{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T21:29:21Z","timestamp":1793741361448,"version":"4.6.1"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100024370","name":"Ministero dell'Istruzione dell'Universit\u00e0 e della Ricerca","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100024370","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004271","name":"University of Rome La Sapienza","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004271","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jns-journal.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of the Neurological Sciences"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.jns.2026.125846","type":"journal-article","created":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T19:57:33Z","timestamp":1772308653000},"page":"125846","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"C","title":["Predictors of short-term, relapse-independent progression in multiple sclerosis: A machine learning approach based on clinical data and conventional MRI-derived features"],"prefix":"10.1016","volume":"483","author":[{"given":"Antonio","family":"Ianniello","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"Barbuti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Francesca","family":"Capobianco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Tranfa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clementina","family":"Miele","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Serena","family":"Ruggieri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giuseppe","family":"Pontillo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sirio","family":"Cocozza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrizia","family":"Pantano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlo","family":"Pozzilli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renato","family":"Cuocolo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria","family":"Petracca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"14","key":"10.1016\/j.jns.2026.125846_bb0005","doi-asserted-by":"crossref","DOI":"10.1177\/1352458520970841","article-title":"Rising prevalence of multiple sclerosis worldwide: insights from the atlas of MS, third edition","volume":"26","author":"Walton","year":"2020","journal-title":"Mult. Scler. J."},{"issue":"9","key":"10.1016\/j.jns.2026.125846_bb0010","doi-asserted-by":"crossref","first-page":"5127","DOI":"10.1007\/s00415-022-11169-w","article-title":"Patients with multiple sclerosis: a burden and cost of illness study","volume":"269","author":"Battaglia","year":"2022","journal-title":"J. Neurol."},{"issue":"1","key":"10.1016\/j.jns.2026.125846_bb0015","doi-asserted-by":"crossref","DOI":"10.1212\/WNL.0000000000209444","article-title":"Using the progression independent of relapse activity framework to unveil the pathobiological foundations of multiple sclerosis","volume":"103","author":"Ciccarelli","year":"2024","journal-title":"Neurology"},{"issue":"9","key":"10.1016\/j.jns.2026.125846_bb0020","doi-asserted-by":"crossref","first-page":"3147","DOI":"10.1093\/brain\/awac016","article-title":"How patients with multiple sclerosis acquire disability","volume":"145","author":"Lublin","year":"2022","journal-title":"Brain"},{"issue":"9","key":"10.1016\/j.jns.2026.125846_bb0025","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1001\/jamaneurol.2020.1568","article-title":"Contribution of relapse-independent progression vs relapse-associated worsening to overall confirmed disability accumulation in typical relapsing multiple sclerosis in a pooled analysis of 2 randomized clinical trials","volume":"77","author":"Kappos","year":"2020","journal-title":"JAMA Neurol."},{"issue":"10","key":"10.1016\/j.jns.2026.125846_bb0030","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1177\/13524585241266180","article-title":"Disability patterns in multiple sclerosis: a meta-analysis on RAW and PIRA in the real-world context","volume":"30","author":"Prosperini","year":"2024","journal-title":"Mult. Scler. J."},{"issue":"5","key":"10.1016\/j.jns.2026.125846_bb0035","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1080\/13543784.2020.1757647","article-title":"New targets and therapeutics for neuroprotection, remyelination and repair in multiple sclerosis","volume":"29","author":"Villoslada","year":"2020","journal-title":"Expert Opin. Investig. Drugs"},{"issue":"5","key":"10.1016\/j.jns.2026.125846_bb0040","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1038\/s41582-019-0170-8","article-title":"Reaching an evidence-based prognosis for personalized treatment of multiple sclerosis","volume":"15","author":"Rotstein","year":"2019","journal-title":"Nat. Rev. Neurol."},{"key":"10.1016\/j.jns.2026.125846_bb0045","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.nicl.2017.10.015","article-title":"Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls","volume":"17","author":"Yoo","year":"2018","journal-title":"Neuroimage Clin."},{"issue":"4","key":"10.1016\/j.jns.2026.125846_bb0050","doi-asserted-by":"crossref","first-page":"1103","DOI":"10.1007\/s11682-018-9926-9","article-title":"Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data","volume":"13","author":"Sacc\u00e0","year":"2019","journal-title":"Brain Imaging Behav."},{"key":"10.1016\/j.jns.2026.125846_bb0055","doi-asserted-by":"crossref","first-page":"724","DOI":"10.1016\/j.nicl.2018.09.002","article-title":"Characterization of relapsing-remitting multiple sclerosis patients using support vector machine classifications of functional and diffusion MRI data","volume":"20","author":"Zurita","year":"2018","journal-title":"Neuroimage Clin."},{"issue":"1","key":"10.1016\/j.jns.2026.125846_bb0060","doi-asserted-by":"crossref","first-page":"2078","DOI":"10.1038\/s41467-021-22265-2","article-title":"Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data","volume":"12","author":"Eshaghi","year":"2021","journal-title":"Nat. Commun."},{"issue":"11","key":"10.1016\/j.jns.2026.125846_bb0065","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.3174\/ajnr.A7274","article-title":"A combined Radiomics and machine learning approach to overcome the Clinicoradiologic paradox in multiple sclerosis","volume":"42","author":"Pontillo","year":"2021","journal-title":"Am. J. Neuroradiol."},{"issue":"8","key":"10.1016\/j.jns.2026.125846_bb0070","doi-asserted-by":"crossref","first-page":"5382","DOI":"10.1007\/s00330-022-08610-z","article-title":"Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach","volume":"32","author":"Pontillo","year":"2022","journal-title":"Eur. Radiol."},{"issue":"10","key":"10.1016\/j.jns.2026.125846_bb0075","doi-asserted-by":"crossref","DOI":"10.1212\/WNL.0000000000209976","article-title":"Disentangling neurodegeneration from aging in multiple sclerosis using deep learning","volume":"103","author":"Pontillo","year":"2024","journal-title":"Neurology"},{"issue":"4","key":"10.1016\/j.jns.2026.125846_bb0080","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1111\/jon.12991","article-title":"Classification of multiple sclerosis patients based on structural disconnection: a robust feature selection approach","volume":"32","author":"Schiavi","year":"2022","journal-title":"J. Neuroimaging"},{"issue":"10","key":"10.1016\/j.jns.2026.125846_bb0085","doi-asserted-by":"crossref","first-page":"6543","DOI":"10.1007\/s00415-024-12651-3","article-title":"Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review","volume":"271","author":"Yousef","year":"2024","journal-title":"J. Neurol."},{"key":"10.1016\/j.jns.2026.125846_bb0090","series-title":"RF-Isolation: A Novel Representation of Structural Connectivity Networks for Multiple Sclerosis Classification","first-page":"158","author":"Mensi","year":"2022"},{"issue":"2","key":"10.1016\/j.jns.2026.125846_bb0095","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1002\/ana.20740","article-title":"Defining the response to interferon-\u03b2 in relapsing-remitting multiple sclerosis patients","volume":"59","author":"R\u00edo","year":"2006","journal-title":"Ann. Neurol."},{"issue":"6","key":"10.1016\/j.jns.2026.125846_bb0100","doi-asserted-by":"crossref","first-page":"614","DOI":"10.1001\/jamaneurol.2025.0495","article-title":"Standardized definition of progression independent of relapse activity (PIRA) in relapsing-remitting multiple sclerosis","volume":"82","author":"M\u00fcller","year":"2025","journal-title":"JAMA Neurol."},{"issue":"3","key":"10.1016\/j.jns.2026.125846_bb0105","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1002\/ana.25808","article-title":"Identifying progression in multiple sclerosis: new perspectives","volume":"88","author":"Filippi","year":"2020","journal-title":"Ann. Neurol."},{"key":"10.1016\/j.jns.2026.125846_bb0110","doi-asserted-by":"crossref","DOI":"10.1016\/j.nicl.2024.103611","article-title":"LST-AI: a deep learning ensemble for accurate MS lesion segmentation","volume":"42","author":"Wiltgen","year":"2024","journal-title":"Neuroimage Clin."},{"issue":"9","key":"10.1016\/j.jns.2026.125846_bb0115","doi-asserted-by":"crossref","first-page":"2062","DOI":"10.1002\/hbm.21344","article-title":"Evaluating and reducing the impact of white matter lesions on brain volume measurements","volume":"33","author":"Battaglini","year":"2012","journal-title":"Hum. Brain Mapp."},{"issue":"3","key":"10.1016\/j.jns.2026.125846_bb0120","doi-asserted-by":"crossref","DOI":"10.1111\/j.1552-6569.2011.00629.x","article-title":"Approaches to normalization of spinal cord volume: application to multiple sclerosis","volume":"22","author":"Healy","year":"2012","journal-title":"J. Neuroimaging"},{"issue":"4","key":"10.1016\/j.jns.2026.125846_bb0125","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0174866","article-title":"Exploration of machine learning techniques in predicting multiple sclerosis disease course","volume":"12","author":"Zhao","year":"2017","journal-title":"PLoS One"},{"issue":"12","key":"10.1016\/j.jns.2026.125846_bb0130","doi-asserted-by":"crossref","first-page":"4834","DOI":"10.1007\/s00415-021-10605-7","article-title":"Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis","volume":"268","author":"Tommasin","year":"2021","journal-title":"J. Neurol."},{"issue":"4","key":"10.1016\/j.jns.2026.125846_bb0135","article-title":"Machine learning in secondary progressive multiple sclerosis: an improved predictive model for short-term disability progression","volume":"5","author":"Law","year":"2019","journal-title":"Mult. Scler. J. Exp. Transl. Clin."},{"key":"10.1016\/j.jns.2026.125846_bb0140","article-title":"Predicting ten-year clinical outcomes in multiple sclerosis with radiomics-based machine learning models","author":"Tranfa","year":"2025","journal-title":"Am. J. Neuroradiol."},{"issue":"3","key":"10.1016\/j.jns.2026.125846_bb0145","doi-asserted-by":"crossref","first-page":"2185","DOI":"10.1007\/s00330-022-09154-y","article-title":"Clinical correlates of R1 relaxometry and magnetic susceptibility changes in multiple sclerosis: a multi-parameter quantitative MRI study of brain iron and myelin","volume":"33","author":"Pontillo","year":"2022","journal-title":"Eur. Radiol."},{"issue":"1","key":"10.1016\/j.jns.2026.125846_bb0150","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1093\/brain\/awz355","article-title":"Long-range connections are more severely damaged and relevant for cognition in multiple sclerosis","volume":"143","author":"Meijer","year":"2020","journal-title":"Brain"},{"issue":"6","key":"10.1016\/j.jns.2026.125846_bb0155","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.1093\/brain\/awy088","article-title":"Progression of regional grey matter atrophy in multiple sclerosis","volume":"141","author":"Eshaghi","year":"2018","journal-title":"Brain"},{"issue":"10","key":"10.1016\/j.jns.2026.125846_bb0160","doi-asserted-by":"crossref","first-page":"4995","DOI":"10.1007\/s00415-023-11829-5","article-title":"The added value of spinal cord lesions to disability accrual in multiple sclerosis","volume":"270","author":"Ruggieri","year":"2023","journal-title":"J. Neurol."},{"key":"10.1016\/j.jns.2026.125846_bb0165","doi-asserted-by":"crossref","DOI":"10.1007\/s00415-021-10576-9","article-title":"A matter of atrophy: differential impact of brain and spine damage on disability worsening in multiple sclerosis","author":"Ruggieri","year":"2021","journal-title":"J. Neurol."},{"issue":"1","key":"10.1016\/j.jns.2026.125846_bb0170","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/ana.26913","article-title":"Determinants and biomarkers of progression independent of relapses in multiple sclerosis","volume":"96","author":"Calabrese","year":"2024","journal-title":"Ann. Neurol."},{"issue":"10","key":"10.1016\/j.jns.2026.125846_bb0175","doi-asserted-by":"crossref","first-page":"4704","DOI":"10.3390\/ijms26104704","article-title":"Biomarkers of progression independent of relapse activity\u2014can we actually measure it yet?","volume":"26","author":"Bsteh","year":"2025","journal-title":"Int. J. Mol. Sci."},{"issue":"5","key":"10.1016\/j.jns.2026.125846_bb0180","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1002\/ana.25463","article-title":"Silent progression in disease activity\u2013free relapsing multiple sclerosis","volume":"85","author":"Cree","year":"2019","journal-title":"Ann. Neurol."},{"issue":"4","key":"10.1016\/j.jns.2026.125846_bb0185","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1007\/s00415-019-09676-4","article-title":"Differentiating societal costs of disability worsening in multiple sclerosis","volume":"267","author":"Ness","year":"2020","journal-title":"J. Neurol."}],"container-title":["Journal of the Neurological Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022510X26001280?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0022510X26001280?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T21:00:26Z","timestamp":1793739626000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0022510X26001280"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":37,"alternative-id":["S0022510X26001280"],"URL":"https:\/\/doi.org\/10.1016\/j.jns.2026.125846","relation":{},"ISSN":["0022-510X"],"issn-type":[{"value":"0022-510X","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Predictors of short-term, relapse-independent progression in multiple sclerosis: A machine learning approach based on clinical data and conventional MRI-derived features","name":"articletitle","label":"Article Title"},{"value":"Journal of the Neurological Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jns.2026.125846","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"125846"}}