Background: Myocardial injury, defined by elevated troponin levels, is associated with adverse outcome in patients with coronavirus disease 2019 (COVID-19). The frequency of cardiac injury remains highly uncertain and confounded in current publications; myocarditis is one of several mechanisms that have been proposed. Methods: We prospectively assessed patients with myocardial injury hospitalized for COVID-19 using transthoracic echocardiography, cardiac magnetic resonance imaging, and endomyocardial biopsy. Results: Eighteen patients with COVID-19 and myocardial injury were included in this study. Echocardiography revealed normal to mildly reduced left ventricular ejection fraction of 52.5% (46.5%–60.5%) but moderately to severely reduced left ventricular global longitudinal strain of −11.2% (−7.6% to −15.1%). Cardiac magnetic resonance showed any myocardial tissue injury defined by elevated T1, extracellular volume, or late gadolinium enhancement with a nonischemic pattern in 16 patients (83.3%). Seven patients (38.9%) demonstrated myocardial edema in addition to tissue injury fulfilling the Lake-Louise criteria for myocarditis. Combining cardiac magnetic resonance with speckle tracking echocardiography demonstrated functional or morphological cardiac changes in 100% of investigated patients. Endomyocardial biopsy was conducted in 5 patients and revealed enhanced macrophage numbers in all 5 patients in addition to lymphocytic myocarditis in 1 patient. SARS-CoV-2 RNA was not detected in any biopsy by quantitative real-time polymerase chain reaction. Finally, follow-up measurements of left ventricular global longitudinal strain revealed significant improvement after a median of 52.0 days (−11.2% [−9.2% to −14.7%] versus −15.6% [−12.5% to −19.6%] at follow-up; P =0.041). Conclusions: In this small cohort of COVID-19 patients with elevated troponin levels, myocardial injury was evidenced by reduced echocardiographic left ventricular strain, myocarditis patterns on cardiac magnetic resonance, and enhanced macrophage numbers but not predominantly lymphocytic myocarditis in endomyocardial biopsies.

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Background and Purpose: Basilar artery occlusion is associated with high morbidity and mortality. Optimal imaging and treatment strategy are still controversial and prognosis estimation challenging. We, therefore, aimed to determine the predictive value of computed tomography perfusion (CTP) parameters for functional outcome in patients with basilar artery occlusion in the context of endovascular treatment. Methods: Patients with basilar artery occlusion who underwent endovascular treatment were selected from a prospectively acquired cohort. Ischemic changes were assessed with the posterior-circulation Acute Stroke Prognosis Early Computed Tomography Score on noncontrast computed tomography, computed tomography angiography (CTA) source images, and CTP maps. Basilar artery on CTA score, posterior-circulation CTA score, and posterior-circulation collateral score were evaluated on CTA. Perfusion deficit volumes were quantified on CTP maps. Good functional outcome was defined as modified Rankin Scale score ≤3 at 90 days. Statistical analysis included binary logistic regressions and receiver operating characteristics analyses. Results: Among 49 patients who matched the inclusion criteria, 24 (49.0%) achieved a good outcome. In univariate analysis, age, National Institutes of Health Stroke Scale score on admission, posterior cerebral artery involvement, absence of or hypoplastic posterior communicating arteries, basilar artery on CTA score, posterior-circulation Acute Stroke Prognosis Early Computed Tomography Score, and perfusion deficit volumes on all CTP parameter maps presented significant association with functional outcome ( P <0.05). In multivariate analyses, Basilar artery on CTA score, posterior-circulation Acute Stroke Prognosis Early Computed Tomography Score (odds ratio range, 1.31–2.10 [95% CI, 1.00–7.24]), and perfusion deficit volumes on all CTP maps (odds ratio range, 0.77–0.98 [95% CI, 0.63–1.00]) remained as independent outcome predictors. Cerebral blood flow deficit volume yielded the best performance for the classification of good clinical outcome with an area under the curve of 0.92 (95% CI, 0.84–0.99). Age and admission National Institutes of Health Stroke Scale had lower discriminatory power (area under the curve, <0.7). Conclusions: CTP imaging parameters contain prognostic information for functional outcome in patients with stroke due to basilar artery occlusion and may identify patients with higher risk of disability at an early stage of hospitalization.

Background: CT colonography does not enable definite differentiation between benign and premalignant colorectal polyps.Purpose: To perform machine learning-based differentiation of benign and premalignant colorectal polyps detected with CT colonography in an average-risk asymptomatic colorectal cancer screening sample with external validation using radiomics. Materials and Methods:In this secondary analysis of a prospective trial, colorectal polyps of all size categories and morphologies were manually segmented on CT colonographic images and were classified as benign (hyperplastic polyp or regular mucosa) or premalignant (adenoma) according to the histopathologic reference standard. Quantitative image features characterizing shape (n = 14), gray level histogram statistics (n = 18), and image texture (n = 68) were extracted from segmentations after applying 22 image filters, resulting in 1906 feature-filter combinations. Based on these features, a random forest classification algorithm was trained to predict the individual polyp character. Diagnostic performance was validated in an external test set. Results:The random forest model was fitted using a training set consisting of 107 colorectal polyps in 63 patients (mean age, 63 years 6 8 [standard deviation]; 40 men) comprising 169 segmentations on CT colonographic images. The external test set included 77 polyps in 59 patients comprising 118 segmentations. Random forest analysis yielded an area under the receiver operating characteristic curve of 0.91 (95% CI: 0.85, 0.96), a sensitivity of 82% (65 of 79) (95% CI: 74%, 91%), and a specificity of 85% (33 of 39) (95% CI: 72%, 95%) in the external test set. In two subgroup analyses of the external test set, the area under the receiver operating characteristic curve was 0.87 in the size category of 6-9 mm and 0.90 in the size category of 10 mm or larger. The most important image feature for decision making (relative importance of 3.7%) was quantifying first-order gray level histogram statistics. Conclusion:In this proof-of-concept study, machine learning-based image analysis enabled noninvasive differentiation of benign and premalignant colorectal polyps with CT colonography.

• Cross-sectional imaging is frequently applied in the diagnosis of LVV. • Navigated, PPU-triggered, T1w-3D mVISTA pre- and post contrast takes 10-12 min. • In this prospective, single-centre study, T1w-3D mVISTA accurately depicted large thoracic vessels. • T1w-3D mVISTA visualized CWT/CCW as correlates of mural inflammation in LVV. • T1w-3D mVISTA might be an alternative diagnostic tool without ionizing radiation.