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

Abstract

Jennifer M Wang,1 Sundaresh Ram,2 Wassim W Labaki,1 MeiLan K Han,1 Craig J Galbán2 1Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, MI, USA; 2Department of Radiology, University of Michigan, Ann Arbor, MI, USACorrespondence: Craig J Galbán, Department of Radiology, University of Michigan, BSRB, Room A506, 109 Zina Pitcher Place, Ann Arbor, MI, 48109-2200, USA, Tel [phone], Fax [phone], Email [email protected]: 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

Keywords

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