Faculty, Staff and Student Publications

Publication Date

12-5-2022

Journal

Nature Communications

DOI

10.1038/s41467-022-33407-5

PMID

36470898

PMCID

PMC9722782

PubMedCentral® Posted Date

12-5-2022

PubMedCentral® Full Text Version

Post-print

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.

Keywords

Humans, Big Data, Glioblastoma, Machine Learning, Rare Diseases, Information Dissemination, CNS cancer, Computer science, Medical imaging, Medical research, Biomedical engineering

Published Open-Access

yes

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