{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T09:27:13Z","timestamp":1772616433995,"version":"3.50.1"},"reference-count":40,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T00:00:00Z","timestamp":1647043200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000245","name":"Michael Smith Foundation for Health Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000245","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>Outside eosinophilia, current clinical asthma phenotypes do not show strong relationships with disease pathogenesis or treatment responses. While chest x\u2010ray computed tomography (CT) phenotypes have previously been explored, functional MRI measurements provide complementary phenotypic information.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To derive novel data\u2010driven asthma phenotypic clusters using functional MRI airway biomarkers that better describe airway pathologies in patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Study Type<\/jats:title><jats:p>Retrospective.<\/jats:p><\/jats:sec><jats:sec><jats:title>Population<\/jats:title><jats:p>A total of 45 patients with asthma who underwent post\u2010bronchodilator<jats:sup>129<\/jats:sup>Xe MRI, volume\u2010matched CT, spirometry and plethysmography within a 90\u2010minute visit.<\/jats:p><\/jats:sec><jats:sec><jats:title>Field Strength\/Sequence<\/jats:title><jats:p>Three\u2010dimensional gradient\u2010recalled echo<jats:sup>129<\/jats:sup>Xe ventilation sequence at 3\u00a0T.<\/jats:p><\/jats:sec><jats:sec><jats:title>Assessment<\/jats:title><jats:p>We measured MRI ventilation defect percent (VDP), CT airway wall\u2010area percent (WA%), wall\u2010thickness (WT, WT* [*normalized for age\/sex\/height]), lumen\u2010area (LA), lumen\u2010diameter (D, D*) and total airway count (TAC). Univariate relationships were utilized to select variables for k\u2010means cluster analysis and phenotypic subgroup generation. Spirometry and plethysmography measurements were compared across imaging\u2010based clusters.<\/jats:p><\/jats:sec><jats:sec><jats:title>Statistical Tests<\/jats:title><jats:p>Spearman correlation (<jats:italic>\u03c1<\/jats:italic>), one\u2010way analysis of variance (ANOVA) or Kruskal\u2013Wallis tests with post hoc Bonferroni correction for multiple comparisons, significance level 0.05.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Based on limited common variance (Kaiser\u2013Meyer\u2013Olkin\u2010measure\u00a0=\u00a00.44), four unique clusters were generated using MRI VDP, TAC, WT* and D* (52\u2009\u00b1\u200914\u2009years, 27 female). Imaging measurements were significantly different across clusters as was the forced expiratory volume in 1\u2010second (FEV<jats:sub>1<\/jats:sub>%<jats:sub>pred<\/jats:sub>), residual volume\/total lung capacity and airways resistance. Asthma\u2010control (<jats:italic>P<\/jats:italic>\u00a0=\u00a00.9), quality\u2010of\u2010life scores (<jats:italic>P<\/jats:italic>\u00a0=\u00a00.7) and the proportions of severe\u2010asthma (<jats:italic>P<\/jats:italic>\u00a0=\u00a00.4) were not significantly different. Cluster1 (<jats:italic>n<\/jats:italic>\u00a0=\u00a015\/8 female) reflected mildly abnormal CT airway measurements and FEV<jats:sub>1<\/jats:sub>with moderately abnormal VDP. Cluster2 (<jats:italic>n<\/jats:italic>\u00a0=\u00a012\/12 female) reflected moderately abnormal TAC, WT and FEV<jats:sub>1<\/jats:sub>. In Cluster3 and Cluster4 (<jats:italic>n<\/jats:italic>\u00a0=\u00a014\/6 female,<jats:italic>n<\/jats:italic>\u00a0=\u00a04\/1 female, respectively), there was severely reduced TAC, D and FEV<jats:sub>1<\/jats:sub>, but Cluster4 also had significantly worse, severely abnormal VDP (7\u2009\u00b1\u20095% vs. 41\u2009\u00b1\u200912%).<\/jats:p><\/jats:sec><jats:sec><jats:title>Data Conclusion<\/jats:title><jats:p>We generated four proof\u2010of\u2010concept MRI\u2010derived clusters of asthma with distinct structure\u2013function pathologies. Cluster analysis of asthma using<jats:sup>129<\/jats:sup>Xe MRI in combination with CT biomarkers is feasible and may challenge currently used paradigms for asthma phenotyping and treatment decisions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Evidence Level<\/jats:title><jats:p>3<\/jats:p><\/jats:sec><jats:sec><jats:title>Technical Efficacy<\/jats:title><jats:p>Stage<\/jats:p><\/jats:sec>","DOI":"10.1002\/jmri.28152","type":"journal-article","created":{"date-parts":[[2022,3,12]],"date-time":"2022-03-12T13:23:21Z","timestamp":1647091401000},"page":"1475-1486","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Pulmonary<scp>MRI<\/scp>and Cluster Analysis Help Identify Novel Asthma Phenotypes"],"prefix":"10.1002","volume":"56","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2541-1645","authenticated-orcid":false,"given":"Rachel L.","family":"Eddy","sequence":"first","affiliation":[{"name":"Centre for Heart Lung 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