{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T21:14:01Z","timestamp":1790716441016,"version":"4.1.0"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T00:00:00Z","timestamp":1677196800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003196","name":"Ministero della Salute","doi-asserted-by":"publisher","award":["RC 2017- 2019"],"award-info":[{"award-number":["RC 2017- 2019"]}],"id":[{"id":"10.13039\/501100003196","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003196","name":"Ministero della Salute","doi-asserted-by":"publisher","award":["RC 2020-2021"],"award-info":[{"award-number":["RC 2020-2021"]}],"id":[{"id":"10.13039\/501100003196","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003196","name":"Ministero della Salute","doi-asserted-by":"publisher","award":["RF 2016-02362914"],"award-info":[{"award-number":["RF 2016-02362914"]}],"id":[{"id":"10.13039\/501100003196","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Quantitative Muscle MRI (qMRI) is a valuable and non-invasive tool to assess disease involvement and progression in neuromuscular disorders being able to detect even subtle changes in muscle pathology. The aim of this study is to evaluate the feasibility of using a conventional short-tau inversion recovery (STIR) sequence to predict fat fraction (FF) and water T2 (wT2) in skeletal muscle introducing a radiomic workflow with standardized feature extraction combined with machine learning algorithms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Twenty-five patients with facioscapulohumeral muscular dystrophy (FSHD) were scanned at calf level using conventional STIR sequence and qMRI techniques. We applied and compared three different radiomics workflows (\n                      <jats:italic>WF1, WF2, WF3<\/jats:italic>\n                      ), combined with seven Machine Learning regression algorithms (linear, ridge and lasso regression, tree, random forest, k-nearest neighbor and support vector machine), on conventional STIR images to predict FF and wT2 for six calf muscles.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The combination of WF3 and K-nearest neighbor resulted to be the best predictor model of qMRI parameters with a mean absolute error about \u00b1 5\n                      <jats:italic>pp<\/jats:italic>\n                      for FF and \u00b1 1.8\n                      <jats:italic>ms<\/jats:italic>\n                      for wT2.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This pilot study demonstrated the possibility to predict qMRI parameters in a cohort of FSHD subjects starting from conventional STIR sequence.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2023.1105276","type":"journal-article","created":{"date-parts":[[2023,2,24]],"date-time":"2023-02-24T02:59:36Z","timestamp":1677207576000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease"],"prefix":"10.3389","volume":"14","author":[{"given":"Giulia","family":"Colelli","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Leonardo","family":"Barzaghi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Matteo","family":"Paoletti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mauro","family":"Monforte","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Niels","family":"Bergsland","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giulia","family":"Manco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xeni","family":"Deligianni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Santini","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"}]},{"given":"Antonietta","family":"Mira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Silvia","family":"Figini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Pichiecchio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2023,2,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"78","DOI":"10.3389\/fneur.2019.00078","article-title":"Advances in quantitative imaging of genetic and acquired myopathies: clinical applications and perspectives","volume":"10","author":"Paoletti","year":"2019","journal-title":"Front Neurol"},{"key":"B2","first-page":"2","article-title":"Muscle MRI in muscular dystrophies","volume":"34","author":"Diaz-Manera","year":"2015","journal-title":"Acta Myologica."},{"key":"B3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3233\/JND-160145","article-title":"Skeletal muscle quantitative nuclear magnetic resonance imaging and spectroscopy as an outcome measure for clinical trials","volume":"3","author":"Carlier","year":"2016","journal-title":"J Neuromusc Dis"},{"key":"B4","doi-asserted-by":"publisher","first-page":"956","DOI":"10.1212\/WNL.0000000000000785","article-title":"Quantitative MRI in muscular dystrophy: an indispensable trial endpoint?","volume":"83","author":"Hollingsworth","year":"2014","journal-title":"Neurology"},{"key":"B5","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1002\/mus.23370","article-title":"Magnetic resonance imaging of muscle disease: a pattern-based approach","volume":"46","author":"Costa","year":"2012","journal-title":"Muscle Nerve"},{"key":"B6","doi-asserted-by":"publisher","first-page":"438","DOI":"10.1007\/s00415-016-8361-3","article-title":"MRI as outcome measure in facioscapulohumeral muscular dystrophy: 1-year follow-up of 45 patients","volume":"264","author":"Andersen","year":"2017","journal-title":"J Neurol"},{"key":"B7","doi-asserted-by":"publisher","first-page":"357","DOI":"10.1212\/WNL.0000000000001783","article-title":"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 abd electrodiagnostic medicine","volume":"85","author":"Tawil","year":"2015","journal-title":"Neurology"},{"key":"B8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/2044-5040-4-12","article-title":"Facioscapulohumeral dystrophy: the path to consensus on pathophysiology","volume":"4","author":"Tawil","year":"2014","journal-title":"Skeletal Muscle"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1178","DOI":"10.1212\/WNL.0000000000000828","article-title":"Severe paraspinal muscle involvement in facioscapulohumeral muscular dystrophy","volume":"83","author":"Dahlqvist","year":"2014","journal-title":"Neurology"},{"key":"B10","doi-asserted-by":"publisher","first-page":"306","DOI":"10.1007\/BF00868438","article-title":"Magnetic resonance imaging of skeletal muscles in idiopathic inflammatory myopathies of adults","volume":"241","author":"Reimers","year":"1994","journal-title":"J Neurol"},{"key":"B11","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1053\/ejpn.2002.0617","article-title":"A short protocol for muscle MRI in children with muscular dystrophies","volume":"6","author":"Mercuri","year":"2002","journal-title":"Eur J Paed Neurol"},{"key":"B12","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1007\/s00330-015-3890-1","article-title":"Muscle MRI findings in facioscapulohumeral muscular dystrophy","volume":"26","author":"Gerevini","year":"2016","journal-title":"Eur Radiol"},{"key":"B13","doi-asserted-by":"publisher","first-page":"566","DOI":"10.1016\/j.neurol.2016.08.002","article-title":"Muscle MRI of facioscapulohumeral dystrophy (FSHD): A growing demand and a promising approach","volume":"172","author":"Fatehi","year":"2016","journal-title":"Revue Neurologique"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1258","DOI":"10.1002\/jcsm.12473","article-title":"R, Pichiecchio A, Tasca G, Ricci E. Tracking muscle wasting and disease activity in facioscapulohumeral muscular dystrophy by qualitative longitudinal imaging","volume":"10","author":"Monforte","year":"2019","journal-title":"J Cachexia Sarcopenia Muscle"},{"key":"B15","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1186\/s12891-020-03910-1","article-title":"Adapting MRI as a clinical outcome measure for a facioscapulohumeral muscular dystrophy trial of prednisone and tacrolimus: case report","volume":"22","author":"Wang","year":"2021","journal-title":"BMC Musculoskelet Disord"},{"key":"B16","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/jmri.20804","article-title":"Muscle MRI in inherited neuromuscular disorders: past present and future","volume":"25","author":"Mercuri","year":"2007","journal-title":"J Int Soc Mag Reson Med"},{"key":"B17","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1212\/01.wnl.0000324927.28817.9b","article-title":"Distinct muscle imaging patterns in myofibrillar myopathies","volume":"71","author":"Fischer","year":"2008","journal-title":"Neurology"},{"key":"B18","doi-asserted-by":"publisher","first-page":"1426","DOI":"10.1038\/s41598-022-04817-8","article-title":"Facioscapulohumeral dystrophy transcriptome signatures correlate with different stages of disease and are marked by different MRI biomarkers","volume":"12","author":"Van den Heuvel","year":"2022","journal-title":"Sci Rep"},{"key":"B19","doi-asserted-by":"publisher","first-page":"1700","DOI":"10.1212\/WNL.0000000000002640","article-title":"Quantitative MRI reveals decelerated fatty infiltration in muscles of active FSHD patients","volume":"86","author":"Janssen","year":"2016","journal-title":"Neurology."},{"key":"B20","doi-asserted-by":"publisher","first-page":"7910","DOI":"10.1007\/s00330-022-08862-9","article-title":"Quantitative water T2 relaxometry in the early detection of neuromuscular diseases: a retrospective biopsy-controlled analysis","volume":"32","author":"Locher","year":"2022","journal-title":"Eur Radiol."},{"key":"B21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in medical imaging\u2014\u201chow-to\u201d guide and critical reflection","volume":"11","author":"Van Timmeren","year":"2020","journal-title":"Insights Imag"},{"key":"B22","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1002\/wics.101","article-title":"Principal component analysis. Wiley interdisciplinary reviews","volume":"2","author":"Abdi","year":"2010","journal-title":"Computat Stat"},{"key":"B23","doi-asserted-by":"publisher","first-page":"039","DOI":"10.1093\/pnasnexus\/pgac039","article-title":"Ranking the information content of distance measures","volume":"1","author":"Glielmo","year":"2022","journal-title":"PNAS Nexus"},{"key":"B24","doi-asserted-by":"publisher","first-page":"109460","DOI":"10.1016\/j.ejrad.2020.109460","article-title":"Texture analysis and machine learning to predict water T2 and fat fraction from non-quantitative MRI of thigh muscles in Facioscapulohumeral muscular dystrophy","volume":"134","author":"Felisaz","year":"2021","journal-title":"Eur J Radiol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"4786","DOI":"10.1158\/0008-5472.CAN-18-0125","article-title":"LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity","volume":"78","author":"Nioche","year":"2018","journal-title":"Cancer Res"},{"key":"B26","doi-asserted-by":"publisher","first-page":"1323","DOI":"10.1016\/j.mri.2012.05.001","article-title":"3D Slicer as an image computing platform for the quantitative imaging network","volume":"30","author":"Fedorov","year":"2012","journal-title":"Mag Resonan Imag"},{"key":"B27","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1007\/s10334-021-00967-4","article-title":"Deep learning for automatic segmentation of thigh and leg muscles","volume":"35","author":"Agosti","year":"2022","journal-title":"Mag Res Mat Physics Biol Med"},{"key":"B28","doi-asserted-by":"publisher","first-page":"S173","DOI":"10.1016\/j.neuroimage.2008.10.055","article-title":"W, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, Smith SM. Bayesian analysis of neuroimaging data in FSL","volume":"45","author":"Woolrich","year":"2009","journal-title":"Neuroimage."},{"key":"B29","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1002\/nbm.2851","article-title":"T2 mapping provides multiple approaches for the characterization of muscle involvement in neuromuscular diseases: a cross-sectional study of lower leg muscles in 5\u201315-year-old boys with Duchenne muscular dystrophy","volume":"26","author":"Arpan","year":"2013","journal-title":"NMR Biomed"},{"key":"B30","article-title":"Optimization of fat-water separation algorithm selection and options using image-based metrics with validation by ISMRM fat-water challenge datasets","author":"Smith","year":"2013","journal-title":"Proceedings of the 21st Annual Meeting of the International Society for Magnetic Resonance in Medicine Salt Lake City Utah"},{"key":"B31","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1002\/jmri.24619","article-title":"Extended phase graphs: dephasing RF pulses and echoes-pure and simple","volume":"41","author":"Weigel","year":"2015","journal-title":"J Mag Res Imag"},{"key":"B32","doi-asserted-by":"publisher","first-page":"387","DOI":"10.3389\/fneur.2021.630387","article-title":"Fast open-source toolkit for water T2 mapping in the presence of fat from multi-echo spin-echo acquisitions for muscle MRI","volume":"248","author":"Santini","year":"2021","journal-title":"Front Neurol"},{"key":"B33","volume-title":"Principal Component Analysis for Special Types of Data.","author":"Jolliffe","year":"2002"},{"key":"B34","article-title":"The Elements of Statistical Learning (Vol. 1)","author":"Friedman","year":"2001"},{"key":"B35","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge regression: Biased estimation for nonorthogonal problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"B36","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J Royal Stat Soc Series B"},{"key":"B37","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/TIT.1967.1053964","article-title":"Nearest neighbor pattern classification","volume":"13","author":"Cover","year":"1967","journal-title":"IEEE Transact Inform Theory"},{"key":"B38","first-page":"5","article-title":"Support vector regression machines","volume":"9","author":"Drucker","year":"1996","journal-title":"Adv Neural Inform Process Systems"},{"key":"B39","article-title":"Classification and regression trees","author":"Breiman","year":"2017","journal-title":"Routledge."},{"key":"B40","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"B41","doi-asserted-by":"publisher","first-page":"669","DOI":"10.1002\/ana.25804","article-title":"MRI in neuromuscular diseases: an emerging diagnostic tool and biomarker for prognosis and efficacy","volume":"88","author":"Dahlqvist","year":"2020","journal-title":"Ann Neurol"},{"key":"B42","doi-asserted-by":"publisher","first-page":"9821","DOI":"10.1038\/s41598-021-89311-3","article-title":"Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies","volume":"11","author":"Nagawa","year":"2021","journal-title":"Sci Rep"},{"key":"B43","doi-asserted-by":"publisher","first-page":"W735","DOI":"10.2214\/AJR.11.8233","article-title":"sequence with increased receiver bandwidth of the inversion pulse for reduction of metallic artifacts","volume":"199","author":"Ulbrich","year":"2012","journal-title":"AJR Am J Roentgenol."},{"key":"B44","unstructured":"2022"}],"container-title":["Frontiers in Neurology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2023.1105276\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,14]],"date-time":"2024-10-14T22:32:42Z","timestamp":1728945162000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fneur.2023.1105276\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,24]]},"references-count":44,"alternative-id":["10.3389\/fneur.2023.1105276"],"URL":"https:\/\/doi.org\/10.3389\/fneur.2023.1105276","relation":{},"ISSN":["1664-2295"],"issn-type":[{"value":"1664-2295","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,24]]},"article-number":"1105276"}}