Baricitinib treatment resolves lower-airway macrophage inflammation and neutrophil recruitment in SARS-CoV-2-infected rhesus macaques.

SARS-CoV-2-induced hypercytokinemia and inflammation are critically associated with COVID-19 severity. Baricitinib, a clinically approved JAK1/JAK2 inhibitor, is currently being investigated in COVID-19 clinical trials. Here, we investigated the immunologic and virologic efficacy of baricitinib in a rhesus macaque model of SARS-CoV-2 infection. Viral shedding measured from nasal and throat swabs, bronchoalveolar lavages, and tissues was not reduced with baricitinib. Type I interferon (IFN) antiviral responses and SARS-CoV-2-specific T cell responses remained similar between the two groups. Animals treated with baricitinib showed reduced inflammation, decreased lung infiltration of inflammatory cells, reduced NETosis activity, and more limited lung pathology. Importantly, baricitinib-treated animals had a rapid and remarkably potent suppression of lung macrophage production of cytokines and chemokines responsible for inflammation and neutrophil recruitment. These data support a beneficial role for, and elucidate the immunological mechanisms underlying, the use of baricitinib as a frontline treatment for inflammation induced by SARS-CoV-2 infection.

Society for Cardiovascular Magnetic Resonance 2021 cases of SCMR and COVID-19 case collection series.

The Society for Cardiovascular Magnetic Resonance (SCMR) is an international society focused on the research, education, and clinical application of cardiovascular magnetic resonance (CMR). "Cases of SCMR" is a case series hosted on the SCMR website ( https://www.scmr.org ) that demonstrates the utility and importance of CMR in the clinical diagnosis and management of cardiovascular disease. The COVID-19 Case Collection highlights the impact of coronavirus disease 2019 (COVID-19) on the heart as demonstrated on CMR. Each case in series consists of the clinical presentation and the role of CMR in diagnosis and guiding clinical management. The cases are all instructive and helpful in the approach to patient management. We present a digital archive of the 2021 Cases of SCMR and the 2020 and 2021 COVID-19 Case Collection series of nine cases as a means of further enhancing the education of those interested in CMR and as a means of more readily identifying these cases using a PubMed or similar literature search engine.

Chest CT and Coronavirus Disease (COVID-19): A Critical Review of the Literature to Date.

OBJECTIVE. Coronavirus disease (COVID-19) is a global pandemic. Studies in the radiology literature have suggested that CT might be sufficiently sensitive and specific in diagnosing COVID-19 when used in lieu of a reverse transcription-polymerase chain reaction test; however, this suggestion runs counter to current society guidelines. The purpose of this article is to critically review some of the most frequently cited studies on the use of CT for detecting COVID-19. CONCLUSION. To date, the radiology literature on COVID-19 has consisted of limited retrospective studies that do not substantiate the use of CT as a diagnostic test for COVID-19.

Confidence-Aware Severity Assessment of Lung Disease from Chest X-Rays Using Deep Neural Network on a Multi-Reader Dataset.

In this study, we present a method based on Monte Carlo Dropout (MCD) as Bayesian neural network (BNN) approximation for confidence-aware severity classification of lung diseases in COVID-19 patients using chest X-rays (CXRs). Trained and tested on 1208 CXRs from Hospital 1 in the USA, the model categorizes severity into four levels (i.e., normal, mild, moderate, and severe) based on lung consolidation and opacity. Severity labels, determined by the median consensus of five radiologists, serve as the reference standard. The model's performance is internally validated against evaluations from an additional radiologist and two residents that were excluded from the median. The performance of the model is further evaluated on additional internal and external datasets comprising 2200 CXRs from the same hospital and 1300 CXRs from Hospital 2 in South Korea. The model achieves an average area under the curve (AUC) of 0.94 ± 0.01 across all classes in the primary dataset, surpassing human readers in each severity class and achieves a higher Kendall correlation coefficient (KCC) of 0.80 ± 0.03. The performance of the model is consistent across varied datasets, highlighting its generalization. A key aspect of the model is its predictive uncertainty (PU), which is inversely related to the level of agreement among radiologists, particularly in mild and moderate cases. The study concludes that the model outperforms human readers in severity assessment and maintains consistent accuracy across diverse datasets. Its ability to provide confidence measures in predictions is pivotal for potential clinical use, underscoring the BNN's role in enhancing diagnostic precision in lung disease analysis through CXR.

COVID-19 pneumonia chest radiographic severity score: variability assessment among experienced and in-training radiologists and creation of a multireader composite score database for artificial intelligence algorithm development.

OBJECTIVE: The purpose was to evaluate reader variability between experienced and in-training radiologists of COVID-19 pneumonia severity on chest radiograph (CXR), and to create a multireader database suitable for AI development. METHODS: In this study, CXRs from polymerase chain reaction positive COVID-19 patients were reviewed. Six experienced cardiothoracic radiologists and two residents classified each CXR according to severity. One radiologist performed the classification twice to assess intraobserver variability. Severity classification was assessed using a 4-class system: normal (0), mild (1), moderate (2), and severe (3). A median severity score (Rad Med) for each CXR was determined for the six radiologists for development of a multireader database (XCOMS). Kendal Tau correlation and percentage of disagreement were calculated to assess variability. RESULTS: A total of 397 patients (1208 CXRs) were included (mean age, 60 years SD ± 1), 189 men). Interobserver variability between the radiologists ranges between 0.67 and 0.78. Compared to the Rad Med score, the radiologists show good correlation between 0.79-0.88. Residents show slightly lower interobserver agreement of 0.66 with each other and between 0.69 and 0.71 with experienced radiologists. Intraobserver agreement was high with a correlation coefficient of 0.77. In 220 (18%), 707 (59%), 259 (21%) and 22 (2%) CXRs there was a 0, 1, 2 or 3 class-difference. In 594 (50%) CXRs the median scores of the residents and the radiologists were similar, in 578 (48%) and 36 (3%) CXRs there was a 1 and 2 class-difference. CONCLUSION: Experienced and in-training radiologists demonstrate good inter- and intraobserver agreement in COVID-19 pneumonia severity classification. A higher percentage of disagreement was observed in moderate cases, which may affect training of AI algorithms. ADVANCES IN KNOWLEDGE: Most AI algorithms are trained on data labeled by a single expert. This study shows that for COVID-19 X-ray severity classification there is significant variability and disagreement between radiologist and between residents.

CT Findings and Patterns of e-Cigarette or Vaping Product Use-Associated Lung Injury: A Multicenter Cohort of 160 Cases.

BACKGROUND: e-Cigarette or vaping-induced lung injury (EVALI) causes a spectrum of CT lung injury patterns. Relative frequencies and associations with vaping behavior are unknown. RESEARCH QUESTION: What are the frequencies of imaging findings and CT patterns in EVALI and what is the relationship to vaping behavior? STUDY DESIGN AND METHODS: CT scans of 160 subjects with EVALI from 15 institutions were retrospectively reviewed. CT findings and patterns were defined and agreed on via consensus. The parenchymal organizing pneumonia (OP) pattern was defined as regional or diffuse ground-glass opacity (GGO) ± consolidation without centrilobular nodules (CNs). An airway-centered OP pattern was defined as diffuse CNs with little or no GGO, whereas a mixed OP pattern was a combination of the two. Other patterns included diffuse alveolar damage (DAD), acute eosinophilic-like pneumonia, and pulmonary hemorrhage. Cases were classified as atypical if they did not fit into a pattern. Imaging findings, pattern frequencies, and injury severity were correlated with substance vaped (marijuana derives [tetrahydrocannabinol] [THC] only, nicotine derivates only, and both), vaping frequency, regional geography, and state recreational THC legality. One-way analysis of variance, χ2 test, and multivariable analyses were used for statistical analysis. RESULTS: A total of 160 patients (79.4% men) with a mean age of 28.2 years (range, 15-68 years) with EVALI underwent CT scan. Seventy-seven (48.1%), 15 (9.4%), and 68 (42.5%) patients admitted to vaping THC, nicotine, or both, respectively. Common findings included diffuse or lower lobe GGO with subpleural (78.1%), lobular (59.4%), or peribronchovascular (PBV) sparing (40%). Septal thickening (50.6%), lymphadenopathy (63.1%), and CNs (36.3%) were common. PBV sparing was associated with younger age (P = .02). Of 160 subjects, 156 (97.5%) had one of six defined patterns. Parenchymal, airway-centered, and mixed OP patterns were seen in 89 (55.6%), 14 (8.8%), and 32 (20%) patients, respectively. Acute eosinophilic-like pneumonia (six of 160, 3.8%), DAD (nine of 160, 5.6%), pulmonary hemorrhage (six of 160, 3.8%), and atypical (four of 160, 2.5%) patterns were less common. Increased vaping frequency was associated with more severe injury (P = .008). Multivariable analysis showed a negative association between vaping for > 6 months and DAD pattern (P = .03). Two subjects (1.25%) with DAD pattern died. There was no relation between pattern and injury severity, geographic location, and state legality of recreational use of THC. INTERPRETATION: EVALI typically causes an OP pattern but exists on a spectrum of acute lung injury. Vaping habits do not correlate with CT patterns except for negative correlation between vaping > 6 months and DAD pattern. PBV sparing, not previously described in acute lung injury, is a common finding.

Second-Opinion Reads in Interstitial Lung Disease Imaging: Added Value of Subspecialty Interpretation.

PURPOSE: The purpose of this study was to determine how often a second-opinion interpretation of interstitial lung disease (ILD) by an academic cardiothoracic radiologist is discordant with the initial interpretation by a nonacademic radiologists and how often the clinical diagnosis determined by multidisciplinary consensus agrees with the initial and second-opinion interpretations. METHODS: This retrospective study included 364 consecutive second-opinion CT examination reports of imaging from nonacademic radiology practices from July 2014 to May 2016. The second-opinion interpretations, provided by seven fellowship-trained cardiothoracic radiologists, were compared with the initial interpretations and the clinical diagnoses determined by multidisciplinary consensus. RESULTS: Two hundred ninety-six consecutive reports met the inclusion criteria, and two hundred had findings of ILD. The initial interpretations lacked specific diagnoses in 41% of reports, but the second-opinion reports lacked specific diagnoses in only 7%. When a diagnosis was provided, the second-opinion diagnosis disagreed with the initial interpretation in 25% of cases. The clinical-consensus diagnosis was concordant with that of the academic radiologists 85% of the time but concordant with the initial interpretation only 44% of the time. The academic radiologists' diagnostic sensitivity was higher than that of the initial radiologists for the four most common diagnoses: usual interstitial pneumonitis (0.91 versus 0.4), sarcoidosis (0.94 versus 0.60), hypersensitivity pneumonitis (0.79 versus 0.17), and nonspecific interstitial pneumonitis (0.72 versus 0.14). CONCLUSIONS: Academic cardiothoracic radiologists were more likely to provide specific diagnoses for ILD, and these diagnoses were more likely to be concordant with the multidisciplinary consensus.

Repaired Congenital Heart Disease in Older Children and Adults: Up-to-Date Practical Assessment and Characteristic Imaging Findings.

Because of a recent increase in survival rates and life expectancy of patients with congenital heart disease (CHD), radiologists are facing new challenges when imaging the peculiar anatomy of individuals with repaired CHD. Cardiac computed tomography and magnetic resonance are paramount noninvasive imaging tools that are useful in assessing patients with repaired CHD, and both techniques are increasingly performed in centers where CHD is not the main specialization. This review provides general radiologists with insight into the main issues of imaging patients with repaired CHD, and the most common findings and complications of each individual pathology and its repair.

Baricitinib treatment resolves lower airway inflammation and neutrophil recruitment in SARS-CoV-2-infected rhesus macaques.

Effective therapeutics aimed at mitigating COVID-19 symptoms are urgently needed. SARS-CoV-2 induced hypercytokinemia and systemic inflammation are associated with disease severity. Baricitinib, a clinically approved JAK1/2 inhibitor with potent anti-inflammatory properties is currently being investigated in COVID-19 human clinical trials. Recent reports suggest that baricitinib may also have antiviral activity in limiting viral endocytosis. Here, we investigated the immunologic and virologic efficacy of baricitinib in a rhesus macaque model of SARS-CoV-2 infection. Viral shedding measured from nasal and throat swabs, bronchoalveolar lavages and tissues was not reduced with baricitinib. Type I IFN antiviral responses and SARS-CoV-2 specific T cell responses remained similar between the two groups. Importantly, however, animals treated with baricitinib showed reduced immune activation, decreased infiltration of neutrophils into the lung, reduced NETosis activity, and more limited lung pathology. Moreover, baricitinib treated animals had a rapid and remarkably potent suppression of alveolar macrophage derived production of cytokines and chemokines responsible for inflammation and neutrophil recruitment. These data support a beneficial role for, and elucidate the immunological mechanisms underlying, the use of baricitinib as a frontline treatment for severe inflammation induced by SARS-CoV-2 infection.

Long-Term Prognostic Value of Stress Perfusion Cardiovascular Magnetic Resonance Imaging.

PURPOSE OF REVIEW: The purpose of this review is to analyze the long-term prognostic value of stress perfusion cardiovascular magnetic resonance (CMR) in patients with suspected or known coronary artery disease (CAD). RECENT FINDINGS: Stress perfusion CMR provides high diagnostic accuracy for detection of CAD, with high sensitivity and relatively lower specificity. A normal stress perfusion CMR examination is highly predictive of overall low patient risk. Conversely, abnormal stress perfusion CMR results are associated with mortality and increased risk for adverse cardiac-related events. Stress perfusion CMR is a useful and robust tool for risk reclassification across different CAD risk categories, and most significant for patients of intermediate risk. Stress CMR is reliable for excluding clinically significant coronary artery disease in patients presenting with low-risk acute chest pain. An ischemic burden threshold of less than 1.5 cardiac segments has been found to be most appropriate for safe deferral from revascularization therapy. A stress perfusion CMR-guided strategy has been shown to be noninferior compared to fractional flow reserve (FFR) for revascularization in patients with stable CAD. In clinical practice, CMR offers a multiplicity of useful techniques besides stress perfusion which may add significant prognostic value when combined with the findings of the stress test itself. Stress perfusion CMR is an accurate noninvasive diagnostic test for patients with suspected CAD and provides strong prognostic value across different risk categories.

Automated regional registration and characterization of corresponding microcalcification clusters on temporal pairs of mammograms for interval change analysis.

A computerized regional registration and characterization system for analysis of microcalcification clusters on serial mammograms is being developed in our laboratory. The system consists of two stages. In the first stage, based on the location of a detected cluster on the current mammogram, a regional registration procedure identifies the local area on the prior that may contain the corresponding cluster. A search program is used to detect cluster candidates within the local area. The detected cluster on the current image is then paired with the cluster candidates on the prior image to form true (TP-TP) or false (TP-FP) pairs. Automatically extracted features were used in a newly designed correspondence classifier to reduce the number of false pairs. In the second stage, a temporal classifier, based on both current and prior information, is used if a cluster has been detected on the prior image, and a current classifier, based on current information alone, is used if no prior cluster has been detected. The data set used in this study consisted of 261 serial pairs containing biopsy-proven calcification clusters. An MQSA radiologist identified the corresponding clusters on the mammograms. On the priors, the radiologist rated the subtlety of 30 clusters (out of the 261 clusters) as 9 or 10 on a scale of 1 (very obvious) to 10 (very subtle). Leave-one-case-out resampling was used for feature selection and classification in both the correspondence and malignant/benign classification schemes. The search program detected 91.2% (238/261) of the clusters on the priors with an average of 0.42 FPs/image. The correspondence classifier identified 86.6% (226/261) of the TP-TP pairs with 20 false matches (0.08 FPs/image) relative to the entire set of 261 image pairs. In the malignant/benign classification stage the temporal classifier achieved a test A(z) of 0.81 for the 246 pairs which contained a detection on the prior. In addition, a classifier was designed by using the clusters on the current mammograms only. It achieved a test A(z) of 0.72 in classifying the clusters as malignant and benign. The difference between the performance of the temporal classifier and the current classifier was statistically significant (p=0.0014). Our interval change analysis system can detect the corresponding cluster on the prior mammogram with high sensitivity, and classify them with a satisfactory accuracy.

Dark-Blood Delayed Enhancement Cardiac Magnetic Resonance of Myocardial Infarction.

OBJECTIVES: This study introduced and validated a novel flow-independent delayed enhancement technique that shows hyperenhanced myocardium while simultaneously suppressing blood-pool signal. BACKGROUND: The diagnosis and assessment of myocardial infarction (MI) is crucial in determining clinical management and prognosis. Although delayed enhancement cardiac magnetic resonance (DE-CMR) is an in vivo reference standard for imaging MI, an important limitation is poor delineation between hyperenhanced myocardium and bright LV cavity blood-pool, which may cause many infarcts to become invisible. METHODS: A canine model with pathology as the reference standard was used for validation (n = 22). Patients with MI and normal controls were studied to ascertain clinical performance (n = 31). RESULTS: In canines, the flow-independent dark-blood delayed enhancement (FIDDLE) technique was superior to conventional DE-CMR for the detection of MI, with higher sensitivity (96% vs. 85%, respectively; p = 0.002) and accuracy (95% vs. 87%, respectively; p = 0.01) and with similar specificity (92% vs, 92%, respectively; p = 1.0). In infarcts that were identified by both techniques, the entire length of the endocardial border between infarcted myocardium and adjacent blood-pool was visualized in 33% for DE-CMR compared with 100% for FIDDLE. There was better agreement for FIDDLE-measured infarct size than for DE-CMR infarct size (95% limits-of-agreement, 2.1% vs. 5.5%, respectively; p < 0.0001). In patients, findings were similar. FIDDLE demonstrated higher accuracy for diagnosis of MI than DE-CMR (100% [95% confidence interval [CI]: 89% to 100%] vs. 84% [95% CI: 66% to 95%], respectively; p = 0.03). CONCLUSIONS: The study introduced and validated a novel CMR technique that improves the discrimination of the border between infarcted myocardium and adjacent blood-pool. This dark-blood technique provides diagnostic performance that is superior to that of the current in vivo reference standard for the imaging diagnosis of MI.

Increased Computed Tomography Dose Due to Miscentering With Use of Automated Tube Voltage Selection: Phantom and Patient Study.

The purpose of the article is to determine if miscentering affected dose with use of automated tube voltage selection software. An anthropomorphic phantom was imaged at different table heights (centered in the computed tomography [CT] gantry, and -6, -3, +3, and +5.7cm relative to the centered position). Topogram magnification, tube voltage selection, and dose were assessed. Effect of table height on dose also was assessed retrospectively in human subjects (n = 50). When the CT table was positioned closer to the x-ray source, subjects appeared up to 33% magnified in topogram images. When subjects appeared magnified in topogram images, automated software selected higher tube potentials and tube currents that were based on the magnified size of the subject rather than the subject׳s true size. Table height strongly correlated with CT dose index (r = 0.98, P < 0.05) and dose length product (r = 0.98, P < 0.05) in the phantom study. Transverse dimension in the topogram highly correlated with dose in human subjects (r = 0.75-0.87, P <0.05). Miscentering results in increased dose due to topogram magnification with automated voltage selection software.

Comparison of similarity measures for the task of template matching of masses on serial mammograms.

We conducted a study to evaluate the effectiveness of twelve different similarity measures in matching the corresponding masses on temporal pairs of current and prior mammograms. To perform this comparison we implemented each of the twelve similarity measures in the final stage of our multistage registration technique for automated registration of breast lesions in serial mammograms. The multistage technique consists of three stages. In the first stage an initial fan-shape search region was estimated on the prior mammogram based on the geometrical position of the mass on the current mammogram. In the second stage, the location of the fan-shape region was refined by warping, based on an affine transformation and simplex optimization. A new refined search region was defined on the prior mammogram. In the third stage, a search for the best match between the lesion template from the current mammogram and a structure on the prior mammogram was carried out within the search region. Our data set consisted of 318 temporal pairs. We performed three experiments, using a different subset of the 318 temporal pairs for each experiment. In each experiment we further tested how the performance of the similarity measures varied as the size of the search region increased or decreased. We evaluated the twelve similarity measures based on four criteria. The first criterion was the mean Euclidean distance, which was the average distance of the true location of the mass to the location detected by the similarity measure. The second criterion was the percentage of temporal pairs that were aligned so that 50% or more of the lesion area overlapped. The third criterion was the percentage of pairs that were aligned so that 75% or more of the lesion area overlapped. The fourth and final criterion was the robustness of the similarity measure. Our results showed that three of the similarity measures, Pearson's correlation, the cosine coefficient, and Goodman and Kruskal's Gamma coefficient, provide significantly higher accuracy (p < 0.05) in the task of matching the corresponding masses on serial mammograms than the other nine similarity measures.