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Abstract Low muscle mass is associated with numerous adverse outcomes independent of other associated comorbid diseases. We aimed to predict and understand an individual’s risk for developing low muscle mass using proteomics and machine learning. We identified eight biomarkers associated with low pectoralis muscle area (PMA). We built three random forest classification models that used either clinical measures, feature selected biomarkers, or both to predict development of low PMA. The area under the receiver operating characteristic curve for each model was: clinical-only = 0.646, biomarker-only = 0.740, and combined = 0.744. We displayed the heterogenetic nature of an individual’s risk for developing low PMA and identified two distinct subtypes of participants who developed low PMA. While additional validation is required, our methods for identifying and understanding individual and group risk for low muscle mass could be used to enable developments in the personalized prevention of low muscle mass.

Abstract Background Quantitative interstitial abnormalities (QIA) are an automated computed tomography (CT) finding of early parenchymal lung disease, associated with worse lung function, reduced exercise capacity, increased respiratory symptoms, and death. The metabolomic perturbations associated with QIA are not well known. We sought to identify plasma metabolites associated with QIA in smokers. We also sought to identify shared and differentiating metabolomics features between QIA and emphysema, another smoking-related advanced radiographic abnormality. Methods In 928 former and current smokers in the Genetic Epidemiology of COPD cohort, we measured QIA and emphysema using an automated local density histogram method and generated metabolite profiles from plasma samples using liquid chromatography–mass spectrometry (Metabolon). We assessed the associations between metabolite levels and QIA using multivariable linear regression models adjusted for age, sex, body mass index, smoking status, pack-years, and inhaled corticosteroid use, at a Benjamini–Hochberg False Discovery Rate p-value of ≤ 0.05. Using multinomial regression models adjusted for these covariates, we assessed the associations between metabolite levels and the following CT phenotypes: QIA-predominant, emphysema-predominant, combined-predominant, and neither- predominant. Pathway enrichment analyses were performed using MetaboAnalyst. Results We found 85 metabolites significantly associated with QIA, with overrepresentation of the nicotinate and nicotinamide, histidine, starch and sucrose, pyrimidine, phosphatidylcholine, lysophospholipid, and sphingomyelin pathways. These included metabolites involved in inflammation and immune response, extracellular matrix remodeling, surfactant, and muscle cachexia. There were 75 metabolites significantly different between QIA-predominant and emphysema-predominant phenotypes, with overrepresentation of the phosphatidylethanolamine, nicotinate and nicotinamide, aminoacyl-tRNA, arginine, proline, alanine, aspartate, and glutamate pathways. Conclusions Metabolomic correlates may lend insight to the biologic perturbations and pathways that underlie clinically meaningful quantitative CT measurements like QIA in smokers.

Abstract Quantitative assessment of emphysema in CT scans has mostly focused on calculating the percentage of lung tissue that is deemed abnormal based on a density thresholding strategy. However, this overall measure of disease burden discards virtually all the spatial information encoded in the scan that is implicitly utilized in a visual assessment. This simplification is likely grouping heterogenous disease patterns and is potentially obscuring clinical phenotypes and variable disease outcomes. To overcome this, several methods that attempt to quantify heterogeneity in emphysema distribution have been proposed. Here, we compare three of those: one based on estimating a power law for the size distribution of contiguous emphysema clusters, a second that looks at the number of emphysema-to-emphysema voxel adjacencies, and a third that applies a parametric spatial point process model to the emphysema voxel locations. This was done using data from 587 individuals from Phase 1 of COPDGene that had an inspiratory CT scan and plasma protein abundance measurements. The associations between these imaging metrics and visual assessment with clinical measures (FEV $$_1$$ 1 , FEV $$_1$$ 1 -FVC ratio, etc.) and plasma protein biomarker levels were evaluated using a variety of regression models. Our results showed that a selection of spatial measures had the ability to discern heterogeneous patterns among CTs that had similar emphysema burdens. The most informative quantitative measure, average cluster size from the point process model, showed much stronger associations with nearly every clinical outcome examined than existing CT-derived emphysema metrics and visual assessment. Moreover, approximately 75% more plasma biomarkers were found to be associated with an emphysema heterogeneity phenotype when accounting for spatial clustering measures than when they were excluded.

Background and objectivesThe scarcity of data for training deep learning models in pediatrics has prompted questions about the feasibility of employing CNNs trained with adult images for pediatric populations. In this work, a pneumonia classification CNN was used as an exploratory example to showcase the adaptability and efficacy of such models in pediatric healthcare settings despite the inherent data constraints.MethodsTo develop a curated training dataset with reduced biases, 46,947 chest X-ray images from various adult datasets were meticulously selected. Two preprocessing approaches were tried to assess the impact of thoracic segmentation on model attention outside the thoracic area. Evaluation of our approach was carried out on a dataset containing 5,856 chest X-rays of children from 1 to 5 years old.ResultsAn analysis of attention maps indicated that networks trained with thorax segmentation placed less attention on regions outside the thorax, thus eliminating potential bias. The ensuing network exhibited impressive performance when evaluated on an adult dataset, achieving a pneumonia discrimination AUC of 0.95. When tested on a pediatric dataset, the pneumonia discrimination AUC reached 0.82.ConclusionsThe results of this study show that adult-trained CNNs can be effectively applied to pediatric populations. This could potentially shift focus towards validating adult models over pediatric population instead of training new CNNs with limited pediatric data. To ensure the generalizability of deep learning models, it is important to implement techniques aimed at minimizing biases, such as image segmentation or low-quality image exclusion.

Abstract Background Sarcopenia, or loss of skeletal muscle mass and decreased contractile strength, contributes to morbidity and mortality in patients with chronic obstructive pulmonary disease (COPD). The severity of sarcopenia in COPD is variable, and there are limited data to explain phenotype heterogeneity. Others have shown that COPD patients with sarcopenia have several hallmarks of cellular senescence, a potential mechanism of primary (age‐related) sarcopenia. We tested if genetic contributors explain the variability in sarcopenic phenotype and accelerated senescence in COPD. Methods To identify gene variants [single nucleotide polymorphisms (SNPs)] associated with sarcopenia in COPD, we performed a genome‐wide association study (GWAS) of fat free mass index (FFMI) in 32 426 non‐Hispanic White (NHW) UK Biobank participants with COPD. Several SNPs within the fat mass and obesity‐associated (FTO) gene were associated with sarcopenia that were validated in an independent COPDGene cohort (n = 3656). Leucocyte telomere length quantified in the UK Biobank cohort was used as a marker of senescence. Experimental validation was done by genetic depletion of FTO in murine skeletal myotubes exposed to prolonged intermittent hypoxia or chronic hypoxia because hypoxia contributes to sarcopenia in COPD. Molecular biomarkers for senescence were also quantified with FTO depletion in murine myotubes. Results Multiple SNPs located in the FTO gene were associated with sarcopenia in addition to novel SNPs both within and in proximity to the gene AC090771.2, which transcribes long non‐coding RNA (lncRNA). To replicate our findings, we performed a GWAS of FFMI in NHW subjects from COPDGene. The SNP most significantly associated with FFMI was on chromosome (chr) 16, rs1558902A > T in the FTO gene (β = 0.151, SE = 0.021, P = 1.40 × 10−12 for UK Biobank |β= 0.220, SE = 0.041, P = 9.99 × 10−8 for COPDGene) and chr 18 SNP rs11664369C > T nearest to the AC090771.2 gene (β = 0.129, SE = 0.024, P = 4.64 × 10−8 for UK Biobank |β = 0.203, SE = 0.045, P = 6.38 × 10−6 for COPDGene). Lower handgrip strength, a measure of muscle strength, but not FFMI was associated with reduced telomere length in the UK Biobank. Experimentally, in vitro knockdown of FTO lowered myotube diameter and induced a senescence‐associated molecular phenotype, which was worsened by prolonged intermittent hypoxia and chronic hypoxia. Conclusions Genetic polymorphisms of FTO and AC090771.2 were associated with sarcopenia in COPD in independent cohorts. Knockdown of FTO in murine myotubes caused a molecular phenotype consistent with senescence that was exacerbated by hypoxia, a common condition in COPD. Genetic variation may interact with hypoxia and contribute to variable severity of sarcopenia and skeletal muscle molecular senescence phenotype in COPD.

Pulmonary arterial hypertension associated with schistosomiasis (SchPAH) and pulmonary arterial hypertension associated with portal hypertension (PoPAH) are lung diseases that develop in the presence of liver diseases. However, mechanistic pathways by which the underlying liver conditions and other drivers contribute to the development and progression of pulmonary arterial hypertension (PAH) are unclear for both etiologies. In turn, these unknowns limit certainty of strategies to prevent, diagnose, and reverse the resultant PAH. Here we consider specific mechanisms that contribute to SchPAH and PoPAH, identifying those that may be shared and those that appear to be unique to each etiology, in the hope that this exploration will both highlight known causal drivers and identify knowledge gaps appropriate for future research. Overall, the key pathophysiologic differences that we identify between SchPAH and PoPAH suggest that they are not variants of a single condition.

Abstract The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists’ severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists’ interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists’ severity score.

Pulmonary hypertension is characterized histologically by intimal and medial thickening in the small pulmonary arteries, eventually resulting in vascular “pruning.” Computed tomography (CT)‐based quantification of pruning is associated with clinical measures of pulmonary hypertension, but it is not established whether CT‐based pruning correlates with histologic arterial remodeling. Our sample consisted of 138 patients who underwent resection for early‐stage lung adenocarcinoma. From histologic sections, we identified small pulmonary arteries and measured the relative area comprising the intima and media (VWA%), with higher VWA% representing greater histologic remodeling. From pre‐operative CTs, we used image analysis algorithms to calculate the small vessel volume fraction (BV5/TBV) as a CT‐based indicator of pruning (lower BV5/TBV represents greater pruning). We investigated relationships of CT pruning and histologic remodeling using Pearson correlation, simple linear regression, and multivariable regression with adjustment for age, sex, height, weight, smoking status, and total pack‐years. We also tested for effect modification by sex and smoking status. In primary models, more severe CT pruning was associated with greater histologic remodeling. The Pearson correlation coefficient between BV5/TBV and VWA% was –0.41, and in linear regression models, VWA% was 3.13% higher (95% CI: 1.95–4.31%, p

Abstract Throughout the course of this Workshop, members of the advocacy community, imaging researchers, computer and measurement scientists, clinicians and policy-focused workshop attendees engaged in cross-cutting discussions from innovative technical aspects of thoracic imaging to policy approaches to ensure equitable access to all at-risk individuals when implementing lung cancer screening services. A major aspect of these implementation discussions was how to efficiently collect routine thoracic CT-based screening with de-identified clinical outcomes data to support the development of robust imaging tools, including responsible AI development, to better detect and manage early lung cancer as well as other major tobacco-related thoracic diseases. A future vision involves routinely collecting a substantial fraction of every thoracic screening CT image to establish a large, curated collection of de-identified thoracic CT images with clinical outcome data to support open research for building better computational imaging tools for early thoracic disease management. Imaging researchers are positioned to develop much better workflow software tools to promote more efficient, outpatient management of the screening process for populations at-risk for lung cancer, especially with the rapid development of promising AI tools. Efficient and effective management tools for the large numbers of at-risk ever smokers could allow the primary care community to discuss lung cancer screening despite their heavy existing clinical demands. Supporting the primary care community in this fashion may significantly improve the current slow uptake of lung cancer screening and save many lives in the process.

The phenotypic characteristics of chronic obstructive pulmonary disease (COPD) in individuals younger than 50 years of age (early COPD) are not well defined. This prospective, multicentre, case–control study sought to describe these characteristics and compare them with those of smokers (≥10 pack-years) of similar age with normal spirometry (controls). We studied 92 cases (post-bronchodilator forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC)

Murine studies have linked TGF-β signaling to emphysema, and human genome-wide association studies (GWAS) studies of lung function and COPD have identified associated regions near genes in the TGF-β superfamily. However, the functional regulatory mechanisms at these loci have not been identified. We performed the largest GWAS of emphysema patterns to date, identifying 10 GWAS loci including an association peak spanning a 200 kb region downstream from TGFB2. Integrative analysis of publicly available eQTL, DNaseI, and chromatin conformation data identified a putative functional variant, rs1690789, that may regulate TGFB2 expression in human fibroblasts. Using chromatin conformation capture, we confirmed that the region containing rs1690789 contacts the TGFB2 promoter in fibroblasts, and CRISPR/Cas-9 targeted deletion of a ~ 100 bp region containing rs1690789 resulted in decreased TGFB2 expression in primary human lung fibroblasts. These data provide novel mechanistic evidence linking genetic variation affecting the TGF-β pathway to emphysema in humans.

There are few studies looking at the pulmonary circulation in subjects with bronchiectasis. We aimed to evaluate the intraparenchymal pulmonary vascular structure, using noncontrast chest computed tomography (CT), and its clinical implications in smokers with radiographic bronchiectasis. Visual bronchiectasis scoring and quantitative assessment of the intraparenchymal pulmonary vasculature were performed on CT scans from 486 smokers. Clinical, lung function and 6-min walk test (6MWT) data were also collected. The ratio of blood vessel volume in vessels

Right Ventricular to Left Ventricular (RV/LV) diameter ratio has been shown to be a prognostic biomarker for patients suffering from acute Pulmonary Embolism (PE). While Computed Tomography Pulmonary Angiography (CTPA) images used to confirm a clinical suspicion of PE do include information of the heart, a numerical RV/LV diameter ratio is not universally reported, likely because of lack in training, inter-reader variability in the measurements, and additional effort by the radiologist. This study designs and validates a completely automated Computer Aided Detection (CAD) system to compute the axial RV/LV diameter ratio from CTPA images so that the RV/LV diameter ratio can be a more objective metric that is consistently reported in patients for whom CTPA diagnoses PE.The CAD system was designed specifically for RV/LV measurements. The system was tested in 198 consecutive CTPA patients with acute PE. Its accuracy was evaluated using reference standard RV/LV radiologist measurements and its prognostic value was established for 30-day PE-specific mortality and a composite outcome of 30-day PE-specific mortality or the need for intensive therapies. The study was Institutional Review Board (IRB) approved and HIPAA compliant.The CAD system analyzed correctly 92.4% (183/198) of CTPA studies. The mean difference between automated and manually computed axial RV/LV ratios was 0.03±0.22. The correlation between the RV/LV diameter ratio obtained by the CAD system and that obtained by the radiologist was high (r=0.81). Compared to the radiologist, the CAD system equally achieved high accuracy for the composite outcome, with areas under the receiver operating characteristic curves of 0.75 vs. 0.78. Similar results were found for 30-days PE-specific mortality, with areas under the curve of 0.72 vs. 0.75.An automated CAD system for determining the CT derived RV/LV diameter ratio in patients with acute PE has high accuracy when compared to manual measurements and similar prognostic significance for two clinical outcomes.