Prospective machine learning CT quantitative evaluation of idiopathic pulmonary fibrosis in patients undergoing anti-fibrotic treatment using low- and ultra-low-dose CT.

Journal: Clinical radiology
Published Date: Mar 1, 2022

Abstract

AIM: To compare the machine learning computed tomography (CT) quantification tool, Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER) to pulmonary function testing (PFT) in assessing idiopathic pulmonary fibrosis (IPF) for patients undergoing treatment and determine the effects of limited (LD) and ultra-low dose (ULD) CT on CALIPER performance.

Authors

  • C W Koo
    Department of Radiology, Mayo Clinic, Rochester, MN, USA. Electronic address: koo.chiwan@mayo.edu.
  • N B Larson
    Department of Quantitative Health Sciences, Division of Clinical Trials and Biostatistics, Mayo Clinic, Rochester, MN, USA.
  • C T Parris-Skeete
    Department of Radiology, Mayo Clinic, Rochester, MN, USA.
  • R A Karwoski
    Biomedical Imaging Resources, Research Applications Solutions, Mayo Clinic, Rochester, MN, USA.
  • S Kalra
    Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Mayo Clinic, Rochester, MN, USA.
  • B J Bartholmai
    Department of Radiology, Mayo Clinic, Rochester, MN, USA.
  • E M Carmona
    Department of Medicine, Division of Pulmonary, Critical Care and Sleep Medicine, Mayo Clinic, Rochester, MN, USA.

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