BACKGROUND AND PURPOSE: Paragangliomas are highly vascularized usually benign neoplasms arising from nerve tissue. Endovascular preoperative embolization is used to facilitate surgery but is often not complete, due to tiny feeding arteries not feasible for selective catheterization. Our purpose was to evaluate angiographic and clinical outcome using Onyx for percutaneous glomus tumor embolization.
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.
BACKGROUND AND PURPOSE:Recovery of aneurysm induced CNP after endosaccular coiling has been reported in the literature. The aim of this study was to assess in detail the parameters that affect the outcome after endovascular treatment of ophthalmoplegic aneurysms due CNP.
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