NeuroImage · 2026
Federated learning for MRI-based BrainAGE: A multicenter study on post-stroke functional outcome prediction.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Roca Vincent, Tommasi Marc, Andrey Paul, Bellet Aurélien, Schirmer Markus D, Henon Hilde, Puy Laurent, Ramon Julien, Kuchcinski Grégory, Bretzner Martin, Lopes Renaud, ARIANES study group
Аннотация
OBJECTIVE: Brain-predicted age difference (BrainAGE) is a neuroimaging biomarker reflecting brain health, with potential implications for post-stroke recovery. However, training robust BrainAGE models requires large, diverse datasets, often restricted by privacy and regulatory concerns. This study evaluates the performance of federated learning (FL) for BrainAGE estimation in ischemic stroke patients treated with mechanical thrombectomy, and investigates its association with clinical phenotypes and functional outcome.
METHODS: We used pre-treatment FLAIR brain images from 1674 stroke patients across 16 hospital centers. We implemented standard machine learning and deep learning models for BrainAGE estimates under three data management strategies: centralized learning (pooled data), FL (local training at each site), and single-site learning. We reported prediction errors and examined associations between BrainAGE and vascular risk factors (e.g., diabetes mellitus, hypertension, smoking), as well as functional outcome at three months post-stroke. Logistic regression evaluated BrainAGE's predictive value for this outcome, adjusting for age, sex, vascular risk factors, stroke severity, time between MRI and arterial puncture, prior intravenous thrombolysis, and recanalization outcome.
RESULTS: While centralized learning yielded the most accurate predictions, FL consistently outperformed single-site models. BrainAGE was significantly higher in patients with diabetes mellitus across all models. Comparisons between patients with good and poor functional outcome, and multivariate predictions of these outcome showed the significance of the association between BrainAGE and post-stroke recovery.
CONCLUSION: FL enables accurate age predictions without data centralization. The strong association between BrainAGE, vascular risk factors, and post-stroke recovery highlights its potential for personalized prognostic modeling in stroke care.
METHODS: We used pre-treatment FLAIR brain images from 1674 stroke patients across 16 hospital centers. We implemented standard machine learning and deep learning models for BrainAGE estimates under three data management strategies: centralized learning (pooled data), FL (local training at each site), and single-site learning. We reported prediction errors and examined associations between BrainAGE and vascular risk factors (e.g., diabetes mellitus, hypertension, smoking), as well as functional outcome at three months post-stroke. Logistic regression evaluated BrainAGE's predictive value for this outcome, adjusting for age, sex, vascular risk factors, stroke severity, time between MRI and arterial puncture, prior intravenous thrombolysis, and recanalization outcome.
RESULTS: While centralized learning yielded the most accurate predictions, FL consistently outperformed single-site models. BrainAGE was significantly higher in patients with diabetes mellitus across all models. Comparisons between patients with good and poor functional outcome, and multivariate predictions of these outcome showed the significance of the association between BrainAGE and post-stroke recovery.
CONCLUSION: FL enables accurate age predictions without data centralization. The strong association between BrainAGE, vascular risk factors, and post-stroke recovery highlights its potential for personalized prognostic modeling in stroke care.