Machine Learning Algorithms Utilizing Quantitative CT Features May Predict Eventual Onset of Bronchiolitis Obliterans Syndrome After Lung Transplantation.

Journal: Academic radiology
PMID: 29472146

Abstract

RATIONALE AND OBJECTIVES: Long-term survival after lung transplantation (LTx) is limited by bronchiolitis obliterans syndrome (BOS), defined as a sustained decline in forced expiratory volume in the first second (FEV) not explained by other causes. We assessed whether machine learning (ML) utilizing quantitative computed tomography (qCT) metrics can predict eventual development of BOS.

Authors

  • Eduardo J Mortani Barbosa
    Perelman School of Medicine, University of Pennsylvania, Departments of Radiology and Medicine, 3400 Spruce Street, Philadelphia, PA 19104. Electronic address: Eduardo.Barbosa@uphs.upenn.edu.
  • Maarten Lanclus
    FLUIDDA nv, Kontich, Belgium.
  • Wim Vos
    FLUIDDA nv, Kontich, Belgium.
  • Cedric Van Holsbeke
    FLUIDDA nv, Kontich, Belgium.
  • William De Backer
    University Hospital Antwerp, Department of Respiratory Medicine, Edegem, Belgium.
  • Jan De Backer
    FLUIDDA nv, Kontich, Belgium.
  • James Lee
    Perelman School of Medicine, University of Pennsylvania, Departments of Radiology and Medicine, 3400 Spruce Street, Philadelphia, PA 19104.

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