{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:28:28Z","timestamp":1780676908866,"version":"3.54.1"},"reference-count":50,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T00:00:00Z","timestamp":1722643200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["R01 NS109439"],"award-info":[{"award-number":["R01 NS109439"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Annals of Neurology"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>To develop a multiparametric machine\u2010learning (ML) framework using high\u2010resolution 3 dimensional (3D) magnetic resonance (MR) fingerprinting (MRF) data for quantitative characterization of focal cortical dysplasia (FCD).<\/jats:p><\/jats:sec><jats:sec><jats:title>Materials<\/jats:title><jats:p>We included 119 subjects, 33 patients with focal epilepsy and histopathologically confirmed FCD, 60 age\u2010 and gender\u2010matched healthy controls (HCs), and 26 disease controls (DCs). Subjects underwent whole\u2010brain 3\u2009Tesla MRF acquisition, the reconstruction of which generated T1 and T2 relaxometry maps. A 3D region of interest was manually created for each lesion, and z\u2010score normalization using HC data was performed. We conducted 2D classification with ensemble models using MRF T1 and T2 mean and standard deviation from gray matter and white matter for FCD versus controls. Subtype classification additionally incorporated entropy and uniformity of MRF metrics, as well as morphometric features from the morphometric analysis program (MAP). We translated 2D results to individual probabilities using the percentage of slices above an adaptive threshold. These probabilities and clinical variables were input into a support vector machine for individual\u2010level classification. Fivefold cross\u2010validation was performed and performance metrics were reported using receiver\u2010operating\u2010characteristic\u2010curve analyses.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>FCD versus HC classification yielded mean sensitivity, specificity, and accuracy of 0.945, 0.980, and 0.962, respectively; FCD versus DC classification achieved 0.918, 0.965, and 0.939. In comparison, visual review of the clinical magnetic resonance imaging (MRI) detected 48% (16\/33) of the lesions by official radiology report. In the subgroup where both clinical MRI and MAP were negative, the MRF\u2010ML models correctly distinguished FCD patients from HCs and DCs in 98.3% of cross\u2010validation trials. Type II versus non\u2010type\u2010II classification exhibited mean sensitivity, specificity, and accuracy of 0.835, 0.823, and 0.83, respectively; type IIa versus IIb classification showed 0.85, 0.9, and 0.87. In comparison, the transmantle sign was present in 58% (7\/12) of the IIb cases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Interpretation<\/jats:title><jats:p>The MRF\u2010ML framework presented in this study demonstrated strong efficacy in noninvasively classifying FCD from normal cortex and distinguishing FCD subtypes. ANN NEUROL 2024;96:944\u2013957<\/jats:p><\/jats:sec>","DOI":"10.1002\/ana.27049","type":"journal-article","created":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T07:35:04Z","timestamp":1722670504000},"page":"944-957","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Multiparametric Characterization of Focal Cortical Dysplasia Using <scp>3D MR<\/scp> Fingerprinting"],"prefix":"10.1002","volume":"96","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5054-9163","authenticated-orcid":false,"given":"Ting\u2010Yu","family":"Su","sequence":"first","affiliation":[{"name":"Epilepsy Center, Neurological Institute Cleveland Clinic  Cleveland OH USA"},{"name":"Department of Biomedical Engineering Case Western Reserve University  Cleveland OH USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joon Yul","family":"Choi","sequence":"additional","affiliation":[{"name":"Epilepsy Center, Neurological Institute 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