavi Tiwari, Ruchika Verma, Rohan Bareja, Ipsa Yadav, Jonathan Chen, Neeraj Kumar, Marion Smits, Sebastian R. Van der voort, Ahmed Alafandi, Fatih Incekara, Maarten M. J. Wijnenga, Georgios Kapsas, Renske Gahrmann, Joost W. Schouten, Hendrikus J. Dubbink, Arnaud J. P. E. Vincent, Martin J. Van den bent, Pim J. French, Stefan Klein, Yading Yuan, Sonam Sharma, Tzu-chi Tseng, Saba Adabi, Simone P. Niclou, Olivier Keunen, Ann-christin Hau, Martin Vallières, David Fortin, Martin Lepage, Bennett Landman, Karthik Ramadass, Kaiwen Xu, Silky Chotai, Lola B. Chambless, Akshitkumar Mistry, Reid C. Thompson, Yuriy Gusev, Krithika Bhuvaneshwar, Anousheh Sayah, Camelia Bencheqroun, Anas Belouali, Subha Madhavan, Thomas C. Booth, Alysha Chelliah, Marc Modat, Haris Shuaib, Carmen Dragos, Aly Abayazeed, Kenneth Kolodziej, Michael Hill, Ahmed Abbassy, Shady Gamal, Mahmoud Mekhaimar, Mohamed Qayati, Mauricio Reyes, Ji Eun Park, Jihye Yun, Ho Sung Kim, Abhishek Mahajan, Mark Muzi, Sean Benson, Regina G. H. Beets-Tan, Jonas Teuwen, Alejandro Herrera-Trujillo, Maria Trujillo, William Escobar, Ana Abello, Jose Bernal, Jhon Gómez, Joseph Choi, Stephen Baek, Yusung Kim, Heba Ismael, Bryan Allen, John M. Buatti, Aikaterini Kotrotsou, Hongwei Li, Tobias Weiss, Michael Weller, Andrea Bink, Bertrand Pouymayou, Hassan F. Shaykh, Joel Saltz, Prateek Prasanna, Sampurna Shrestha, Kartik M. Mani, David Payne, Tahsin Kurc, Enrique Pelaez, Heydy Franco-Maldonado, Francis Loayza, Sebastian Quevedo, Pamela Guevara, Esteban Torche, Cristobal Mendoza, Franco Vera, Elvis Ríos, Eduardo López, Sergio A. Velastin, Godwin Ogbole, Mayowa Soneye, Dotun Oyekunle, Olubunmi Odafe-Oyibotha, Babatunde Osobu, Mustapha Shu’aibu, Adeleye Dorcas, Farouk Dako, Amber L. Simpson, Mohammad Hamghalam, Jacob J. Peoples, Ricky Hu, Anh Tran, Danielle Cutler, Fabio Y. Moraes, Michael A. Boss, James Gimpel, Deepak Kattil Veettil, Kendall Schmidt, Brian Bialecki, Sailaja Marella, Cynthia Price, Lisa Cimino, Charles Apgar, Prashant Shah, Bjoern Menze, Jill S. Barnholtz-Sloan, Jason Martin, Spyridon Bakas

Original languageEnglish
Article number7346
JournalNature Communications
Volume13
Issue number1
DOIs
Published5 Dec 2022

Bibliographical note

Funding Information: Research and main methodological developments reported in this publication were partly supported by the National Institutes of Health (NIH) under award numbers NIH/NCI:U01CA242871 (S. Bakas), NIH/NINDS:R01NS042645 (C. Davatzikos), NIH/NCI:U24CA189523 (C. Davatzikos), NIH/NCI:U24CA215109 (J. Saltz), NIH/NCI:U01CA248226 (P. Tiwari), NIH/NCI:P30CA51008 (Y. Gusev), NIH:R50CA211270 (M. Muzi), NIH/NCATS:UL1TR001433 (Y. Yuan), NIH/NIBIB:R21EB030209 (Y. Yuan), NIH/NCI:R37CA214955 (A. Rao), and NIH:R01CA233888 (A.L. Simpson). The authors would also like to acknowledge the following NIH funded awards for the multi-site clinical trial (NCT00884741, RTOG0825/ACRIN6686): U10CA21661, U10CA37422, U10CA180820, U10CA180794, U01CA176110, R01CA082500, CA079778, CA080098, CA180794, CA180820, CA180822, CA180868. Research reported in this publication was also partly supported by the National Science Foundation, under award numbers 2040532 (S. Baek), and 2040462 (B. Landman). Funding Information: Research and main methodological developments reported in this publication were partly supported by the National Institutes of Health (NIH) under award numbers NIH/NCI:U01CA242871 (S. Bakas), NIH/NINDS:R01NS042645 (C. Davatzikos), NIH/NCI:U24CA189523 (C. Davatzikos), NIH/NCI:U24CA215109 (J. Saltz), NIH/NCI:U01CA248226 (P. Tiwari), NIH/NCI:P30CA51008 (Y. Gusev), NIH:R50CA211270 (M. Muzi), NIH/NCATS:UL1TR001433 (Y. Yuan), NIH/NIBIB:R21EB030209 (Y. Yuan), NIH/NCI:R37CA214955 (A. Rao), and NIH:R01CA233888 (A.L. Simpson). The authors would also like to acknowledge the following NIH funded awards for the multi-site clinical trial (NCT00884741, RTOG0825/ACRIN6686): U10CA21661, U10CA37422, U10CA180820, U10CA180794, U01CA176110, R01CA082500, CA079778, CA080098, CA180794, CA180820, CA180822, CA180868. Research reported in this publication was also partly supported by the National Science Foundation, under award numbers 2040532 (S. Baek), and 2040462 (B. Landman). Research reported in this publication was also supported by i) a research grant from Varian Medical Systems (Palo Alto, CA, USA) (Y.Yuan), (ii) the Ministry of Health of the Czech Republic (Grant Nr. NU21-08-00359) (M.Kerkovský and M.Kozubek), (iii) Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 404521405, SFB 1389, Work Package C02, and Priority Program 2177 “Radiomics: Next Generation of Biomedical Imaging” (KI 2410/1-1 ∣ MA 6340/18-1) (P. Vollmuth), (iv) DFG Project-ID B12, SFB 824 (B. Wiestler), (v) the Helmholtz Association (funding number ZT-I-OO1 4) (K. Maier-Hein), vi) the Dutch Cancer Society (KWF project number EMCR 2015-7859) (S.R. van der Voort), (vii) the Chilean National Agency for Research and Development (ANID-Basal FB0008 (AC3E) and FB210017 (CENIA)) (P. Guevara), viii) the Canada CIFAR AI Chairs Program (M. Vallières), (ix) Leeds Hospital Charity (Ref: 9RO1/1403) (S. Currie), (x) the Cancer Research UK funding for the Leeds Radiotherapy Research Centre of Excellence (RadNet) and the grant number C19942/A28832 (S. Currie), (xi) Medical Research Council (MRC) Doctoral Training Program in Precision Medicine (Award Reference No. 2096671) (J. Bernal), (xii) The European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (Grant Agreement No. 757173) (B.Glocker), (xiii) The UKRI London Medical Imaging & Artificial Intelligence Centre for Value-Based Healthcare (K. Kamnitsas), (xiv) Wellcome/Engineering and Physical Sciences Research Council (EPSRC) Center for Medical Engineering (WT 203148/Z/16/Z) (T.C. Booth), (xv) American Cancer Society Research Scholar Grant RSG-16-005-01 (A. Rao), (xvi) the Department of Defense (DOD) Peer Reviewed Cancer Research Program (PRCRP) W81XWH-18-1-0404, Dana Foundation David Mahoney Neuroimaging Program, the V Foundation Translational Research Award, Johnson & Johnson WiSTEM2D Award (P. Tiwari), (xvii) RSNA Research & Education Foundation under grant number RR2011 (E.Calabrese), (xviii) the National Research Fund of Luxembourg (FNR) (grant number: C20/BM/14646004/GLASS-LUX/Niclou) (S.P.Niclou), xix) EU Marie Curie FP7-PEOPLE-2012-ITN project TRANSACT (PITN-GA-2012-316679) and the Swiss National Science Foundation (project number 140958) (J. Slotboom), and (xx) CNPq 303808/2018-7 and FAPESP 2014/12236-1 (A. Xavier Falcão). The content of this publication is solely the responsibility of the authors and does not represent the official views of the NIH, the NSF, the RSNA R&E Foundation, or any of the additional funding bodies. Publisher Copyright: © 2022, The Author(s).

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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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