{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T22:23:26Z","timestamp":1788992606430,"version":"build-2803163510"},"reference-count":33,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T00:00:00Z","timestamp":1750723200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Transplantation"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Introduction<\/jats:title><jats:p>Primary graft dysfunction (PGD) is a significant barrier to survival in lung transplant (LTx) recipients. PGD in patients with systemic sclerosis (SSc) remains especially underrepresented in research.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We investigated 92 SSc recipients (mean age 51 years \u00b1 10) who underwent bilateral LTx between 2007 and 2020. PGD was defined as grade 3 PGD at 72\u00a0h post\u2010LTx. A comprehensive set of CT image features was automatically computed from recipient chest CT scans using deep learning algorithms. Volumetric analysis of recipients\u2019 lungs and chest cavity was used to estimate lung\u2010size matching. Four machine learning (ML) algorithms were developed to predict PGD, including multivariate logistic regression, support vector machine (SVM), random forest classifier (RFC), and multilayer perceptron (MLP).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>PGD was significantly associated with BMI &gt;30\u00a0kg\/m<jats:sup>2<\/jats:sup> (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.009), African American race (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.011), lower Preop FEV1 (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.002) and FVC (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.004), longer waitlist time (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.014), higher lung allocation score (LAS) (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.028), and interstitial lung disease (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.050). From CT analysis, PGD was significantly associated with decreased lung volume (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.001), increased heart\u2010chest cavity volume ratio (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.001), epicardial (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.033) and total heart (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.049) adipose tissue, and five cardiopulmonary features (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.050). Oversized donor allografts estimated using CT analysis were significantly associated with PGD (<jats:italic>p<\/jats:italic>\u00a0&lt;\u00a00.050). The MLP model achieved a maximum AUROC of 0.85 (95% CI: 0.81\u20130.88) in predicting PGD with four features: <jats:italic>Preop FEV1, heart\u2010chest cavity volume ratio, waitlist time, and donor to recipient chest cavity volume ratio<\/jats:italic>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>CT\u2010derived features are significantly associated with PGD, and models incorporating these features can predict PGD in SSc recipients.<\/jats:p><\/jats:sec>","DOI":"10.1111\/ctr.70217","type":"journal-article","created":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T07:39:56Z","timestamp":1750750796000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine\u2010Learning and CT Features"],"prefix":"10.1111","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1267-1533","authenticated-orcid":false,"given":"Jatin","family":"Singh","sequence":"first","affiliation":[{"name":"Department of Radiology University of Pittsburgh  Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Meng","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Pittsburgh  Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joseph K.","family":"Leader","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Pittsburgh  Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Ryan","sequence":"additional","affiliation":[{"name":"Division of Lung Transplant and Lung Failure Department of Cardiothoracic Surgery University of Pittsburgh School of Medicine  Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lucas","family":"Pu","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Pittsburgh  Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6494-599X","authenticated-orcid":false,"given":"Rachel","family":"Deitz","sequence":"additional","affiliation":[{"name":"Division of Lung Transplant and Lung Failure Department of Cardiothoracic Surgery University of Pittsburgh School of Medicine  Pittsburgh Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9849-6371","authenticated-orcid":false,"given":"Ernest G.","family":"Chan","sequence":"additional","affiliation":[{"name":"Department of Surgery Section of Thoracic Surgery University of Chicago  Chicago Illinois 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