Longitudinal Imaging-Based Clusters in Former Smokers of the COPD Cohort Associate with Clinical Characteristics: The SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)

Abstract

Purpose Quantitative computed tomography (qCT) imaging-based cluster analysis identified clinically meaningful COPD former-smoker subgroups (clusters) based on cross-sectional data. We aimed to identify progression clusters for former smokers using longitudinal data. Patients and Methods We selected 472 former smokers from SPIROMICS with a baseline visit and a one-year follow-up visit. A total of 150 qCT imaging-based variables, comprising 75 variables at baseline and their corresponding progression rates, were derived from the respective inspiration and expiration scans of the two visits. The COPD progression clusters identified were then associated with subject demography, clinical variables and biomarkers. Results COPD severities at baseline increased with increasing cluster number. Cluster 1 patients were an obese subgroup with rapid progression of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%). Cluster 2 exhibited a decrease of fSAD% and Emph%, an increase of tissue fraction at total lung capacity and airway narrowing over one year. Cluster 3 showed rapid expansion of Emph% and an attenuation of fSAD%. Cluster 4 demonstrated severe emphysema and fSAD and significant structural alterations at baseline with rapid progression of fSAD% over one year. Subjects with different progression patterns in the same cross-sectional cluster were identified by longitudinal clustering. Conclusion qCT imaging-based metrics at two visits for former smokers allow for the derivation of four statistically stable clusters associated with unique progression patterns and clinical characteristics. Use of baseline variables and their progression rates enables identification of longitudinal clusters, resulting in a refinement of cross-sectional clusters.

Chronic Obstructive Pulmonary Disease and Small Airways Diseases

“…It is unclear if QLI provides unique metrics that differ from the insights gained from clinical symptoms and pulmonary function testing. Longitudinal QLI has been able to identify specific subgroups at higher risk for progression of disease over time 54 and in this context might be helpful in identifying patients in need of more aggressive treatment and encouragement of smoking cessation.…”

Machine Learning-based Prediction of Relative Regional Air Volume Change from Healthy Human Lung CTs

“…Pulmonary function test (PFT) results, such as forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC), are used to determine the stages of COPD [18]. However, since the PFT reflects only the whole lung function, and not regional lung decline of functional features before the destruction of lung tissue [19,20], early detection or selfrecognition is difficult until the whole lung function is severely declined. Detection and management of early COPD have recently received a lot of attention [21].…”

“…Detection and management of early COPD have recently received a lot of attention [21]. Machine learning approaches with imaging have been capable of characterizing early stage progression of COPD [20]. Furthermore, machine learning has been widely used to investigate various aspects of COVID-19 [22][23][24].…”

Contrastive learning and subtyping of post-COVID-19 lung computed tomography images

“…Chest X-ray and computed tomography (CT) scans are widely used to examine patients with COVID-19 ( Mukherjee et al, 2021 ; Song et al, 2021 ; Wang et al, 2021 ; Zou et al, 2021 ; Mahbub et al, 2022 ; Santosh et al, 2022 ). With medical care and management of post-COVID-19 subjects being recognized as a top research priority by professional societies, follow-up evaluations of COVID-19 survivors based on chest X-ray or CT scans along with clinical assessment have been recommended ( Zheng et al, 2020 ).…”