European radiology
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To compare the chest computed tomography (CT) findings of coronavirus disease 2019 (COVID-19) to other non-COVID viral pneumonia. ⋯ • Most common CT findings of coronavirus disease 2019 (COVID-19) were a predominant pattern of ground-glass opacity (GGO), followed by a mixed pattern of GGO and consolidation, bilateral disease, peripheral distribution, and lower lobe involvement. • Most frequent CT findings of non-COVID viral pneumonia were a predominantly mixed pattern of GGO and consolidation, followed by a predominant pattern of GGO, bilateral disease, random or diffuse distribution, and lower lobe involvement. • COVID-19 pneumonia presented a higher prevalence of peripheral distribution, and involvement of upper and middle lobes compared with non-COVID viral pneumonia.
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Observational Study
Quantitative chest CT analysis in COVID-19 to predict the need for oxygenation support and intubation.
Lombardy (Italy) was the epicentre of the COVID-19 pandemic in March 2020. The healthcare system suffered from a shortage of ICU beds and oxygenation support devices. In our Institution, most patients received chest CT at admission, only interpreted visually. Given the proven value of quantitative CT analysis (QCT) in the setting of ARDS, we tested QCT as an outcome predictor for COVID-19. ⋯ • Quantitative computer-aided analysis of chest CT (QCT) provides new metrics of COVID-19. • The compromised lung volume measured in the - 500, 100 HU interval predicts oxygenation support and intubation and is a risk factor for in-hospital death. • Compromised lung values in the 6-23% range prompt oxygenation therapy; values above 23% increase the need for intubation.
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To develop and validate a radiomics model for predicting 2019 novel coronavirus (COVID-19) pneumonia. ⋯ • A radiomics model showed good performance for prediction 2019 novel coronavirus pneumonia and favorable discrimination for other types of pneumonia on CT images. • A central or peripheral distribution, a maximum lesion range > 10 cm, the involvement of all five lobes, hilar and mediastinal lymph node enlargement, and no pleural effusion is associated with an increased risk of 2019 novel coronavirus pneumonia. • A radiomics model was superior to a clinical model in predicting 2019 novel coronavirus pneumonia.
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To determine normal pericoronary adipose tissue mean attenuation (PCATMA) values for left the anterior descending (LAD), left circumflex (LCX), and right coronary artery (RCA) in patients without plaques on coronary CT angiography (cCTA), taking into account tube voltage influence. ⋯ • In patients without plaque on cCTA, PCATMA differs slightly by coronary artery (LAD, LCX, RCA). • Tube voltage of cCTA affects PCATMA measurement, with mean PCATMA increasing linearly with increasing kV. • For longitudinal cCTA analysis of PCATMA , the use of equal kV setting is strongly recommended.
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To develop a fully automated AI system to quantitatively assess the disease severity and disease progression of COVID-19 using thick-section chest CT images. ⋯ • A deep learning-based AI system was able to accurately segment the infected lung regions by COVID-19 using the thick-section CT scans (Dice coefficient ≥ 0.74). • The computed imaging biomarkers were able to distinguish between the non-severe and severe COVID-19 stages (area under the receiver operating characteristic curve 0.97). • The infection volume changes computed by the AI system were able to assess the COVID-19 progression (Cohen's kappa 0.8220).