Aerodynamic Simulation of Small Airway Resistance: A New Imaging Biomarker for Chronic Obstructive Pulmonary Disease.

Purpose: To develop a novel method for calculating small airway resistance using computational fluid dynamics (CFD) based on CT data and evaluate its value to identify COPD. Patients and Methods: 24 subjects who underwent chest CT scans and pulmonary function tests between August 2020 and December 2020 were enrolled retrospectively. Subjects were divided into three groups: normal (10), high-risk (6), and COPD (8). The airway from the trachea down to the sixth generation of bronchioles was reconstructed by a 3D slicer. The small airway resistance (RSA) and RSA as a percentage of total airway resistance (RSA%) were calculated by CFD combined with airway resistance and FEV1 measured by pulmonary function test. A correlation analysis was conducted between RSA and pulmonary function parameters, including FEV1/FVC, FEV1% predicted, MEF50% predicted, MEF75% predicted and MMEF75/25% predicted. Results: The RSA and RSA% were significantly different among the three groups (p<0.05) and related to FEV1/FVC (r = -0.70, p < 0.001; r = -0.67, p < 0.001), FEV1% predicted (r = -0.60, p = 0.002; r = -0.57, p = 0.004), MEF50% predicted (r = -0.64, p = 0.001; r = -0.64, p = 0.001), MEF75% predicted (r = -0.71, p < 0.001; r = -0.60, p = 0.002) and MMEF 75/25% predicted (r = -0.64, p = 0.001; r = -0.64, p = 0.001). Conclusion: Airway CFD is a valuable method for estimating the small airway resistance, where the derived RSA will aid in the early diagnosis of COPD.

Doppler-derived pulmonary pulse transit time measurements in chronic obstructive pulmonary disease: Reproducibility and cardiopulmonary function.

INTRODUCTION: Doppler-derived pulmonary pulse transit time (pPTT) is an auspicious hemodynamic marker in chronic pulmonary diseases. The aim is to compare four distinct pPTT measurements and its relation to right cardiac and pulmonary function. METHODS: Prospectively, 25 chronic obstructive pulmonary disease (COPD) patients (four patients excluded) and 32 healthy subjects underwent repeated distinct pPTT measurements, standard echocardiography, and pulmonary function testing on the same day. pPTT was defined as the interval from the R or Q-wave in the electrocardiogram to the corresponding pulse wave Doppler peak late systolic (S) 2 or diastolic (D) pulmonary vein flow velocity (pPTT R-S, Q-S, R-D, Q-D). Reproducibility was assessed using Bland-Altman analysis, coefficient of variation (COV), intraclass correlation coefficient (ICC), and power calculations. Associations with right ventricular RV tissue and pulse wave Doppler velocities (RV E', RV S', RV A', RV E, RV A, RV E/E', RV E/A), TAPSE, right ventricular fractional area change, left ventricular systolic and diastolic function (LV ejection fraction, E, A, E/A, E/E', septal E', lateral E'), LA diameters, as well as forced expiratory volume in 1 s, forced vital capacity (FVC) predicted (%), and in liters were analyzed. RESULTS: There was no significant difference and no bias between pPTT measures (p range: .1-.9). COV was in COPD 1.2%-2.3%, in healthy subjects 1.0%-3.1%. ICC ranged from .92 (COPD) to .96 (healthy subjects). In COPD significant correlations were found for pPTT R-S, Q-S and R-D with RV E`, (all > ρ: .49, < p = .0364), pPTT R-S, Q-S with RV E/E` (both > ρ: .49, < p = .0291), pPTT Q-S with RV S´ (ρ: .58, p = .0134), RV A (ρ: .59, p = .0339) and heart rate > ρ: -.39, < p = .0297). pPTT R-S, R-D showed significant correlations with FVC predicted (%) (ρ: .48 p = .0224) and FVC (l) (ρ:.47 p = .0347). CONCLUSIONS: All pPTT measures exhibited high reproducibility. In COPD patients pPTT measures correlate with diastolic right ventricular function. Defining Q as starting point seems clinically advantageous considering electromechanical desynchrony in patients with conduction disorders.

Which is a real valuable screening tool for lung cancer and measure thoracic diseases, chest radiography or low-dose computed tomography?: A review on the current status of Japan and other countries.

Chest radiography (CR) has been used as a screening tool for lung cancer and the use of low-dose computed tomography (LDCT) is not recommended in Japan. We need to reconsider whether CR really contributes to the early detection of lung cancer. In addition, we have not well discussed about other major thoracic disease detection by CR and LDCT compared with lung cancer despite of its high frequency. We review the usefulness of CR and LDCT as veridical screening tools for lung cancer and other thoracic diseases. In the case of lung cancer, many studies showed that LDCT has capability of early detection and improving outcomes compared with CR. Recent large randomized trial also supports former results. In the case of chronic obstructive pulmonary disease (COPD), LDCT contributes to early detection and leads to the implementation of smoking cessation treatments. In the case of pulmonary infections, LDCT can reveal tiny inflammatory changes that are not observed on CR, though many of these cases improve spontaneously. Therefore, LDCT screening for pulmonary infections may be less useful. CR screening is more suitable for the detection of pulmonary infections. In the case of cardiovascular disease (CVD), CR may be a better screening tool for detecting cardiomegaly, whereas LDCT may be a more useful tool for detecting vascular changes. Therefore, the current status of thoracic disease screening is that LDCT may be a better screening tool for detecting lung cancer, COPD, and vascular changes. CR may be a suitable screening tool for pulmonary infections and cardiomegaly.

Who is at risk of lung nodules on low-dose CT in a Western country? A population-based approach.

BACKGROUND: This population-based study aimed to identify the risk factors for lung nodules in a Western European general population. METHODS: We quantified the presence or absence of lung nodules among 12 055 participants of the Dutch population-based ImaLife (Imaging in Lifelines) study (age ≥45 years) who underwent low-dose chest computed tomography. Outcomes included the presence of 1) at least one solid lung nodule (volume ≥30 mm3) and 2) a clinically relevant lung nodule (volume ≥100 mm3). Fully adjusted multivariable logistic regression models were applied overall and stratified by smoking status to identify independent risk factors for the presence of nodules. RESULTS: Among the 12 055 participants (44.1% male; median age 60 years; 39.9% never-smokers; 98.7% White), we found lung nodules in 41.8% (5045 out of 12 055) and clinically relevant nodules in 11.4% (1377 out of 12 055); the corresponding figures among never-smokers were 38.8% and 9.5%, respectively. Factors independently associated with increased odds of having any lung nodule included male sex, older age, low educational level, former smoking, asbestos exposure and COPD. Among never-smokers, a family history of lung cancer increased the odds of both lung nodules and clinically relevant nodules. Among former and current smokers, low educational level was positively associated with lung nodules, whereas being overweight was negatively associated. Among current smokers, asbestos exposure and low physical activity were associated with clinically relevant nodules. CONCLUSIONS: The study provides a large-scale evaluation of lung nodules and associated risk factors in a Western European general population: lung nodules and clinically relevant nodules were prevalent, and never-smokers with a family history of lung cancer were a non-negligible group.

Chest CT-based automated vertebral fracture assessment using artificial intelligence and morphologic features.

BACKGROUND: Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD. PURPOSE: We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts. METHODS: A chest CT-based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant et al. The computational method was accomplished in the following steps: (1) computation of a voxel-level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi-parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior-posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi-site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n = 3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n = 80) and validation (n = 40) datasets for the DL-based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n = 236) was evaluated. RESULTS: The vertebral segmentation module achieved a Dice score of .984 as compared to manual outlining results as reference (n = 100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being .980 and .989, respectively. The vertebral labelling module achieved 100% accuracy (n = 100). For low dose CT, the segmentation module produced image-level minimum and maximum Dice scores of .995 and .999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n = 236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants. CONCLUSIONS: Our CT-based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population-based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population-based studies has increased to reduce cumulative radiation exposure.

Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable condition, provided it is identified early and managed effectively. This study aims to develop an advanced COPD diagnostic model by integrating deep learning and radiomics features. METHODS: We utilized a dataset comprising CT images from 2,983 participants, of which 2,317 participants also provided epidemiological data through questionnaires. Deep learning features were extracted using a Variational Autoencoder, and radiomics features were obtained using the PyRadiomics package. Multi-Layer Perceptrons were used to construct models based on deep learning and radiomics features independently, as well as a fusion model integrating both. Subsequently, epidemiological questionnaire data were incorporated to establish a more comprehensive model. The diagnostic performance of standalone models, the fusion model and the comprehensive model was evaluated and compared using metrics including accuracy, precision, recall, F1-score, Brier score, receiver operating characteristic curves, and area under the curve (AUC). RESULTS: The fusion model exhibited outstanding performance with an AUC of 0.952, surpassing the standalone models based solely on deep learning features (AUC = 0.844) or radiomics features (AUC = 0.944). Notably, the comprehensive model, incorporating deep learning features, radiomics features, and questionnaire variables demonstrated the highest diagnostic performance among all models, yielding an AUC of 0.971. CONCLUSION: We developed and implemented a data fusion strategy to construct a state-of-the-art COPD diagnostic model integrating deep learning features, radiomics features, and questionnaire variables. Our data fusion strategy proved effective, and the model can be easily deployed in clinical settings. TRIAL REGISTRATION: Not applicable. This study is NOT a clinical trial, it does not report the results of a health care intervention on human participants.

Longitudinal assessment of interstitial lung abnormalities on CT in patients with COPD using artificial intelligence-based segmentation: a prospective observational study.

BACKGROUND: Interstitial lung abnormalities (ILAs) on CT may affect the clinical outcomes in patients with chronic obstructive pulmonary disease (COPD), but their quantification remains unestablished. This study examined whether artificial intelligence (AI)-based segmentation could be applied to identify ILAs using two COPD cohorts. METHODS: ILAs were diagnosed visually based on the Fleischner Society definition. Using an AI-based method, ground-glass opacities, reticulations, and honeycombing were segmented, and their volumes were summed to obtain the percentage ratio of interstitial lung disease-associated volume to total lung volume (ILDvol%). The optimal ILDvol% threshold for ILA detection was determined in cross-sectional data of the discovery and validation cohorts. The 5-year longitudinal changes in ILDvol% were calculated in discovery cohort patients who underwent baseline and follow-up CT scans. RESULTS: ILAs were found in 32 (14%) and 15 (10%) patients with COPD in the discovery (n = 234) and validation (n = 153) cohorts, respectively. ILDvol% was higher in patients with ILAs than in those without ILA in both cohorts. The optimal ILDvol% threshold in the discovery cohort was 1.203%, and good sensitivity and specificity (93.3% and 76.3%) were confirmed in the validation cohort. 124 patients took follow-up CT scan during 5 ± 1 years. 8 out of 124 patients (7%) developed ILAs. In a multivariable model, an increase in ILDvol% was associated with ILA development after adjusting for age, sex, BMI, and smoking exposure. CONCLUSION: AI-based CT quantification of ILDvol% may be a reproducible method for identifying and monitoring ILAs in patients with COPD.

Pulmonary blood volume measured by 82Rb-PET in patients with chronic obstructive pulmonary disease: a retrospective cohort study.

In patients with chronic obstructive pulmonary disease (COPD), pulmonary vascular dysfunction and destruction are observable before the onset of detectable emphysema, but it is unknown whether this is associated with central hypovolemia. We investigated if patients with COPD have reduced pulmonary blood volume (PBV) evaluated by 82Rb-positron emission tomography (PET) at rest and during adenosine-induced hyperemia. This single-center retrospective cohort study assessed 6,301 82Rb-PET myocardial perfusion imaging (MPI) examinations performed over a 6-yr period. We compared 77 patients with COPD with 44 healthy kidney donors (controls). Cardiac output ([Formula: see text]) and mean 82Rb bolus transit time (MBTT) were used to calculate PBV. [Formula: see text] was similar at rest (COPD: 3,649 ± 120 mL vs. control: 3,891 ± 160 mL, P = 0.368) but lower in patients with COPD compared with controls during adenosine infusion (COPD: 5,432 ± 124 mL vs. control: 6,185 ± 161 mL, P < 0.050). MBTT was shorter in patients with COPD compared with controls at rest (COPD: 8.7 ± 0.28 s vs. control: 11.4 ± 0.37 s, P < 0.001) and during adenosine infusion (COPD: 9.2 ± 0.28 s vs. control: 10.2 ± 0.37 s, P < 0.014). PBV was lower in patients with COPD, even after adjustment for body surface area, sex, and age at rest [COPD: 530 (29) mL vs. 708 (38) mL, P < 0.001] and during adenosine infusion [COPD: 826 (29) mL vs. 1,044 (38) mL, P < 0.001]. In conclusion, patients with COPD show evidence of central hypovolemia, but it remains to be determined whether this has any diagnostic or prognostic impact.NEW & NOTEWORTHY The present study demonstrated that patients with chronic obstructive pulmonary disease (COPD) exhibit central hypovolemia compared with healthy controls. Pulmonary blood volume may thus be a relevant physiological and/or clinical outcome measure in future COPD studies.

Pulmonary volumes and signs of chronic airflow limitation in quantitative computed tomography.

BACKGROUND: Computed tomography (CT) offers pulmonary volumetric quantification but is not commonly used in healthy individuals due to radiation concerns. Chronic airflow limitation (CAL) is one of the diagnostic criteria for chronic obstructive pulmonary disease (COPD), where early diagnosis is important. Our aim was to present reference values for chest CT volumetric and radiodensity measurements and explore their potential in detecting early signs of CAL. METHODS: From the population-based Swedish CArdioPulmonarybioImage Study (SCAPIS), 294 participants aged 50-64, were categorized into non-CAL (n = 258) and CAL (n = 36) groups based on spirometry. From inspiratory and expiratory CT images we compared lung volumes, mean lung density (MLD), percentage of low attenuation volume (LAV%) and LAV cluster volume between groups, and against reference values from static pulmonary function test (PFT). RESULTS: The CAL group exhibited larger lung volumes, higher LAV%, increased LAV cluster volume and lower MLD compared to the non-CAL group. Lung volumes significantly deviated from PFT values. Expiratory measurements yielded more reliable results for identifying CAL compared to inspiratory. Using a cut-off value of 0.6 for expiratory LAV%, we achieved sensitivity, specificity and positive/negative predictive values of 72%, 85% and 40%/96%, respectively. CONCLUSION: We present volumetric reference values from inspiratory and expiratory chest CT images for a middle-aged healthy cohort. These results are not directly comparable to those from PFTs. Measures of MLD and LAV can be valuable in the evaluation of suspected CAL. Further validation and refinement are necessary to demonstrate its potential as a decision support tool for early detection of COPD.

Right main pulmonary artery distensibility on dynamic ventilation CT and its association with respiratory function.

BACKGROUND: Heartbeat-based cross-sectional area (CSA) changes in the right main pulmonary artery (MPA), which reflects its distensibility associated with pulmonary hypertension, can be measured using dynamic ventilation computed tomography (DVCT) in patients with and without chronic obstructive pulmonary disease (COPD) during respiratory dynamics. We investigated the relationship between MPA distensibility (MPAD) and respiratory function and how heartbeat-based CSA is related to spirometry, mean lung density (MLD), and patient characteristics. METHODS: We retrospectively analyzed DVCT performed preoperatively in 37 patients (20 female and 17 males) with lung cancer aged 70.6 ± 7.9 years (mean ± standard deviation), 18 with COPD and 19 without. MPA-CSA was separated into respiratory and heartbeat waves by discrete Fourier transformation. For the cardiac pulse-derived waves, CSA change (CSAC) and CSA change ratio (CSACR) were calculated separately during inhalation and exhalation. Spearman rank correlation was computed. RESULT: In the group without COPD as well as all cases, CSACR exhalation was inversely correlated with percent residual lung volume (%RV) and RV/total lung capacity (r = -0.68, p = 0.003 and r = -0.58, p = 0.014). In contrast, in the group with COPD, CSAC inhalation was correlated with MLDmax and MLD change rate (MLDmax/MLDmin) (r = 0.54, p = 0.020 and r = 0.64, p = 0.004) as well as CSAC exhalation and CSACR exhalation. CONCLUSION: In patients with insufficient exhalation, right MPAD during exhalation was decreased. Also, in COPD patients with insufficient exhalation, right MPAD was reduced during inhalation as well as exhalation, which implied that exhalation impairment is a contributing factor to pulmonary hypertension complicated with COPD. RELEVANCE STATEMENT: Assessment of MPAD in different respiratory phases on DVCT has the potential to be utilized as a non-invasive assessment for pulmonary hypertension due to lung disease and/or hypoxia and elucidation of its pathogenesis. KEY POINTS: • There are no previous studies analyzing all respiratory phases of right main pulmonary artery distensibility (MPAD). • Patients with exhalation impairment decreased their right MPAD. • Analysis of MPAD on dynamic ventilation computed tomography contributes to understanding the pathogenesis of pulmonary hypertension due to lung disease and/or hypoxia in patients with expiratory impairment.

Association between the Static and Dynamic Lung Function and CT-Derived Thoracic Skeletal Muscle Measurements-A Retrospective Analysis of a 12-Month Observational Follow-Up Pilot Study.

BACKGROUND: Patients with chronic obstructive pulmonary disease (COPD) with low skeletal muscle mass and severe airway obstruction have higher mortality risks. However, the relationship between dynamic/static lung function (LF) and thoracic skeletal muscle measurements (SMM) remains unclear. This study explored patient characteristics (weight, BMI, exacerbations, dynamic/static LF, sex differences in LF and SMM, and the link between LF and SMM changes. METHODS: A retrospective analysis of a 12-month prospective follow-up study patients with stable COPD undergoing standardized treatment, covering mild to severe stages, was conducted. The baseline and follow-up assessments included computed tomography and body plethysmography. RESULTS: This study included 35 patients (17 females and 18 males). This study revealed that females had more stable LF but tended to have greater declines in SMM areas and indices than males (-5.4% vs. -1.9%, respectively), despite the fact that females were younger and had higher LF and less exacerbation than males. A multivariate linear regression showed a negative association between the inspiratory capacity/total lung capacity ratio (IC/TLC) and muscle fat area. CONCLUSIONS: The findings suggest distinct LF and BC progression patterns between male and female patients with COPD. A low IC/TLC ratio may predict increased muscle fat. Further studies are necessary to understand these relationships better.

Preserved Ratio Impaired Spirometry (PRISm): A Global Epidemiological Overview, Radiographic Characteristics, Comorbid Associations, and Differentiation from Chronic Obstructive Pulmonary Disease.

Preserved Ratio Impaired Spirometry (PRISm) manifests notable epidemiological disparities across the globe, with its prevalence and influential factors showcasing pronounced diversities among various geographical territories and demographics. The prevalence of PRISm fluctuates considerably among regions such as Latin America, the United States, and Asian nations, potentially correlating with a myriad of determinants, including socioeconomic status, environmental factors, and lifestyle modalities. Concurrently, the link between PRISm and health risks and other disorders, especially its distinction and interrelation with chronic obstructive pulmonary disease (COPD), has become a pivotal subject of scientific enquiry. Radiographic anomalies, such as perturbations in the pulmonary parenchyma and structural alterations, are posited as salient characteristics of PRISm. Furthermore, PRISm unveils intricate associations with multiple comorbidities, inclusive of hypertension and type 2 diabetes, thereby amplifying the intricacy in comprehending and managing this condition. In this review, we aim to holistically elucidate the epidemiological peculiarities of PRISm, its potential aetiological contributors, its nexus with COPD, and its association with radiographic aberrations and other comorbidities. An integrative understanding of these dimensions will provide pivotal insights for the formulation of more precise and personalised preventative and therapeutic strategies.

Advances in COPD imaging using CT and MRI: linkage with lung physiology and clinical outcomes.

Recent years have witnessed major advances in lung imaging in patients with COPD. These include significant refinements in images obtained by computed tomography (CT) scans together with the introduction of new techniques and software that aim for obtaining the best image whilst using the lowest possible radiation dose. Magnetic resonance imaging (MRI) has also emerged as a useful radiation-free tool in assessing structural and more importantly functional derangements in patients with well-established COPD and smokers without COPD, even before the existence of overt changes in resting physiological lung function tests. Together, CT and MRI now allow objective quantification and assessment of structural changes within the airways, lung parenchyma and pulmonary vessels. Furthermore, CT and MRI can now provide objective assessments of regional lung ventilation and perfusion, and multinuclear MRI provides further insight into gas exchange; this can help in structured decisions regarding treatment plans. These advances in chest imaging techniques have brought new insights into our understanding of disease pathophysiology and characterising different disease phenotypes. The present review discusses, in detail, the advances in lung imaging in patients with COPD and how structural and functional imaging are linked with common resting physiological tests and important clinical outcomes.

Visualizing pursed lips breathing of patients with chronic obstructive pulmonary disease through evaluation of global and regional ventilation using electrical impedance tomography.

Objective. This study aims to explore the possibility of using electrical impedance tomography (EIT) to assess pursed lips breathing (PLB) performance of patients with chronic obstructive pulmonary disease (COPD).Methods. 32 patients with COPD were assigned equally to either the conventional group or the EIT guided group. All patients were taught to perform PLB by a physiotherapist without EIT in the conventional group or with EIT in the EIT guided group for 10 min. The ventilation of all patients in the final test were continuously monitored using EIT and the PLB performances were rated by another physiotherapist before and after reviewing EIT. The global and regional ventilation between two groups as well as between quite breathing (QB) and PLB were compared and rating scores with and without EIT were also compared.Results.For global ventilation, the inspiratory depth and the ratio of expiratory-to-inspiratory time during PLB was significantly larger than those during QB for both group (P< 0.001). The inspiratory depth and the ratio of expiratory-to-inspiratory time during PLB in the EIT guided group were higher compared to those in the conventional group (P< 0.001), as well as expiratory flow expiratory uniformity and respiratory stability were better (P< 0.001). For regional ventilation, center of ventilation significantly decreased during PLB (P< 0.05). The expiratory time constant during PLB in the EIT guided group was greater than that in the conventional group (P< 0.001). Additionally, Bland-Altman plots analysis suggested a high concordance between subjective rating and rating with the help of EIT, but the score rated after EIT observation significantly lower than that rated subjectively in both groups (score drop of -2.68 ± 1.1 in the conventional group and -1.19 ± 0.72 in the EIT guided group,P< 0.01).Conclusion.EIT could capture the details of PLB maneuver, which might be a potential tool to quantitatively evaluate PLB performance and thus assist physiotherapists to teach PLB maneuver to patients.

Local heterogeneity of normal lung parenchyma and small airways disease are associated with COPD severity and progression.

BACKGROUND: Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline. METHODS: PRM metrics of normal lung (PRMNorm) and functional SAD (PRMfSAD) were generated from CT scans collected as part of the COPDGene study (n = 8956). Volume density (V) and Euler-Poincaré Characteristic (χ) image maps, measures of the extent and coalescence of pocket formations (i.e., topologies), respectively, were determined for both PRMNorm and PRMfSAD. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV1 decline using a machine learning model. RESULTS: Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRMfSAD and PRMNorm were independently associated with the amount of emphysema. Readouts χfSAD (ß of 0.106, p < 0.001) and VfSAD (ß of 0.065, p = 0.004) were also independently associated with FEV1% predicted. The machine learning model using PRM topologies as inputs predicted FEV1 decline over five years with an AUC of 0.69. CONCLUSIONS: We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRMfSAD and PRMNorm may show promise as an early indicator of emphysema onset and COPD progression.

Traditional and Advanced Echocardiographic Evaluation in Chronic Obstructive Pulmonary Disease: The Forgotten Relation.

Chronic obstructive pulmonary disease (COPD) is a significant preventable and treatable clinical disorder defined by a persistent, typically progressive airflow obstruction. This disease has a significant negative impact on mortality and morbidity worldwide. However, the complex interaction between the heart and lungs is usually underestimated, necessitating more attention to improve clinical outcomes and prognosis. Indeed, COPD significantly impacts ventricular function, right and left chamber architecture, tricuspid valve functionality, and pulmonary blood vessels. Accordingly, more emphasis should be paid to their diagnosis since cardiac alterations may occur very early before COPD progresses and generate pulmonary hypertension (PH). Echocardiography enables a quick, noninvasive, portable, and accurate assessment of such changes. Indeed, recent advancements in imaging technology have improved the characterization of the heart chambers and made it possible to investigate the association between a few cardiac function indexes and clinical and functional aspects of COPD. This review aims to describe the intricate relation between COPD and heart changes and provide basic and advanced echocardiographic methods to detect early right ventricular and left ventricular morphologic alterations and early systolic and diastolic dysfunction. In addition, it is crucial to comprehend the clinical and prognostic significance of functional tricuspid regurgitation in COPD and PH and the currently available transcatheter therapeutic approaches for its treatment. Moreover, it is also essential to assess noninvasively PH and pulmonary resistance in patients with COPD by applying new echocardiographic parameters. In conclusion, echocardiography should be used more frequently in assessing patients with COPD because it may aid in discovering previously unrecognized heart abnormalities and selecting the most appropriate treatment to improve the patient's symptoms, quality of life, and survival.

COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images.

Chronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a significant health, economic, and social burden. Detecting the COPD stage can provide a timely warning for prompt intervention in COPD patients. However, existing methods based on inspiratory (IN) and expiratory (EX) chest CT images are not sufficiently accurate and efficient in COPD stage detection. The lung region images are autonomously segmented from IN and EX chest CT images to extract the 1 , 781 × 2 lung radiomics and 13 , 824 × 2 3D CNN features. Furthermore, a strategy for concatenating and selecting features was employed in COPD stage detection based on radiomics and 3D CNN features. Finally, we combine all the radiomics, 3D CNN features, and factor risks (age, gender, and smoking history) to detect the COPD stage based on the Auto-Metric Graph Neural Network (AMGNN). The AMGNN with radiomics and 3D CNN features achieves the best performance at 89.7 % of accuracy, 90.9 % of precision, 89.5 % of F1-score, and 95.8 % of AUC compared to six classic machine learning (ML) classifiers. Our proposed approach demonstrates high accuracy in detecting the stage of COPD using both IN and EX chest CT images. This method can potentially establish an efficient diagnostic tool for patients with COPD. Additionally, we have identified radiomics and 3D CNN as more appropriate biomarkers than Parametric Response Mapping (PRM). Moreover, our findings indicate that expiration yields better results than inspiration in detecting the stage of COPD.

CT whole lung radiomic nomogram: a potential biomarker for lung function evaluation and identification of COPD.

BACKGROUND: Computed tomography (CT) plays a great role in characterizing and quantifying changes in lung structure and function of chronic obstructive pulmonary disease (COPD). This study aimed to explore the performance of CT-based whole lung radiomic in discriminating COPD patients and non-COPD patients. METHODS: This retrospective study was performed on 2785 patients who underwent pulmonary function examination in 5 hospitals and were divided into non-COPD group and COPD group. The radiomic features of the whole lung volume were extracted. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection and radiomic signature construction. A radiomic nomogram was established by combining the radiomic score and clinical factors. Receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) were used to evaluate the predictive performance of the radiomic nomogram in the training, internal validation, and independent external validation cohorts. RESULTS: Eighteen radiomic features were collected from the whole lung volume to construct a radiomic model. The area under the curve (AUC) of the radiomic model in the training, internal, and independent external validation cohorts were 0.888 [95% confidence interval (CI) 0.869-0.906], 0.874 (95%CI 0.844-0.904) and 0.846 (95%CI 0.822-0.870), respectively. All were higher than the clinical model (AUC were 0.732, 0.714, and 0.777, respectively, P < 0.001). DCA demonstrated that the nomogram constructed by combining radiomic score, age, sex, height, and smoking status was superior to the clinical factor model. CONCLUSIONS: The intuitive nomogram constructed by CT-based whole-lung radiomic has shown good performance and high accuracy in identifying COPD in this multicenter study.