Federated learning enables big data for rare cancer boundary detection
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Published:2022-12-05
Issue:1
Volume:13
Page:
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ISSN:2041-1723
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Container-title:Nature Communications
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language:en
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Short-container-title:Nat Commun
Author:
Pati SarthakORCID, Baid UjjwalORCID, Edwards BrandonORCID, Sheller Micah, Wang Shih-Han, Reina G. Anthony, Foley PatrickORCID, Gruzdev Alexey, Karkada DeepthiORCID, Davatzikos ChristosORCID, Sako ChiharuORCID, Ghodasara SatyamORCID, Bilello Michel, Mohan SuyashORCID, Vollmuth PhilippORCID, Brugnara GianlucaORCID, Preetha Chandrakanth J.ORCID, Sahm FelixORCID, Maier-Hein KlausORCID, Zenk MaximilianORCID, Bendszus Martin, Wick WolfgangORCID, Calabrese EvanORCID, Rudie JeffreyORCID, Villanueva-Meyer Javier, Cha Soonmee, Ingalhalikar Madhura, Jadhav ManaliORCID, Pandey UmangORCID, Saini Jitender, Garrett JohnORCID, Larson Matthew, Jeraj Robert, Currie StuartORCID, Frood RussellORCID, Fatania KaviORCID, Huang Raymond Y., Chang Ken, Balaña CarmenORCID, Capellades Jaume, Puig Josep, Trenkler JohannesORCID, Pichler Josef, Necker GeorgORCID, Haunschmidt AndreasORCID, Meckel StephanORCID, Shukla Gaurav, Liem Spencer, Alexander Gregory S.ORCID, Lombardo Joseph, Palmer Joshua D.ORCID, Flanders Adam E., Dicker Adam P.ORCID, Sair Haris I.ORCID, Jones Craig K.ORCID, Venkataraman ArchanaORCID, Jiang MeiruiORCID, So Tiffany Y.ORCID, Chen ChengORCID, Heng Pheng Ann, Dou Qi, Kozubek MichalORCID, Lux FilipORCID, Michálek JanORCID, Matula PetrORCID, Keřkovský MilošORCID, Kopřivová TerezaORCID, Dostál MarekORCID, Vybíhal VáclavORCID, Vogelbaum Michael A., Mitchell J. Ross, Farinhas JoaquimORCID, Maldjian Joseph A., Yogananda Chandan Ganesh Bangalore, Pinho Marco C., Reddy Divya, Holcomb James, Wagner Benjamin C., Ellingson Benjamin M., Cloughesy Timothy F.ORCID, Raymond Catalina, Oughourlian Talia, Hagiwara Akifumi, Wang ChencaiORCID, To Minh-Son, Bhardwaj Sargam, Chong Chee, Agzarian MarcORCID, Falcão Alexandre XavierORCID, Martins Samuel B.ORCID, Teixeira Bernardo C. A.ORCID, Sprenger FláviaORCID, Menotti DavidORCID, Lucio Diego R., LaMontagne PamelaORCID, Marcus Daniel, Wiestler BenediktORCID, Kofler FlorianORCID, Ezhov IvanORCID, Metz MarieORCID, Jain RajanORCID, Lee MatthewORCID, Lui Yvonne W.ORCID, McKinley RichardORCID, Slotboom JohannesORCID, Radojewski Piotr, Meier Raphael, Wiest RolandORCID, Murcia Derrick, Fu Eric, Haas Rourke, Thompson John, Ormond David RyanORCID, Badve ChaitraORCID, Sloan Andrew E., Vadmal Vachan, Waite KristinORCID, Colen Rivka R., Pei Linmin, Ak MuratORCID, Srinivasan Ashok, Bapuraj J. RajivORCID, Rao Arvind, Wang NicholasORCID, Yoshiaki Ota, Moritani Toshio, Turk Sevcan, Lee JoonsangORCID, Prabhudesai Snehal, Morón FannyORCID, Mandel JacobORCID, Kamnitsas KonstantinosORCID, Glocker BenORCID, Dixon Luke V. M.ORCID, Williams MatthewORCID, Zampakis PeterORCID, Panagiotopoulos VasileiosORCID, Tsiganos PanagiotisORCID, Alexiou Sotiris, Haliassos IliasORCID, Zacharaki Evangelia I.ORCID, Moustakas KonstantinosORCID, Kalogeropoulou ChristinaORCID, Kardamakis Dimitrios M., Choi Yoon SeongORCID, Lee Seung-KooORCID, Chang Jong HeeORCID, Ahn Sung SooORCID, Luo Bing, Poisson LailaORCID, Wen NingORCID, Tiwari Pallavi, Verma Ruchika, Bareja Rohan, Yadav Ipsa, Chen JonathanORCID, Kumar NeerajORCID, Smits MarionORCID, van der Voort Sebastian R., Alafandi Ahmed, Incekara Fatih, Wijnenga Maarten M. J., Kapsas GeorgiosORCID, Gahrmann RenskeORCID, Schouten Joost W., Dubbink Hendrikus J.ORCID, Vincent Arnaud J. P. E.ORCID, van den Bent Martin J.ORCID, French Pim J.ORCID, Klein StefanORCID, Yuan YadingORCID, Sharma Sonam, Tseng Tzu-Chi, Adabi Saba, Niclou Simone P.ORCID, Keunen OlivierORCID, Hau Ann-ChristinORCID, Vallières MartinORCID, Fortin David, Lepage MartinORCID, Landman BennettORCID, Ramadass Karthik, Xu KaiwenORCID, Chotai Silky, Chambless Lola B., Mistry Akshitkumar, Thompson Reid C., Gusev YuriyORCID, Bhuvaneshwar KrithikaORCID, Sayah AnoushehORCID, Bencheqroun Camelia, Belouali AnasORCID, Madhavan Subha, Booth Thomas C.ORCID, Chelliah Alysha, Modat Marc, Shuaib HarisORCID, Dragos CarmenORCID, Abayazeed Aly, Kolodziej Kenneth, Hill Michael, Abbassy Ahmed, Gamal Shady, Mekhaimar Mahmoud, Qayati MohamedORCID, Reyes MauricioORCID, Park Ji Eun, Yun Jihye, Kim Ho SungORCID, Mahajan AbhishekORCID, Muzi MarkORCID, Benson SeanORCID, Beets-Tan Regina G. H., Teuwen Jonas, Herrera-Trujillo AlejandroORCID, Trujillo Maria, Escobar William, Abello Ana, Bernal JoseORCID, Gómez Jhon, Choi Joseph, Baek StephenORCID, Kim Yusung, Ismael Heba, Allen BryanORCID, Buatti John M.ORCID, Kotrotsou Aikaterini, Li Hongwei, Weiss TobiasORCID, Weller MichaelORCID, Bink AndreaORCID, Pouymayou BertrandORCID, Shaykh Hassan F., Saltz JoelORCID, Prasanna Prateek, Shrestha SampurnaORCID, Mani Kartik M.ORCID, Payne DavidORCID, Kurc TahsinORCID, Pelaez EnriqueORCID, Franco-Maldonado Heydy, Loayza FrancisORCID, Quevedo SebastianORCID, Guevara PamelaORCID, Torche Esteban, Mendoza CristobalORCID, Vera Franco, Ríos ElvisORCID, López EduardoORCID, Velastin Sergio A., Ogbole GodwinORCID, Soneye Mayowa, Oyekunle DotunORCID, Odafe-Oyibotha Olubunmi, Osobu Babatunde, Shu’aibu Mustapha, Dorcas Adeleye, Dako FaroukORCID, Simpson Amber L., Hamghalam Mohammad, Peoples Jacob J.ORCID, Hu Ricky, Tran AnhORCID, Cutler DanielleORCID, Moraes Fabio Y.ORCID, Boss Michael A.ORCID, Gimpel JamesORCID, Veettil Deepak KattilORCID, Schmidt Kendall, Bialecki BrianORCID, Marella Sailaja, Price Cynthia, Cimino Lisa, Apgar Charles, Shah PrashantORCID, Menze Bjoern, Barnholtz-Sloan Jill S.ORCID, Martin JasonORCID, Bakas SpyridonORCID
Abstract
AbstractAlthough 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.
Publisher
Springer Science and Business Media LLC
Subject
General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary
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