SPIROMICS protocol for multicenter quantitative computed tomography to phenotype the lungs Academic Article uri icon


MeSH Major

  • Adrenal Cortex Hormones
  • Adrenergic beta-2 Receptor Agonists
  • Bronchodilator Agents
  • Cardiovascular Diseases
  • Fibrinogen
  • Muscarinic Antagonists
  • Precision Medicine
  • Pulmonary Disease, Chronic Obstructive


  • Multidetector row computed tomography (MDCT) is increasingly taking a central role in identifying subphenotypes within chronic obstructive pulmonary disease (COPD), asthma, and other lung-related disease populations, allowing for the quantification of the amount and distribution of altered parenchyma along with the characterization of airway and vascular anatomy. The embedding of quantitative CT (QCT) into a multicenter trial with a variety of scanner makes and models along with the variety of pressures within a clinical radiology setting has proven challenging, especially in the context of a longitudinal study. SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study), sponsored by the National Institutes of Health, has established a QCT lung assessment system (QCT-LAS), which includes scanner-specific imaging protocols for lung assessment at total lung capacity and residual volume. Also included are monthly scanning of a standardized test object and web-based tools for subject registration, protocol assignment, and data transmission coupled with automated image interrogation to assure protocol adherence. The SPIROMICS QCT-LAS has been adopted and contributed to by a growing number of other multicenter studies in which imaging is embedded. The key components of the SPIROMICS QCT-LAS along with evidence of implementation success are described herein. While imaging technologies continue to evolve, the required components of a QCT-LAS provide the framework for future studies, and the QCT results emanating from SPIROMICS and the growing number of other studies using the SPIROMICS QCT-LAS will provide a shared resource of image-derived pulmonary metrics.

publication date

  • October 2016



  • Academic Article



  • eng

PubMed Central ID

  • PMC5074650

Digital Object Identifier (DOI)

  • 10.1164/rccm.201506-1208PP

PubMed ID

  • 27482984

Additional Document Info

start page

  • 794

end page

  • 806


  • 194


  • 7