{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T12:02:43Z","timestamp":1793793763013,"version":"4.6.1"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T00:00:00Z","timestamp":1761523200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010269","name":"Wellcome Trust","doi-asserted-by":"publisher","award":["223521\/Z\/21\/Z"],"award-info":[{"award-number":["223521\/Z\/21\/Z"]}],"id":[{"id":"10.13039\/100010269","id-type":"DOI","asserted-by":"publisher"}]},{"name":"NIHR Biomedical Research Centre Sheffield","award":["NIHR203321"],"award-info":[{"award-number":["NIHR203321"]}]},{"DOI":"10.13039\/100006662","name":"NIHR","doi-asserted-by":"publisher","award":["AI_AWARD01706"],"award-info":[{"award-number":["AI_AWARD01706"]}],"id":[{"id":"10.13039\/100006662","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000276","name":"Department of Health and Social Care","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100000276","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Unenhanced chest CT is frequently used to assess lung malignancy and parenchymal disease. Harnessing CT data to quantify cardiac and vascular structures has the potential to improve the diagnosis of heart failure and pulmonary hypertension (PH). This study aims to develop a deep learning model to segment and analyse cardiothoracic structures from unenhanced CT images to diagnose PH, pre-capillary PH and PH associated with left heart disease (LHD).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>A twelve-structure cardiothoracic segmentation model was developed using an institutional cohort (n = 55, 35\/9\/11 training\/validation\/testing). Model performance was evaluated using Dice similarity coefficients (DSC). Volumetric measurements were compared to manual values using intra-class correlation (ICC) and visually assessed by four observers using an external cohort (n = 50, from 26 hospitals). Univariable and multivariable regression analyses were performed using a cohort of 368 patients (254\/114 training\/testing). Receiver-operating characteristic curves were plotted and the area under the curves (AUC) with confidence intervals (CI) were calculated. The model yielded a DSC segmentation performance of \u22650.87 for 9\/12 segmented structures and ICC &amp;gt; 0.95 for 10\/12 structures. Most of the segmented structures scored as excellent in the external cohort visual assessment. Diagnostic accuracy for predicting PH was high [AUC = 0.88 (CI: 0.80\u20130.96), sensitivity = 70%, specificity = 100%], including pre-capillary PH [AUC = 0.84 (CI: 0.74\u20130.94), sensitivity = 72%, specificity = 94%] and PH-LHD [AUC = 0.86 (CI: 0.79\u20130.93), sensitivity = 94%, specificity = 63%].<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>A fully automated model for multi-structure cardiothoracic segmentation on unenhanced CT is achievable. The model can predict PH and identify patients with pre-capillary PH and PH-LHD with promising performance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/ehjdh\/ztaf124","type":"journal-article","created":{"date-parts":[[2025,10,25]],"date-time":"2025-10-25T12:03:34Z","timestamp":1761393814000},"source":"Crossref","is-referenced-by-count":3,"title":["A fully automated explainable predictive model for diagnosing pre-capillary and post-capillary pulmonary hypertension on routine unenhanced CT: results from the ASPIRE registry"],"prefix":"10.1093","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-8014-4924","authenticated-orcid":false,"given":"Turki Nasser","family":"Alnasser","sequence":"first","affiliation":[{"name":"School of Medicine and Population Health, The University of Sheffield , Sheffield S10 2TN ,","place":["United Kingdom"]},{"name":"Radiological Sciences Department, College of Applied Medical Science, King Saud bin Abdulaziz University for Health 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Population Health, The University of Sheffield , Sheffield S10 2TN ,","place":["United Kingdom"]},{"name":"3D Imaging Lab, Sheffield Teaching Hospitals NHSFT , Sheffield ,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lojain F","family":"Abdulaal","sequence":"additional","affiliation":[{"name":"School of Medicine and Population Health, The University of Sheffield , Sheffield S10 2TN ,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khalid S","family":"Alghamdi","sequence":"additional","affiliation":[{"name":"School of Medicine and Population Health, The University of Sheffield , Sheffield S10 2TN ,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pankaj","family":"Garg","sequence":"additional","affiliation":[{"name":"Norwich Medical School, Faculty of Medicine and Health Sciences, University of East Anglia , Norwich ,","place":["United 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Population Health, The University of Sheffield , Sheffield S10 2TN ,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David G","family":"Kiely","sequence":"additional","affiliation":[{"name":"School of Medicine and Population Health, The University of Sheffield , Sheffield S10 2TN ,","place":["United Kingdom"]},{"name":"Insigneo Institute, Faculty of Engineering, The University of Sheffield , Sheffield ,","place":["United Kingdom"]},{"name":"Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Trust , Sheffield ,","place":["United Kingdom"]},{"name":"National Institute for Health and Care Research, Sheffield Biomedical Research Centre , Sheffield ,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9960-7587","authenticated-orcid":false,"given":"Samer","family":"Alabed","sequence":"additional","affiliation":[{"name":"School of Medicine and Population 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