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Related Experiment Video

Updated: Sep 18, 2025

Unilateral Lung Volume Analysis Using Micro-CT for Enhanced Assessment of Pulmonary Fibrosis in Preclinical Models
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Predicting Primary Graft Dysfunction in Systemic Sclerosis Lung Transplantation Using Machine-Learning and CT

Jatin Singh1, Xin Meng1, Joseph K Leader1

  • 1Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

Clinical Transplantation
|June 24, 2025
PubMed
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This summary is machine-generated.

Primary graft dysfunction (PGD) after lung transplant is a challenge, especially for systemic sclerosis patients. Advanced CT imaging and machine learning accurately predict PGD risk using specific patient and donor characteristics.

Area of Science:

  • Cardiology
  • Pulmonology
  • Radiology
  • Transplant Surgery
  • Medical Imaging
  • Machine Learning

Background:

  • Primary graft dysfunction (PGD) is a major obstacle to long-term survival in lung transplant (LTx) recipients.
  • PGD research in patients with systemic sclerosis (SSc) is notably limited.
  • Understanding PGD predictors in SSc patients is crucial for improving transplant outcomes.

Purpose of the Study:

  • To identify clinical and imaging predictors of PGD in SSc patients undergoing bilateral LTx.
  • To develop and evaluate machine learning (ML) models for PGD prediction using CT-derived features.
  • To assess the association between donor-recipient size matching and PGD in this population.
Keywords:
acute rejectionlung transplantationmachine learningprimary graft dysfunctionsclerodermasystemic sclerosis

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Main Methods:

  • Investigated 92 SSc recipients undergoing bilateral LTx (2007-2020).
  • Utilized deep learning for automated CT image feature extraction and volumetric analysis for lung-size matching.
  • Developed four ML algorithms (logistic regression, SVM, RFC, MLP) to predict PGD (defined as grade 3 at 72h post-LTx).

Main Results:

  • PGD was linked to higher BMI, African American race, lower preoperative FEV1 and FVC, longer waitlist times, and higher LAS.
  • CT analysis revealed associations between PGD and reduced lung volume, increased heart-chest cavity ratio, and greater epicardial/total heart adipose tissue.
  • Oversized donor allografts, identified via CT, were significantly associated with PGD (p < 0.050).
  • The MLP model, using FEV1, heart-chest cavity ratio, waitlist time, and donor-recipient chest cavity volume ratio, achieved an AUROC of 0.85.

Conclusions:

  • CT-derived imaging features significantly correlate with PGD development in SSc LTx recipients.
  • Predictive models incorporating these CT features demonstrate strong potential for PGD risk assessment.
  • These findings highlight the utility of advanced imaging and ML in optimizing lung transplant outcomes for SSc patients.