Fiscone, CristianaSighinolfi, GiovanniManners, David NeilMotta, LorenzoVenturi, GretaPanzera, IvanZaccagna, FulvioRundo, LeonardoLugaresi, AlessandraLodi, RaffaeleTonon, CaterinaCastelli, Mauro2024-06-052024-06-052024-06-042052-4463PURE: 89923480PURE UUID: eaf74bd3-de98-45d8-a2c8-6748d71f31afScopus: 85195249527WOS: 001276270200005ORCID: /0000-0002-8793-1451/work/160960491PubMed: 38834674PubMedCentral: PMC11150520http://hdl.handle.net/10362/168225Fiscone, C., Sighinolfi, G., Manners, D. N., Motta, L., Venturi, G., Panzera, I., Zaccagna, F., Rundo, L., Lugaresi, A., Lodi, R., Tonon, C., & Castelli, M. (2024). Multiparametric MRI dataset for susceptibility-based radiomic feature extraction and analysis. Scientific Data, 11, 1-11. Article 575. https://doi.org/10.1038/s41597-024-03418-6 --- This work was supported by national funds through the FCT (Fundação para a Ciência e a Tecnologia) by the project UIDB/04152/2020 (DOI: https://doi.org/10.54499/UIDB/04152/2020) - Centro de Investigação em Gestão de Informação – MagIC/NOVA IMS. This work was supported by NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006)—A multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022). The publication of this article was supported by the ‘‘Ricerca Corrente’’ funding from the Italian Ministry of Health.Multiple sclerosis (MS) is a progressive demyelinating disease impacting the central nervous system. Conventional Magnetic Resonance Imaging (MRI) techniques (e.g., T2w images) help diagnose MS, although they sometimes reveal non-specific lesions. Quantitative MRI techniques are capable of quantifying imaging biomarkers in vivo, offering the potential to identify specific signs related to pre-clinical inflammation. Among those techniques, Quantitative Susceptibility Mapping (QSM) is particularly useful for studying processes that influence the magnetic properties of brain tissue, such as alterations in myelin concentration. Because of its intrinsic quantitative nature, it is particularly well-suited to be analyzed through radiomics, including techniques that extract a high number of complex and multi-dimensional features from radiological images. The dataset presented in this work provides information about normal-appearing white matter (NAWM) in a cohort of MS patients and healthy controls. It includes QSM-based radiomic features from NAWM and its tracts, and MR sequences necessary to implement the pipeline: T1w, T2w, QSM, DWI. The workflow is outlined in this article, along with an application showing feature reliability assessment.112500873engBiomarkersMultiple sclerosisStatistics and ProbabilityInformation SystemsEducationComputer Science ApplicationsStatistics, Probability and UncertaintyLibrary and Information SciencesSDG 3 - Good Health and Well-beingjournal article10.1038/s41597-024-03418-6http://10.5281/zenodo.10931120https://www.scopus.com/pages/publications/85195249527https://www.webofscience.com/wos/woscc/full-record/WOS:001276270200005