Real-time 3-D needle guidance based on CBCT is feasible for TIPSS placement. In terms of puncture attempts, duration and dose, CBCT guidance was not inferior to the control groups and may be a valuable support for interventionists in TIPSS procedures.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing coronavirus disease 2019 (COVID-19) induces lung injury of varying severity, potentially causing severe acute respiratory distress syndrome (ARDS). Pulmonary injury patterns in COVID-19 patients differ from those in patients with other causes of ARDS. We aimed to explore the frequency and pathogenesis of cavitary lung lesions in critically ill patients with COVID-19. Retrospective study in 39 critically ill adult patients hospitalized with severe acute respiratory syndrome coronavirus 2 including lung injury of varying severity in a tertiary care referral center during March and May 2020, Berlin/Germany. We observed lung cavitations in an unusually large proportion of 22/39 (56%) COVID-19 patients treated on intensive care units (ICU), including 3/5 patients without mechanical ventilation. Median interquartile range (IQR) time between onset of symptoms and ICU admission was 11.5 (6.25–17.75) days. In 15 patients, lung cavitations were already present on the first CT scan, performed after ICU admission; in seven patients they developed during a subsequent median (IQR) observation period of 48 (35–58) days. In seven patients we found at least one cavitation with a diameter > 2 cm (maximum 10 cm). Patients who developed cavitations were older and had a higher body mass index. Autopsy findings in three patients revealed that the cavitations reflected lung infarcts undergoing liquefaction, secondary to thrombotic pulmonary artery branch occlusions. Lung cavitations appear to be a frequent complication of severely ill COVID-19 patients, probably related to the prothrombotic state associated with COVID-19.

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Objectives This study aims to better characterize potential responders of Y-90-radioembolization at baseline through analysis of clinical variables and contrast enhanced (CE) MRI tumor volumetry in order to adjust therapeutic regimens early on and to improve treatment outcomes. Methods Fifty-eight HCC patients who underwent Y-90-radioembolization at our center between 10/2008 and 02/2017 were retrospectively included. Pre- and post-treatment target lesion volumes were measured as total tumor volume (TTV) and enhancing tumor volume (ETV). Survival analysis was performed with Cox regression models to evaluate 65% ETV reduction as surrogate endpoint for treatment efficacy. Univariable and multivariable logistic regression analyses were used to evaluate the combination of baseline clinical variables and tumor volumetry as predictors of ≥ 65% ETV reduction. Results Mean patients’ age was 66 (SD 8.7) years, and 12 were female (21%). Sixty-seven percent of patients suffered from liver cirrhosis. Median survival was 11 months. A threshold of ≥ 65% in ETV reduction allowed for a significant (p = 0.04) separation of the survival curves with a median survival of 11 months in non-responders and 17 months in responders. Administered activity per tumor volume did predict neither survival nor ETV reduction. A baseline ETV/TTV ratio greater than 50% was the most important predictor of arterial devascularization (odds ratio 6.3) in a statistically significant (p = 0.001) multivariable logistic regression model. The effect size was strong with a Cohen’s f of 0.89. Conclusion We present a novel approach to identify promising candidates for Y-90 radioembolization at pre-treatment baseline MRI using tumor volumetry and clinical baseline variables. Key Points • A decrease of 65% enhancing tumor volume (ETV) on follow-up imaging 2–3 months after Y-90 radioembolization of HCC enables the early prediction of significantly improved median overall survival (11 months vs. 17 months, p = 0.04). • Said decrease in vascularization is predictable at baseline: an ETV greater than 50% is the most important variable in a multivariable logistic regression model that predicts responders at a high level of significance (p = 0.001) with an area under the curve of 87%.