Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine‐Learning and CT Features
CT-derived features are significantly associated with PGD, and models incorporating these features can predict PGD in SSc recipients.
Authors
- Joseph K. Leader
- Ernest G. Chan (ORCID: https://orcid.org/0000-0001-9849-6371)
- Norihisa Shigemura (ORCID: https://orcid.org/0000-0003-0438-1632)
- Jiantao Pu (ORCID: https://orcid.org/0000-0003-2127-5313)
- Lucas Pu (ORCID: https://orcid.org/0009-0005-4506-7617)
- Jatin Singh (ORCID: https://orcid.org/0000-0003-1267-1533)
- Chadi A. Hage (ORCID: https://orcid.org/0000-0002-0582-4362)
- Rachel L. Deitz (ORCID: https://orcid.org/0000-0002-6494-599X)
- Xin Meng (ORCID: https://orcid.org/0000-0001-7727-6046)
- Pablo G. Sánchez (ORCID: https://orcid.org/0000-0003-3607-0345)
- John P. Ryan (ORCID: https://orcid.org/0000-0003-4466-4418)
Institutions
- University of Pittsburgh (US)
- University of Chicago (US)
Publication Details
- Journal
- Clinical Transplantation
- Published
- 2025-06-24
- DOI
- https://doi.org/10.1111/ctr.70217
- Citations
- 1
- Primary Topic
- Transplantation: Methods and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.01
Funders
- National Institutes of Health