Document Type
Article
Publication Date
12-5-2022
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.
Recommended Citation
Pati, Sarthak; Baid, Ujjwal; Edwards, Brandon; Sheller, Micah; Wang, Shih-Han; Reina, G. Anthony; Foley, Patrick; Gruzdev, Alexey; Karkada, Deepthi; Davatzikos, Christos; Sako, Chiharu; Ghodasara, Satyam; Bilello, Michel; Mohan, Suyash; Vollmuth, Philipp; Brugnara, Gianluca; Preetha, Chandrakanth J.; Sahm, Felix; Maier-Hein, Klaus; Zenk, Maximilian; Bendszus, Martin; Wick, Wolfgang; Calabrese, Evan; Rudie, Jeffrey; Villanueva-Meyer, Javier; Cha, Soonmee; Ingalhalikar, Madhura; Jadhav, Manali; Pandey, Umang; Saini, Jitender; Garrett, John; Larson, Matthew; Jeraj, Robert; Currie, Stuart; Frood, Russell; Fatania, Kavi; Huang, Raymond Y.; Chang, Ken; Balaña, Carmen; Capellades, Jaume; Puig, Josep; Trenkler, Johannes; Pichler, Josef; Necker, Georg; Haunschmidt, Andreas; Meckel, Stephan; Shukla, Gaurav; Liem, Spencer; Alexander, Gregory S; Lombardo, Joseph; Palmer, Joshua D.; Flanders, Adam E.; Dicker, Adam P.; Sair, Haris I.; Jones, Craig K.; Venkataraman, Archana; Jiang, Meirui; So, Tiffany Y.; Chen, Cheng; Heng, Pheng Ann; Dou, Qi; Kozubek, Michal; Lux, Filip; Michálek, Jan; Matula, Petr; Keřkovský, Miloš; Kopřivová, Tereza; Dostál, Marek; Vybíhal, Václav; Vogelbaum, Michael A.; Mitchell, J. Ross; Farinhas, Joaquim; Maldjian, Joseph A.; Yogananda, Chandan Ganesh Bangalore; Pinho, Marco C.; Reddy, Divya; Holcomb, James; Wagner, Benjamin C.; Ellingson, Benjamin M.; Cloughesy, Timothy F.; Raymond, Catalina; Oughourlian, Talia; Hagiwara, Akifumi; Wang, Chencai; To, Minh-Son; Bhardwaj, Sargam; Chong, Chee; Agzarian, Marc; Falcão, Alexandre Xavier; Martins, Samuel B.; Teixeira, Bernardo C. A.; Sprenger, Flávia; Menotti, David; Lucio, Diego R.; LaMontagne, Pamela; Marcus, Daniel; Wiestler, Benedikt; Kofler, Florian; Ezhov, Ivan; Metz, Marie; Jain, Rajan; Lee, Matthew; Lui, Yvonne W.; McKinley, Richard; Slotboom, Johannes; Radojewski, Piotr; Meier, Raphael; Wiest, Roland; Murcia, Derrick; Fu, Eric; Haas, Rourke; Thompson, John; Ormond, David Ryan; Badve, Chaitra; Sloan, Andrew E.; Vadmal, Vachan; Waite, Kristin; Colen, Rivka R.; Pei, Linmin; Ak, Murat; Srinivasan, Ashok; Bapuraj, J. Rajiv; Rao, Arvind; Wang, Nicholas; Yoshiaki, Ota; Moritani, Toshio; Turk, Sevcan; Lee, Joonsang; Prabhudesai, Snehal; Morón, Fanny; Mandel, Jacob; Kamnitsas, Konstantinos; Glocker, Ben; Dixon, Luke V. M.; Williams, Matthew; Zampakis, Peter; Panagiotopoulos, Vasileios; Tsiganos, Panagiotis; Alexiou, Sotiris; Haliassos, Ilias; Zacharaki, Evangelia I; Moustakas, Konstantinos; Kalogeropoulou, Christina; Kardamakis, Dimitrios M.; Choi, Yoon Seong; Lee, Seung-Koo; Chang, Jong Hee; Ahn, Sung Soo; Luo, Bing; Poisson, Laila; Wen, Ning; Tiwari, Pallavi; Verma, Ruchika; Bareja, Rohan; Yadav, Ipsa; Chen, Jonathan; Kumar, Neeraj; Smits, Marion; van der Voort, Sebastian R.; Alafandi, Ahmed; Incekara, Fatih; Wijnenga, Maarten M. J.; Kapsas, Georgios; Gahrmann, Renske; Schouten, Joost W; Dubbink, Hendrikus J.; Vincent, Arnaud J. P. E.; van den Bent, Martin J.; French, Pim J.; Klein, Stefan; Yuan, Yading; Sharma, Sonam; Tseng, Tzu-Chi; Adabi, Saba; Niclou, Simone P.; Keunen, Olivier; Hau, Ann-Christin; Vallières, Martin; Fortin, David; Lepage, Martin; Landman, Bennett; Ramadass, Karthik; Xu, Kaiwen; Chotai, Silky; Chambless, Lola B.; Mistry, Akshitkumar; Thompson, Reid C.; Gusev, Yuriy; Bhuvaneshwar, Krithika; Sayah, Anousheh; Bencheqroun, Camelia; Belouali, Anas; Madhavan, Subha; Booth, Thomas C.; Chelliah, Alysha; Modat, Marc; Shuaib, Haris; Dragos, Carmen; Abayazeed, Aly; Kolodziej, Kenneth; Hill, Michael; Abbassy, Ahmed; Gamal, Shady; Mekhaimar, Mahmoud; Qayati, Mohamed; Reyes, Mauricio; Park, Ji Eun; Yun, Jihye; Kim, Ho Sung; Mahajan, Abhishek; Muzi, Mark; Benson, Sean; Beets-Tan, Regina G. H.; Teuwen, Jonas; Herrera-Trujillo, Alejandro; Trujillo, Maria; Escobar, William; Abello, Ana; Bernal, Jose; Gómez, Jhon; Choi, Joseph; Baek, Stephen; Kim, Yusung; Ismael, Heba; Allen, Bryan; Buatti, John M.; Kotrotsou, Aikaterini; Li, Hongwei; Weiss, Tobias; Weller, Michael; Bink, Andrea; Pouymayou, Bertrand; Shaykh, Hassan F.; Saltz, Joel; Prasanna, Prateek; Shrestha, Sampurna; Mani, Kartik M.; Payne, David; Kurc, Tahsin; Pelaez, Enrique; Franco-Maldonado, Heydy; Loayza, Francis; Quevedo, Sebastian; Guevara, Pamela; Torche, Esteban; Mendoza, Cristobal; Vera, Franco; Ríos, Elvis; López, Eduardo; Velastin, Sergio A.; Ogbole, Godwin; Soneye, Mayowa; Oyekunle, Dotun; Odafe-Oyibotha, Olubunmi; Osobu, Babatunde; Shu'aibu, Mustapha; Dorcas, Adeleye; Dako, Farouk; Simpson, Amber L.; Hamghalam, Mohammad; Peoples, Jacob J.; Hu, Ricky; Tran, Anh; Cutler, Danielle; Moraes, Fabio Y.; Boss, Michael A.; Gimpel, James; Veettil, Deepak Kattil; Schmidt, Kendall; Bialecki, Brian; Marella, Sailaja; Price, Cynthia; Cimino, Lisa; Apgar, Charles; Shah, Prashant; Menze, Bjoern; Barnholtz-Sloan, Jill S.; Martin, Jason; and Bakas, Spyridon, "Federated Learning Enables Big Data for Rare Cancer Boundary Detection" (2022). Department of Radiation Oncology Faculty Papers. Paper 171.
https://jdc.jefferson.edu/radoncfp/171
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PubMed ID
36470898
Language
English
Comments
This article is the author's final published version in Nature Communications, Volume 13, Issue 1, December 2022, Article number 7346.
The published version is available at https://doi.org/10.1038/s41467-022-33407-5. Copyright © The Author(s) 2022.