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

Rationale: Rates of emphysema progression vary in chronic obstructive pulmonary disease (COPD), and the relationship with vascular and airway pathophysiology remain unclear. Objective: We sought to determine if indices of peripheral (segmental and beyond) pulmonary arterial (PA) dilation measured via computed tomography (CT) are associated with a 1-year index of emphysema (EI: %voxels<-950HU) progression. Methods: 599 GOLD 0-3 former and never-smokers were evaluated from the SubPopulations and InterMediate Outcome Measures in COPD Study (SPIROMICS) cohort: rapid-emphysema-progressors (RP, n=188; 1-year ΔEI>1%), non-progressors (NP, n=301; 1-year ΔEI±0.5%) and never-smokers (NS: N=110). Segmental PA cross-sectional areas were standardized to associated airway luminal areas (Segmental : Pulmonary Artery-to-Airway Ratio: PAARseg). Full inspiratory CT scan-derived total (arteries + veins) pulmonary vascular volume (TPVV) was compared to vessel volume with radius smaller than 0.75mm (SVV.75/TPVV). Airway-to-lung ratios (an index of dysanapsis and COPD risk) were compared to TPVV-lung-volume-ratios. Results: Compared with NP, RP exhibited significantly larger PAARseg (0.73±0.29 vs. 0.67±0.23; p=0.001), lower TPVV-to-lung-volume ratio (3.21%±0.42% vs. 3.48%±0.38%; p=5.0 x 10-12), lower airway-to-lung-volume ratio (0.031±0.003 vs. 0.034±0.004; p=6.1 x 10-13) and larger SVV.75/TPVV (37.91%±4.26% vs. 35.53±4.89; p=1.9 x 10-7). In adjusted analyses, a 1-SD increment in PAARseg was associated with a 98.4% higher rate of severe exacerbations (95%CI: 29 to 206%; p = 0.002) and 79.3% higher in odds of being in the rapid emphysema progression group (95%CI: 24% to 157%; p = 0.001). At year-2 followup, the CT-defined RP group demonstrated a significant decline in post-bronchodilator-FEV1% predicted. Conclusion: 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.

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.

Response to high dose ipilimumab plus temozolomide after progression on standard or low dose ipilimumab in advanced melanoma: a retrospective analysis.

Background: Despite advancements in checkpoint inhibitor-based immunotherapy, patients with advanced melanoma who have progressed on standard dose ipilimumab (Ipi) + nivolumab continue to have poor prognosis. Several studies support a dose-response activity of Ipi, and one promising combination is Ipi 10mg/kg (Ipi10) + temozolomide (TMZ). Methods: We performed a retrospective cohort analysis of patients with advanced melanoma treated with Ipi10+TMZ in the immunotherapy refractory/resistant setting (n = 6), using similar patients treated with Ipi3+TMZ (n = 6) as comparison. Molecular profiling by whole exome sequencing (WES) and RNA-seq of tumors harvested through one responder's treatment was performed. Results: With a median follow up of 119 days, patients treated with Ipi10+TMZ had statistically significant longer median progression free survival of 144.5 days (range 27-219) vs 44 (26-75) in Ipi3+TMZ, p=0.04, and a trend for longer median overall survival of 154.5 days (27-537) vs 89.5 (26-548). All patients in the Ipi10 cohort had progressed on prior Ipi+Nivo. WES revealed only 12 shared somatic mutations including BRAF V600E. RNA-seq showed enrichment of inflammatory signatures, including interferon responses in metastatic lesions after standard dose Ipi + nivo and Ipi10 + TMZ compared to the primary tumor, and downregulated negative immune regulators including Wnt and TGFb signaling. Conclusion: Ipi10+TMZ demonstrated efficacy including dramatic responses in patients with advanced melanoma refractory to prior Ipi + anti-PD1, even with CNS metastases. Molecular data suggest a potential threshold of Ipi dose for activation of sufficient anti-tumor immune response, and higher dose Ipi is required for some patients.

Localization supervision of chest x-ray classifiers using label-specific eye-tracking annotation.

Convolutional neural networks (CNNs) have been successfully applied to chest x-ray (CXR) images. Moreover, annotated bounding boxes have been shown to improve the interpretability of a CNN in terms of localizing abnormalities. However, only a few relatively small CXR datasets containing bounding boxes are available, and collecting them is very costly. Opportunely, eye-tracking (ET) data can be collected during the clinical workflow of a radiologist. We use ET data recorded from radiologists while dictating CXR reports to train CNNs. We extract snippets from the ET data by associating them with the dictation of keywords and use them to supervise the localization of specific abnormalities. We show that this method can improve a model's interpretability without impacting its image-level classification.

Quantifying the Preferential Direction of the Model Gradient in Adversarial Training With Projected Gradient Descent.

Adversarial training, especially projected gradient descent (PGD), has proven to be a successful approach for improving robustness against adversarial attacks. After adversarial training, gradients of models with respect to their inputs have a preferential direction. However, the direction of alignment is not mathematically well established, making it difficult to evaluate quantitatively. We propose a novel definition of this direction as the direction of the vector pointing toward the closest point of the support of the closest inaccurate class in decision space. To evaluate the alignment with this direction after adversarial training, we apply a metric that uses generative adversarial networks to produce the smallest residual needed to change the class present in the image. We show that PGD-trained models have a higher alignment than the baseline according to our definition, that our metric presents higher alignment values than a competing metric formulation, and that enforcing this alignment increases the robustness of models.

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.

REFLACX, a dataset of reports and eye-tracking data for localization of abnormalities in chest x-rays.

Deep learning has shown recent success in classifying anomalies in chest x-rays, but datasets are still small compared to natural image datasets. Supervision of abnormality localization has been shown to improve trained models, partially compensating for dataset sizes. However, explicitly labeling these anomalies requires an expert and is very time-consuming. We propose a potentially scalable method for collecting implicit localization data using an eye tracker to capture gaze locations and a microphone to capture a dictation of a report, imitating the setup of a reading room. The resulting REFLACX (Reports and Eye-Tracking Data for Localization of Abnormalities in Chest X-rays) dataset was labeled across five radiologists and contains 3,032 synchronized sets of eye-tracking data and timestamped report transcriptions for 2,616 chest x-rays from the MIMIC-CXR dataset. We also provide auxiliary annotations, including bounding boxes around lungs and heart and validation labels consisting of ellipses localizing abnormalities and image-level labels. Furthermore, a small subset of the data contains readings from all radiologists, allowing for the calculation of inter-rater scores.

PLOSL: Population learning followed by one shot learning pulmonary image registration using tissue volume preserving and vesselness constraints.

This paper presents the Population Learning followed by One Shot Learning (PLOSL) pulmonary image registration method. PLOSL is a fast unsupervised learning-based framework for 3D-CT pulmonary image registration algorithm based on combining population learning (PL) and one-shot learning (OSL). The PLOSL image registration has the advantages of the PL and OSL approaches while reducing their respective drawbacks. The advantages of PLOSL include improved performance over PL, substantially reducing OSL training time and reducing the likelihood of OSL getting stuck in local minima. PLOSL pulmonary image registration uses tissue volume preserving and vesselness constraints for registration of inspiration-to-expiration and expiration-to-inspiration pulmonary CT images. A coarse-to-fine convolution encoder-decoder CNN architecture is used to register large and small shape features. During training, the sum of squared tissue volume difference (SSTVD) compensates for intensity differences between inspiration and expiration computed tomography (CT) images and the sum of squared vesselness measure difference (SSVMD) helps match the lung vessel tree. Results show that the PLOSL (SSTVD+SSVMD) algorithm achieved subvoxel landmark error while preserving pulmonary topology on the SPIROMICS data set, the public DIR-LAB COPDGene and 4DCT data sets.

Forced Expiratory Flow at 25%-75% Links COPD Physiology to Emphysema and Disease Severity in the SPIROMICS Cohort.

BACKGROUND: Forced expiratory volume in 1 second (FEV1) is central to the diagnosis of chronic obstructive pulmonary disease (COPD) but is imprecise in classifying disease burden. We examined the potential of the maximal mid-expiratory flow rate (forced expiratory flow rate between 25% and 75% [FEF25%-75%]) as an additional tool for characterizing pathophysiology in COPD. OBJECTIVE: To determine whether FEF25%-75% helps predict clinical and radiographic abnormalities in COPD. STUDY DESIGN AND METHODS: The SubPopulations and InteRediate Outcome Measures In COPD Study (SPIROMICS) enrolled a prospective cohort of 2978 nonsmokers and ever-smokers, with and without COPD, to identify phenotypes and intermediate markers of disease progression. We used baseline data from 2771 ever-smokers from the SPIROMICS cohort to identify associations between percent predicted FEF25%-75% (%predFEF25%-75%) and both clinical markers and computed tomography (CT) findings of smoking-related lung disease. RESULTS: Lower %predFEF25-75% was associated with more severe disease, manifested radiographically by increased functional small airways disease, emphysema (most notably with homogeneous distribution), CT-measured residual volume, total lung capacity (TLC), and airway wall thickness, and clinically by increased symptoms, decreased 6-minute walk distance, and increased bronchodilator responsiveness (BDR). A lower %predFEF25-75% remained significantly associated with increased emphysema, functional small airways disease, TLC, and BDR after adjustment for FEV1 or forced vital capacity (FVC). INTERPRETATION: The %predFEF25-75% provides additional information about disease manifestation beyond FEV1. These associations may reflect loss of elastic recoil and air trapping from emphysema and intrinsic small airways disease. Thus, %predFEF25-75% helps link the anatomic pathology and deranged physiology of COPD.

Mucus Plugs and Emphysema in the Pathophysiology of Airflow Obstruction and Hypoxemia in Smokers.

Rationale: The relative roles of mucus plugs and emphysema in mechanisms of airflow limitation and hypoxemia in smokers with chronic obstructive pulmonary disease (COPD) are uncertain.Objectives: To relate image-based measures of mucus plugs and emphysema to measures of airflow obstruction and oxygenation in patients with COPD.Methods: We analyzed computed tomographic (CT) lung images and lung function in participants in the Subpopulations and Intermediate Outcome Measures in COPD Study. Radiologists scored mucus plugs on CT lung images, and imaging software automatically quantified emphysema percentage. Unadjusted and adjusted relationships between mucus plug score, emphysema percentage, and lung function were determined using regression.Measurements and Main Results: Among 400 smokers, 229 (57%) had mucus plugs and 207 (52%) had emphysema, and subgroups could be identified with mucus-dominant and emphysema-dominant disease. Only 33% of smokers with high mucus plug scores had mucus symptoms. Mucus plug score and emphysema percentage were independently associated with lower values for FEV1 and peripheral oxygen saturation (P < 0.001). The relationships between mucus plug score and lung function outcomes were strongest in smokers with limited emphysema (P < 0.001). Compared with smokers with low mucus plug scores, those with high scores had worse COPD Assessment Test scores (17.4 ± 7.7 vs. 14.4 ± 13.3), more frequent annual exacerbations (0.75 ± 1.1 vs. 0.43 ± 0.85), and shorter 6-minute-walk distance (329 ± 115 vs. 392 ± 117 m) (P < 0.001).Conclusions: Symptomatically silent mucus plugs are highly prevalent in smokers and independently associate with lung function outcomes. These data provide rationale for targeting patients with mucus-high/emphysema-low COPD in clinical trials of mucoactive treatments.Clinical trial registered with www.clinicaltrials.gov (NCT01969344).

Prediction of Obstructive Lung Disease from Chest Radiographs via Deep Learning Trained on Pulmonary Function Data.

Background: Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is often underdiagnosed. Purpose: To develop machine learning methods to predict COPD using chest radiographs and a convolutional neural network (CNN) trained with near-concurrent pulmonary function test (PFT) data. Comparison is made to natural language processing (NLP) of the associated radiologist text reports. Materials and Methods: This IRB-approved single-institution retrospective study uses 6749 two-view chest radiograph exams (2012-2017, 4436 unique subjects, 54% female, 46% male), same-day associated radiologist text reports, and PFT exams acquired within 180 days. The Image Model (Resnet18 pre-trained with ImageNet CNN) is trained using frontal and lateral radiographs and PFTs with 10% of the subjects for validation and 19% for testing. The NLP Model is trained using radiologist text reports and PFTs. The primary metric of model comparison is the area under the receiver operating characteristic curve (AUC). Results: The Image Model achieves an AUC of 0.814 for prediction of obstructive lung disease (FEV1/FVC <0.7) from chest radiographs and performs better than the NLP Model (AUC 0.704, p<0.001) from radiologist text reports where FEV1 = forced expiratory volume in 1 second and FVC = forced vital capacity. The Image Model performs better for prediction of severe or very severe COPD (FEV1 <0.5) with an AUC of 0.837 versus the NLP model AUC of 0.770 (p<0.001). Conclusion: A CNN Image Model trained on physiologic lung function data (PFTs) can be applied to chest radiographs for quantitative prediction of obstructive lung disease with good accuracy.

Feasibility of multiple-view myocardial perfusion MRI using radial simultaneous multi-slice acquisitions.

PURPOSE: Dynamic contrast enhanced MRI of the heart typically acquires 2-4 short-axis (SA) slices to detect and characterize coronary artery disease. This acquisition scheme is limited by incomplete coverage of the left ventricle. We studied the feasibility of using radial simultaneous multi-slice (SMS) technique to achieve SA, 2-chamber and/or 4-chamber long-axis (2CH LA and/or 4CH LA) coverage with and without electrocardiography (ECG) gating using a motion-robust reconstruction framework. METHODS: 12 subjects were scanned at rest and/or stress, free breathing, with or without ECG gating. Multiple sets of radial SMS k-space were acquired within each cardiac cycle, and each SMS set sampled 3 parallel slices that were either SA, 2CH LA, or 4CH LA slices. The radial data was interpolated onto Cartesian space using an SMS GRAPPA operator gridding method. Self-gating and respiratory states binning of the data were done. The binning information as well as a pixel tracking spatiotemporal constrained reconstruction method were applied to obtain motion-robust image reconstructions. Reconstructions with and without the pixel tracking method were compared for signal-to-noise ratio and contrast-to-noise ratio. RESULTS: Full coverage of the heart (at least 3 SA and 3 LA slices) during the first pass of contrast at every heartbeat was achieved by using the radial SMS acquisition. The proposed pixel tracking reconstruction improves the average SNR and CNR by 21% and 30% respectively, and reduces temporal blurring for both gated and ungated acquisitions. CONCLUSION: Acquiring simultaneous multi-slice SA, 2CH LA and/or 4CH LA myocardial perfusion images in every heartbeat is feasible in both gated and ungated acquisitions. This can add confidence when detecting and characterizing coronary artery disease by revealing ischemia in different views, and by providing apical coverage that is improved relative to SA slices alone. The proposed pixel tracking framework improves the reconstruction while adding little computational cost.

CT measurements of central pulmonary vasculature as predictors of severe exacerbation in COPD.

To identify a predictive value for the exacerbation status of chronic obstructive pulmonary disease (COPD) subjects, we evaluated the relationship between pulmonary vascular measurements on chest CT and severe COPD exacerbation.Six hundred three subjects enrolled in the COPDGene population were included and divided into nonexacerbator (n = 313) and severe exacerbator (n = 290) groups, based on whether they had an emergency room visit and/or hospitalization for COPD exacerbation. We measured the diameter of the main pulmonary artery (MPA) and ascending aorta (AA) at 2 different sites of the MPA (the tubular midportion and bifurcation) on both axial images and multiplanar reconstructions. Using multiple logistic regression analyses, we evaluated the relationship between each CT-measured pulmonary vasculature and exacerbation status.Axial and multiplanar MPA to AA diameter ratios (PA:AA ratios) at the tubular midportion and the axial PA:AA ratios at the bifurcation indicated significant association with severe exacerbation. The strongest association was found with the axial PA:mean AA ratio at the bifurcation (adjusted odds ratio [OR] = 12.53, 95% confidence interval [CI] = 2.35-66.74, P = .003) and the axial PA:major AA ratio at the tubular midportion (adjusted OR = 10.72, 95% CI = 1.99-57.86, P = .006). No differences were observed in the MPA diameter. Receiver operating characteristic analysis of these variables indicates that they may serve as a good predictive value for severe exacerbation (area under the curve, 0.77-0.78). The range of cut-off value for PA:AA ratio was 0.8 to 0.87.CT-measured PA:AA ratios at either the bifurcation or the tubular site, measured either on axial or multiplanar images, are useful for identification of the risk of severe exacerbation, and consequently can be helpful in guiding the management of COPD. Although CT measurement was used at the level of pulmonary bifurcation in previous studies, we suggest that future studies should monitor the tubular site of the MPA for maximum diagnostic value of CT in pulmonary hypertension or severe COPD exacerbation, as the tubular site of the MPA remains relatively constant on CT images.

Distance between the left atrium and the vertebral body is predictive of esophageal movement in serial MR imaging.

PURPOSE: MRI or CT imaging can be used to identify the esophageal location prior to left atrial ablation, but the esophagus may move making the location unreliable when ablating to minimize esophageal injury. The aim of this study was to evaluate esophageal position and movement based on serial MRI imaging with the goal of identifying imaging and clinical characteristics that can predict the esophageal movement. METHODS: Fifty patients undergoing 190 MRI scans were analyzed. The relative position of the esophagus in each MRI along with clinical and imaging characteristics was quantified, including the gap between the left atrium (LA) and the vertebral body (GAP), an anatomic space in which the esophagus can move. RESULTS: A mean of 3.8 MRIs was analyzed per patient. Sixteen patients (32.0%) experienced significant lateral esophageal movement of more than 10 mm. In the significant movement group, body mass index (BMI) was higher (33.0 ± 6.5 vs 28.8 ± 5.3, p = 0.02) and the GAP was significantly larger (7.1 ± 2.5 vs 4.8 ± 5.1 mm, p = 0.04). Multivariate logistic regression analysis revealed that the GAP ≤ 4.5 mm was the only independent predictor of the esophagus not moving (odds ratio = 9.25, 95% confidence interval = 1.72 to 49.67, p = 0.0095). CONCLUSIONS: A GAP of less than 4.5 mm between the LA and the vertebral body is associated with lack of esophageal movement (< 10 mm). This suggests that the measurement of GAP < 4.5 mm may be used to predict the esophageal location in patients undergoing atrial ablation.

4D Flow Assessment of Vorticity in Right Ventricular Diastolic Dysfunction.

Diastolic dysfunction, a leading cause of heart failure in the US, is a complex pathology which manifests morphological and hemodynamic changes in the heart and circulatory system. Recent advances in time-resolved phase-contrast cardiac magnetic resonance imaging (4D Flow) have allowed for characterization of blood flow in the right ventricle (RV) and right atrium (RA), including calculation of vorticity and qualitative visual assessment of coherent flow patterns. We hypothesize that right ventricular diastolic dysfunction (RVDD) is associated with changes in vorticity and right heart blood flow. This paper presents background on RVDD, and 4D Flow tools and techniques used for quantitative and qualitative analysis of cardiac flows in the normal and disease states. In this study, 20 patients with RVDD and 14 controls underwent cardiac 4D Flow and echocardiography. A method for determining the time-step for peak early diastole using 4D Flow data is described. Spatially integrated early diastolic vorticity was extracted from the RV, RA, and combined RV/RA regions of each subject using a range of vorticity thresholding and scaling methods. Statistically significant differences in vorticity were found in the RA and combined RA/RV in RVDD subjects compared to controls when vorticity vectors were both thresholded and scaled by cardiac index.

Idiopathic Pulmonary Fibrosis: Data-driven Textural Analysis of Extent of Fibrosis at Baseline and 15-Month Follow-up.

Purpose To evaluate associations between pulmonary function and both quantitative analysis and visual assessment of thin-section computed tomography (CT) images at baseline and at 15-month follow-up in subjects with idiopathic pulmonary fibrosis (IPF). Materials and Methods This retrospective analysis of preexisting anonymized data, collected prospectively between 2007 and 2013 in a HIPAA-compliant study, was exempt from additional institutional review board approval. The extent of lung fibrosis at baseline inspiratory chest CT in 280 subjects enrolled in the IPF Network was evaluated. Visual analysis was performed by using a semiquantitative scoring system. Computer-based quantitative analysis included CT histogram-based measurements and a data-driven textural analysis (DTA). Follow-up CT images in 72 of these subjects were also analyzed. Univariate comparisons were performed by using Spearman rank correlation. Multivariate and longitudinal analyses were performed by using a linear mixed model approach, in which models were compared by using asymptotic χ2 tests. Results At baseline, all CT-derived measures showed moderate significant correlation (P < .001) with pulmonary function. At follow-up CT, changes in DTA scores showed significant correlation with changes in both forced vital capacity percentage predicted (ρ = -0.41, P < .001) and diffusing capacity for carbon monoxide percentage predicted (ρ = -0.40, P < .001). Asymptotic χ2 tests showed that inclusion of DTA score significantly improved fit of both baseline and longitudinal linear mixed models in the prediction of pulmonary function (P < .001 for both). Conclusion When compared with semiquantitative visual assessment and CT histogram-based measurements, DTA score provides additional information that can be used to predict diminished function. Automatic quantification of lung fibrosis at CT yields an index of severity that correlates with visual assessment and functional change in subjects with IPF. © RSNA, 2017.

Texture-based Quantification of Centrilobular Emphysema and Centrilobular Nodularity in Longitudinal CT Scans of Current and Former Smokers.

RATIONALE AND OBJECTIVES: The effect of smoking cessation on centrilobular emphysema (CLE) and centrilobular nodularity (CN), two manifestations of smoking-related lung injury on computed tomography (CT) images, has not been clarified. The objective of this study is to leverage texture analysis to investigate differences in extent of CLE and CN between current and former smokers. MATERIALS AND METHODS: Chest CT scans from 350 current smokers, 401 former smokers, and 25 control subjects were obtained from the multicenter COPDGene Study, a Health Insurance Portability and Accountability Act-compliant study approved by the institutional review board of each participating clinical study center. Additionally, for 215 of these subjects, a follow-up CT scan was obtained approximately 5 years later. For each CT scan, 5000 circular regions of interest (ROIs) of 35-pixel diameter were randomly selected throughout the lungs. The patterns present in each ROI were summarized by 50 computer-extracted texture features. A logistic regression classifier was leveraged to classify each ROI as normal lung, CLE, or CN, and differences in the percentages of normal lung, CLE, and CN by study group were assessed. RESULTS: Former smokers had significantly more CLE (P <0.01) but less CN (P <0.001) than did current smokers, even after adjustment for important covariates such as patient age, GOLD stage, smoking history, forced expiratory volume in 1 second, gas trapping, and scanner model. Among patients with longitudinal CT scans, continued smoking led to a slight increase in CLE (P = 0.13), whereas sustained abstinence from smoking led to further reduction in CN (P <0.05). CONCLUSIONS: The proposed texture-based approach quantifies the extent of CN and CLE with high precision. Differences in smoking-related lung disease between longitudinal scans of current smokers and longitudinal scans of former smokers suggest that CN may be reversible on smoking cessation.

Vorticity is a marker of diastolic ventricular interdependency in pulmonary hypertension.

Our objective was to determine whether left ventricular (LV) vorticity (ω), the local spinning motion of a fluid element, correlated with markers of ventricular interdependency in pulmonary hypertension (PH). Maladaptive ventricular interdependency is associated with interventricular septal shift, impaired LV performance, and poor outcomes in PH patients, yet the pathophysiologic mechanisms underlying fluid-structure interactions in ventricular interdependency are incompletely understood. Because conformational changes in chamber geometry affect blood flow formations and dynamics, LV ω may be a marker of LV-RV (right ventricular) interactions in PH. Echocardiography was performed for 13 PH patients and 10 controls for assessment of interdependency markers, including eccentricity index (EI), and biventricular diastolic dysfunction, including mitral valve (MV) and tricuspid valve (TV) early and late velocities (E and A, respectively) as well as MV septal and lateral early tissue Doppler velocities (e'). Same-day 4-dimensional cardiac magnetic resonance was performed for LV E (early)-wave ω measurement. LV E-wave ω was significantly decreased in PH patients (P = 0.008) and correlated with diastolic EI (Rho = -0.53, P = 0.009) as well as with markers of LV diastolic dysfunction, including MV E(Rho = 0.53, P = 0.011), E/A (Rho = 0.56, P = 0.007), septal e' (Rho = 0.63, P = 0.001), and lateral e' (Rho = 0.57, P = 0.007). Furthermore, LV E-wave ω was associated with indices of RV diastolic dysfunction, including TV e' (Rho = 0.52, P = 0.012) and TV E/A (Rho = 0.53, P = 0.009). LV E-wave ω is decreased in PH and correlated with multiple echocardiographic markers of ventricular interdependency. LV ω may be a novel marker for fluid-tissue biomechanical interactions in LV-RV interdependency.

4D magnetic resonance flow imaging for estimating pulmonary vascular resistance in pulmonary hypertension.

PURPOSE: To develop an estimate of pulmonary vascular resistance (PVR) using blood flow measurements from 3D velocity-encoded phase contract magnetic resonance imaging (here termed 4D MRI). MATERIALS AND METHODS: In all, 17 patients with pulmonary hypertension (PH) and five controls underwent right heart catheterization (RHC), 4D and 2D Cine MRI (1.5T) within 24 hours. MRI was used to compute maximum spatial peak systolic vorticity in the main pulmonary artery (MPA) and right pulmonary artery (RPA), cardiac output, and relative area change in the MPA. These parameters were combined in a four-parameter multivariate linear regression model to arrive at an estimate of PVR. Agreement between model predicted and measured PVR was also evaluated using Bland-Altman plots. Finally, model accuracy was tested by randomly withholding a patient from regression analysis and using them to validate the multivariate equation. RESULTS: A decrease in vorticity in the MPA and RPA were correlated with an increase in PVR (MPA: R(2) = 0.54, P < 0.05; RPA: R(2) = 0.75, P < 0.05). Expanding on this finding, we identified a multivariate regression equation that accurately estimates PVR (R(2) = 0.94, P < 0.05) across severe PH and normotensive populations. Bland-Altman plots showed 95% of the differences between predicted and measured PVR to lie within 1.49 Wood units. Model accuracy testing revealed a prediction error of ∼20%. CONCLUSION: A multivariate model that includes MPA relative area change and flow characteristics, measured using 4D and 2D Cine MRI, offers a promising technique for noninvasively estimating PVR in PH patients. J. MAGN. RESON. IMAGING 2016;44:914-922.