Exploratory Study on COPD Phenotypes and their Progression: Integrating SPECT and qCT Imaging Analysis.

Abstract

Background:

The objective of this study is to understand chronic obstructive pulmonary disease (COPD) phenotypes and their progressions by quantifying heterogeneities of lung ventilation from the single photon emission computed tomography (SPECT) images and establishing associations with the quantitative computed tomography (qCT) imaging-based clusters and variables.

Methods:

Eight COPD patients completed a longitudinal study of three visits with intervals of about a year. CT scans of these subjects at residual volume, functional residual capacity, and total lung capacity were taken for all visits. The functional and structural qCT-based variables were derived, and the subjects were classified into the qCT-based clusters. In addition, the SPECT variables were derived to quantify the heterogeneity of lung ventilation. The correlations between the key qCT-based variables and SPECT-based variables were examined.

Results:

The SPECT-based coefficient of variation (CVTotal), a measure of ventilation heterogeneity, showed strong correlations (|r| ≥ 0.7) with the qCT-based functional small airway disease percentage (fSAD%Total) and emphysematous tissue percentage (Emph%Total) in the total lung on cross-sectional data. As for the two-year changes, the SPECT-based maximum tracer concentration (TCmax), a measure of hot spots, exhibited strong negative correlations with fSAD%Total, Emph%Total, average airway diameter in the left upper lobe, and airflow distribution in the middle and lower lobes.

Conclusion:

Small airway disease is highly associated with the heterogeneity of ventilation in COPD lungs. TCmax is a more sensitive functional biomarker for COPD progression than CVTotal. Besides fSAD%Total and Emph%Total, segmental airways narrowing and imbalanced ventilation between upper and lower lobes may contribute to the development of hot spots over time.

• Authors: Frank Li, Xuan Zhang, Alejandro P Comellas, Eric A Hoffman, Michael M Graham, Ching-Long Lin
• Journal: medRxiv : the preprint server for health sciences (medRxiv) (Apr 13 2024) United States
• PMID: 38645219 (Publication Type: Preprint)