{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T17:17:12Z","timestamp":1792171032703,"version":"4.4.0"},"reference-count":35,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2016,6,1]],"date-time":"2016-06-01T00:00:00Z","timestamp":1464739200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2016,6,1]],"date-time":"2016-06-01T00:00:00Z","timestamp":1464739200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006662","name":"National Institute on Handicapped Research","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100006662","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000858","name":"University of Sheffield","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Pulm. circ."],"published-print":{"date-parts":[[2016,6]]},"abstract":"<jats:p>Accurately identifying patients with pulmonary hypertension (PH) using noninvasive methods is challenging, and right heart catheterization (RHC) is the gold standard. Magnetic resonance imaging (MRI) has been proposed as an alternative to echocardiography and RHC in the assessment of cardiac function and pulmonary hemodynamics in patients with suspected PH. The aim of this study was to assess whether machine learning using computational modeling techniques and image\u2010based metrics of PH can improve the diagnostic accuracy of MRI in PH. Seventy\u2010two patients with suspected PH attending a referral center underwent RHC and MRI within 48 hours. Fifty\u2010seven patients were diagnosed with PH, and 15 had no PH. A number of functional and structural cardiac and cardiovascular markers derived from 2\u00a0mathematical models and also solely from MRI of the main pulmonary artery and heart were integrated into a classification algorithm to investigate the diagnostic utility of the combination of the individual markers. A physiological marker based on the quantification of wave reflection in the pulmonary artery was shown to perform best individually, but optimal diagnostic performance was found by the combination of several image\u2010based markers. Classifier results, validated using leave\u2010one\u2010out cross validation, demonstrated that combining computation\u2010derived metrics reflecting hemodynamic changes in the pulmonary vasculature with measurement of right ventricular morphology and function, in a decision support algorithm, provides a method to noninvasively diagnose PH with high accuracy (92%). The high diagnostic accuracy of these MRI\u2010based model parameters may reduce the need for RHC in patients with suspected PH.<\/jats:p>","DOI":"10.1086\/686020","type":"journal-article","created":{"date-parts":[[2016,2,4]],"date-time":"2016-02-04T16:01:30Z","timestamp":1454601690000},"page":"181-190","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":47,"title":["Diagnosis of Pulmonary Hypertension from Magnetic Resonance Imaging\u2013Based Computational Models and Decision Tree Analysis"],"prefix":"10.1002","volume":"6","author":[{"given":"Angela","family":"Lungu","sequence":"first","affiliation":[{"name":"Cardiovascular Science Department University of Sheffield Sheffield South Yorkshire United Kingdom"},{"name":"Insigneo Institute for in silico Medicine University of Sheffield Sheffield South Yorkshire United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew 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