SPIROMICS Protocol for Multicenter Quantitative Computed Tomography to Phenotype the Lungs. Academic Article uri icon

Overview

abstract

  • 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.

authors

  • Kaner, Robert J.
  • Sieren, Jered P
  • Newell, John D
  • Barr, R Graham
  • Bleecker, Eugene R
  • Burnette, Nathan
  • Carretta, Elizabeth E
  • Couper, David
  • Goldin, Jonathan
  • Guo, Junfeng
  • Han, MeiLan K
  • Hansel, Nadia N
  • Kanner, Richard E
  • Kazerooni, Ella A
  • Martinez, Fernando J
  • Rennard, Stephen
  • Woodruff, Prescott G
  • Hoffman, Eric A

publication date

  • October 1, 2016

Research

keywords

  • Asthma
  • Emphysema
  • Lung
  • Multidetector Computed Tomography
  • Pulmonary Disease, Chronic Obstructive

Identity

PubMed Central ID

  • PMC5074650

Scopus Document Identifier

  • 84992363493

Digital Object Identifier (DOI)

  • 10.1164/rccm.201506-1208PP

PubMed ID

  • 27482984

Additional Document Info

volume

  • 194

issue

  • 7