Strain Analysis in Patients at High-Risk for COPD Using Four-Dimensional Dynamic-Ventilation CT

Current advances in pulmonary functional imaging

Recent advances in imaging analysis have enabled evaluation of ventilation and perfusion in specific regions by chest computed tomography (CT) and magnetic resonance imaging (MRI), in addition to modalities including dynamic chest radiography, scintigraphy, positron emission tomography (PET), ultrasound, and electrical impedance tomography (EIT). In this review, an overview of current functional imaging techniques is provided for each modality. Advances in chest CT have allowed for the analysis of local volume changes and small airway disease in addition to emphysema, using the Jacobian determinant and parametric response mapping with inspiratory and expiratory images. Airway analysis can reveal characteristics of airway lesions in chronic obstructive pulmonary disease (COPD) and bronchial asthma, and the contribution of dysanapsis to obstructive diseases. Chest CT is also employed to measure pulmonary blood vessels, interstitial lung abnormalities, and mediastinal and chest wall components including skeletal muscle and bone. Dynamic CT can visualize lung deformation in respective portions. Pulmonary MRI has been developed for the estimation of lung ventilation and perfusion, mainly using hyperpolarized 129Xe. Oxygen-enhanced and proton-based MRI, without a polarizer, has potential clinical applications. Dynamic chest radiography is gaining traction in Japan for ventilation and perfusion analysis. Single photon emission CT can be used to assess ventilation-perfusion (V˙/Q˙) mismatch in pulmonary vascular diseases and COPD. PET/CT V˙/Q˙ imaging has also been demonstrated using "Galligas". Both ultrasound and EIT can detect pulmonary edema caused by acute respiratory distress syndrome. Familiarity with these functional imaging techniques will enable clinicians to utilize these systems in clinical practice.

Early Diagnosis of High-Risk Chronic Obstructive Pulmonary Disease Based on Quantitative High-Resolution Computed Tomography Measurements

Purpose

Quantitative computed tomography (QCT) techniques, focusing on airway anatomy and emphysema, may help to detect early structural changes of COPD disease. This retrospective study aims to identify high-risk COPD participants by using QCT measurements.

Patients and Methods

We enrolled 140 participants from the Second Affiliated Hospital of Shenyang Medical College who completed inspiratory high-resolution CT scans, pulmonary function tests (PFTs), and clinical characteristics recorded. They were diagnosed Non-COPD by PFT value of FEV1/FVC >70% and divided into two groups according percentage predicted FEV1 (FEV1%), low-risk COPD group: FEV1% ≥ 95%, high-risk group: 80% < FEV1% < 95%. The QCT measurements were analyzed by the Student's t-test (or Mann-Whitney U-test) method. Then, feature candidates were identified using the LASSO method. Meanwhile, the correlation between QCT measurements and PFTs was assessed by the Spearman rank correlation test. Furthermore, support vector machine (SVM) was performed to identify high-risk COPD participants. The performance of the models was evaluated in terms of accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-score, and area under the ROC curve (AUC), with p <0.05 considered statistically significant.

Results

The SVM based on QCT measurements achieved good performance in identifying high-risk COPD patients with 85.71% of ACC, 88.34% of SEN, 84.00% of SPE, 83.33% of F1-score, and 0.93 of AUC. Further, QCT measurements integration of clinical data improved the performance with an ACC of 90.48%. The emphysema index (%LAA-950) of left lower lung was negatively correlated with PFTs (P < 0.001). The airway anatomy indexes of lumen diameter (LD) were correlated with PFTs.

Conclusion

QCT measurements combined with clinical information could provide an effective tool for an early diagnosis of high-risk COPD. The QCT indexes can be used to assess the pulmonary function status of high-risk COPD.