Chest CT Airway and Vascular Measurements in Females with COPD or Long-COVID.

Chest CT provides a way to quantify pulmonary airway and vascular tree measurements. In patients with COPD, CT airway measurement differences in females are concomitant with worse quality-of-life and other outcomes. CT total airway count (TAC), airway lumen area (LA), and wall thickness (WT) also differ in females with long-COVID. Our objective was to evaluate CT airway and pulmonary vascular and quality-of-life measurements in females with COPD as compared to ex-smokers and patients with long-COVID. Chest CT was acquired 3-months post-COVID-19 infection in females with long-COVID for comparison with the same inspiratory CT in female ex-smokers and COPD patients. TAC, LA, WT, and pulmonary vascular measurements were quantified. Linear regression models were adjusted for confounders including age, height, body-mass-index, lung volume, pack-years and asthma diagnosis. Twenty-one females (53 ± 14 years) with long-COVID, 17 female ex-smokers (69 ± 9 years) and 13 female COPD (67 ± 6 years) patients were evaluated. In the absence of differences in quality-of-life scores, females with long-COVID reported significantly different LA (p = 0.006) compared to ex-smokers but not COPD (p = 0.7); WT% was also different compared to COPD (p = 0.009) but not ex-smokers (p = 0.5). In addition, there was significantly greater pulmonary small vessel volume (BV5) in long-COVID as compared to female ex-smokers (p = 0.045) and COPD (p = 0.003) patients and different large (BV10) vessel volume as compared to COPD (p = 0.03). In females with long-COVID and highly abnormal quality-of-life scores, there was CT evidence of airway remodelling, similar to ex-smokers and patients with COPD, but there was no evidence of pulmonary vascular remodelling.Clinical Trial Registration: www.clinicaltrials.gov NCT05014516 and NCT02279329.

Machine learning and magnetic resonance image texture analysis predicts accelerated lung function decline in ex-smokers with and without chronic obstructive pulmonary disease.

Purpose: Our objective was to train machine-learning algorithms on hyperpolarized He 3 magnetic resonance imaging (MRI) datasets to generate models of accelerated lung function decline in participants with and without chronic-obstructive-pulmonary-disease. We hypothesized that hyperpolarized gas MRI ventilation, machine-learning, and multivariate modeling could be combined to predict clinically-relevant changes in forced expiratory volume in 1 s ( FEV 1 ) across 3 years. Approach: Hyperpolarized He 3 MRI was acquired using a coronal Cartesian fast gradient recalled echo sequence with a partial echo and segmented using a k-means clustering algorithm. A maximum entropy mask was used to generate a region-of-interest for texture feature extraction using a custom-developed algorithm and the PyRadiomics platform. The principal component and Boruta analyses were used for feature selection. Ensemble-based and single machine-learning classifiers were evaluated using area-under-the-receiver-operator-curve and sensitivity-specificity analysis. Results: We evaluated 88 ex-smoker participants with 31 ± 7 months follow-up data, 57 of whom (22 females/35 males, 70 ± 9 years) had negligible changes in FEV 1 and 31 participants (7 females/24 males, 68 ± 9 years) with worsening FEV 1 ≥ 60 mL / year . In addition, 3/88 ex-smokers reported a change in smoking status. We generated machine-learning models to predict FEV 1 decline using demographics, spirometry, and texture features, with the later yielding the highest classification accuracy of 81%. The combined model (trained on all available measurements) achieved the overall best classification accuracy of 82%; however, it was not significantly different from the model trained on MRI texture features alone. Conclusion: For the first time, we have employed hyperpolarized He 3 MRI ventilation texture features and machine-learning to identify ex-smokers with accelerated decline in FEV 1 with 82% accuracy.

129Xe MRI Ventilation Textures and Longitudinal Quality-of-Life Improvements in Long-COVID.

RATIONALE AND OBJECTIVES: It remains difficult to predict longitudinal outcomes in long-COVID, even with chest CT and functional MRI. 129Xe MRI reflects airway dysfunction, measured using ventilation defect percent (VDP) and in long-COVID patients, MRI VDP was abnormal, suggestive of airways disease. While MRI VDP and quality-of-life improved 15-month post-COVID infection, both remained abnormal. To better understand the relationship of airways disease and quality-of-life improvements in patients with long-COVID, we extracted 129Xe ventilation MRI textures and generated machine-learning models in an effort to predict improved quality-of-life, 15-month post-infection. MATERIALS AND METHODS: Long-COVID patients provided written-informed consent to 3-month and 15-month post-infection visits. Pyradiomics was used to extract 129Xe ventilation MRI texture features, which were ranked using a Random-Forest classifier. Top-ranking features were used in classification models to dichotomize patients based on St. George's Respiratory Questionnaire (SGRQ) score improvement greater than the minimal-clinically-important-difference (MCID). Classification performance was evaluated using the area under the receiver-operator-characteristic-curve (AUC), sensitivity, and specificity. RESULTS: 120 texture features were extracted from 129Xe ventilation MRI in 44 long-COVID participants (54 ± 14 years), including 30 (52 ± 12 years) with ΔSGRQ≥MCID and 14 (58 ± 18 years) with ΔSGRQ

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.

Review of quantitative and functional lung imaging evidence of vaping-related lung injury.

Introduction: The pulmonary effects of e-cigarette use (or vaping) became a healthcare concern in 2019, following the rapid increase of e-cigarette-related or vaping-associated lung injury (EVALI) in young people, which resulted in the critical care admission of thousands of teenagers and young adults. Pulmonary functional imaging is well-positioned to provide information about the acute and chronic effects of vaping. We generated a systematic review to retrieve relevant imaging studies that describe the acute and chronic imaging findings that underly vaping-related lung structure-function abnormalities. Methods: A systematic review was undertaken on June 13th, 2023 using PubMed to search for published manuscripts using the following criteria: [("Vaping" OR "e-cigarette" OR "EVALI") AND ("MRI" OR "CT" OR "Imaging")]. We included only studies involving human participants, vaping/e-cigarette use, and MRI, CT and/or PET. Results: The search identified 445 manuscripts, of which 110 (668 unique participants) specifically mentioned MRI, PET or CT imaging in cases or retrospective case series of patients who vaped. This included 105 manuscripts specific to CT (626 participants), three manuscripts which mainly used MRI (23 participants), and two manuscripts which described PET findings (20 participants). Most studies were conducted in North America (n = 90), with the remaining studies conducted in Europe (n = 15), Asia (n = 4) and South America (n = 1). The vast majority of publications described case studies (n = 93) and a few described larger retrospective or prospective studies (n = 17). In e-cigarette users and patients with EVALI, key CT findings included ground-glass opacities, consolidations and subpleural sparing, MRI revealed abnormal ventilation, perfusion and ventilation/perfusion matching, while PET showed evidence of pulmonary inflammation. Discussion and conclusion: Pulmonary structural and functional imaging abnormalities were common in patients with EVALI and in e-cigarette users with or without respiratory symptoms, which suggests that functional MRI may be helpful in the investigation of the pulmonary health effects associated with e-cigarette use.

129Xe MRI and Oscillometry of Irritant-Induced Asthma After Bronchial Thermoplasty.

Irritant-induced asthma (IIA) may develop after acute inhalational exposure in individuals without preexisting asthma. The effect of bronchial thermoplasty to treat intractable, worsening IIA has not yet been described. We evaluated a previously healthy 52-year-old man after inhalation of an unknown white powder. His pulmonary function and symptoms/quality of life worsened over 4 years, despite maximal guidelines-based asthma therapy. We acquired 129Xe MRI and pulmonary function test measurements on three occasions including before and after bronchial thermoplasty treatment. Seven months after bronchial thermoplasty, improved MRI ventilation and oscillometry small airway resistance were observed. Spirometry and asthma control did not improve until 19 months after bronchial thermoplasty, 5.5 years postexposure. Together, oscillometry measurements of the small airways and 129Xe MRI provided effort-independent, sensitive, and objective measurements of response to therapy. Improved MRI and oscillometry small airway resistance measurements temporally preceded improved airflow obstruction and may be considered for complex asthma cases.

Review of Hyperpolarized Pulmonary Functional 129 Xe MR for Long-COVID.

The respiratory consequences of acute COVID-19 infection and related symptoms tend to resolve 4 weeks post-infection. However, for some patients, new, recurrent, or persisting symptoms remain beyond the acute phase and persist for months, post-infection. The symptoms that remain have been referred to as long-COVID. A number of research sites employed 129 Xe magnetic resonance imaging (MRI) during the pandemic and evaluated patients post-infection, months after hospitalization or home-based care as a way to better understand the consequences of infection on 129 Xe MR gas-exchange and ventilation imaging. A systematic review and comprehensive search were employed using MEDLINE via PubMed (April 2023) using the National Library of Medicine's Medical Subject Headings and key words: post-COVID-19, MRI, 129 Xe, long-COVID, COVID pneumonia, and post-acute COVID-19 syndrome. Fifteen peer-reviewed manuscripts were identified including four editorials, a single letter to the editor, one review article, and nine original research manuscripts (2020-2023). MRI and MR spectroscopy results are summarized from these prospective, controlled studies, which involved small sample sizes ranging from 9 to 76 participants. Key findings included: 1) 129 Xe MRI gas-exchange and ventilation abnormalities, 3 months post-COVID-19 infection, and 2) a combination of MRI gas-exchange and ventilation abnormalities alongside persistent symptoms in patients hospitalized and not hospitalized for COVID-19, 1-year post-infection. The persistence of respiratory symptoms and 129 Xe MRI abnormalities in the context of normal or nearly normal pulmonary function test results and chest computed tomography (CT) was consistent. Longitudinal improvements were observed in long-term follow-up of long-COVID patients but mean 129 Xe gas-exchange, ventilation heterogeneity values and symptoms remained abnormal, 1-year post-infection. Pulmonary functional MRI using inhaled hyperpolarized 129 Xe gas has played a role in detecting gas-exchange and ventilation abnormalities providing complementary information that may help develop our understanding of the root causes of long-COVID. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 5.

Machine Learning and CT Texture Features in Ex-smokers with no CT Evidence of Emphysema and Mildly Abnormal Diffusing Capacity.

RATIONALE AND OBJECTIVES: Ex-smokers without spirometry or CT evidence of chronic obstructive pulmonary disease (COPD) but with mildly abnormal diffusing capacity of the lungs for carbon monoxide (DLCO) are at higher risk of developing COPD. It remains difficult to make clinical management decisions for such ex-smokers without other objective assessments consistent with COPD. Hence, our objective was to develop a machine-learning and CT texture-analysis pipeline to dichotomize ex-smokers with normal and abnormal DLCO (DLCO≥75%pred and DLCO<75%pred). MATERIALS AND METHODS: In this retrospective study, 71 ex-smokers (50-85yrs) without COPD underwent spirometry, plethysmography, thoracic CT, and 3He MRI to generate ventilation defect percent (VDP) and apparent diffusion coefficients (ADC). PyRadiomics was utilized to extract 496 CT texture-features; Boruta and principal component analysis were used for feature selection and various models were investigated for classification. Machine-learning classifiers were evaluated using area under the receiver operator characteristic curve (AUC), sensitivity, specificity, and F1-measure. RESULTS: Of 71 ex-smokers without COPD, 29 with mildly abnormal DLCO had significantly different MRI ADC (p < .001), residual-volume to total-lung-capacity ratio (p = .003), St. George's Respiratory Questionnaire (p = .029), and six-minute-walk distance (6MWD) (p < .001), but similar relative area of the lung < -950 Hounsfield-units (RA950) (p = .9) compared to 42 ex-smokers with normal DLCO. Logistic-regression machine-learning mixed-model trained on selected texture-features achieved the best classification accuracy of 87%. All clinical and imaging measurements were outperformed by high-high-pass filter high-gray-level-run-emphasis texture-feature (AUC=0.81), which correlated with DLCO (ρ = -0.29, p = .02), MRI ADC (ρ = 0.23, p = .048), and 6MWD (ρ = -0.25, p = .02). CONCLUSION: In ex-smokers with no CT evidence of emphysema, machine-learning models exclusively trained on CT texture-features accurately classified ex-smokers with abnormal diffusing capacity, outperforming conventional quantitative CT measurements.

Chest MRI and CT Predictors of 10-Year All-Cause Mortality in COPD.

Pulmonary imaging measurements using magnetic resonance imaging (MRI) and computed tomography (CT) have the potential to deepen our understanding of chronic obstructive pulmonary disease (COPD) by measuring airway and parenchymal pathologic information that cannot be provided by spirometry. Currently, MRI and CT measurements are not included in mortality risk predictions, diagnosis, or COPD staging. We evaluated baseline pulmonary function, MRI and CT measurements alongside imaging texture-features to predict 10-year all-cause mortality in ex-smokers with (n = 93; 31 females; 70 ± 9years) and without (n = 69; 29 females, 69 ± 9years) COPD. CT airway and vessel measurements, helium-3 (3He) MRI ventilation defect percent (VDP) and apparent diffusion coefficients (ADC) were quantified. MRI and CT texture-features were extracted using PyRadiomics (version2.2.0). Associations between 10-year all-cause mortality and all clinical and imaging measurements were evaluated using multivariable regression model odds-ratios. Machine-learning predictive models for 10-year all-cause mortality were evaluated using area-under-receiver-operator-characteristic-curve (AUC), sensitivity and specificity analyses. DLCO (%pred) (HR = 0.955, 95%CI: 0.934-0.976, p < 0.001), MRI ADC (HR = 1.843, 95%CI: 1.260-2.871, p < 0.001), and CT informational-measure-of-correlation (HR = 3.546, 95% CI: 1.660-7.573, p = 0.001) were the strongest predictors of 10-year mortality. A machine-learning model trained on clinical, imaging, and imaging textures was the best predictive model (AUC = 0.82, sensitivity = 83%, specificity = 84%) and outperformed the solely clinical model (AUC = 0.76, sensitivity = 77%, specificity = 79%). In ex-smokers, regardless of COPD status, addition of CT and MR imaging texture measurements to clinical models provided unique prognostic information of mortality risk that can allow for better clinical management.Clinical Trial Registration: www.clinicaltrials.gov NCT02279329.

Reduced Total Airway Count and Airway Wall Tapering after Three-Years in Ex-Smokers.

Computed tomography (CT) total-airway-count (TAC) and airway wall-thickness differ across chronic obstructive pulmonary disease (COPD) severities, but longitudinal insights are lacking. The aim of this study was to evaluate longitudinal CT airway measurements over three-years in ex-smokers. In this prospective convenience sample study, ex-smokers with (n = 50; 13 female; age = 70 ± 9 years; pack-years = 43 ± 26) and without (n = 40; 17 female; age = 69 ± 10 years; pack-years = 31 ± 17) COPD completed CT, 3He magnetic resonance imaging (MRI), and pulmonary function tests at baseline and three-year follow-up. CT TAC, airway wall-area (WA), lumen-area (LA), and wall-area percent (WA%) were generated. Emphysema was quantified as the relative-area-of-the-lung with attenuation < -950 Hounsfield-units (RA950). MRI ventilation-defect-percent (VDP) was also quantified. Differences over time were evaluated using paired-samples t tests. Multivariable prediction models using the backwards approach were generated. After three-years, forced-expiratory-volume in 1-second (FEV1) was not different in ex-smokers with (p = 0.4) and without (p = 0.5) COPD, whereas RA950 was (p < 0.001, p = 0.02, respectively). In ex-smokers without COPD, there was no change in TAC (p = 0.2); however, LA (p = 0.009) and WA% (p = 0.01) were significantly different. In ex-smokers with COPD, TAC (p < 0.001), WA (p = 0.04), LA (p < 0.001), and WA% (p < 0.001) were significantly different. In all ex-smokers, TAC was related to VDP (baseline: ρ = -0.30, p = 0.005; follow-up: ρ = -0.33, p = 0.002). In significant multivariable models, baseline airway wall-thickness was predictive of TAC worsening. After three-years, in the absence of FEV1 worsening, TAC diminished only in ex-smokers with COPD and airway walls were thinner in all ex-smokers. These longitudinal findings suggest that the evaluation of CT airway remodeling may be a useful clinical tool for predicting disease progression and managing COPD.Clinical trial registration: www.clinicaltrials.gov NCT02279329.

Association of Intima-Media Thickness Measured at the Common Carotid Artery With Incident Carotid Plaque: Individual Participant Data Meta-Analysis of 20 Prospective Studies.

Background The association between common carotid artery intima-media thickness (CCA-IMT) and incident carotid plaque has not been characterized fully. We therefore aimed to precisely quantify the relationship between CCA-IMT and carotid plaque development. Methods and Results We undertook an individual participant data meta-analysis of 20 prospective studies from the Proof-ATHERO (Prospective Studies of Atherosclerosis) consortium that recorded baseline CCA-IMT and incident carotid plaque involving 21 494 individuals without a history of cardiovascular disease and without preexisting carotid plaque at baseline. Mean baseline age was 56 years (SD, 9 years), 55% were women, and mean baseline CCA-IMT was 0.71 mm (SD, 0.17 mm). Over a median follow-up of 5.9 years (5th-95th percentile, 1.9-19.0 years), 8278 individuals developed first-ever carotid plaque. We combined study-specific odds ratios (ORs) for incident carotid plaque using random-effects meta-analysis. Baseline CCA-IMT was approximately log-linearly associated with the odds of developing carotid plaque. The age-, sex-, and trial arm-adjusted OR for carotid plaque per SD higher baseline CCA-IMT was 1.40 (95% CI, 1.31-1.50; I2=63.9%). The corresponding OR that was further adjusted for ethnicity, smoking, diabetes, body mass index, systolic blood pressure, low- and high-density lipoprotein cholesterol, and lipid-lowering and antihypertensive medication was 1.34 (95% CI, 1.24-1.45; I2=59.4%; 14 studies; 16 297 participants; 6381 incident plaques). We observed no significant effect modification across clinically relevant subgroups. Sensitivity analysis restricted to studies defining plaque as focal thickening yielded a comparable OR (1.38 [95% CI, 1.29-1.47]; I2=57.1%; 14 studies; 17 352 participants; 6991 incident plaques). Conclusions Our large-scale individual participant data meta-analysis demonstrated that CCA-IMT is associated with the long-term risk of developing first-ever carotid plaque, independent of traditional cardiovascular risk factors.

Undersampled Diffusion-Weighted 129Xe MRI Morphometry of Airspace Enlargement: Feasibility in Chronic Obstructive Pulmonary Disease.

Multi-b diffusion-weighted hyperpolarized gas MRI measures pulmonary airspace enlargement using apparent diffusion coefficients (ADC) and mean linear intercepts (Lm). Rapid single-breath acquisitions may facilitate clinical translation, and, hence, we aimed to develop single-breath three-dimensional multi-b diffusion-weighted 129Xe MRI using k-space undersampling. We evaluated multi-b (0, 12, 20, 30 s/cm2) diffusion-weighted 129Xe ADC/morphometry estimates using a fully sampled and retrospectively undersampled k-space with two acceleration-factors (AF = 2 and 3) in never-smokers and ex-smokers with chronic obstructive pulmonary disease (COPD) or alpha-one anti-trypsin deficiency (AATD). For the three sampling cases, mean ADC/Lm values were not significantly different (all p > 0.5); ADC/Lm values were significantly different for the COPD subgroup (0.08 cm2s-1/580 µm, AF = 3; all p < 0.001) as compared to never-smokers (0.05 cm2s-1/300 µm, AF = 3). For never-smokers, mean differences of 7%/7% and 10%/7% were observed between fully sampled and retrospectively undersampled (AF = 2/AF = 3) ADC and Lm values, respectively. For the COPD subgroup, mean differences of 3%/4% and 11%/10% were observed between fully sampled and retrospectively undersampled (AF = 2/AF = 3) ADC and Lm, respectively. There was no relationship between acceleration factor with ADC or Lm (p = 0.9); voxel-wise ADC/Lm measured using AF = 2 and AF = 3 were significantly and strongly related to fully-sampled values (all p < 0.0001). Multi-b diffusion-weighted 129Xe MRI is feasible using two different acceleration methods to measure pulmonary airspace enlargement using Lm and ADC in COPD participants and never-smokers.

129Xe MRI Ventilation Defects in Asthma: What is the Upper Limit of Normal and Minimal Clinically Important Difference?

RATIONALE AND OBJECTIVES: The minimal clinically important difference (MCID) and upper limit of normal (ULN) for MRI ventilation defect percent (VDP) were previously reported for hyperpolarized 3He gas MRI. Hyperpolarized 129Xe VDP is more sensitive to airway dysfunction than 3He, therefore the objective of this study was to determine the ULN and MCID for 129Xe MRI VDP in healthy and asthma participants. MATERIALS AND METHODS: We retrospectively evaluated healthy and asthma participants who underwent spirometry and 129XeMRI on a single visit; participants with asthma completed the asthma control questionnaire (ACQ-7). The MCID was estimated using distribution- (smallest detectable difference [SDD]) and anchor-based (ACQ-7) methods. Two observers measured VDP (semiautomated k-means-cluster segmentation algorithm) in 10 participants with asthma, five-times each in random order, to determine SDD. The ULN was estimated based on the 95% confidence interval of the relationships between VDP and age. RESULTS: Mean VDP was 1.6 ± 1.2% for healthy (n = 27) and 13.7 ± 12.9% for asthma participants (n = 55). ACQ-7 and VDP were correlated (r = .37, p = .006; VDP = 3.5·ACQ + 4.9). The anchor-based MCID was 1.75% while the mean SDD and distribution-based MCID was 2.25%. VDP was correlated with age for healthy participants (p = .56, p =.003; VDP = .04·Age-.01). The ULN for all healthy participants was 2.0%. By age tertiles, the ULN was 1.3% ages 18-39 years, 2.5% for 40-59 years and 3.8% for 60-79 years. CONCLUSION: The 129Xe MRI VDP MCID was estimated in participants with asthma; the ULN was estimated in healthy participants across a range of ages, both of which provide a way to interpret VDP measurements in clinical investigations.