Browsing by Author "Forkert, Nils D"

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    ItemOpen Access
    An international study presenting a federated learning AI platform for pediatric brain tumors.
    (Nature communications, 2024-09) Lee, Edward H; Han, Michelle; Wright, Jason; Kuwabara, Michael; Mevorach, Jacob; Fu, Gang; Choudhury, Olivia; Ratan, Ujjwal; Zhang, Michael; Wagner, Matthias W; Goetti, Robert; Toescu, Sebastian; Perreault, Sebastien; Dogan, Hakan; Altinmakas, Emre; Mohammadzadeh, Maryam; Szymanski, Kathryn A; Campen, Cynthia J; Lai, Hollie; Eghbal, Azam; Radmanesh, Alireza; Mankad, Kshitij; Aquilina, Kristian; Said, Mourad; Vossough, Arastoo; Oztekin, Ozgur; Ertl-Wagner, Birgit; Poussaint, Tina; Thompson, Eric M; Ho, Chang Y; Jaju, Alok; Curran, John; Ramaswamy, Vijay; Cheshier, Samuel H; Grant, Gerald A; Wong, S Simon; Moseley, Michael E; Lober, Robert M; Wilms, Mattias; Forkert, Nils D; Vitanza, Nicholas A; Miller, Jeffrey H; Prolo, Laura M; Yeom, Kristen W
    While multiple factors impact disease, artificial intelligence (AI) studies in medicine often use small, non-diverse patient cohorts due to data sharing and privacy issues. Federated learning (FL) has emerged as a solution, enabling training across hospitals without direct data sharing. Here, we present FL-PedBrain, an FL platform for pediatric posterior fossa brain tumors, and evaluate its performance on a diverse, realistic, multi-center cohort. Pediatric brain tumors were targeted due to the scarcity of such datasets, even in tertiary care hospitals. Our platform orchestrates federated training for joint tumor classification and segmentation across 19 international sites. FL-PedBrain exhibits less than a 1.5% decrease in classification and a 3% reduction in segmentation performance compared to centralized data training. FL boosts segmentation performance by 20 to 30% on three external, out-of-network sites. Finally, we explore the sources of data heterogeneity and examine FL robustness in real-world scenarios with data imbalances.
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    ItemOpen Access
    Intracranial Artery Morphology in Pediatric Moya Moya Disease and Moya Moya Syndrome.
    (Neurosurgery, 2022-11) Yedavalli, Vivek S; Quon, Jennifer L; Tong, Elizabeth; van Staalduinen, Eric K; Mouches, Pauline; Kim, Lily H; Steinberg, Gary K; Grant, Gerald A; Yeom, Kristen W; Forkert, Nils D

    Background

    Moya Moya disease (MMD) and Moya Moya syndrome (MMS) are cerebrovascular disorders, which affect the internal carotid arteries (ICAs). Diagnosis and surveillance of MMD/MMS in children mostly rely on qualitative evaluation of vascular imaging, especially MR angiography (MRA).

    Objective

    To quantitatively characterize arterial differences in pediatric patients with MMD/MMS compared with normal controls.

    Methods

    MRA data sets from 17 presurgery MMD/MMS (10M/7F, mean age = 10.0 years) patients were retrospectively collected and compared with MRA data sets of 98 children with normal vessel morphology (49 male patients; mean age = 10.6 years). Using a level set segmentation method with anisotropic energy weights, the cerebral arteries were automatically extracted and used to compute the radius of the ICA, middle cerebral artery (MCA), anterior cerebral artery (ACA), posterior cerebral artery (PCA), and basilar artery (BA). Moreover, the density and the average radius of all arteries in the MCA, ACA, and PCA flow territories were quantified.

    Results

    Statistical analysis revealed significant differences comparing children with MMD/MMS and those with normal vasculature ( P < .001), whereas post hoc analyses identified significantly smaller radii of the ICA, MCA-M1, MCA-M2, and ACA ( P < .001) in the MMD/MMS group. No significant differences were found for the radii of the PCA and BA or any artery density and average artery radius measurement in the flow territories ( P > .05).

    Conclusion

    His study describes the results of an automatic approach for quantitative characterization of the cerebrovascular system in patients with MMD/MMS with promising preliminary results for quantitative surveillance in pediatric MMD/MMS management.