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Pontillo, G; Prados, F; Colman, J; Kanber, B; Abdel-Mannan, O; Al-Araji, S; Bellenberg, B; Bianchi, A; Bisecco, A; Brownlee, WJ; Brunetti, A; Cagol, A; Calabrese, M; Castellaro, M; Christensen, R; Cocozza, S; Colato, E; Collorone, S; Cortese, R; De, Stefano, N; Enzinger, C; Filippi, M; Foster, MA; Gallo, A; Gasperini, C; Gonzalez-Escamilla, G; Granziera, C; Groppa, S; Hacohen, Y; Harbo, HFF; He, A; Hogestol, EA; Kuhle, J; Llufriu, S; Lukas, C; Martinez-Heras, E; Messina, S; Moccia, M; Mohamud, S; Nistri, R; Nygaard, GO; Palace, J; Petracca, M; Pinter, D; Rocca, MA; Rovira, A; Ruggieri, S; Sastre-Garriga, J; Strijbis, EM; Toosy, AT; Uher, T; Valsasina, P; Vaneckova, M; Vrenken, H; Wingrove, J; Yam, C; Schoonheim, MM; Ciccarelli, O; Cole, JH; Barkhof, F.
Disentangling Neurodegeneration From Aging in Multiple Sclerosis Using Deep Learning: The Brain-Predicted Disease Duration Gap.
Neurology. 2024; 103(10):e209976 Doi: 10.1212/WNL.0000000000209976 [OPEN ACCESS]
Web of Science PubMed FullText FullText_MUG

Co-authors Med Uni Graz: Enzinger Christian; Pinter Daniela Theresia
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Abstract:: BACKGROUND AND OBJECTIVES: Disentangling brain aging from disease-related neurodegeneration in patients with multiple sclerosis (PwMS) is increasingly topical. The brain-age paradigm offers a window into this problem but may miss disease-specific effects. In this study, we investigated whether a disease-specific model might complement the brain-age gap (BAG) by capturing aspects unique to MS. METHODS: In this retrospective study, we collected 3D T1-weighted brain MRI scans of PwMS to build (1) a cross-sectional multicentric cohort for age and disease duration (DD) modeling and (2) a longitudinal single-center cohort of patients with early MS as a clinical use case. We trained and evaluated a 3D DenseNet architecture to predict DD from minimally preprocessed images while age predictions were obtained with the DeepBrainNet model. The brain-predicted DD gap (the difference between predicted and actual duration) was proposed as a DD-adjusted global measure of MS-specific brain damage. Model predictions were scrutinized to assess the influence of lesions and brain volumes while the DD gap was biologically and clinically validated within a linear model framework assessing its relationship with BAG and physical disability measured with the Expanded Disability Status Scale (EDSS). RESULTS: We gathered MRI scans of 4,392 PwMS (69.7% female, age: 42.8 ± 10.6 years, DD: 11.4 ± 9.3 years) from 15 centers while the early MS cohort included 749 sessions from 252 patients (64.7% female, age: 34.5 ± 8.3 years, DD: 0.7 ± 1.2 years). Our model predicted DD better than chance (mean absolute error = 5.63 years, R² = 0.34) and was nearly orthogonal to the brain-age model (correlation between DD and BAGs: r = 0.06 [0.00-0.13], p = 0.07). Predictions were influenced by distributed variations in brain volume and, unlike brain-predicted age, were sensitive to MS lesions (difference between unfilled and filled scans: 0.55 years [0.51-0.59], p < 0.001). DD gap significantly explained EDSS changes (B = 0.060 [0.038-0.082], p < 0.001), adding to BAG (ΔR² = 0.012, p < 0.001). Longitudinally, increasing DD gap was associated with greater annualized EDSS change (r = 0.50 [0.39-0.60], p < 0.001), with an incremental contribution in explaining disability worsening compared with changes in BAG alone (ΔR² = 0.064, p < 0.001). DISCUSSION: The brain-predicted DD gap is sensitive to MS-related lesions and brain atrophy, adds to the brain-age paradigm in explaining physical disability both cross-sectionally and longitudinally, and may be used as an MS-specific biomarker of disease severity and progression.
Find related publications in this database (using NLM MeSH Indexing): Humans - administration & dosage; Deep Learning - administration & dosage; Multiple Sclerosis - diagnostic imaging, pathology; Female - administration & dosage; Male - administration & dosage; Adult - administration & dosage; Middle Aged - administration & dosage; Aging - pathology, physiology; Brain - diagnostic imaging, pathology; Magnetic Resonance Imaging - administration & dosage; Retrospective Studies - administration & dosage; Cross-Sectional Studies - administration & dosage; Longitudinal Studies - administration & dosage; Neurodegenerative Diseases - diagnostic imaging