Coronavirus disease 2019 (COVID-19) has recently become a worldwide outbreak with several millions of people infected and more than 160.000 deaths. A fast and accurate diagnosis in this outbreak is critical to isolate and treat patients. Radiology plays an important role in the diagnosis and management of the patients. Among various imaging modalities, chest CT has received attention with its higher sensitivity and specificity rates. Shortcomings of the real-time reverse transcriptase-polymerase chain reaction test, including inappropriate sample collection and analysis methods, initial false negative results, and limited availability has led to widespread use of chest CT in the diagnostic algorithm. This review summarizes the role of radiology in COVID-19 pneumonia, diagnostic accuracy of imaging, and chest CT findings of the disease.

Empty azygos fissure implies dislocation of the azygos vein to the mediastinal side of the right upper lobe from azygos fissure, which is usually secondary to pneumothorax, pleural effusion, parenchymal fibrosis, vertebral collapse or persistent vomiting. We are presenting here a case where a separated azygos vein in CT and radiography images was noted. Moreover, in the follow-up images, it appeared that the complete reexpansion of the right lung resulted in reentrance of the azygos vein into azygos fissure in its native position.

Chest computed tomography (CT) imaging with the use of an artificial intelligence (AI) analysis program has been helpful for the rapid evaluation of large numbers of patients during the COVID-19 pandemic. We have previously demonstrated that adults with COVID-19 infection with high-risk obstructive sleep apnea (OSA) have poorer clinical outcomes than COVID-19 patients with low-risk OSA. In the current secondary analysis, we evaluated the association of AI-guided CT-based severity scores (SSs) with short-term outcomes in the same cohort. In total, 221 patients (mean age of 52.6 ± 15.6 years, 59% men) with eligible chest CT images from March to May 2020 were included. The AI program scanned the CT images in 3D, and the algorithm measured volumes of lobes and lungs as well as high-opacity areas, including ground glass and consolidation. An SS was defined as the ratio of the volume of high-opacity areas to that of the total lung volume. The primary outcome was the need for supplemental oxygen and hospitalization over 28 days. A receiver operating characteristic (ROC) curve analysis of the association between an SS and the need for supplemental oxygen revealed a cut-off score of 2.65 on the CT images, with a sensitivity of 81% and a specificity of 56%. In a multivariate logistic regression model, an SS > 2.65 predicted the need for supplemental oxygen, with an odds ratio (OR) of 3.98 (95% confidence interval (CI) 1.80–8.79; p < 0.001), and hospitalization, with an OR of 2.40 (95% CI 1.23–4.71; p = 0.011), adjusted for age, sex, body mass index, diabetes, hypertension, and coronary artery disease. We conclude that AI-guided CT-based SSs can be used for predicting the need for supplemental oxygen and hospitalization in patients with COVID-19 pneumonia.

Background: We have previously demonstrated that high-risk obstructive sleep apnea (HR-OSA), based on a modified Berlin Questionnaire (mBQ), is linked to worse clinical outcomes. Chest computed tomography (CT) imaging with the implementation of an artificial intelligence (AI) analysis program has been a valuable tool for the speedy assessment of huge numbers of patients during the COVID-19 epidemic. In the current study, we addressed how the severity of AI-guided, CT-based total opacity ratio (TOR) scores are associated with high-risk OSA and short-term outcomes in the same cohort. Methods: The ratio of the volume of high opacity areas to that of the total lung volume constituted the TOR. We arbitrarily applied thresholds of <5 (no or mild TOR), ≥5 and <15 (moderate TOR), and ≥15 (severe TOR). Results: In total, 221 patients were included. HR-OSA was observed among 11.0% of the no or mild TOR group, 22.2% of the moderate TOR group, and 38.7% of the severe TOR group (p < 0.001). In a logistic regression analysis, HR-OSA was associated with a severe TOR with an adjusted odds ratio of 3.06 (95% confidence interval [CI] 1.27–7.44; p = 0.01). A moderate TOR predicted clinical worsening with an adjusted hazard ratio (HR) of 1.93 (95% CI 1.00–3.72; p = 0.05) and a severe TOR predicted worsening with an HR of 3.06 (95% CI 1.56–5.99; p = 0.001). Conclusions: Our results offer additional radiological proof of the relationship between HR-OSA and worse outcomes in patients with COVID-19 pneumonia. A TOR may also potentially indicate the individuals that are at higher risk of HR-OSA, enabling early intervention and management strategies. The clinical significance of TOR thresholds needs further evaluation in larger samples.

Background: Obstructive sleep apnea (OSA) is associated with increased cardiovascular morbidity; however, its relationship with subclinical coronary atherosclerosis detected incidentally on routine chest computed tomography (CT) remains incompletely defined. We aimed to evaluate the association between questionnaire-based OSA risk and moderate-to-severe coronary artery calcification (CAC) in patients without known cardiac disease undergoing non-contrast chest CT for non-cardiac indications. Methods: In this prospective cross-sectional study, 268 consecutive adults undergoing routine non-contrast chest CT were included. OSA risk was assessed using the Berlin Questionnaire (BQ) and a modified BQ (mBQ), excluding hypertension and obesity components. CAC was quantified using the Agatston method on non-gated CT images, and moderate-to-severe CAC was defined as a score > 100. Multivariable logistic regression models were adjusted for age, sex, smoking status, alcohol use, obesity, lung disease, diabetes mellitus and hypertension. Results: Moderate-to-severe CAC was substantially more prevalent among patients at high risk for OSA than among those at low risk (43.1% vs. 12.0%, p < 0.001). In unadjusted analyses, high-risk OSA was strongly associated with CAC > 100. After multivariable adjustment, BQ-defined high-risk OSA remained independently associated with moderate-to-severe CAC (adjusted odds ratio [OR] 2.74, 95% confidence interval [CI] 1.29–5.78, p = 0.008). Similar results were observed with the mBQ (adjusted OR 2.62, 95% CI 1.27–5.41, p = 0.009). Increased snoring intensity was also independently associated with CAC > 100 (adjusted OR 2.25, 95% CI 1.07–4.72, p = 0.032). Conclusions: Questionnaire-defined high-risk OSA is independently associated with moderate-to-severe incidental CAC detected on routine chest CT. These findings support the integration of sleep-related risk assessment into opportunistic cardiovascular imaging frameworks and highlight the potential role of thoracic CT in multidimensional cardiovascular risk stratification.

Objective: Diffuse pulmonary opacities are seen in both pulmonary edema (PE) and pulmonary infections such as viral pneumonia (VP) and Pneumocystis jirovecii pneumonia (PJP), and distinguishing these from each other might be challenging. We aimed to show whether subcutaneous fat tissue density (SFTD) measurement in computed tomography scans helps to distinguish PE from VP and PJP. Material and Methods: In this retrospective study, SFTD, defined in terms of Hounsfield unit (HU) was evaluated in computed tomography scans of 99 patients with PE, 103 patients with VP and PJP, and 110 healthy individuals. The SFTD was calculated by using a region of interest with a diameter of approximately 1 cm in the subcutaneous adipose tissue, located 3 cm superior-inferior and medial-lateral to the junction of the midclavicular line and the xiphoid tip on the chest wall, blinded to the clinical data. Results: Median SFTD was -82.5 HU [interquartile range (IQR) -88.7; -67.4] in patients with PE, -117.7 HU (IQR - 121.3; -111.8) in patients with VP and PJP, and -120.3 HU (IQR -123.1; -117.0) in the control group (p<0.001). All patients with PE had SFTD at least -95 HU. None of the patients with VP and PJP as well as controls had SFTD less more -101 HU. Conclusion: Our results suggest that a SFTD measurement above -95 HU might be useful in differentiating PE from VP and PJP pneumonia in patients with diffuse lung opacities.