Structural Predictors of Lung Function Decline in Young Smokers with Normal Spirometry.

Rationale: Chronic obstructive pulmonary disease (COPD) due to tobacco smoking commonly presents when extensive lung damage has occurred. Objectives: We hypothesized that structural change would be detected early in the natural history of COPD and would relate to loss of lung function with time. Methods: We recruited 431 current smokers (median age, 39 yr; 16 pack-years smoked) and recorded symptoms using the COPD Assessment Test (CAT), spirometry, and quantitative thoracic computed tomography (QCT) scans at study entry. These scan results were compared with those from 67 never-smoking control subjects. Three hundred sixty-eight participants were followed every six months with measurement of postbronchodilator spirometry for a median of 32 months. The rate of FEV1 decline, adjusted for current smoking status, age, and sex, was related to the initial QCT appearances and symptoms, measured using the CAT. Measurements and Main Results: There were no material differences in demography or subjective CT appearances between the young smokers and control subjects, but 55.7% of the former had CAT scores greater than 10, and 24.2% reported chronic bronchitis. QCT assessments of disease probability-defined functional small airway disease, ground-glass opacification, bronchovascular prominence, and ratio of small blood vessel volume to total pulmonary vessel volume were increased compared with control subjects and were all associated with a faster FEV1 decline, as was a higher CAT score. Conclusions: Radiological abnormalities on CT are already established in young smokers with normal lung function and are associated with FEV1 loss independently of the impact of symptoms. Structural abnormalities are present early in the natural history of COPD and are markers of disease progression. Clinical trial registered with www.clinicaltrials.gov (NCT03480347).

Association of Ground Glass Opacities with Systemic Inflammation and Progression of Emphysema.

RATIONAL: Ground glass opacities (GGO) in the absence of interstitial lung disease are understudied. OBJECTIVE: To assess the association of GGO with white blood cells (WBCs) and progression of quantified chest CT emphysema. METHODS: We analyzed data of participants in the Subpopulations and Intermediate Outcome Measures In COPD Study (SPIROMICS). Chest radiologists and pulmonologists labeled regions of the lung as GGO and adaptive multiple feature method (AMFM) trained the computer to assign those labels to image voxels and quantify the volume of the lung with GGO (%GGOAMFM). We used multivariable linear regression, zero-inflated negative binomial, and proportional hazards regression models to assess the association of %GGOAMFM with WBC, changes in %emphysema, and clinical outcomes. MEASUREMENTS AND MAIN RESULTS: Among 2,714 participants, 1,680 had COPD and 1,034 had normal spirometry. Among COPD participants, based on the multivariable analysis, current smoking and chronic productive cough was associated with higher %GGOAMFM. Higher %GGOAMFM was cross-sectionally associated with higher WBCs and neutrophils levels. Higher %GGOAMFM per interquartile range at visit 1 (baseline) was associated with an increase in emphysema at one-year follow visit by 11.7% (Relative increase; 95%CI 7.5-16.1%;P<0.001). We found no association between %GGOAMFM and one-year FEV1 decline but %GGOAMFM was associated with exacerbations and all-cause mortality during a median follow-up time of 1,544 days (Interquartile Interval=1,118-2,059). Among normal spirometry participants, we found similar results except that %GGOAMFM was associated with progression to COPD at one-year follow-up. CONCLUSIONS: Our findings suggest that GGOAMFM is associated with increased systemic inflammation and emphysema progression.

Associations of pulmonary microvascular blood volume with per cent emphysema and CT emphysema subtypes in the community: the MESA Lung study.

BACKGROUND: Pulmonary microvasculature alterations are implicated in emphysema pathogenesis, but the association between pulmonary microvascular blood volume (PMBV) and emphysema has not been directly assessed at scale, and prior studies have used non-specific measures of emphysema. METHODS: The Multi-Ethnic Study of Atherosclerosis Lung Study invited participants recruited from the community without renal impairment to undergo contrast-enhanced dual-energy CT. Pulmonary blood volume was calculated by material decomposition; PMBV was defined as blood volume in the peripheral 2 cm of the lung. Non-contrast CT was acquired to assess per cent emphysema and novel CT emphysema subtypes, which include the diffuse emphysema subtype and small-airways-related combined bronchitic-apical emphysema subtype. Generalised linear regression models included age, sex, race/ethnicity, body size, smoking, total lung volume and small airway count. RESULTS: Among 495 participants, 53% were never-smokers and the race/ethnic distribution was 35% white, 31% black, 15% Hispanic and 18% Asian. Mean PMBV was 352±120 mL; mean per cent emphysema was 4.95±4.75%. Lower PMBV was associated with greater per cent emphysema (-0.90% per 100 mL PMBV, 95% CI: -1.29 to -0.51). The association was of larger magnitude in participants with 10 or more pack-years smoking and airflow obstruction, but present among participants with no smoking history or airflow limitation, and was specific to the diffuse CT emphysema subtype (-1.48% per 100 mL PMBV, 95% CI: -2.31 to -0.55). CONCLUSION: In this community-based study, lower PMBV was associated with greater per cent emphysema, including in participants without a smoking history or airflow limitation, and was specific to the diffuse CT emphysema subtype.

The pleural gradient does not reflect the superimposed pressure in patients with class III obesity.

BACKGROUND: The superimposed pressure is the primary determinant of the pleural pressure gradient. Obesity is associated with elevated end-expiratory esophageal pressure, regardless of lung disease severity, and the superimposed pressure might not be the only determinant of the pleural pressure gradient. The study aims to measure partitioned respiratory mechanics and superimposed pressure in a cohort of patients admitted to the ICU with and without class III obesity (BMI ≥ 40 kg/m2), and to quantify the amount of thoracic adipose tissue and muscle through advanced imaging techniques. METHODS: This is a single-center observational study including ICU-admitted patients with acute respiratory failure who underwent a chest computed tomography scan within three days before/after esophageal manometry. The superimposed pressure was calculated from lung density and height of the largest axial lung slice. Automated deep-learning pipelines segmented lung parenchyma and quantified thoracic adipose tissue and skeletal muscle. RESULTS: N = 18 participants (50% female, age 60 [30-66] years), with 9 having BMI < 30 and 9  ≥ 40 kg/m2. Groups showed no significant differences in age, sex, clinical severity scores, or mortality. Patients with BMI ≥ 40 exhibited higher esophageal pressure (15.8 ± 2.6 vs. 8.3 ± 4.9 cmH2O, p = 0.001), higher pleural pressure gradient (11.1 ± 4.5 vs. 6.3 ± 4.9 cmH2O, p = 0.04), while superimposed pressure did not differ (6.8 ± 1.1 vs. 6.5 ± 1.5 cmH2O, p = 0.59). Subcutaneous and intrathoracic adipose tissue were significantly higher in subjects with BMI ≥ 40 and correlated positively with esophageal pressure and pleural pressure gradient (p < 0.05). Muscle areas did not differ between groups. CONCLUSIONS: In patients with class III obesity, the superimposed pressure does not approximate the pleural pressure gradient, which is higher than in patients with lower BMI. The quantity and distribution of subcutaneous and intrathoracic adiposity also contribute to increased pleural pressure gradients in individuals with BMI ≥ 40. This study introduces a novel physiological concept that provides a solid rationale for tailoring mechanical ventilation in patients with high BMI, where specific guidelines recommendations are lacking.

Electrical Impedance Tomography Identifies Evolution of Regional Perfusion in a Porcine Model of Acute Respiratory Distress Syndrome.

BACKGROUND: Bedside electrical impedance tomography could be useful to visualize evolving pulmonary perfusion distributions when acute respiratory distress syndrome worsens or in response to ventilatory and positional therapies. In experimental acute respiratory distress syndrome, this study evaluated the agreement of electrical impedance tomography and dynamic contrast-enhanced computed tomography perfusion distributions at two injury time points and in response to increased positive end-expiratory pressure (PEEP) and prone position. METHODS: Eleven mechanically ventilated (VT 8 ml · kg-1) Yorkshire pigs (five male, six female) received bronchial hydrochloric acid (3.5 ml · kg-1) to invoke lung injury. Electrical impedance tomography and computed tomography perfusion images were obtained at 2 h (early injury) and 24 h (late injury) after injury in supine position with PEEP 5 and 10 cm H2O. In eight animals, electrical impedance tomography and computed tomography perfusion imaging were also conducted in the prone position. Electrical impedance tomography perfusion (QEIT) and computed tomography perfusion (QCT) values (as percentages of image total) were compared in eight vertical regions across injury stages, levels of PEEP, and body positions using mixed-effects linear regression. The primary outcome was agreement between QEIT and QCT, defined using limits of agreement and Pearson correlation coefficient. RESULTS: Pao2/Fio2 decreased over the course of the experiment (healthy to early injury, -253 [95% CI, -317 to -189]; early to late injury, -88 [95% CI, -151 to -24]). The limits of agreement between QEIT and QCT were -4.66% and 4.73% for the middle 50% quantile of average regional perfusion, and the correlation coefficient was 0.88 (95% CI, 0.86 to 0.90]; P < 0.001). Electrical impedance tomography and computed tomography showed similar perfusion redistributions over injury stages and in response to increased PEEP. QEIT redistributions after positional therapy underestimated QCT in ventral regions and overestimated QCT in dorsal regions. CONCLUSIONS: Electrical impedance tomography closely approximated computed tomography perfusion measures in experimental acute respiratory distress syndrome, in the supine position, over injury progression and with increased PEEP. Further validation is needed to determine the accuracy of electrical impedance tomography in measuring perfusion redistributions after positional changes.

Unsupervised segmentation and quantification of COVID-19 lesions on computed Tomography scans using CycleGAN.

BACKGROUND: Lesion segmentation is a critical step in medical image analysis, and methods to identify pathology without time-intensive manual labeling of data are of utmost importance during a pandemic and in resource-constrained healthcare settings. Here, we describe a method for fully automated segmentation and quantification of pathological COVID-19 lung tissue on chest Computed Tomography (CT) scans without the need for manually segmented training data. METHODS: We trained a cycle-consistent generative adversarial network (CycleGAN) to convert images of COVID-19 scans into their generated healthy equivalents. Subtraction of the generated healthy images from their corresponding original CT scans yielded maps of pathological tissue, without background lung parenchyma, fissures, airways, or vessels. We then used these maps to construct three-dimensional lesion segmentations. Using a validation dataset, Dice scores were computed for our lesion segmentations and other published segmentation networks using ground truth segmentations reviewed by radiologists. RESULTS: The COVID-to-Healthy generator eliminated high Hounsfield unit (HU) voxels within pulmonary lesions and replaced them with lower HU voxels. The generator did not distort normal anatomy such as vessels, airways, or fissures. The generated healthy images had higher gas content (2.45 ± 0.93 vs 3.01 ± 0.84 L, P < 0.001) and lower tissue density (1.27 ± 0.40 vs 0.73 ± 0.29 Kg, P < 0.001) than their corresponding original COVID-19 images, and they were not significantly different from those of the healthy images (P < 0.001). Using the validation dataset, lesion segmentations scored an average Dice score of 55.9, comparable to other weakly supervised networks that do require manual segmentations. CONCLUSION: Our CycleGAN model successfully segmented pulmonary lesions in mild and severe COVID-19 cases. Our model's performance was comparable to other published models; however, our model is unique in its ability to segment lesions without the need for manual segmentations.

Diminishing Efficacy of Prone Positioning With Late Application in Evolving Lung Injury.

OBJECTIVES: It is not known how lung injury progression during mechanical ventilation modifies pulmonary responses to prone positioning. We compared the effects of prone positioning on regional lung aeration in late versus early stages of lung injury. DESIGN: Prospective, longitudinal imaging study. SETTING: Research imaging facility at The University of Pennsylvania (Philadelphia, PA) and Medical and Surgical ICUs at Massachusetts General Hospital (Boston, MA). SUBJECTS: Anesthetized swine and patients with acute respiratory distress syndrome (acute respiratory distress syndrome). INTERVENTIONS: Lung injury was induced by bronchial hydrochloric acid (3.5 mL/kg) in 10 ventilated Yorkshire pigs and worsened by supine nonprotective ventilation for 24 hours. Whole-lung CT was performed 2 hours after hydrochloric acid (Day 1) in both prone and supine positions and repeated at 24 hours (Day 2). Prone and supine images were registered (superimposed) in pairs to measure the effects of positioning on the aeration of each tissue unit. Two patients with early acute respiratory distress syndrome were compared with two patients with late acute respiratory distress syndrome, using electrical impedance tomography to measure the effects of body position on regional lung mechanics. MEASUREMENTS AND MAIN RESULTS: Gas exchange and respiratory mechanics worsened over 24 hours, indicating lung injury progression. On Day 1, prone positioning reinflated 18.9% ± 5.2% of lung mass in the posterior lung regions. On Day 2, position-associated dorsal reinflation was reduced to 7.3% ± 1.5% (p < 0.05 vs Day 1). Prone positioning decreased aeration in the anterior lungs on both days. Although prone positioning improved posterior lung compliance in the early acute respiratory distress syndrome patients, it had no effect in late acute respiratory distress syndrome subjects. CONCLUSIONS: The effects of prone positioning on lung aeration may depend on the stage of lung injury and duration of prior ventilation; this may limit the clinical efficacy of this treatment if applied late.

Lung distribution of gas and blood volume in critically ill COVID-19 patients: a quantitative dual-energy computed tomography study.

BACKGROUND: Critically ill COVID-19 patients have pathophysiological lung features characterized by perfusion abnormalities. However, to date no study has evaluated whether the changes in the distribution of pulmonary gas and blood volume are associated with the severity of gas-exchange impairment and the type of respiratory support (non-invasive versus invasive) in patients with severe COVID-19 pneumonia. METHODS: This was a single-center, retrospective cohort study conducted in a tertiary care hospital in Northern Italy during the first pandemic wave. Pulmonary gas and blood distribution was assessed using a technique for quantitative analysis of dual-energy computed tomography. Lung aeration loss (reflected by percentage of normally aerated lung tissue) and the extent of gas:blood volume mismatch (percentage of non-aerated, perfused lung tissue-shunt; aerated, non-perfused dead space; and non-aerated/non-perfused regions) were evaluated in critically ill COVID-19 patients with different clinical severity as reflected by the need for non-invasive or invasive respiratory support. RESULTS: Thirty-five patients admitted to the intensive care unit between February 29th and May 30th, 2020 were included. Patients requiring invasive versus non-invasive mechanical ventilation had both a lower percentage of normally aerated lung tissue (median [interquartile range] 33% [24-49%] vs. 63% [44-68%], p < 0.001); and a larger extent of gas:blood volume mismatch (43% [30-49%] vs. 25% [14-28%], p = 0.001), due to higher shunt (23% [15-32%] vs. 5% [2-16%], p = 0.001) and non-aerated/non perfused regions (5% [3-10%] vs. 1% [0-2%], p = 0.001). The PaO2/FiO2 ratio correlated positively with normally aerated tissue (ρ = 0.730, p < 0.001) and negatively with the extent of gas-blood volume mismatch (ρ = - 0.633, p < 0.001). CONCLUSIONS: In critically ill patients with severe COVID-19 pneumonia, the need for invasive mechanical ventilation and oxygenation impairment were associated with loss of aeration and the extent of gas:blood volume mismatch.

Computed tomography assessment of PEEP-induced alveolar recruitment in patients with severe COVID-19 pneumonia.

BACKGROUND: There is a paucity of data concerning the optimal ventilator management in patients with COVID-19 pneumonia; particularly, the optimal levels of positive-end expiratory pressure (PEEP) are unknown. We aimed to investigate the effects of two levels of PEEP on alveolar recruitment in critically ill patients with severe COVID-19 pneumonia. METHODS: A single-center cohort study was conducted in a 39-bed intensive care unit at a university-affiliated hospital in Genoa, Italy. Chest computed tomography (CT) was performed to quantify aeration at 8 and 16 cmH2O PEEP. The primary endpoint was the amount of alveolar recruitment, defined as the change in the non-aerated compartment at the two PEEP levels on CT scan. RESULTS: Forty-two patients were included in this analysis. Alveolar recruitment was median [interquartile range] 2.7 [0.7-4.5] % of lung weight and was not associated with excess lung weight, PaO2/FiO2 ratio, respiratory system compliance, inflammatory and thrombophilia markers. Patients in the upper quartile of recruitment (recruiters), compared to non-recruiters, had comparable clinical characteristics, lung weight and gas volume. Alveolar recruitment was not different in patients with lower versus higher respiratory system compliance. In a subgroup of 20 patients with available gas exchange data, increasing PEEP decreased respiratory system compliance (median difference, MD - 9 ml/cmH2O, 95% CI from - 12 to - 6 ml/cmH2O, p < 0.001) and the ventilatory ratio (MD - 0.1, 95% CI from - 0.3 to - 0.1, p = 0.003), increased PaO2 with FiO2 = 0.5 (MD 24 mmHg, 95% CI from 12 to 51 mmHg, p < 0.001), but did not change PaO2 with FiO2 = 1.0 (MD 7 mmHg, 95% CI from - 12 to 49 mmHg, p = 0.313). Moreover, alveolar recruitment was not correlated with improvement of oxygenation or venous admixture. CONCLUSIONS: In patients with severe COVID-19 pneumonia, higher PEEP resulted in limited alveolar recruitment. These findings suggest limiting PEEP strictly to the values necessary to maintain oxygenation, thus avoiding the use of higher PEEP levels.

LungViT: Ensembling Cascade of Texture Sensitive Hierarchical Vision Transformers for Cross-Volume Chest CT Image-to-Image Translation.

Chest computed tomography (CT) at inspiration is often complemented by an expiratory CT to identify peripheral airways disease. Additionally, co-registered inspiratory-expiratory volumes can be used to derive various markers of lung function. Expiratory CT scans, however, may not be acquired due to dose or scan time considerations or may be inadequate due to motion or insufficient exhale; leading to a missed opportunity to evaluate underlying small airways disease. Here, we propose LungViT- a generative adversarial learning approach using hierarchical vision transformers for translating inspiratory CT intensities to corresponding expiratory CT intensities. LungViT addresses several limitations of the traditional generative models including slicewise discontinuities, limited size of generated volumes, and their inability to model texture transfer at volumetric level. We propose a shifted-window hierarchical vision transformer architecture with squeeze-and-excitation decoder blocks for modeling dependencies between features. We also propose a multiview texture similarity distance metric for texture and style transfer in 3D. To incorporate global information into the training process and refine the output of our model, we use ensemble cascading. LungViT is able to generate large 3D volumes of size 320×320×320 . We train and validate our model using a diverse cohort of 1500 subjects with varying disease severity. To assess model generalizability beyond the development set biases, we evaluate our model on an out-of-distribution external validation set of 200 subjects. Clinical validation on internal and external testing sets shows that synthetic volumes could be reliably adopted for deriving clinical endpoints of chronic obstructive pulmonary disease.

Mechanical ventilation guided by driving pressure optimizes local pulmonary biomechanics in an ovine model.

Mechanical ventilation exposes the lung to injurious stresses and strains that can negatively affect clinical outcomes in acute respiratory distress syndrome or cause pulmonary complications after general anesthesia. Excess global lung strain, estimated as increased respiratory system driving pressure, is associated with mortality related to mechanical ventilation. The role of small-dimension biomechanical factors underlying this association and their spatial heterogeneity within the lung are currently unknown. Using four-dimensional computed tomography with a voxel resolution of 2.4 cubic millimeters and a multiresolution convolutional neural network for whole-lung image segmentation, we dynamically measured voxel-wise lung inflation and tidal parenchymal strains. Healthy or injured ovine lungs were evaluated as the mechanical ventilation positive end-expiratory pressure (PEEP) was titrated from 20 to 2 centimeters of water. The PEEP of minimal driving pressure (PEEPDP) optimized local lung biomechanics. We observed a greater rate of change in nonaerated lung mass with respect to PEEP below PEEPDP compared with PEEP values above this threshold. PEEPDP similarly characterized a breaking point in the relationships between PEEP and SD of local tidal parenchymal strain, the 95th percentile of local strains, and the magnitude of tidal overdistension. These findings advance the understanding of lung collapse, tidal overdistension, and strain heterogeneity as local triggers of ventilator-induced lung injury in large-animal lungs similar to those of humans and could inform the clinical management of mechanical ventilation to improve local lung biomechanics.

Deep Learning Estimation of Small Airways Disease from Inspiratory Chest CT is Associated with FEV1 Decline in COPD.

Rationale: Quantifying functional small airways disease (fSAD) requires additional expiratory computed tomography (CT) scan, limiting clinical applicability. Artificial intelligence (AI) could enable fSAD quantification from chest CT scan at total lung capacity (TLC) alone (fSADTLC). Objectives: To evaluate an AI model for estimating fSADTLC and study its clinical associations in chronic obstructive pulmonary disease (COPD). Methods: We analyzed 2513 participants from the SubPopulations and InteRmediate Outcome Measures in COPD Study (SPIROMICS). Using a subset (n = 1055), we developed a generative model to produce virtual expiratory CTs for estimating fSADTLC in the remaining 1458 SPIROMICS participants. We compared fSADTLC with dual volume, parametric response mapping fSADPRM. We investigated univariate and multivariable associations of fSADTLC with FEV1, FEV1/FVC, six-minute walk distance (6MWD), St. George's Respiratory Questionnaire (SGRQ), and FEV1 decline. The results were validated in a subset (n = 458) from COPDGene study. Multivariable models were adjusted for age, race, sex, BMI, baseline FEV1, smoking pack years, smoking status, and percent emphysema. Measurements and Main Results: Inspiratory fSADTLC was highly correlated with fSADPRM in SPIROMICS (Pearson's R = 0.895) and COPDGene (R = 0.897) cohorts. In SPIROMICS, fSADTLC was associated with FEV1 (L) (adj.ß = -0.034, P < 0.001), FEV1/FVC (adj.ß = -0.008, P < 0.001), SGRQ (adj.ß = 0.243, P < 0.001), and FEV1 decline (mL / year) (adj.ß = -1.156, P < 0.001). fSADTLC was also associated with FEV1 (L) (adj.ß = -0.032, P < 0.001), FEV1/FVC (adj.ß = -0.007, P < 0.001), SGRQ (adj.ß = 0.190, P = 0.02), and FEV1 decline (mL / year) (adj.ß = -0.866, P = 0.001) in COPDGene. We found fSADTLC to be more repeatable than fSADPRM with intraclass correlation of 0.99 (95% CI: 0.98, 0.99) vs. 0.83 (95% CI: 0.76, 0.88). Conclusions: Inspiratory fSADTLC captures small airways disease as reliably as fSADPRM and is associated with FEV1 decline.

Vessel and Airway Characteristics in One-Year Computed Tomography-defined Rapid Emphysema Progression: SPIROMICS.

Rationale: Rates of emphysema progression vary in chronic obstructive pulmonary disease (COPD), and the relationships with vascular and airway pathophysiology remain unclear. Objectives: We sought to determine if indices of peripheral (segmental and beyond) pulmonary arterial dilation measured on computed tomography (CT) are associated with a 1-year index of emphysema (EI; percentage of voxels <-950 Hounsfield units) progression. Methods: Five hundred ninety-nine former and never-smokers (Global Initiative for Chronic Obstructive Lung Disease stages 0-3) were evaluated from the SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study) cohort: rapid emphysema progressors (RPs; n = 188, 1-year ΔEI > 1%), nonprogressors (n = 301, 1-year ΔEI ± 0.5%), and never-smokers (n = 110). Segmental pulmonary arterial cross-sectional areas were standardized to associated airway luminal areas (segmental pulmonary artery-to-airway ratio [PAARseg]). Full-inspiratory CT scan-derived total (arteries and veins) pulmonary vascular volume (TPVV) was compared with small vessel volume (radius smaller than 0.75 mm). Ratios of airway to lung volume (an index of dysanapsis and COPD risk) were compared with ratios of TPVV to lung volume. Results: Compared with nonprogressors, RPs exhibited significantly larger PAARseg (0.73 ± 0.29 vs. 0.67 ± 0.23; P = 0.001), lower ratios of TPVV to lung volume (3.21 ± 0.42% vs. 3.48 ± 0.38%; P = 5.0 × 10-12), lower ratios of airway to lung volume (0.031 ± 0.003 vs. 0.034 ± 0.004; P = 6.1 × 10-13), and larger ratios of small vessel volume to TPVV (37.91 ± 4.26% vs. 35.53 ± 4.89%; P = 1.9 × 10-7). In adjusted analyses, an increment of 1 standard deviation in PAARseg was associated with a 98.4% higher rate of severe exacerbations (95% confidence interval, 29-206%; P = 0.002) and 79.3% higher odds of being in the RP group (95% confidence interval, 24-157%; P = 0.001). At 2-year follow-up, the CT-defined RP group demonstrated a significant decline in postbronchodilator percentage predicted forced expiratory volume in 1 second. Conclusions: Rapid one-year progression of emphysema was associated with indices indicative of higher peripheral pulmonary vascular resistance and a possible role played by pulmonary vascular-airway dysanapsis.

Predicting pulmonary ventilation damage after radiation therapy for nonsmall cell lung cancer using a ResNet generative adversarial network.

BACKGROUND: Functional lung avoidance radiation therapy (RT) is a technique being investigated to preferentially avoid specific regions of the lung that are predicted to be more susceptible to radiation-induced damage. Reducing the dose delivered to high functioning regions may reduce the occurrence radiation-induced lung injuries (RILIs) and toxicities. However, in order to develop effective lung function-sparing plans, accurate predictions of post-RT ventilation change are needed to determine which regions of the lung should be spared. PURPOSE: To predict pulmonary ventilation change following RT for nonsmall cell lung cancer using machine learning. METHODS: A conditional generative adversarial network (cGAN) was developed with data from 82 human subjects enrolled in a randomized clinical trial approved by the institution's IRB to predict post-RT pulmonary ventilation change. The inputs to the network were the pre-RT pulmonary ventilation map and radiation dose distribution. The loss function was a combination of the binary cross-entropy loss and an asymmetrical structural similarity index measure (aSSIM) function designed to increase penalization of under-prediction of ventilation damage. Network performance was evaluated against a previously developed polynomial regression model using a paired sample t-test for comparison. Evaluation was performed using eight-fold cross-validation. RESULTS: From the eight-fold cross-validation, we found that relative to the polynomial model, the cGAN model significantly improved predicting regions of ventilation damage following radiotherapy based on true positive rate (TPR), 0.14±0.15 to 0.72±0.21, and Dice similarity coefficient (DSC), 0.19±0.16 to 0.46±0.14, but significantly declined in true negative rate, 0.97±0.05 to 0.62±0.21, and accuracy, 0.79±0.08 to 0.65±0.14. Additionally, the average true positive volume increased from 104±119 cc in the POLY model to 565±332 cc in the cGAN model, and the average false negative volume decreased from 654±361 cc in the POLY model to 193±163 cc in the cGAN model. CONCLUSIONS: The proposed cGAN model demonstrated significant improvement in TPR and DSC. The higher sensitivity of the cGAN model can improve the clinical utility of functional lung avoidance RT by identifying larger volumes of functional lung that can be spared and thus decrease the probability of the patient developing RILIs.

Direct estimation of regional lung volume change from paired and single CT images using residual regression neural network.

BACKGROUND: Chest computed tomography (CT) enables characterization of pulmonary diseases by producing high-resolution and high-contrast images of the intricate lung structures. Deformable image registration is used to align chest CT scans at different lung volumes, yielding estimates of local tissue expansion and contraction. PURPOSE: We investigated the utility of deep generative models for directly predicting local tissue volume change from lung CT images, bypassing computationally expensive iterative image registration and providing a method that can be utilized in scenarios where either one or two CT scans are available. METHODS: A residual regression convolutional neural network, called Reg3DNet+, is proposed for directly regressing high-resolution images of local tissue volume change (i.e., Jacobian) from CT images. Image registration was performed between lung volumes at total lung capacity (TLC) and functional residual capacity (FRC) using a tissue mass- and structure-preserving registration algorithm. The Jacobian image was calculated from the registration-derived displacement field and used as the ground truth for local tissue volume change. Four separate Reg3DNet+ models were trained to predict Jacobian images using a multifactorial study design to compare the effects of network input (i.e., single image vs. paired images) and output space (i.e., FRC vs. TLC). The models were trained and evaluated on image datasets from the COPDGene study. Models were evaluated against the registration-derived Jacobian images using local, regional, and global evaluation metrics. RESULTS: Statistical analysis revealed that both factors - network input and output space - were significant determinants for change in evaluation metrics. Paired-input models performed better than single-input models, and model performance was better in the output space of FRC rather than TLC. Mean structural similarity index for paired-input models was 0.959 and 0.956 for FRC and TLC output spaces, respectively, and for single-input models was 0.951 and 0.937. Global evaluation metrics demonstrated correlation between registration-derived Jacobian mean and predicted Jacobian mean: coefficient of determination (r2 ) for paired-input models was 0.974 and 0.938 for FRC and TLC output spaces, respectively, and for single-input models was 0.598 and 0.346. After correcting for effort, registration-derived lobar volume change was strongly correlated with the predicted lobar volume change: for paired-input models r2 was 0.899 for both FRC and TLC output spaces, and for single-input models r2 was 0.803 and 0.862, respectively. CONCLUSIONS: Convolutional neural networks can be used to directly predict local tissue mechanics, eliminating the need for computationally expensive image registration. Networks that use paired CT images acquired at TLC and FRC allow for more accurate prediction of local tissue expansion compared to networks that use a single image. Networks that only require a single input image still show promising results, particularly after correcting for effort, and allow for local tissue expansion estimation in cases where multiple CT scans are not available. For single-input networks, the FRC image is more predictive of local tissue volume change compared to the TLC image.

Attention U-net for automated pulmonary fissure integrity analysis in lung computed tomography images.

Computed Tomography (CT) imaging is routinely used for imaging of the lungs. Deep learning can effectively automate complex and laborious tasks in medical imaging. In this work, a deep learning technique is utilized to assess lobar fissure completeness (also known as fissure integrity) from pulmonary CT images. The human lungs are divided into five separate lobes, divided by the lobar fissures. Fissure integrity assessment is important to endobronchial valve treatment screening. Fissure integrity is known to be a biomarker of collateral ventilation between lobes impacting the efficacy of valves designed to block airflow to diseased lung regions. Fissure integrity is also likely to impact lobar sliding which has recently been shown to affect lung biomechanics. Further widescale study of fissure integrity's impact on disease susceptibility and progression requires rapid, reproducible, and noninvasive fissure integrity assessment. In this paper we describe IntegrityNet, an attention U-Net based automatic fissure integrity analysis tool. IntegrityNet is able to predict fissure integrity with an accuracy of 95.8%, 96.1%, and 89.8% for left oblique, right oblique, and right horizontal fissures, compared to manual analysis on a dataset of 82 subjects. We also show that our method is robust to COPD severity and reproducible across subject scans acquired at different time points.

A specific combination of laboratory data is associated with overweight lungs in patients with COVID-19 pneumonia at hospital admission: secondary cross-sectional analysis of a randomized clinical trial.

Background: Lung weight may be measured with quantitative chest computed tomography (CT) in patients with COVID-19 to characterize the severity of pulmonary edema and assess prognosis. However, this quantitative analysis is often not accessible, which led to the hypothesis that specific laboratory data may help identify overweight lungs. Methods: This cross-sectional study was a secondary analysis of data from SARITA2, a randomized clinical trial comparing nitazoxanide and placebo in patients with COVID-19 pneumonia. Adult patients (≥18 years) requiring supplemental oxygen due to COVID-19 pneumonia were enrolled between April 20 and October 15, 2020, in 19 hospitals in Brazil. The weight of the lungs as well as laboratory data [hemoglobin, leukocytes, neutrophils, lymphocytes, C-reactive protein, D-dimer, lactate dehydrogenase (LDH), and ferritin] and 47 additional specific blood biomarkers were assessed. Results: Ninety-three patients were included in the study: 46 patients presented with underweight lungs (defined by ≤0% of excess lung weight) and 47 patients presented with overweight lungs (>0% of excess lung weight). Leukocytes, neutrophils, D-dimer, and LDH were higher in patients with overweight lungs. Among the 47 blood biomarkers investigated, interferon alpha 2 protein was higher and leukocyte inhibitory factor was lower in patients with overweight lungs. According to CombiROC analysis, the combinations of D-dimer/LDH/leukocytes, D-dimer/LDH/neutrophils, and D-dimer/LDH/leukocytes/neutrophils achieved the highest area under the curve with the best accuracy to detect overweight lungs. Conclusion: The combinations of these specific laboratory data: D-dimer/LDH/leukocytes or D-dimer/LDH/neutrophils or D-dimer/LDH/leukocytes/neutrophils were the best predictors of overweight lungs in patients with COVID-19 pneumonia at hospital admission. Clinical trial registration: Brazilian Registry of Clinical Trials (REBEC) number RBR-88bs9x and ClinicalTrials.gov number NCT04561219.

Improving pulmonary perfusion assessment by dynamic contrast-enhanced computed tomography in an experimental lung injury model.

Pulmonary perfusion has been poorly characterized in acute respiratory distress syndrome (ARDS). Optimizing protocols to measure pulmonary blood flow (PBF) via dynamic contrast-enhanced (DCE) computed tomography (CT) could improve understanding of how ARDS alters pulmonary perfusion. In this study, comparative evaluations of injection protocols and tracer-kinetic analysis models were performed based on DCE-CT data measured in ventilated pigs with and without lung injury. Ten Yorkshire pigs (five with lung injury, five healthy) were anesthetized, intubated, and mechanically ventilated; lung injury was induced by bronchial hydrochloric acid instillation. Each DCE-CT scan was obtained during a 30-s end-expiratory breath-hold. Reproducibility of PBF measurements was evaluated in three pigs. In eight pigs, undiluted and diluted Isovue-370 were separately injected to evaluate the effect of contrast viscosity on estimated PBF values. PBF was estimated with the peak-enhancement and the steepest-slope approach. Total-lung PBF was estimated in two healthy pigs to compare with cardiac output measured invasively by thermodilution in the pulmonary artery. Repeated measurements in the same animals yielded a good reproducibility of computed PBF maps. Injecting diluted isovue-370 resulted in smaller contrast-time curves in the pulmonary artery (P < 0.01) and vein (P < 0.01) without substantially diminishing peak signal intensity (P = 0.46 in the pulmonary artery) compared with the pure contrast agent since its viscosity is closer to that of blood. As compared with the peak-enhancement model, PBF values estimated by the steepest-slope model with diluted contrast were much closer to the cardiac output (R2 = 0.82) as compared with the peak-enhancement model. DCE-CT using the steepest-slope model and diluted contrast agent provided reliable quantitative estimates of PBF.NEW & NOTEWORTHY Dynamic contrast-enhanced CT using a lower-viscosity contrast agent in combination with tracer-kinetic analysis by the steepest-slope model improves pulmonary blood flow measurements and assessment of regional distributions of lung perfusion.

Predicting severe chronic obstructive pulmonary disease exacerbations using quantitative CT: a retrospective model development and external validation study.

BACKGROUND: Quantitative CT is becoming increasingly common for the characterisation of lung disease; however, its added potential as a clinical tool for predicting severe exacerbations remains understudied. We aimed to develop and validate quantitative CT-based models for predicting severe chronic obstructive pulmonary disease (COPD) exacerbations. METHODS: We analysed the Subpopulations and Intermediate Outcome Measures In COPD Study (SPIROMICS) cohort, a multicentre study done at 12 clinical sites across the USA, of individuals aged 40-80 years from four strata: individuals who never smoked, individuals who smoked but had normal spirometry, individuals who smoked and had mild to moderate COPD, and individuals who smoked and had severe COPD. We used 3-year follow-up data to develop logistic regression classifiers for predicting severe exacerbations. Predictors included age, sex, race, BMI, pulmonary function, exacerbation history, smoking status, respiratory quality of life, and CT-based measures of density gradient texture and airway structure. We externally validated our models in a subset from the Genetic Epidemiology of COPD (COPDGene) cohort. Discriminative model performance was assessed using the area under the receiver operating characteristic curve (AUC), which was also compared with other predictors, including exacerbation history and the BMI, airflow obstruction, dyspnoea, and exercise capacity (BODE) index. We evaluated model calibration using calibration plots and Brier scores. FINDINGS: Participants in SPIROMICS were enrolled between Nov 12, 2010, and July 31, 2015. Participants in COPDGene were enrolled between Jan 10, 2008, and April 15, 2011. We included 1956 participants from the SPIROMICS cohort who had complete 3-year follow-up data: the mean age of the cohort was 63·1 years (SD 9·2) and 1017 (52%) were men and 939 (48%) were women. Among the 1956 participants, 434 (22%) had a history of at least one severe exacerbation. For the CT-based models, the AUC was 0·854 (95% CI 0·852-0·855) for at least one severe exacerbation within 3 years and 0·931 (0·930-0·933) for consistent exacerbations (defined as ≥1 acute episode in each of the 3 years). Models were well calibrated with low Brier scores (0·121 for at least one severe exacerbation; 0·039 for consistent exacerbations). For the prediction of at least one severe event during 3-year follow-up, AUCs were significantly higher with CT biomarkers (0·854 [0·852-0·855]) than exacerbation history (0·823 [0·822-0·825]) and BODE index 0·812 [0·811-0·814]). 6965 participants were included in the external validation cohort, with a mean age of 60·5 years (SD 8·9). In this cohort, AUC for at least one severe exacerbation was 0·768 (0·767-0·769; Brier score 0·088). INTERPRETATION: CT-based prediction models can be used for identification of patients with COPD who are at high risk of severe exacerbations. The newly identified CT biomarkers could potentially enable investigation into underlying disease mechanisms responsible for exacerbations. FUNDING: National Institutes of Health and the National Heart, Lung, and Blood Institute.

CT-derived vessel segmentation for analysis of post-radiation therapy changes in vasculature and perfusion.

Vessel segmentation in the lung is an ongoing challenge. While many methods have been able to successfully identify vessels in normal, healthy, lungs, these methods struggle in the presence of abnormalities. Following radiotherapy, these methods tend to identify regions of radiographic change due to post-radiation therapytoxicities as vasculature falsely. By combining texture analysis and existing vasculature and masking techniques, we have developed a novel vasculature segmentation workflow that improves specificity in irradiated lung while preserving the sensitivity of detection in the rest of the lung. Furthermore, radiation dose has been shown to cause vascular injury as well as reduce pulmonary function post-RT. This work shows the improvements our novel vascular segmentation method provides relative to existing methods. Additionally, we use this workflow to show a dose dependent radiation-induced change in vasculature which is correlated with previously measured perfusion changes (R 2 = 0.72) in both directly irradiated and indirectly damaged regions of perfusion. These results present an opportunity to extend non-contrast CT-derived models of functional change following radiation therapy.