Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach(24 views) Pontillo G, Penna S, Cocozza S, Quarantelli M, Gravina M, Lanzillo R, Marrone S, Costabile T, Inglese M, Morra VB, Riccio D, Elefante A, Petracca M, Sansone C, Brunetti A
Department of Advanced Biomedical Sciences, University "Federico II", Via Pansini 5, 80131, Naples, Italy. giuseppe.pontillo@unina.it.
Department of Electrical Engineering and Information Technology (DIETI), University "Federico II", Naples, Italy. giuseppe.pontillo@unina.it.
Institute of Biostructure and Bioimaging, National Research Council, Naples, Italy.
Department of Neurosciences and Reproductive and Odontostomatological Sciences, University "Federico II", Naples, Italy.
Multiple Sclerosis Centre, II Division of Neurology, Department of Clinical and Experimental Medicine, "Luigi Vanvitelli" University, Naples, Italy.
Ospedale Policlinico San Martino IRCCS, Genoa, Italy.
References: Not available.
Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach
OBJECTIVES: To stratify patients with multiple sclerosis (pwMS) based on brain MRI-derived volumetric features using unsupervised machine learning. METHODS: The 3-T brain MRIs of relapsing-remitting pwMS including 3D-T1w and FLAIR-T2w sequences were retrospectively collected, along with Expanded Disability Status Scale (EDSS) scores and long-term (10 ± 2 years) clinical outcomes (EDSS, cognition, and progressive course). From the MRIs, volumes of demyelinating lesions and 116 atlas-defined gray matter regions were automatically segmented and expressed as z-scores referenced to external populations. Following feature selection, baseline MRI-derived biomarkers entered the Subtype and Stage Inference (SuStaIn) algorithm, which estimates subgroups characterized by distinct patterns of biomarker evolution and stages within subgroups. The trained model was then applied to longitudinal MRIs. Stability of subtypes and stage change over time were assessed via Krippendorf's α and multilevel linear regression models, respectively. The prognostic relevance of SuStaIn classification was assessed with ordinal/logistic regression analyses. RESULTS: We selected 425 pwMS (35.9 ± 9.9 years; F/M: 301/124), corresponding to 1129 MRI scans, along with healthy controls (N = 148; 35.9 ± 13.0 years; F/M: 77/71) and external pwMS (N = 80; 40.4 ± 11.9 years; F/M: 56/24) as reference populations. Based on 11 biomarkers surviving feature selection, two subtypes were identified, designated as "deep gray matter (DGM)-first" subtype (N = 238) and "cortex-first" subtype (N = 187) according to the atrophy pattern. Subtypes were consistent over time (α = 0.806), with significant annual stage increase (b = 0.20; p < 0.001). EDSS was associated with stage and DGM-first subtype (p ≤ 0.02). Baseline stage predicted long-term disability, transition to progressive course, and cognitive impairment (p ≤ 0.03), with the latter also associated with DGM-first subtype (p = 0.005). CONCLUSIONS: Unsupervised learning modelling of brain MRI-derived volumetric features provides a biologically reliable and prognostically meaningful stratification of pwMS. KEY POINTS: • The unsupervised modelling of brain MRI-derived volumetric features can provide a single-visit stratification of multiple sclerosis patients. • The so-obtained classification tends to be consistent over time and captures disease-related brain damage progression, supporting the biological reliability of the model. • Baseline stratification predicts long-term clinical disability, cognition, and transition to secondary progressive course.
Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach
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Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach
Antonini A, Vitale C, Barone P, Cilia R, Righini A, Bonuccelli U, Abbruzzese G, Ramat S, Petrone A, Quatrale R, Marconi R, Ceravolo R, Stefani A, Lopiano L, Zappia M, Capus L, Morgante L, Tamma F, Tinazzi M, Colosimo C, Guerra UP, Valzania F, Fagioli G, Distefano A, Bagnato A, Feggi L, Anna S, Maria Teresa Rosaria De Cr, Nobili F, Mazzuca N, Baldari S, Eleopra R, Bestetti A, Benti R, Varrone A, Volterrani D, Massa R, Stocchi F, Schillaci O, Dore F, Zibetti M, Castellano G, Battista SG, Giorgetti G * The relationship between cerebral vascular disease and parkinsonism: The VADO study(659 views) Parkinsonism Relat D (ISSN: 1353-8020, 1873-5126, 1873-5126electronic), 2012; 18(6): 775-780. Impact Factor:3.274 ViewExport to BibTeXExport to EndNote
Malvindi MA, Greco A, Conversano F, Figuerola A, Corti M, Bonora M, Lascialfari A, Doumari HA, Moscardini M, Cingolani R, Gigli G, Casciaro S, Pellegrino T, Ragusa A * MR Contrast Agents(402 views) Small Animal Imaging, 2011 Jul 8; 21(13): 2548-2555. Impact Factor:1.784 ViewExport to BibTeXExport to EndNote
Ntziachristos V, Cuénod CA, Fournier L, Balvay D, Pradel C, Siauve N, Clement O, Jouannot E, Lucidarme O, Vecchio SD, Salvatore M, Law B, Tung C-H, Jain RK, Fukumura D, Munn LL, Brown EB, Schellenberger E, Montet X, Weissleder R, Clerck ND, Postnov A * Tumor Imaging(461 views) Textbook Of In Vivo Imaging In Vertebrates (ISSN: 9780-4700), 2007 Jul 16; 1: 277-309. Impact Factor:1.148 ViewExport to BibTeXExport to EndNote
Hesse B, Tagil K, Cuocolo A, Anagnostopoulos C, Bardies M, Bax J, Bengel F, Busemann Sokole E, Davies G, Dondi M, Edenbrandt L, Franken P, Kjaer A, Knuuti J, Lassmann M, Ljungberg M, Marcassa C, Marie PY, Mckiddie F, O'connor M, Prvuolovich E, Underwood R * 3. 0 T perfusion MR imaging(990 views) Rivista Di Neuroradiologia (ISSN: 1120-9976), 2004; 17(6): 807-812. Impact Factor:0.023 ViewExport to BibTeXExport to EndNote
Testino G, Leone S, Fagoonee S, Del Bas JM, Rodriguez B, Puiggros F, Marine S, Rodriguez MA, Morina D, Armengol L, Caimari A, Arola L, Cimini FA, Barchetta I, Carotti S, Bertoccini L, Baroni MG, Vespasiani-gentilucci U, Cavallo MG, Morini S, Nelson JE, Roth CL, Wilson LA, Yates KP, Aouizerat B, Morgan-stevenson V, Whalen E, Hoofnagle A, Mason M, Gersuk V, Yeh MM, Kowdley KV, Lee SM, Jun DW, Cho YK, Jang KS, Kucukazman M, Ata N, Dal K, Yeniova AO, Kefeli A, Basyigit S, Aktas B, Akin KO, Agladioglu K, Ure OS, Topal F, Nazligul Y, Beyan E, Ertugrul DT, Catena C, Cosma C, Camozzi V, Plebani M, Ermani M, Sechi LA, Fallo F, Goto Y, Ray MB, Mendenhall CL, French SW, Gartside PS Serum vitamin A deficiency and increased intrahepatic expression of cytokeratin antigen in alcoholic liver disease(707 views) Hepatology (ISSN: 1827-1669electronic, 0026-4806linking), 1988 Sep; 83120693611123109(5): 1019-1026. Impact Factor:0.913 ViewExport to BibTeXExport to EndNote
422 Records (401 excluding Abstracts). Total impact factor: 1358.087 (1311.947 excluding Abstracts). Total 5 year impact factor: 1418.67 (1368.112 excluding Abstracts).