Myelin-reactive B cells exacerbate CD4+ T cell-driven CNS autoimmunity in an IL-23-dependent manner.

B cells and T cells collaborate in multiple sclerosis (MS) pathogenesis. IgH[MOG] mice possess a B cell repertoire skewed to recognize myelin oligodendrocyte glycoprotein (MOG). Here, we show that upon immunization with the T cell-obligate autoantigen, MOG[35-55], IgH[MOG] mice develop rapid and exacerbated experimental autoimmune encephalomyelitis (EAE) relative to wildtype (WT) counterparts, characterized by aggregation of T and B cells in the IgH[MOG] meninges and by CD4+ T helper 17 (Th17) cells in the CNS. Production of the Th17 maintenance factor IL-23 is observed from IgH[MOG] CNS-infiltrating and meningeal B cells, and in vivo blockade of IL-23p19 attenuates disease severity in IgH[MOG] mice. In the CNS parenchyma and dura mater of IgH[MOG] mice, we observe an increased frequency of CD4+PD-1+CXCR5- T cells that share numerous characteristics with the recently described T peripheral helper (Tph) cell subset. Further, CNS-infiltrating B and Tph cells from IgH[MOG] mice show increased reactive oxygen species (ROS) production. Meningeal inflammation, Tph-like cell accumulation in the CNS and B/Tph cell production of ROS were all reduced upon p19 blockade. Altogether, MOG-specific B cells promote autoimmune inflammation of the CNS parenchyma and meninges in an IL-23-dependent manner.

A deep learning-based algorithm improves radiology residents' diagnoses of acute pulmonary embolism on CT pulmonary angiograms.

PURPOSE: To compare radiology residents' diagnostic performances to detect pulmonary emboli (PEs) on CT pulmonary angiographies (CTPAs) with deep-learning (DL)-based algorithm support and without. METHODS: Fully anonymized CTPAs (n = 207) of patients suspected of having acute PE served as input for PE detection using a previously trained and validated DL-based algorithm. Three residents in their first three years of training, blinded to the index report and clinical history, read the CTPAs first without, and 2 months later with the help of artificial intelligence (AI) output, to diagnose PE as present, absent or indeterminate. We evaluated concordances and discordances with the consensus-reading results of two experts in chest imaging. RESULTS: Because the AI algorithm failed to analyze 11 CTPAs, 196 CTPAs were analyzed; 31 (15.8 %) were PE-positive. Good-classification performance was higher for residents with AI-algorithm support than without (AUROCs: 0.958 [95 % CI: 0.921-0.979] vs. 0.894 [95 % CI: 0.850-0.931], p < 0.001, respectively). The main finding was the increased sensitivity of residents' diagnoses using the AI algorithm (92.5 % vs. 81.7 %, respectively). Concordance between residents (kappa: 0.77 [95 % CI: 0.76-0.78]; p < 0.001) improved with AI-algorithm use (kappa: 0.88 [95 % CI: 0.87-0.89]; p < 0.001). CONCLUSION: The AI algorithm we used improved between-resident agreements to interpret CTPAs for suspected PE and, hence, their diagnostic performances.

Primary immunodeficiency diseases of adults: a review of pulmonary complication imaging findings.

Our objective in this review is to familiarize radiologists with the spectrum of initial and progressive CT manifestations of pulmonary complications observed in adult patients with primary immunodeficiency diseases, including primary antibody deficiency (PAD), hyper-IgE syndrome (HIES), and chronic granulomatous disease (CGD). In patients with PAD, recurrent pulmonary infections may lead to airway remodeling with bronchial wall-thickening, bronchiectasis, mucus-plugging, mosaic perfusion, and expiratory air-trapping. Interstitial lung disease associates pulmonary lymphoid hyperplasia, granulomatous inflammation, and organizing pneumonia and is called granulomatous-lymphocytic interstitial lung disease (GLILD). The CT features of GLILD are solid and semi-solid pulmonary nodules and areas of air space consolidation, reticular opacities, and lymphadenopathy. These features may overlap those of mucosa-associated lymphoid tissue (MALT) lymphoma, justifying biopsies. In patients with HIES, particularly the autosomal dominant type (Job syndrome), recurrent pyogenic infections lead to permanent lung damage. Secondary infections with aspergillus species develop in pre-existing pneumatocele and bronchiectasis areas, leading to chronic airway infection. The complete spectrum of CT pulmonary aspergillosis may be seen including aspergillomas, chronic cavitary pulmonary aspergillosis, allergic bronchopulmonary aspergillosis (ABPA)-like pattern, mixed pattern, and invasive. Patients with CGD present with recurrent bacterial and fungal infections leading to parenchymal scarring, traction bronchiectasis, cicatricial emphysema, airway remodeling, and mosaicism. Invasive aspergillosis, the major cause of mortality, manifests as single or multiple nodules, areas of airspace consolidation that may be complicated by abscess, empyema, or contiguous extension to the pleura or chest wall. CLINICAL RELEVANCE STATEMENT: Awareness of the imaging findings spectrum of pulmonary complications that can occur in adult patients with primary immunodeficiency diseases is important to minimize diagnostic delay and improve patient outcomes. KEY POINTS: • Unexplained bronchiectasis, associated or not with CT findings of obliterative bronchiolitis, should evoke a potential diagnosis of primary autoantibody deficiency. • The CT evidence of various patterns of aspergillosis developed in severe bronchiectasis or pneumatocele in a young adult characterizes the pulmonary complications of hyper-IgE syndrome. • In patients with chronic granulomatous disease, invasive aspergillosis is relatively frequent, often asymptomatic, and sometimes mimicking or associated with non-infectious inflammatory pulmonary lesions.

Deep Learning-Based Algorithm for Automatic Detection of Pulmonary Embolism in Chest CT Angiograms.

PURPOSE: Since the prompt recognition of acute pulmonary embolism (PE) and the immediate initiation of treatment can significantly reduce the risk of death, we developed a deep learning (DL)-based application aimed to automatically detect PEs on chest computed tomography angiograms (CTAs) and alert radiologists for an urgent interpretation. Convolutional neural networks (CNNs) were used to design the application. The associated algorithm used a hybrid 3D/2D UNet topology. The training phase was performed on datasets adequately distributed in terms of vendors, patient age, slice thickness, and kVp. The objective of this study was to validate the performance of the algorithm in detecting suspected PEs on CTAs. METHODS: The validation dataset included 387 anonymized real-world chest CTAs from multiple clinical sites (228 U.S. cities). The data were acquired on 41 different scanner models from five different scanner makers. The ground truth (presence or absence of PE on CTA images) was established by three independent U.S. board-certified radiologists. RESULTS: The algorithm correctly identified 170 of 186 exams positive for PE (sensitivity 91.4% [95% CI: 86.4-95.0%]) and 184 of 201 exams negative for PE (specificity 91.5% [95% CI: 86.8-95.0%]), leading to an accuracy of 91.5%. False negative cases were either chronic PEs or PEs at the limit of subsegmental arteries and close to partial volume effect artifacts. Most of the false positive findings were due to contrast agent-related fluid artifacts, pulmonary veins, and lymph nodes. CONCLUSIONS: The DL-based algorithm has a high degree of diagnostic accuracy with balanced sensitivity and specificity for the detection of PE on CTAs.

Dual-Labelled Nanoparticles Inform on the Stability of Fluorescent Labels In Vivo.

Fluorescent labelling is commonly used to monitor the biodistribution of nanomedicines. However, meaningful interpretation of the results requires that the fluorescent label remains attached to the nanomedicine. In this work, we explore the stability of three fluorophores (BODIPY650, Cyanine 5 and AZ647) attached to polymeric hydrophobic biodegradable anchors. Using dual-labelled poly(ethylene glycol)-b-poly(lactic acid) (PEG-PLA) nanoparticles that are both radioactive and fluorescent, we investigated how the properties of the fluorophores impact the stability of the labelling in vitro and in vivo. Results suggest that the more hydrophilic dye (AZ647) is released faster from nanoparticles, and that this instability results in misinterpretation of in vivo data. While hydrophobic dyes are likely more suitable to track nanoparticles in biological environments, quenching of the fluorescence inside the nanoparticles can also introduce artefacts. Altogether, this work raises awareness about the importance of stable labelling methods when investigating the biological fate of nanomedicines.

The mechanisms of anti-PEG immune response are different in the spleen and the lymph nodes.

Polyethylene glycol (PEG) is a common ingredient in nanomedicines and pharmaceuticals. Recent studies show that approximately 20-70% of humans have anti-PEG antibodies that can recognize the polymer. Because these anti-PEG antibodies can reduce the effectiveness of certain PEGylated therapeutics, understanding how these immunoglobulins are produced is important. In this work, we investigate the mechanisms of the anti-PEG immune response, following the injection of polymeric nanoparticles by different routes of administration. We observed that the extent of systemic absorption and splenic deposition cannot predict the production of anti-PEG IgM - possibly because redundant biological pathways can be involved. Data obtained by surgically removing the spleen or depleting the complement activity suggest that the mechanisms behind the anti-PEG immune response differ between intravenous and subcutaneous injections. While B cells from the spleen appear to necessitate complement proteins to interact with nanoparticles, internalization by follicular B cells from the lymph nodes is unaffected by depletion of the cascade. This study confirms that the biological mechanisms involved in the immune recognition of nanomedicines varies based on the administration route. This knowledge can be utilized to use nanomedicines to engage the immune system in differentiated ways.

The Potential Role of Artificial Intelligence in Lung Cancer Screening Using Low-Dose Computed Tomography.

Two large randomized controlled trials of low-dose CT (LDCT)-based lung cancer screening (LCS) in high-risk smoker populations have shown a reduction in the number of lung cancer deaths in the screening group compared to a control group. Even if various countries are currently considering the implementation of LCS programs, recurring doubts and fears persist about the potentially high false positive rates, cost-effectiveness, and the availability of radiologists for scan interpretation. Artificial intelligence (AI) can potentially increase the efficiency of LCS. The objective of this article is to review the performances of AI algorithms developed for different tasks that make up the interpretation of LCS CT scans, and to estimate how these AI algorithms may be used as a second reader. Despite the reduction in lung cancer mortality due to LCS with LDCT, many smokers die of comorbid smoking-related diseases. The identification of CT features associated with these comorbidities could increase the value of screening with minimal impact on LCS programs. Because these smoking-related conditions are not systematically assessed in current LCS programs, AI can identify individuals with evidence of previously undiagnosed cardiovascular disease, emphysema or osteoporosis and offer an opportunity for treatment and prevention.

Size Exclusion of Radioactive Polymers (SERP) informs on the biodegradation of trimethyl chitosan and biodegradable polymer nanoparticles in vitro and in vivo.

Preparation of drug delivery systems and nanomedicines necessitates the use of biocompatible excipients that are readily eliminated from the body. The systematic preclinical development of novel materials requires tools to evaluate their pharmacokinetics, biodistribution and excretion. Herein, we propose a technique called Size Exclusion of Radioactive Polymers (SERP) to trail the disposition of a radiolabeled polymer and its nanoparticles using chromatography in the presence of complex biological media such as blood, urine and feces. Trimethyl chitosan (TMC) is a polysaccharide of natural origin showing promise for controlled and targeted drug delivery applications. SERP was used to monitor degradation of radiolabeled TMC and its nanoparticles in vitro in the presence of strong acid, enzymes released by macrophages, as well as in vivo after administration to rats. Excretion of the radiolabeled TMC nanoparticles in urine and feces was monitored for 14 days after dosing to healthy rats, confirming that the polymer could be readily eliminated from the body. This work demonstrates the ability of SERP to understand the biological journey of biomaterials in vivo. Paving the way to understand the fate of polymers and nanoparticles in complex environments, the technique might facilitate the development of safer and better tolerated nanomedicines.

Idiopathic inflammatory myopathies: CT characteristics of interstitial lung disease and their association(s) with myositis-specific autoantibodies.

OBJECTIVES: Interstitial lung disease (ILD), one of the most common extramuscular manifestations of idiopathic inflammatory myopathies (IIMs), carries a poor prognosis. Myositis-specific autoantibody (MSA)-positivity is a key finding for IIM diagnosis. We aimed to identify IIM-associated lung patterns, evaluate potential CT-ILD finding-MSA relationships, and assess intra- and interobserver reproducibility in a large IIM population. METHODS: All consecutive IIM patients (2003-2019) were included. Two chest radiologists retrospectively assessed all chest CT scans. Multiple correspondence and hierarchical cluster analyses of CT findings identified and characterized ILD-patient subgroups. Classification and regression-tree analyses highlighted CT-scan variables predicting three patterns. Three independent radiologists read CT scans twice to assign patients according to CT-ILD-pattern clusters. RESULTS: Among 257 IIM patients, 94 (36.6%) had ILDs; 87 (93%) of them were MSA-positive. ILD-IIM distribution was 54 (57%) ASyS, 21 (22%) DM, 15 (16%) IMNM, and 4 (4%) IBM. Cluster analysis identified three ILD-patient subgroups. Consolidation characterized cluster 1, with significantly (p < 0.05) more frequent anti-MDA5-autoantibody-positivity. Significantly more cluster-2 patients had a reticular pattern, without cysts and with few consolidations. All cluster-3 patients had cysts and anti-PL12 autoantibodies. Clusters 2 and 3 included significantly more ASyS patients. Intraobserver concordances to classify patients into those three clusters were good-to-excellent (Cohen κ 0.64-0.81), with good interobserver reliability (Fleiss's κ 0.56). CONCLUSION: Despite the observed IIM heterogeneity, CT-scan criteria enabled ILD assignment to the three clusters, which were associated with MSAs. Radiologist identification of those clusters could facilitate diagnostic screening and therapeutics. Interstitial lung disease in patients with idiopathic inflammatory myopathy could be classified into three clusters according to CT-scan criteria, and these clusters were significantly associated with myositis-specific autoantibodies. KEY POINTS: • Cluster analysis discerned three homogeneous groups of interstitial lung disease (ILD) for which cysts, consolidations, and reticular pattern were discriminatory, and associated with myositis-specific autoantibodies. • Like muscle- and extramuscular-specific phenotypes, myositis-specific autoantibodies are also associated with specific ILD patterns in patients with idiopathic inflammatory myopathies.

X-ray dark-field chest imaging for detection and quantification of emphysema in patients with chronic obstructive pulmonary disease: a diagnostic accuracy study.

BACKGROUND: Although advanced medical imaging technologies give detailed diagnostic information, a low-dose, fast, and inexpensive option for early detection of respiratory diseases and follow-ups is still lacking. The novel method of x-ray dark-field chest imaging might fill this gap but has not yet been studied in living humans. Enabling the assessment of microstructural changes in lung parenchyma, this technique presents a more sensitive alternative to conventional chest x-rays, and yet requires only a fraction of the dose applied in CT. We studied the application of this technique to assess pulmonary emphysema in patients with chronic obstructive pulmonary disease (COPD). METHODS: In this diagnostic accuracy study, we designed and built a novel dark-field chest x-ray system (Technical University of Munich, Munich, Germany)-which is also capable of simultaneously acquiring a conventional thorax radiograph (7 s, 0·035 mSv effective dose). Patients who had undergone a medically indicated chest CT were recruited from the department of Radiology and Pneumology of our site (Klinikum rechts der Isar, Technical University of Munich, Munich, Germany). Patients with pulmonary pathologies, or conditions other than COPD, that might influence lung parenchyma were excluded. For patients with different disease stages of pulmonary emphysema, x-ray dark-field images and CT images were acquired and visually assessed by five readers. Pulmonary function tests (spirometry and body plethysmography) were performed for every patient and for a subgroup of patients the measurement of diffusion capacity was performed. Individual patient datasets were statistically evaluated using correlation testing, rank-based analysis of variance, and pair-wise post-hoc comparison. FINDINGS: Between October, 2018 and December, 2019 we enrolled 77 patients. Compared with CT-based parameters (quantitative emphysema ρ=-0·27, p=0·089 and visual emphysema ρ=-0·45, p=0·0028), the dark-field signal (ρ=0·62, p<0·0001) yields a stronger correlation with lung diffusion capacity in the evaluated cohort. Emphysema assessment based on dark-field chest x-ray features yields consistent conclusions with findings from visual CT image interpretation and shows improved diagnostic performance than conventional clinical tests characterising emphysema. Pair-wise comparison of corresponding test parameters between adjacent visual emphysema severity groups (CT-based, reference standard) showed higher effect sizes. The mean effect size over the group comparisons (absent-trace, trace-mild, mild-moderate, and moderate-confluent or advanced destructive visual emphysema grades) for the COPD assessment test score is 0·21, for forced expiratory volume in 1 s (FEV1)/functional vital capacity is 0·25, for FEV1% of predicted is 0·23, for residual volume % of predicted is 0·24, for CT emphysema index is 0·35, for dark-field signal homogeneity within lungs is 0·38, for dark-field signal texture within lungs is 0·38, and for dark-field-based emphysema severity is 0·42. INTERPRETATION: X-ray dark-field chest imaging allows the diagnosis of pulmonary emphysema in patients with COPD because this technique provides relevant information representing the structural condition of lung parenchyma. This technique might offer a low radiation dose alternative to CT in COPD and potentially other lung disorders. FUNDING: European Research Council, Deutsche Forschungsgemeinschaft, Royal Philips, and Karlsruhe Nano Micro Facility.

Automated AI-Driven CT Quantification of Lung Disease Predicts Adverse Outcomes in Patients Hospitalized for COVID-19 Pneumonia.

The purpose of our work was to assess the independent and incremental value of AI-derived quantitative determination of lung lesions extent on initial CT scan for the prediction of clinical deterioration or death in patients hospitalized with COVID-19 pneumonia. 323 consecutive patients (mean age 65 ± 15 years, 192 men), with laboratory-confirmed COVID-19 and an abnormal chest CT scan, were admitted to the hospital between March and December 2020. The extent of consolidation and all lung opacities were quantified on an initial CT scan using a 3D automatic AI-based software. The outcome was known for all these patients. 85 (26.3%) patients died or experienced clinical deterioration, defined as intensive care unit admission. In multivariate regression based on clinical, biological and CT parameters, the extent of all opacities, and extent of consolidation were independent predictors of adverse outcomes, as were diabetes, heart disease, C-reactive protein, and neutrophils/lymphocytes ratio. The association of CT-derived measures with clinical and biological parameters significantly improved the risk prediction (p = 0.049). Automated quantification of lung disease at CT in COVID-19 pneumonia is useful to predict clinical deterioration or in-hospital death. Its combination with clinical and biological data improves risk prediction.

Isolating Nanoparticles from Complex Biological Media by Immunoprecipitation.

Polyethylene glycol (PEG) is considered the gold standard to prepare long circulating nanoparticles. The hydrophilic layer that sterically protects PEGylated nanomedicines also impedes their separation from biological media. In this study, we describe an immunoprecipitation method using AntiPEG antibodies cross-linked to magnetic beads to extract three types of radiolabeled PEGylated systems: polymeric nanoparticles, liposomes, and therapeutic proteins. The potential of the method is emphasized by isolating these systems after in vivo administration and ex vivo incubation in human biological fluids. Immunoprecipitation also allows a unique perspective on the size distribution of nanoparticles in the bloodstream after intravenous and intraperitoneal administrations. Further, we highlight the potential of the approach to inform on nanomaterial-associated drug in plasma as well as help characterize the protein corona. Altogether, we believe this method answers an unmet need in nanomedicine research and will contribute a fresh perspective on the interactions of nanomedicines with biological systems.

Lung Transplantation: CT Assessment of Chronic Lung Allograft Dysfunction (CLAD).

Chronic lung allograft rejection remains one of the major causes of morbi-mortality after lung transplantation. The term Chronic Lung Allograft Dysfunction (CLAD) has been proposed to describe the different processes that lead to a significant and persistent deterioration in lung function without identifiable causes. The two main phenotypes of CLAD are Bronchiolitis Obliterans Syndrome (BOS) and Restrictive Allograft Syndrome (RAS), each of them characterized by particular functional and imaging features. These entities can be associated (mixed phenotype) or switched from one to the other. If CLAD remains a clinical diagnosis based on spirometry, computed tomography (CT) scan plays an important role in the diagnosis and follow-up of CLAD patients, to exclude identifiable causes of functional decline when CLAD is first suspected, to detect early abnormalities that can precede the diagnosis of CLAD (particularly RAS), to differentiate between the obstructive and restrictive phenotypes, and to detect exacerbations and evolution from one phenotype to the other. Recognition of early signs of rejection is crucial for better understanding of physiopathologic pathways and optimal management of patients.

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort.

OBJECTIVES: To investigate machine learning classifiers and interpretable models using chest CT for detection of COVID-19 and differentiation from other pneumonias, interstitial lung disease (ILD) and normal CTs. METHODS: Our retrospective multi-institutional study obtained 2446 chest CTs from 16 institutions (including 1161 COVID-19 patients). Training/validation/testing cohorts included 1011/50/100 COVID-19, 388/16/33 ILD, 189/16/33 other pneumonias, and 559/17/34 normal (no pathologies) CTs. A metric-based approach for the classification of COVID-19 used interpretable features, relying on logistic regression and random forests. A deep learning-based classifier differentiated COVID-19 via 3D features extracted directly from CT attenuation and probability distribution of airspace opacities. RESULTS: Most discriminative features of COVID-19 are the percentage of airspace opacity and peripheral and basal predominant opacities, concordant with the typical characterization of COVID-19 in the literature. Unsupervised hierarchical clustering compares feature distribution across COVID-19 and control cohorts. The metrics-based classifier achieved AUC = 0.83, sensitivity = 0.74, and specificity = 0.79 versus respectively 0.93, 0.90, and 0.83 for the DL-based classifier. Most of ambiguity comes from non-COVID-19 pneumonia with manifestations that overlap with COVID-19, as well as mild COVID-19 cases. Non-COVID-19 classification performance is 91% for ILD, 64% for other pneumonias, and 94% for no pathologies, which demonstrates the robustness of our method against different compositions of control groups. CONCLUSIONS: Our new method accurately discriminates COVID-19 from other types of pneumonia, ILD, and CTs with no pathologies, using quantitative imaging features derived from chest CT, while balancing interpretability of results and classification performance and, therefore, may be useful to facilitate diagnosis of COVID-19. KEY POINTS: • Unsupervised clustering reveals the key tomographic features including percent airspace opacity and peripheral and basal opacities most typical of COVID-19 relative to control groups. • COVID-19-positive CTs were compared with COVID-19-negative chest CTs (including a balanced distribution of non-COVID-19 pneumonia, ILD, and no pathologies). Classification accuracies for COVID-19, pneumonia, ILD, and CT scans with no pathologies are respectively 90%, 64%, 91%, and 94%. • Our deep learning (DL)-based classification method demonstrates an AUC of 0.93 (sensitivity 90%, specificity 83%). Machine learning methods applied to quantitative chest CT metrics can therefore improve diagnostic accuracy in suspected COVID-19, particularly in resource-constrained environments.

Vascular Ehlers-Danlos syndrome (vEDS): CT and histologic findings of pleural and lung parenchymal damage.

OBJECTIVES: To describe CT features of lung involvement in patients with vascular Ehlers-Danlos syndrome (vEDS), a rare genetic condition caused by pathogenic variants within the COL3A1 gene, characterized by recurrent arterial, digestive, and pulmonary events. MATERIAL AND METHODS: All consecutive vEDS patients referred to the national tertiary referral center for vEDS, between 2004 and 2016, were included. Chest CT scans obtained during the initial vascular work-up were reviewed retrospectively by two chest radiologists for lung involvement. Five surgical samples underwent histologic examination. RESULTS: Among 136 enrolled patients (83 women, 53 men; mean age 37 years) with molecularly confirmed vEDS, 24 (17.6%) had a history of respiratory events: 17 with pneumothorax, 4 with hemothorax, and 3 with hemoptysis that required thoracic surgery in 11. CT scans detected lung parenchymal abnormalities in 78 (57.3%) patients: emphysema (mostly centrilobular and paraseptal) in 44 (32.3%), comparable for smokers and non-smokers; clusters of calcified small pulmonary nodules in 9 (6.6%); and cavitated nodules in 4 (2.9%). Histologic examination of surgical samples found arterial abnormalities, emphysema with alveolar ruptures in 3, accompanied by diffuse hemorrhage and increased hemosiderin resorption. CONCLUSION: In vEDS patients, identification of lung parenchymal abnormalities is common on CT. The most frequently observed CT finding was emphysema suggesting alveolar wall rupture which might facilitate the diagnostic screening of the disease in asymptomatic carriers of a genetic COL3A1 gene mutation. The prognostic value and evolution of these parenchymal abnormalities remain to be evaluated. KEY POINTS: • Patients with vEDS can have lung parenchymal changes on top of or next to thoracal vascular abnormalities and that these changes can be present in asymptomatic cases. • The presence of these parenchymal changes is associated with a slightly higher incidence of respiratory events (although not statistically significant). • Identification of the described CT pattern by radiologists and chest physicians may facilitate diagnostic screening.