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.

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.

COVID-19 pneumonia: high diagnostic accuracy of chest CT in patients with intermediate clinical probability.

OBJECTIVES: To assess inter-reader agreements and diagnostic accuracy of chest CT to identify COVID-19 pneumonia in patients with intermediate clinical probability during an acute disease outbreak. METHODS: From March 20 to April 8, 319 patients (mean age 62.3 years old) consecutive patients with an intermediate clinical probability of COVID-19 pneumonia underwent a chest CT scan. Two independent chest radiologists blinded to clinical information and RT-PCR results retrospectively reviewed and classified images on a 1-5 confidence level scale for COVID-19 pneumonia. Agreements between radiologists were assessed with kappa statistics. Diagnostic accuracy of chest CT compared with RT-PCR assay and patient outcomes was measured using receiver operating characteristics (ROC). Positive predictive value (PPV) and negative predictive value (NPV) for COVID-19 pneumonia were calculated. RESULTS: Inter-observer agreement for highly probable (kappa: 0.83 [p < .001]) and highly probable or probable (kappa: 0.82 [p < .001]) diagnosis of COVID-19 pneumonia was very good. RT-PCR tests performed in 307 patients were positive in 174 and negative in 133. The areas under the curve (AUC) were 0.94 and 0.92 respectively. With a disease prevalence of 61.2%, PPV were 95.9% and 94.3%, and NPV 84.4% and 77.1%. CONCLUSION: During acute COVID-19 outbreak, chest CT scan may be used for triage of patients with intermediate clinical probability with very good inter-observer agreements and diagnostic accuracy. KEY POINTS: • Concordances between two chest radiologists to diagnose or exclude a COVID-19 pneumonia in 319 consecutive patients with intermediate clinical probability were very good (kappa: 0.82; p < .001). • When compared with RT-PCR results and patient outcomes, the diagnostic accuracy of CT to identify COVID-19 pneumonia was high for both radiologists (AUC: 0.94 and 0.92). • With a disease prevalence of 61.2% in the studied population, the positive predictive values of CT for diagnosing COVID-19 pneumonia were 95.9% and 94.3% with negative predictive values of 84.4% and 77.1%.

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.

Relationship of spirometric, body plethysmographic, and diffusing capacity parameters to emphysema scores derived from CT scans.

Phenotyping of chronic obstructive pulmonary disease (COPD) with computed tomography (CT) is used to distinguish between emphysema- and airway-dominated type. The phenotype is reflected in correlations with lung function measures. Among these, the relative value of body plethysmography has not been quantified. We addressed this question using CT scans retrospectively collected from clinical routine in a large COPD cohort. Three hundred and thirty five patients with baseline data of the German COPD cohort COPD and Systemic Consequences-Comorbidities Network were included. CT scans were primarily evaluated using a qualitative binary emphysema score. The binary score was positive for emphysema in 52.5% of patients, and there were significant differences between the positive/negative groups regarding forced expiratory volume in 1 second (FEV1), FEV1/forced vital capacity (FVC), intrathoracic gas volume (ITGV), residual volume (RV), specific airway resistance (sRaw), transfer coefficient (KCO), transfer factor for carbon monoxide (TLCO), age, pack-years, and body mass index (BMI). Stepwise discriminant analyses revealed the combination of FEV1/FVC, RV, sRaw, and KCO to be significantly related to the binary emphysema score. The additional positive predictive value of body plethysmography, however, was only slightly higher than that of the conventional combination of spirometry and diffusing capacity, which if taken alone also achieved high predictive values, in contrast to body plethysmography. The additional information on the presence of CT-diagnosed emphysema as conferred by body plethysmography appeared to be minor compared to the well-known combination of spirometry and CO diffusing capacity.

CT-based Visual Classification of Emphysema: Association with Mortality in the COPDGene Study.

Purpose To determine whether visually assessed patterns of emphysema at CT might provide a simple assessment of mortality risk among cigarette smokers. Materials and Methods Of the first 4000 cigarette smokers consecutively enrolled between 2007 and 2011 in this COPDGene study, 3171 had data available for both visual emphysema CT scores and survival. Each CT scan was retrospectively visually scored by two analysts using the Fleischner Society classification system. Severity of emphysema was also evaluated quantitatively by using percentage lung volume occupied by low-attenuation areas (voxels with attenuation of -950 HU or less) (LAA-950). Median duration of follow-up was 7.4 years. Regression analysis for the relationship between imaging patterns and survival was based on the Cox proportional hazards model, with adjustment for age, race, sex, height, weight, pack-years of cigarette smoking, current smoking status, educational level, LAA-950, and (in a second model) forced expiratory volume in 1 second (FEV1). Results Observer agreement in visual scoring was good (weighted κ values, 0.71-0.80). There were 519 deaths in the study cohort. Compared with subjects who did not have visible emphysema, mortality was greater in those with any grade of emphysema beyond trace (adjusted hazard ratios, 1.7, 2.5, 5.0, and 4.1, respectively, for mild centrilobular emphysema, moderate centrilobular emphysema, confluent emphysema, and advanced destructive emphysema, P < .001). This increased mortality generally persisted after adjusting for LAA-950. Conclusion The visual presence and severity of emphysema is associated with significantly increased mortality risk, independent of the quantitative severity of emphysema. Online supplemental material is available for this article.

CT-Definable Subtypes of Chronic Obstructive Pulmonary Disease: A Statement of the Fleischner Society.

The purpose of this statement is to describe and define the phenotypic abnormalities that can be identified on visual and quantitative evaluation of computed tomographic (CT) images in subjects with chronic obstructive pulmonary disease (COPD), with the goal of contributing to a personalized approach to the treatment of patients with COPD. Quantitative CT is useful for identifying and sequentially evaluating the extent of emphysematous lung destruction, changes in airway walls, and expiratory air trapping. However, visual assessment of CT scans remains important to describe patterns of altered lung structure in COPD. The classification system proposed and illustrated in this article provides a structured approach to visual and quantitative assessment of COPD. Emphysema is classified as centrilobular (subclassified as trace, mild, moderate, confluent, and advanced destructive emphysema), panlobular, and paraseptal (subclassified as mild or substantial). Additional important visual features include airway wall thickening, inflammatory small airways disease, tracheal abnormalities, interstitial lung abnormalities, pulmonary arterial enlargement, and bronchiectasis.

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.

Contrast-enhanced ultrasound (CEUS) follow-up after radiofrequency ablation or cryoablation of focal liver lesions: treated-area patterns and their changes over time.

OBJECTIVES: To describe the early patterns of liver lesions successfully treated with radiofrequency ablation (RFA) or cryoablation (CA) and their changes over time. METHODS: Twenty-two RFA-treated and 17 CA-treated patients underwent CEUS from week 1 to year 3 post-ablation. Patterns, margins and volumes of RF-induced and cryolesions were evaluated and compared over time. RESULTS: After contrast enhancement, shortly after ablation, undefined margins with persistent enhancing small vessels penetrating >2 mm into the treated zone were significantly more frequent after CA (67 %) than RFA (22 %) (P < 0.02). During the arterial phase, a thin, enhancing marginal rim was seen during week 1 (T1) in around 28 % of RF lesions, while 75 % of cryolesions had thick enhancing rims (P < 0.02). The mean RF-induced lesion volume, maximum at T1 (44.1 ± 37.5 ml), shrank slowly over time, remaining clearly visible at 1 year (8.3 ± 7.4 ml). Cryolesions shrank faster (P = 0.009), from an average of 16.6 ± 7.1 ml at T1 to 1.7 ± 1.3 ml 1-year post-ablation. CONCLUSION: RF-induced and cryolesions differ, particularly their margins and shrinkage rates. Knowing these differences allows avoidance of incomplete treatment or falsely diagnosed recurrence, especially after CA. KEY POINTS: • Contrast-enhanced ultrasound (CEUS) provides new follow-up information following hepatic radiological inyervention. • CEUS provides good visualisation of vascular modifications after thermoablation. • RFA-induced lesions and cryoablated lesions differ. • Knowledge about RF and cryolesion patterns is essential for correct CEUS follow-up. • Cryolesions have thicker peripheral inflammatory reactions and shrink faster than RFA-induced lesions.

Relationship between the airway wall area and asthma control score in moderate persistent asthma.

OBJECTIVES: To assess the association between airway wall area and clinical asthma control, assessed by the Asthma Control Test (ACT). METHODS: This cross-sectional study evaluated 96 adults for asthma control ["at least well controlled" (ACT ≥ 20; n = 52) or "not well controlled" (ACT < 20; n = 44) and airway dimensions: luminal area (LA), wall area (WA) and WA%], obtained using automated dedicated software measurements from volumetric CT images. Results were analysed for segmental bronchi, subsegmental bronchi in the right upper lobe and basilar segments, both uncorrected and corrected for body surface area (BSA). RESULTS: For all bronchi corrected for BSA, there was no correlation between airway wall area and ACT score. There was a weak but statistically significant correlation between uncorrected WA and ACT score (r = -0.203; P = 0.047); WA values were numerically higher in the "not well-controlled" versus the "at least well-controlled asthma" subgroups. For sub-segmental bronchi, there was a correlation between the ACT score and both WA/BSA (r = -0.204; P = 0.047) and WA (r = -0.249; P = 0.014), and for upper lobe bronchi, between the ACT score and WA (r = -0.207; P = 0.044). CONCLUSION: We demonstrated a correlation between subsegmental bronchial airway measurements and clinical control of asthma; this is probably a reflection of airway remodelling and structural changes in chronic poorly controlled asthma. KEY POINTS: • Volumetric computed tomography offers new insights into bronchial morphology. • The relationship between current asthma control and airway wall abnormalities is assessed. • Some relationships between airway wall area and clinical control were demonstrated. • We observed less shape variation of bronchi in "not well-controlled" asthma patients.

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.

A combined pulmonary-radiology workshop for visual evaluation of COPD: study design, chest CT findings and concordance with quantitative evaluation.

UNLABELLED: The purposes of this study were: to describe chest CT findings in normal non-smoking controls and cigarette smokers with and without COPD; to compare the prevalence of CT abnormalities with severity of COPD; and to evaluate concordance between visual and quantitative chest CT (QCT) scoring. METHODS: Volumetric inspiratory and expiratory CT scans of 294 subjects, including normal non-smokers, smokers without COPD, and smokers with GOLD Stage I-IV COPD, were scored at a multi-reader workshop using a standardized worksheet. There were 58 observers (33 pulmonologists, 25 radiologists); each scan was scored by 9-11 observers. Interobserver agreement was calculated using kappa statistic. Median score of visual observations was compared with QCT measurements. RESULTS: Interobserver agreement was moderate for the presence or absence of emphysema and for the presence of panlobular emphysema; fair for the presence of centrilobular, paraseptal, and bullous emphysema subtypes and for the presence of bronchial wall thickening; and poor for gas trapping, centrilobular nodularity, mosaic attenuation, and bronchial dilation. Agreement was similar for radiologists and pulmonologists. The prevalence on CT readings of most abnormalities (e.g. emphysema, bronchial wall thickening, mosaic attenuation, expiratory gas trapping) increased significantly with greater COPD severity, while the prevalence of centrilobular nodularity decreased. Concordances between visual scoring and quantitative scoring of emphysema, gas trapping and airway wall thickening were 75%, 87% and 65%, respectively. CONCLUSIONS: Despite substantial inter-observer variation, visual assessment of chest CT scans in cigarette smokers provides information regarding lung disease severity; visual scoring may be complementary to quantitative evaluation.

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.

Pulmonary involvement in Erdheim-Chester disease: a single-center study of thirty-four patients and a review of the literature.

OBJECTIVE: Erdheim-Chester disease (ECD) is a rare form of non-Langerhans' cell histiocytosis that may present with pulmonary involvement. We undertook the current study to evaluate the characteristic features of pulmonary involvement in ECD, in the largest single-center series of patients reported to date. METHODS: We performed a retrospective study of the characteristics of 34 consecutive patients with biopsy-proven ECD who were referred to the internal medicine department of Pitié-Salpêtrière Hospital between 1981 and November 2008. RESULTS: Data were obtained from 23 men and 11 women. The median age at the time of diagnosis was 53.7 years (range 16-73 years), and the median followup was 3.5 years (1.4-5.3 years). Eight patients (24%) had pulmonary symptoms. High-resolution computed tomography (HRCT) scans of the chest revealed involvement of lung parenchyma in 18 patients (53%) and of the pleura in 14 patients (41%). Bronchoalveolar lavage fluid analysis revealed the presence of an opalescent aspirate in all the patients studied. Treatment with corticosteroids and/or interferon-α (IFNα) resulted in a marked improvement of the pulmonary lesions in only a single patient. Comparison of the survival between patients with and those without pulmonary involvement yielded no significant difference between the groups (P = 0.82). CONCLUSION: Pulmonary involvement in ECD has been overlooked in previous studies. HRCT reveals typical lesions in most patients. There is no clear response of these lesions to corticosteroids and IFNα. The overall prognosis of the disease is poor, but pulmonary involvement does not appear to be a major prognostic factor in ECD.