CT-Based Commercial Software Applications: Improving Patient Care Through Accurate COPD Subtyping

Abstract

Chronic obstructive pulmonary disease (COPD) is heterogenous in its clinical manifestations and disease progression. Patients often have disease courses that are difficult to predict with readily available data, such as lung function testing. The ability to better classify COPD into well-defined groups will allow researchers and clinicians to tailor novel therapies, monitor their effects, and improve patient-centered outcomes. Different modalities of assessing these COPD phenotypes are actively being studied, and an area of great promise includes the use of quantitative computed tomography (QCT) techniques focused on key features such as airway anatomy, lung density, and vascular morphology. Over the last few decades, companies around the world have commercialized automated CT software packages that have proven immensely useful in these endeavors. This article reviews the key features of several commercial platforms, including the technologies they are based on, the metrics they can generate, and their clinical correlations and applications. While such tools are increasingly being used in research and clinical settings, they have yet to be consistently adopted for diagnostic work-up and treatment planning, and their full potential remains to be explored.

Keywords:

• lung disease
• medical imaging
• phenotyping
• clinical
• quantitative

Abbreviations

COPD, chronic obstructive pulmonary disease; FRI, functional respiratory imaging; fSAD, functional small airways disease; PFT, pulmonary function testing; PRM, parametric response mapping; QCT, quantitative computed tomography.

Acknowledgments

The authors thank Lee Olsen for assisting with manuscript preparation and editing.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

Dr. Galbán is co-inventor and patent holder of Parametric Response Mapping, which the University of Michigan has licensed to Imbio, LLC, and has a financial interest in Imbio, LLC. Dr. Labaki reports personal fees from Konica Minolta and Continuing Education Alliance. Dr. Han reports personal fees from GlaxoSmithKline, AstraZeneca, Boehringer Ingelheim, Cipla, Chiesi, Novartis, Pulmonx, Teva, Verona, Merck, Mylan, Sanofi, DevPro, Aerogen, Polarian, Regeneron, United Therapeutics, UpToDate, Medscape, and Integrity. She has received either in kind research support or funds paid to the institution from the National Institutes of Health (NIH), Novartis, Sunovion, Nuvaira, Sanofi, AstraZeneca, Boehringer Ingelheim, Gala Therapeutics, Biodesix, the COPD Foundation, and the American Lung Association. She has participated in Data Safety Monitoring Boards for Novartis and Medtronic with funds paid to the institution. She has received stock options from Meissa Vaccines. The authors report no other conflicts of interest in this work.

Additional information

Funding

The research for this review was funded by the National Heart, Lung, and Blood Institute/NIH (grants R01HL139690 and R01HL150023).