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Background: Chest computed tomography findings are helpful for understanding the pathophysiology of severe acute respiratory distress syndrome (ARDS). However, there is no large, multicenter, chest computed tomography registry for patients requiring veno-venous extracorporeal membrane oxygenation (V-V ECMO). The aim of this study was to describe chest computed tomography findings at V-V ECMO initiation and to evaluate the association between the findings and outcomes in severe ARDS. Methods: This multicenter, retrospective cohort study enrolled patients with severe ARDS on V-V ECMO, who were admitted to the intensive care units of 24 hospitals in Japan between January 1, 2012, and December 31, 2022. Results: The primary outcome was 90-day in-hospital mortality. The secondary outcomes were the successful liberation from V-V ECMO and the values of static lung compliance. Among the 697 registry patients, of the 582 patients who underwent chest computed tomography at V-V ECMO initiation, 394 survived and 188 died. Multivariate Cox regression showed that traction bronchiectasis and subcutaneous emphysema increased the risk of 90-day in-hospital mortality (hazard ratio [95% confidence interval] 1.77 [1.19–2.63], p = 0.005 and 1.97 [1.02–3.79], p = 0.044, respectively). The presence of traction bronchiectasis was also associated with decreased successful liberation from V-V ECMO (odds ratio: 0.27 [0.14–0.52], p < 0.001). Lower static lung compliance was associated with some chest computed tomography findings related to changes outside of pulmonary opacity, but not with the findings related to pulmonary opacity. Conclusions: Traction bronchiectasis and subcutaneous emphysema increased the risk of 90-day in-hospital mortality in patients with severe ARDS who required V-V ECMO. [ABSTRACT FROM AUTHOR]

Objectives: To compare the [18F]FDG PET/CT findings of untreated sarcoidosis and malignant lymphoma (ML) and develop convolutional neural network (CNN) models to differentiate between these diseases using maximum intensity projection (MIP) [18F]FDG PET images. Methods: We retrospectively collected data on consecutive patients newly diagnosed with sarcoidosis and ML who underwent [18F]FDG PET/CT before treatment. Two nuclear radiologists reviewed the images. CNN models were created using MIP PET images and evaluated with k-fold cross-validation. The points of interest were visualized using gradient-weighted class activation mapping (Grad-CAM). Results: A total of 56 patients with sarcoidosis and 62 patients with ML were included. Patients with sarcoidosis had more prominent FDG accumulation in the mediastinal lymph nodes and lung lesions, while those with ML had more prominent accumulation in the cervical lymph nodes (all p < 0.001). For the mediastinal lymph nodes, sarcoidosis patients had significant FDG accumulation in the level 2, 4, 7, and 10 lymph nodes (all p < 0.01). Otherwise, the accumulation in ML patients tended to be in the level 1 lymph nodes (p = 0.08). The CNN model using frontal and lateral MIP images achieved an average accuracy of 0.890 (95% CI: 0.804–0.977), a sensitivity of 0.898 (95% CI: 0.782–1.000), a specificity of 0.907 (95% CI: 0.799–1.000), and an area under the curve of 0.963 (95% CI: 0.899–1.000). Grad-CAM showed that the model focused on the sites of abnormal FDG accumulation. Conclusions: CNN models based on differences in FDG accumulation sites archive high performance in differentiating between sarcoidosis and ML. Clinical relevance statement: We developed a CNN model using MIP images of [18F]FDG PET/CT to distinguish between sarcoidosis and malignant lymphoma. It achieved high performance and could be useful in diagnosing diseases with involvement across organs and lymph nodes. Key Points: • There are differences in FDG distribution when comparing whole-body [18F]FDG PET/CT findings in patients with sarcoidosis and malignant lymphoma before treatment. • Convolutional neural networks, a type of deep learning technique, trained with maximum-intensity projection PET images from two angles showed high performance. • A deep learning model that utilizes differences in FDG distribution may be helpful in differentiating between diseases with lesions that are characteristically widespread among organs and lymph nodes. [ABSTRACT FROM AUTHOR]

Background: It is clinically important to predict difficulty in short-term liberation from veno-venous extracorporeal membrane oxygenation (V-V ECMO) in patients with severe acute respiratory distress syndrome (ARDS) at the time of initiation of the support. The aim of this study was to identify the characteristics of pulmonary opacities on chest CT that is associated with difficulty in short-term liberation from V-V ECMO (< 14 days). Methods: This multicenter retrospective study was conducted in adult patients initiated on V-V ECMO for severe ARDS between January 2014 and June 2022. The pulmonary opacities on CT at the time of initiation of the ECMO support were evaluated in a blinded manner, focusing on the following three characteristics of the opacities: (1) their distribution (focal/diffuse on the dorso-ventral axis or unilateral/bilateral on the left-right axis); (2) their intensity (pure ground glass/pure consolidation/mixed pattern); and (3) the degree of fibroproliferation (signs of traction bronchiectasis or reticular opacities). Results: Among the 153 patients, 72 (47%) were successfully liberated from ECMO in the short term, while short-term liberation failed in the remaining 81 (53%) patients. Multivariate logistic regression analysis showed that the presence of mixed-pattern pulmonary opacities and signs of traction bronchiectasis, but not the distribution of the opacities, were independently associated with difficulty in short-term liberation (OR [95% CI]; 4.8 [1.4–16.5] and 3.9 [1.4–11.2], respectively). Conclusions: The presence of a mixed pattern of the pulmonary opacities and signs of traction bronchiectasis on the chest CT were independently associated with difficulty in short-term liberation from V-V ECMO in severe ARDS patients. [ABSTRACT FROM AUTHOR]