Related Experiment Video
Updated: Jul 27, 2025
02:09
Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
Published on: April 12, 2024
641
Inspiratory and Expiratory Computed Tomography Imaging Clusters Reflect Functional Characteristics in Chronic
Yumiko Matsuo1,2, Emiko Ogawa1,2, Yoko Tsunoda1
1Division of Respiratory Medicine, Department of Internal Medicine, Shiga University of Medical Science, Otsu, Shiga, Japan.
Summary
Disease probability measure (DPM) clustering reveals distinct chronic obstructive pulmonary disease (COPD) phenotypes. These clusters characterize disease progression, showing increased emphysema over three years across all groups.
Area of Science:
- Pulmonary Medicine
- Radiology
- Medical Imaging Analysis
Background:
- Chronic obstructive pulmonary disease (COPD) is a progressive lung disease characterized by airflow limitation.
- Disease Probability Measure (DPM) offers voxel-wise imaging assessment of gas-trapping and emphysematous lesions.
- Understanding COPD progression through imaging parameters is crucial for patient management.
Purpose of the Study:
- To elucidate COPD progression by performing cluster analysis using DPM parameters.
- To identify distinct COPD phenotypes based on imaging characteristics.
- To analyze the 3-year disease progression within identified clusters.
Main Methods:
- Hierarchical cluster analysis was performed on DPM parameters from 131 COPD patients (84 with 3-year follow-up).
- Quantitative measurements included percentage of low attenuation volume (LAV%) and airway wall area (√Aaw at Pi10).
- Five clusters were defined: Normal (NL), Normal-GasTrap (NL-GT), GasTrap (GT), GasTrap-Emphysema (GT-EM), and Emphysema (EM).
Main Results:
- Women were predominantly diagnosed with GT. Forced expiratory volume in 1 s decreased progressively from NL to EM clusters.
- DPMEmph correlated with LAV%, and airway wall area was significantly higher in four clusters compared to NL.
- DPMEmph increased over 3 years in all clusters; DPMNormal increased only in the GT cluster.
Conclusions:
- DPM-based clustering effectively reflects COPD characteristics and pathophysiology.
- Imaging parameters can differentiate COPD phenotypes and track disease progression.
- This approach aids in understanding the heterogeneous nature of COPD.
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