A paper authored by Sanghun Choi, assistant research scientist at IIHR--Hydroscience and Engineering, with Ching-Long Lin serving as senior corresponding author, has been published by the Journal of Allergy and Clinical Immunology (JACI, http://www.jacionline.org/). The paper is entitled “Quantitative computed tomography imaging-based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes."
“The goal is to use imaging-based variables to identify patient clusters via machine learning and establish their associations with clinical characteristics, Lin noted. "The clustering membership may be used as a basis for therapeutic interventions.” The patient population under study is derived from the NIH-sponsored Severe Asthma Research Program (SARP), seeking to understand why some asthma subjects are unresponsive to standard therapies.
Imaging variables, including airway diameter, wall thickness, and air trapping, have been found to be important metrics when differentiating patients with severe asthma from those with nonsevere asthma and healthy subjects. In this new study, additional measures were introduced which better relate to the airway measures which influence flow patterns within the airway tree which in turn influence inhaled particle deposition.
The JACI is the #1 most-cited allergy/immunology journal. To access the paper, go to http://www.jacionline.org/article/S0091-6749(17)30146-X/fulltext.
Co-authors include Dr. Eric A. Hoffman, UI director, Advanced Pulmonary Physiomic Imaging Laboratory: APPIL, professor of radiology, internal medicine, and biomedical engineering, and four faculty researchers from three other major institutions associated with the SARP study.
The work is primarily funded by the National Institutes of Health, and is related to another recent FDA grant for Lin.
The research team is in the process of applying the multi scale imaging-based clustering analysis (MICA) developed in the JACI paper to chronic obstructive pulmonary disease (COPD).