{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T23:58:22Z","timestamp":1789516702528,"version":"build-2803163510"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T00:00:00Z","timestamp":1630886400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Neurol"],"published-print":{"date-parts":[[2022,4]]},"DOI":"10.1007\/s00415-021-10786-1","type":"journal-article","created":{"date-parts":[[2021,9,5]],"date-time":"2021-09-05T22:02:36Z","timestamp":1630879356000},"page":"2055-2063","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Diagnostic magnetic resonance imaging biomarkers for facioscapulohumeral muscular dystrophy identified by machine learning"],"prefix":"10.1007","volume":"269","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4327-6969","authenticated-orcid":false,"given":"Mauro","family":"Monforte","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sara","family":"Bortolani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eleonora","family":"Torchia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lara","family":"Cristiano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Laschena","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tommaso","family":"Tartaglione","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enzo","family":"Ricci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giorgio","family":"Tasca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,6]]},"reference":[{"key":"10786_CR1","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1097\/WCO.0b013e328330a572","volume":"22","author":"GW Padberg","year":"2009","unstructured":"Padberg GW, van Engelen BG (2009) Facioscapulohumeral muscular dystrophy. Curr Opin Neurol 22:539\u2013542. https:\/\/doi.org\/10.1097\/WCO.0b013e328330a572","journal-title":"Curr Opin Neurol"},{"key":"10786_CR2","doi-asserted-by":"publisher","first-page":"1650","DOI":"10.1126\/science.1189044","volume":"329","author":"RJLF Lemmers","year":"2010","unstructured":"Lemmers RJLF, van der Vliet PJ, Klooster R et al (2010) A unifying genetic model for facioscapulohumeral muscular dystrophy. Science 329:1650\u20131653. https:\/\/doi.org\/10.1126\/science.1189044","journal-title":"Science"},{"key":"10786_CR3","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1212\/WNL.0000000000001783","volume":"85","author":"R Tawil","year":"2015","unstructured":"Tawil R, Kissel JT, Heatwole C, Pandya S, Gronseth G, Benatar M (2015) Evidence-based guideline summary: Evaluation, diagnosis, and management of facioscapulohumeral muscular dystrophy: report of the guideline development, dissemination, and implementation subcommittee of the American Academy of Neurology and the Practice Issues Review Panel of the American Association of Neuromuscular & Electrodiagnostic Medicine. Neurology 85:357\u2013364. https:\/\/doi.org\/10.1212\/WNL.0000000000001783","journal-title":"Neurology"},{"key":"10786_CR4","doi-asserted-by":"publisher","first-page":"230","DOI":"10.1016\/j.clineuro.2011.10.022","volume":"114","author":"E Pastorello","year":"2012","unstructured":"Pastorello E, Cao M, Trevisan CP (2012) Atypical onset in a series of 122 cases with facioscapulohumeral muscular dystrophy. Clin Neurol Neurosurg 114:230\u2013234. https:\/\/doi.org\/10.1016\/j.clineuro.2011.10.022","journal-title":"Clin Neurol Neurosurg"},{"key":"10786_CR5","doi-asserted-by":"publisher","first-page":"1204","DOI":"10.1007\/s00415-016-8123-2","volume":"263","author":"G Ricci","year":"2016","unstructured":"Ricci G, Ruggiero L, Vercelli L et al (2016) A novel clinical tool to classify facioscapulohumeral muscular dystrophy phenotypes. J Neurol 263:1204\u20131214. https:\/\/doi.org\/10.1007\/s00415-016-8123-2","journal-title":"J Neurol"},{"key":"10786_CR6","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2020.4040","volume":"3","author":"L Ruggiero","year":"2020","unstructured":"Ruggiero L, Mele F, Manganelli F et al (2020) Phenotypic variability among patients with D4Z4 reduced allele facioscapulohumeral muscular dystrophy. JAMA Netw Open 3:e204040. https:\/\/doi.org\/10.1001\/jamanetworkopen.2020.4040","journal-title":"JAMA Netw Open"},{"key":"10786_CR7","doi-asserted-by":"publisher","first-page":"628","DOI":"10.1016\/j.ajhg.2012.02.019","volume":"90","author":"I Scionti","year":"2012","unstructured":"Scionti I, Greco F, Ricci G et al (2012) Large-scale population analysis challenges the current criteria for the molecular diagnosis of facioscapulohumeral muscular dystrophy. Am J Hum Genet 90:628\u2013635. https:\/\/doi.org\/10.1016\/j.ajhg.2012.02.019","journal-title":"Am J Hum Genet"},{"key":"10786_CR8","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1016\/j.gde.2015.08.007","volume":"33","author":"L Daxinger","year":"2015","unstructured":"Daxinger L, Tapscott SJ, van der Maarel SM (2015) Genetic and epigenetic contributors to FSHD. Curr Opin Genet Dev 33:56\u201361. https:\/\/doi.org\/10.1016\/j.gde.2015.08.007","journal-title":"Curr Opin Genet Dev"},{"key":"10786_CR9","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1136\/jmedgenet-2011-100101","volume":"49","author":"S Sacconi","year":"2012","unstructured":"Sacconi S, Cama\u00f1o P, de Greef JC et al (2012) Patients with a phenotype consistent with facioscapulohumeral muscular dystrophy display genetic and epigenetic heterogeneity. J Med Genet 49:41\u201346. https:\/\/doi.org\/10.1136\/jmedgenet-2011-100101","journal-title":"J Med Genet"},{"key":"10786_CR10","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1038\/ejhg.2012.42","volume":"20","author":"A Leidenroth","year":"2012","unstructured":"Leidenroth A, Sorte HS, Gilfillan G, Ehrlich M, Lyle R, Hewitt JE (2012) Diagnosis by sequencing: correction of misdiagnosis from FSHD2 to LGMD2A by whole-exome analysis. Eur J Hum Genet 20:999\u20131003. https:\/\/doi.org\/10.1038\/ejhg.2012.42","journal-title":"Eur J Hum Genet"},{"key":"10786_CR11","doi-asserted-by":"publisher","first-page":"956","DOI":"10.1002\/mus.24661","volume":"52","author":"G Tasca","year":"2015","unstructured":"Tasca G, Monforte M, De Fino C, Kley RA, Ricci E, Mirabella M (2015) Magnetic resonance imaging pattern recognition in sporadic inclusion-body myositis. Muscle Nerve 52:956\u2013962. https:\/\/doi.org\/10.1002\/mus.24661","journal-title":"Muscle Nerve"},{"key":"10786_CR12","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1136\/jnnp-2017-316736","volume":"89","author":"G Tasca","year":"2018","unstructured":"Tasca G, Monforte M, D\u00edaz-Manera J et al (2018) MRI in sarcoglycanopathies: a large international cohort study. J Neurol Neurosurg Psychiatry 89:72\u201377. https:\/\/doi.org\/10.1136\/jnnp-2017-316736","journal-title":"J Neurol Neurosurg Psychiatry"},{"key":"10786_CR13","doi-asserted-by":"publisher","first-page":"827","DOI":"10.1016\/j.nmd.2019.08.011","volume":"29","author":"JW Chardon","year":"2019","unstructured":"Chardon JW, D\u00edaz-Manera J, Tasca G et al (2019) MYO-MRI diagnostic protocols in genetic myopathies. Neuromuscul Disord 29:827\u2013841. https:\/\/doi.org\/10.1016\/j.nmd.2019.08.011","journal-title":"Neuromuscul Disord"},{"key":"10786_CR14","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.1111\/j.1468-1331.2012.03753.x","volume":"19","author":"G Tasca","year":"2012","unstructured":"Tasca G, Monforte M, Iannaccone E et al (2012) Muscle MRI in female carriers of dystrophinopathy. Eur J Neurol 19:1256\u20131260. https:\/\/doi.org\/10.1111\/j.1468-1331.2012.03753.x","journal-title":"Eur J Neurol"},{"key":"10786_CR15","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0100292","volume":"9","author":"G Tasca","year":"2014","unstructured":"Tasca G, Monforte M, Iannaccone E et al (2014) Upper girdle imaging in facioscapulohumeral muscular dystrophy. PLoS ONE 9:e100292. https:\/\/doi.org\/10.1371\/journal.pone.0100292","journal-title":"PLoS ONE"},{"key":"10786_CR16","doi-asserted-by":"publisher","first-page":"854","DOI":"10.1002\/ana.24640","volume":"79","author":"G Tasca","year":"2016","unstructured":"Tasca G, Monforte M, Ottaviani P et al (2016) Magnetic resonance imaging in a large cohort of facioscapulohumeral muscular dystrophy patients: pattern refinement and implications for clinical trials. Ann Neurol 79:854\u2013864. https:\/\/doi.org\/10.1002\/ana.24640","journal-title":"Ann Neurol"},{"key":"10786_CR17","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/NEJMra1814259","volume":"380","author":"A Rajkomar","year":"2019","unstructured":"Rajkomar A, Dean J, Kohane I (2019) Machine learning in medicine. N Engl J Med 380:1347\u20131358. https:\/\/doi.org\/10.1056\/NEJMra1814259","journal-title":"N Engl J Med"},{"key":"10786_CR18","doi-asserted-by":"publisher","first-page":"2604","DOI":"10.1111\/ene.14446","volume":"27","author":"G Giacomucci","year":"2020","unstructured":"Giacomucci G, Monforte M, Diaz-Manera J et al (2020) Deep phenotyping of facioscapulohumeral muscular dystrophy type 2 by magnetic resonance imaging. Eur J Neurol 27:2604\u20132615. https:\/\/doi.org\/10.1111\/ene.14446","journal-title":"Eur J Neurol"},{"key":"10786_CR19","doi-asserted-by":"publisher","first-page":"2271","DOI":"10.1002\/art.40320","volume":"69","author":"IE Lundberg","year":"2017","unstructured":"Lundberg IE, Tj\u00e4rnlund A, Bottai M et al (2017) 2017 European League Against Rheumatism\/American College of Rheumatology Classification Criteria for Adult and Juvenile Idiopathic Inflammatory Myopathies and Their Major Subgroups. Arthritis Rheumatol 69:2271\u20132282. https:\/\/doi.org\/10.1002\/art.40320","journal-title":"Arthritis Rheumatol"},{"key":"10786_CR20","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1002\/mus.26403","volume":"59","author":"D G\u00f3mez-Andr\u00e9s","year":"2019","unstructured":"G\u00f3mez-Andr\u00e9s D, D\u00edaz J, Munell F et al (2019) Disease duration and disability in dysfeRlinopathy can be described by muscle imaging using heatmaps and random forests. Muscle Nerve 59:436\u2013444. https:\/\/doi.org\/10.1002\/mus.26403","journal-title":"Muscle Nerve"},{"key":"10786_CR21","unstructured":"R Core Team (2020) R: A Language and Environment for Statistical Computing [online]. R Found. Stat. Comput. Vienna Austria. https:\/\/www.R-project.org"},{"key":"10786_CR22","doi-asserted-by":"publisher","first-page":"e1094","DOI":"10.1212\/WNL.0000000000009068","volume":"94","author":"J Verd\u00fa-D\u00edaz","year":"2020","unstructured":"Verd\u00fa-D\u00edaz J, Alonso-P\u00e9rez J, Nu\u00f1ez-Peralta C et al (2020) Accuracy of a machine learning muscle MRI-based tool for the diagnosis of muscular dystrophies. Neurology 94:e1094\u2013e1102. https:\/\/doi.org\/10.1212\/WNL.0000000000009068","journal-title":"Neurology"},{"key":"10786_CR23","doi-asserted-by":"publisher","first-page":"2025","DOI":"10.1016\/S0140-6736(19)32910-1","volume":"394","author":"E Mercuri","year":"2019","unstructured":"Mercuri E, B\u00f6nnemann CG, Muntoni F (2019) Muscular dystrophies. The Lancet 394:2025\u20132038. https:\/\/doi.org\/10.1016\/S0140-6736(19)32910-1","journal-title":"The Lancet"},{"key":"10786_CR24","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1016\/j.nmd.2015.10.005","volume":"26","author":"R Tawil","year":"2016","unstructured":"Tawil R, Padberg GW, Shaw DW, van der Maarel SM, Tapscott SJ, Workshop Participants FSHD (2016) Clinical trial preparedness in facioscapulohumeral muscular dystrophy: clinical, tissue, and imaging outcome measures 29\u201330 May 2015, Rochester, New York. Neuromuscul Disord 26:181\u2013186. https:\/\/doi.org\/10.1016\/j.nmd.2015.10.005","journal-title":"Neuromuscul Disord"},{"key":"10786_CR25","doi-asserted-by":"publisher","first-page":"441","DOI":"10.3233\/JND-200554","volume":"8","author":"LH Wang","year":"2021","unstructured":"Wang LH, Tawil R (2021) Current therapeutic approaches in FSHD. J Neuromuscul Dis 8:441\u2013451. https:\/\/doi.org\/10.3233\/JND-200554","journal-title":"J Neuromuscul Dis"},{"key":"10786_CR26","doi-asserted-by":"publisher","first-page":"2959","DOI":"10.1007\/s12035-017-0563-x","volume":"55","author":"G Tasca","year":"2018","unstructured":"Tasca G, Monforte M, Corbi M et al (2018) Muscle microdialysis to investigate inflammatory biomarkers in facioscapulohumeral muscular dystrophy. Mol Neurobiol 55:2959\u20132966. https:\/\/doi.org\/10.1007\/s12035-017-0563-x","journal-title":"Mol Neurobiol"},{"key":"10786_CR27","doi-asserted-by":"publisher","first-page":"290","DOI":"10.3390\/ijms22010290","volume":"22","author":"V Corasolla Carregari","year":"2020","unstructured":"Corasolla Carregari V, Monforte M, Di Maio G et al (2020) Proteomics of muscle microdialysates identifies potential circulating biomarkers in facioscapulohumeral muscular dystrophy. Int J Mol Sci 22:290. https:\/\/doi.org\/10.3390\/ijms22010290","journal-title":"Int J Mol Sci"},{"key":"10786_CR28","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1093\/hmg\/ddy364","volume":"28","author":"LH Wang","year":"2019","unstructured":"Wang LH, Friedman SD, Shaw D et al (2019) MRI-informed muscle biopsies correlate MRI with pathology and DUX4 target gene expression in FSHD. Hum Mol Genet 28:476\u2013486. https:\/\/doi.org\/10.1093\/hmg\/ddy364","journal-title":"Hum Mol Genet"},{"key":"10786_CR29","unstructured":"Padberg, GW (1982) Facioscapulohumeral disease. PhD thesis. Leiden Neth. Leiden Univeristy Press. https:\/\/openaccess.leidenuniv.nl\/handle\/1887\/25818"},{"key":"10786_CR30","doi-asserted-by":"publisher","first-page":"e444","DOI":"10.1212\/WNL.0000000000005915","volume":"91","author":"M Wohlgemuth","year":"2018","unstructured":"Wohlgemuth M, Lemmers RJ, Jonker M et al (2018) A family-based study into penetrance in facioscapulohumeral muscular dystrophy type 1. Neurology 91:e444\u2013e454. https:\/\/doi.org\/10.1212\/WNL.0000000000005915","journal-title":"Neurology"}],"container-title":["Journal of Neurology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00415-021-10786-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00415-021-10786-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00415-021-10786-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,3,22]],"date-time":"2022-03-22T12:09:55Z","timestamp":1647950995000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00415-021-10786-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,6]]},"references-count":30,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,4]]}},"alternative-id":["10786"],"URL":"https:\/\/doi.org\/10.1007\/s00415-021-10786-1","relation":{},"ISSN":["0340-5354","1432-1459"],"issn-type":[{"value":"0340-5354","type":"print"},{"value":"1432-1459","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,6]]},"assertion":[{"value":"28 April 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 August 2021","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 August 2021","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 September 2021","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declaration"}},{"value":"M. Monforte and E. Ricci provided central reading services for MRI scans generated in aTyr\u2019s clinical trials of Resolaris (ATYR1940), pursuant to the terms of a Master Academic Services Agreement with the Catholic University of the Sacred Heart; E. Ricci was the site principal investigator for some of these trials; S. Bortolani, E. Torchia, L. Cristiano, F. Laschena, T. Tartaglione and G. Tasca have no conflicts of interest to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Participants gave written informed consent to the study, and approval from the ethics committee of the Universit\u00e0 Cattolica del Sacro Cuore (Rome, IT) was obtained.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and participant consent"}}]}}