J 2022

Federated learning enables big data for rare cancer boundary detection

PATI, Sarthak, Ujjwal BAID, Brandon EDWARDS, Micah SHELLER, Shih-Han WANG et. al.

Basic information

Original name

Federated learning enables big data for rare cancer boundary detection

Authors

PATI, Sarthak, 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, 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 (203 Czech Republic, guarantor, belonging to the institution), Filip LUX (203 Czech Republic, belonging to the institution), Jan MICHÁLEK (203 Czech Republic, belonging to the institution), Petr MATULA (203 Czech Republic, belonging to the institution), Miloš KEŘKOVSKÝ (203 Czech Republic, belonging to the institution), Tereza KOPŘIVOVÁ (203 Czech Republic, belonging to the institution), Marek DOSTÁL (203 Czech Republic, belonging to the institution), Václav VYBÍHAL (203 Czech Republic, belonging to the institution), 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, Vera FRANCO, 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 and Spyridon BAKAS

Edition

Nature Communications, London, Nature Publishing Group, 2022, 2041-1723

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

United Kingdom of Great Britain and Northern Ireland

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

Impact factor

Impact factor: 16.600

RIV identification code

RIV/00216224:14330/22:00129718

Organization unit

Faculty of Informatics

UT WoS

000984380900001

Keywords in English

Biomedical engineering; CNS cancer; Computer science; Medical imaging; Medical research

Tags

International impact, Reviewed
Změněno: 18/8/2023 07:58, RNDr. Pavel Šmerk, Ph.D.

Abstract

V originále

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.

Links

MUNI/A/1145/2021, interní kód MU
Name: Rozsáhlé výpočetní systémy: modely, aplikace a verifikace XI. (Acronym: SV-FI MAV XI.)
Investor: Masaryk University
NU21-08-00359, research and development project
Name: Klasifikace mozkových tumorů pomocí pokročilých metod analýzy dat multimodálního MR zobrazení difuze
Investor: Ministry of Health of the CR, Classification of brain tumors using advanced techniques of multimodal diffusion MRI data, Subprogram 1 - standard