Management of COPD: Is there a role for quantitative imaging?

Experimental feasibility of xenon-enhanced dual-energy radiography for imaging of lung function

Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide. We experimentally investigated the feasibility of two-dimensional xenon-enhanced dual-energy (XeDE) radiography for imaging of lung function. We optimized image quality under quantum-noise-limited conditions using a chest phantom consisting of a rectangular chamber representing the thoracic volume and PMMA slabs simulating x-ray attenuation by soft tissue. A sealed, air-filled cavity with thin PMMA walls was positioned inside the chamber to simulate a 2 cm thick ventilation defect. The chamber was ventilated with xenon and dual-energy imaging was performed using a diagnostic x-ray tube and a flat-panel detector. The contrast-to-noise ratio of ventilation defects normalized by patient x-ray exposure maximized at a kV-pair of approximately 60/140-kV and when approximately one third of the total exposure was allocated to the HE image. We used the optimized technique to image a second phantom that contained lung-parenchyma-mimicking PMMA clutter, rib-mimicking aluminum slats and an insert that simulated ventilation defects with thicknesses ranging from 0.5 cm to 2 cm and diameters ranging from 1 cm to 2 cm. From the resulting images we computed the area under the receiver operating characteristic curve (AUC) of the non-prewhitening model observer with an eye filter and internal noise. For a xenon concentration of 75%, good AUCs (i.e. 0.8-0.9) to excellent AUCs (i.e. >0.9) were obtained when the defect diameter is greater than 1.3 cm and defect thickness is 1 cm. When the xenon concentration was reduced to 50%, the AUC was ∼0.9 for defects 1.2 cm in diameter and ∼1.5 cm in thickness. Two-dimensional XeDE radiography may therefore enable detection of functional abnormalities associated with early-stage COPD, for which xenon ventilation defects can occupy up to 20% of the lung volume, and should be further developed as a low-cost alternative to MRI-based approaches and a low-dose alternative to CT-based approaches.

Deep radiomics-based survival prediction in patients with chronic obstructive pulmonary disease

Heterogeneous clinical manifestations and progression of chronic obstructive pulmonary disease (COPD) affect patient health risk assessment, stratification, and management. Pulmonary function tests are used to diagnose and classify the severity of COPD, but they cannot fully represent the type or range of pathophysiologic abnormalities of the disease. To evaluate whether deep radiomics from chest computed tomography (CT) images can predict mortality in patients with COPD, we designed a convolutional neural network (CNN) model for extracting representative features from CT images and then performed random survival forest to predict survival in COPD patients. We trained CNN-based binary classifier based on six-minute walk distance results (> 440 m or not) and extracted high-throughput image features (i.e., deep radiomics) directly from the last fully connected layer of it. The various sizes of fully connected layers and combinations of deep features were experimented using a discovery cohort with 344 patients from the Korean Obstructive Lung Disease cohort and an external validation cohort with 102 patients from Penang General Hospital in Malaysia. In the integrative analysis of discovery and external validation cohorts, with combining 256 deep features from the coronal slice of the vertebral body and two sagittal slices of the left/right lung, deep radiomics for survival prediction achieved concordance indices of 0.8008 (95% CI, 0.7642–0.8373) and 0.7156 (95% CI, 0.7024–0.7288), respectively. Deep radiomics from CT images could be used to predict mortality in COPD patients.

Echo Time-Dependent Observed Lung T1 in Patients With Chronic Obstructive Pulmonary Disease in Correlation With Quantitative Imaging and Clinical Indices

BACKGROUND

There is a clinical need for imaging-derived biomarkers for the management of chronic obstructive pulmonary disease (COPD). Observed pulmonary T₁ (T₁ (TE)) depends on the echo-time (TE) and reflects regional pulmonary function.

PURPOSE

To investigate the potential diagnostic value of T₁ (TE) for the assessment of lung disease in COPD patients by determining correlations with clinical parameters and quantitative CT.

STUDY TYPE

Prospective non-randomized diagnostic study.

POPULATION

Thirty COPD patients (67.7 ± 6.6 years). Data from a previous study (15 healthy volunteers [26.2 ± 3.9 years) were used as reference.

FIELD STRENGTH/SEQUENCE

Study participants were examined at 1.5 T using dynamic contrast-enhanced three-dimensional gradient echo keyhole perfusion sequence and a multi-echo inversion recovery two-dimensional UTE (ultra-short TE) sequence for T₁ (TE) mapping at TE_1-5  = 70 μsec, 500 μsec, 1200 μsec, 1650 μsec, and 2300 μsec.

ASSESSMENT

Perfusion images were scored by three radiologists. T₁ (TE) was automatically quantified. Computed tomography (CT) images were quantified in software (qCT). Clinical parameters including pulmonary function testing were also acquired.

STATISTICAL TESTS

Spearman rank correlation coefficients (ρ) were calculated between T₁ (TE) and perfusion scores, clinical parameters and qCT. A P-value <0.05 was considered statistically significant.

RESULTS

Median values were T₁ (TE_1-5 ) = 644 ± 78 msec, 835 ± 92 msec, 835 ± 87 msec, 831 ± 131 msec, 893 ± 220 msec, all significantly shorter than previously reported in healthy subjects. A significant increase of T₁ was observed from TE₁ to TE₂ , with no changes from TE₂ to TE₃ (P = 0.48), TE₃ to TE₄ (P = 0.94) or TE₄ to TE₅ (P = 0.02) which demonstrates an increase at shorter TEs than in healthy subjects. Moderate to strong Spearman's correlations between T₁ and parameters including the predicted diffusing capacity for carbon monoxide (DLCO, ρ < 0.70), mean lung density (MLD, ρ < 0.72) and the perfusion score (ρ > -0.69) were found. Overall, correlations were strongest at TE₂ , weaker at TE₁ and rarely significant at TE₄ -TE₅ .

DATA CONCLUSION

In COPD patients, the increase of T₁ (TE) with TE occurred at shorter TEs than previously found in healthy subjects. Together with the lack of correlation between T₁ and clinical parameters of disease at longer TEs, this suggests that T₁ (TE) quantification in COPD patients requires shorter TEs. The TE-dependence of correlations implies that T₁ (TE) mapping might be developed further to provide diagnostic information beyond T₁ at a single TE.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Pulmonary Functional Imaging: Part 2-State-of-the-Art Clinical Applications and Opportunities for Improved Patient Care

Pulmonary functional imaging may be defined as the regional quantification of lung function by using primarily CT, MRI, and nuclear medicine techniques. The distribution of pulmonary physiologic parameters, including ventilation, perfusion, gas exchange, and biomechanics, can be noninvasively mapped and measured throughout the lungs. This information is not accessible by using conventional pulmonary function tests, which measure total lung function without viewing the regional distribution. The latter is important because of the heterogeneous distribution of virtually all lung disorders. Moreover, techniques such as hyperpolarized xenon 129 and helium 3 MRI can probe lung physiologic structure and microstructure at the level of the alveolar-air and alveolar-red blood cell interface, which is well beyond the spatial resolution of other clinical methods. The opportunities, challenges, and current stage of clinical deployment of pulmonary functional imaging are reviewed, including applications to chronic obstructive pulmonary disease, asthma, interstitial lung disease, pulmonary embolism, and pulmonary hypertension. Among the challenges to the deployment of pulmonary functional imaging in routine clinical practice are the need for further validation, establishment of normal values, standardization of imaging acquisition and analysis, and evidence of patient outcomes benefit. When these challenges are addressed, it is anticipated that pulmonary functional imaging will have an expanding role in the evaluation and management of patients with lung disease.

Radiomics approach for survival prediction in chronic obstructive pulmonary disease

OBJECTIVES

To apply radiomics analysis for overall survival prediction in chronic obstructive pulmonary disease (COPD), and evaluate the performance of the radiomics signature (RS).

METHODS

This study included 344 patients from the Korean Obstructive Lung Disease (KOLD) cohort. External validation was performed on a cohort of 112 patients. In total, 525 chest CT-based radiomics features were semi-automatically extracted. The five most useful features for survival prediction were selected by least absolute shrinkage and selection operation (LASSO) Cox regression analysis and used to generate a RS. The ability of the RS for classifying COPD patients into high or low mortality risk groups was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis.

RESULTS

The five features remaining after the LASSO analysis were %LAA_-950, AWT_Pi10_6^th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA_18mm. The RS demonstrated a C-index of 0.774 in the discovery group and 0.805 in the validation group. Patients with a RS greater than 1.053 were classified into the high-risk group and demonstrated worse overall survival than those in the low-risk group in both the discovery (log-rank test, < 0.001; hazard ratio [HR], 5.265) and validation groups (log-rank test, < 0.001; HR, 5.223). For both groups, RS was significantly associated with overall survival after adjustments for patient age and body mass index.

CONCLUSIONS

A radiomics approach for survival prediction and risk stratification in COPD patients is feasible, and the constructed radiomics model demonstrated acceptable performance. The RS derived from chest CT data of COPD patients was able to effectively identify those at increased risk of mortality.

KEY POINTS

• A total of 525 chest CT-based radiomics features were extracted and the five radiomics features of %LAA_-950, AWT_Pi10_6^th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA_18mm were selected to generate a radiomics model. • A radiomics model for predicting survival of COPD patients demonstrated reliable performance with a C-index of 0.774 in the discovery group and 0.805 in the validation group. • Radiomics approach was able to effectively identify COPD patients with an increased risk of mortality, and patients assigned to the high-risk group demonstrated worse overall survival in both the discovery and validation groups.

The emerging role of quantitative imaging in asthma

Quantitative imaging of the lung has proved to be a valuable tool that has improved our understanding of asthma. CT, MRI, and positron emission tomography have all been utilized in asthma with each modality having its own distinct advantages and disadvantages. Research has now demonstrated that quantitative imaging plays a valuable role in characterizing asthma phenotypes and endotypes, as well as potentially predicting future asthma morbidity. Nonetheless, future research is needed in order to minimize radiation exposure, standardize reporting, and further delineate how imaging can predict longitudinal outcomes. With future work, quantitative imaging may make its way into the clinical care of asthma and change our practice.

What's New on Quantitative CT Analysis as a Tool to Predict Growth in Persistent Pulmonary Subsolid Nodules? A Literature Review

Pulmonary subsolid nodules (SSNs) are observed not infrequently on thin-section chest computed tomography (CT) images. SSNs persisting after a follow-up period of three to six months have a high likelihood of being pre-malignant or malignant lesions. Malignant SSNs usually represent the histologic spectrum of pulmonary adenocarcinomas, and pulmonary adenocarcinomas presenting as SSNs exhibit quite heterogeneous behavior. In fact, while most lesions show an indolent course and may grow very slowly or remain stable for many years, others may exhibit significant growth in a relatively short time. Therefore, it is not yet clear which persistent SSNs should be surgically removed and for how many years stable SSNs should be monitored. In order to solve these two open issues, the use of quantitative analysis has been proposed to define the "tailored" management of persistent SSNs. The main purpose of this review was to summarize recent results about quantitative CT analysis as a diagnostic tool for predicting the behavior of persistent SSNs. Thus, a literature search was conducted in PubMed/MEDLINE, Scopus, and Web of Science databases to find original articles published from January 2014 to October 2019. The results of the selected studies are presented and compared in a narrative way.

Imaging Advances in Chronic Obstructive Pulmonary Disease. Insights from the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) Study

The Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study, which began in 2007, is an ongoing multicenter observational cohort study of more than 10,000 current and former smokers. The study is aimed at understanding the etiology, progression, and heterogeneity of chronic obstructive pulmonary disease (COPD). In addition to genetic analysis, the participants have been extensively characterized by clinical questionnaires, spirometry, volumetric inspiratory and expiratory computed tomography, and longitudinal follow-up, including follow-up computed tomography at 5 years after enrollment. The purpose of this state-of-the-art review is to summarize the major advances in our understanding of COPD resulting from the imaging findings in the COPDGene study. Imaging features that are associated with adverse clinical outcomes include early interstitial lung abnormalities, visual presence and pattern of emphysema, the ratio of pulmonary artery to ascending aortic diameter, quantitative evaluation of emphysema, airway wall thickness, and expiratory gas trapping. COPD is characterized by the early involvement of the small conducting airways, and the addition of expiratory scans has enabled measurement of small airway disease. Computational advances have enabled indirect measurement of nonemphysematous gas trapping. These metrics have provided insights into the pathogenesis and prognosis of COPD and have aided early identification of disease. Important quantifiable extrapulmonary findings include coronary artery calcification, cardiac morphology, intrathoracic and extrathoracic fat, and osteoporosis. Current active research includes identification of novel quantitative measures for emphysema and airway disease, evaluation of dose reduction techniques, and use of deep learning for phenotyping COPD.

Quantitative Imaging Markers of Lung Function in a Smoking Population Distinguish COPD Subgroups with Differential Lung Cancer Risk

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition with respect to onset, progression, and response to therapy. Incorporating clinical- and imaging-based features to refine COPD phenotypes provides valuable information beyond that obtained from traditional clinical evaluations. We characterized the spectrum of COPD-related phenotypes in a sample of former and current smokers and evaluated how these subgroups differ with respect to sociodemographic characteristics, COPD-related comorbidities, and subsequent risk of lung cancer.

METHODS

White (N = 659) and African American (N = 520) male and female participants without lung cancer (controls) in the INHALE study who completed a chest CT scan, interview, and spirometry test were used to define distinct COPD-related subgroups based on hierarchical clustering. Seven variables were used to define clusters: pack years, quit years, FEV₁/FVC, % predicted FEV₁, and from quantitative CT (qCT) imaging, % emphysema, % air trapping, and mean lung density ratio. Cluster definitions were then applied to INHALE lung cancer cases (N = 576) to evaluate lung cancer risk.

RESULTS

Five clusters were identified that differed significantly with respect to sociodemographic (e.g., race, age) and clinical (e.g., BMI, limitations due to breathing difficulties) characteristics. Increased risk of lung cancer was associated with increasingly detrimental lung function clusters (when ordered from most detrimental to least detrimental).

CONCLUSIONS

Measures of lung function vary considerably among smokers and are not fully explained by smoking intensity.

IMPACT

Combining clinical (spirometry) and radiologic (qCT) measures of COPD defines a spectrum of lung disease that predicts lung cancer risk differentially among patient clusters.

Quantitative assessment of pulmonary vascular alterations in chronic obstructive lung disease: Associations with pulmonary function test and survival in the KOLD cohort

PURPOSE

Despite the high prevalence of pulmonary vascular alterations and their substantial impact on chronic obstructive pulmonary disease (COPD), tools for the direct in vivo assessment of pulmonary vascular alterations remain limited. Thus, the purpose of this study was to automatically extract pulmonary vessels from volumetric chest CT and evaluate the associations between the derived quantitative pulmonary vessel features and clinical parameters, including survival, in COPD patients.

METHODS

This study included 344 adult COPD patients. Pulmonary vessels were automatically extracted from volumetric chest CT data. Quantitative pulmonary vessel features were obtained from various lung surface areas (LSAs), which are theoretical surface areas drawn at different depths from the pleural borders. The total number of vessels (N_total) and number of vessels with vessel area (VA) less than 5 mm² (N_<5mm) were counted as both robust values and as values per 10 cm² of LSA (N_total/LSA; N_<5mm/LSA). The average VA (VA_mean) and percentage of measured VA in the corresponding LSA (%VA) were measured. Associations between quantitative pulmonary vessel features and clinical parameters, including survival and the pulmonary function test (PFT), were evaluated.

RESULTS

The pulmonary vessels were automatically extracted with 100% technical success. Cox regression analysis showed N_total/LSA, N_<5mm/LSA, VA_mean, and %VA to be significant predictors of survival (hazard ratio (HR), 0.80, 0.75, 0.70, 0.49, respectively). Patients classified into high-risk groups by %VA_18mm (cut-off = 3.258), chosen because it demonstrated the strongest statistical influence on survival in a univariate Cox analysis, were associated with worse overall survival before (HR, 4.83; p < 0.001) and after adjustment for patient age and BMI (HR, 2.18; p = 0.014). Of the quantitative pulmonary vessel features, N_total/LSA, N_<5mm/LSA, and %VA were correlated with FEV₁, FEV₁/FVC, and DLCO in all LSAs. The strongest correlation with PFTs was noted at LSA_9mm for both N_total (FEV₁, r = 0.33; FEV₁/FVC, r = 0.51) and N_<5mm (FEV₁, r = 0.35; FEV₁/FVC, r = 0.52). For %VA, the association was most evident at LSA_18mm (FEV₁, r = 0.27; FEV₁/FVC, r = 0.47). Significant moderate to strong correlations were consistently observed between the extent of emphysema and quantitative pulmonary vessel features (r = 0.44-0.66; all p < 0.001).

CONCLUSIONS

The automated extraction of pulmonary vessels and their quantitative assessment are technically feasible. Various quantitative pulmonary vessel features demonstrated significant relationships with survival and PFT in COPD patients. Of the various quantitative features, the percentage of total VA measured at 18 mm depth from the pleural surface (%VA_18mm) and the number of small vessels counted per 10 cm² of LSA at 9 mm depth from the pleural surface (N_<5mm/LSA_9mm) had the strongest predictability for the clinical parameters.