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Abstract Objectives Critically ill patients with severe pancreatitis exhibit substantial muscle wasting, which limits in-hospital and post-hospital outcomes. Survivors of critical illness undergo extensive recovery processes. Previous studies have explored pancreatic function, quality of life, and costs post-hospitalization for AP patients, but none have comprehensively quantified muscle loss and recovery post-discharge. By applying an AI-based automated segmentation tool, we aimed to quantify muscle mass recovery in ICU patients after discharge. Materials Muscle segmentation was performed on 22 patients, with a minimum of three measurements taken during hospitalization and one clinically indicated examination after hospital discharge. Changes in psoas muscle area (PMA) between admission, discharge and follow up were calculated. T-Test was performed to identify significant differences between patients able and not able to recover their muscle mass. Results Monitoring PMA shows muscle loss during and gain after hospitalization: The mean PMA at the first scan before or at ICU admission (TP1) was 17.08 cm², at the last scan before discharge (TP2), mean PMA was 9.61 cm². The percentage change in PMA between TP1 and TP2 ranged from − 85.42% to -2.89%, with a mean change of -40.18%. The maximum muscle decay observed during the stay was − 50.61%. After a mean follow-up period of 438.73 days most patients (81%) were able to increase their muscle mass. Compared to muscle status at TP1, only 27% of patients exhibited full recovery, with the majority still presenting a deficit of 31.96%. Conclusion Muscle recovery in ICU patients suffering from severe AP is highly variable, with only about one third of patients recovering to their initial physical status. Opportunistic screening of post-ICU patient recovery using clinically indicated imaging and AI-based segmentation tools enables precise quantification of patients’ muscle status and can be employed to identify individuals who fail to recover and would benefit from secondary rehabilitation. Understanding the dynamics of muscle atrophy may improve prognosis and support personalized patient care.

Abstract Metal artifacts notoriously pose significant challenge in computed tomography (CT), leading to inaccuracies in image formation and interpretation. Artifact reduction tools have been designed to improve cone beam computed tomography (CBCT) image quality by reducing artifacts caused by certain high-density materials. Metal artifact reduction (MAR) tools are specific algorithms that are applied during image reconstruction to minimize or eliminate artifacts degrading CBCT images. The purpose of the study is to evaluate the effect of a MAR algorithm on image quality in CBCT performed for evaluating patients before transarterial radioembolization (TARE). We retrospectively included 40 consecutive patients (aged 65 ± 13 years; 23 males) who underwent 45 CBCT examinations (Allura FD 20, XperCT Roll protocol, Philips Healthcare, Best, The Netherlands) in the setting of evaluation for TARE between January 2017 and December 2018. Artifacts caused by coils, catheters, and surgical clips were scored subjectively by four readers on a 5-point scale (1 = artifacts affecting diagnostic information to 5 = no artifacts) using a side-by-side display of uncorrected and MAR-corrected images. In addition, readers scored tumor visibility and vessel discrimination. MAR-corrected images were assigned higher scores, indicating better image quality. The differences between the measurements with and without MAR were most impressive for coils with a mean improvement of 1.6 points (95%CI [1.5 1.8]) on the 5-point likert scale, followed by catheters 1.4 points (95%CI [1.3 1.5]) and clips 0.7 points (95%CI [0.3 1.1]). Improvements for other artifact sources were consistent but relatively small (below 0.25 points on average). Interrater agreement was good to perfect (Kendall’s W coefficient = 0.68–0.95) and was higher for MAR-corrected images, indicating that MAR improves diagnostic accuracy. A metal artifact reduction algorithm can improve diagnostic and interventional accuracy of cone beam CT in patients undergoing radioembolization by reducing artifacts caused by diagnostic catheters and coils, lowering interference of metal artifacts with adjacent major structures, and improving tumor visibility.

Abstract Objectives To assess the feasibility of long-term muscle monitoring, we implemented an AI-guided segmentation approach on clinically indicated Computed Tomography (CT) examinations conducted throughout the hospitalization period of patients admitted to the intensive care unit (ICU) with acute pancreatitis (AP). In addition, we aimed to investigate the potential of muscle monitoring for early detection of patients at nutritional risk and those experiencing adverse outcomes. This cohort served as a model for potential integration into clinical practice. Materials Retrospective cohort study including 100 patients suffering from AP that underwent a minimum of three CT scans during hospitalization, totaling 749 assessments. Sequential segmentation of psoas muscle area (PMA) was performed and was relative muscle loss per day for the entire monitoring period, as well as for the interval between each consecutive scan was calculated. Subgroup and outcome analyses were performed including ANOVA. Discriminatory power of muscle decay rates was evaluated using ROC analysis. Results Monitoring PMA decay revealed significant long-term losses of 48.20% throughout the hospitalization period, with an average daily decline of 0.98%. Loss rates diverged significantly between survival groups, with 1.34% PMA decay per day among non-survivors vs. 0.74% in survivors. Overweight patients exhibited significantly higher total PMA losses (52.53 vs. 42.91%; p = 0.02) and average PMA loss per day (of 1.13 vs. 0.80%; p = 0.039). The first and the maximum decay rate, in average available after 6.16 and 17.03 days after ICU admission, showed convincing discriminatory power for survival in ROC analysis (AUC 0.607 and 0.718). Both thresholds for maximum loss (at 3.23% decay per day) and for the initial loss rate (at 1.98% per day) proved to be significant predictors of mortality. Conclusions The innovative AI-based PMA segmentation method proved robust and effortless, enabling the first comprehensive assessment of muscle wasting in a large cohort of intensive care pancreatitis patients. Findings revealed significant muscle wasting (48.20% on average), particularly notable in overweight individuals. Higher rates of initial and maximum muscle loss, detectable early, correlated strongly with survival. Integrating this tool into routine clinical practice will enable continuous muscle status tracking and early identification of those at risk for unfavorable outcomes.

Abstract Background and Aims Intrahepatic cholangiocarcinoma (iCC) is an aggressive tumor, usually detected at an advanced stage. Our aim was to investigate the potential of body composition analysis (BCA) derived from presurgical staging computed tomography (CT) in predicting perisurgical complications. Methods In this retrospective cohort study, we enrolled 86 patients who underwent CT imaging prior to liver surgery. Cox and logistic regression were performed to assess risk factors for prolonged hospital and intensive care unit (ICU) stays, as well as the occurrence of various complications. BCA parameters served as covariates besides conventional risk factors. Results Postoperative complications after resection of iCC significantly prolonged the overall length of hospitalization (p

Abstract Objectives SARS-CoV-2 virus infection can lead to acute respiratory distress syndrome (ARDS), which can be complicated by severe muscle wasting. Until now, data on muscle loss of critically ill COVID-19 patients are limited, while computed tomography (CT) scans for clinical follow-up are available. We sought to investigate the parameters of muscle wasting in these patients by being the first to test the clinical application of body composition analysis (BCA) as an intermittent monitoring tool. Materials BCA was conducted on 54 patients, with a minimum of three measurements taken during hospitalization, totaling 239 assessments. Changes in psoas- (PMA) and total abdominal muscle area (TAMA) were assessed by linear mixed model analysis. PMA was calculated as relative muscle loss per day for the entire monitoring period, as well as for the interval between each consecutive scan. Cox regression was applied to analyze associations with survival. Receiver operating characteristic (ROC) analysis and Youden index were used to define a decay cut-off. Results Intermittent BCA revealed significantly higher long-term PMA loss rates of 2.62% (vs. 1.16%, p

Background: Patients diagnosed with Marfan syndrome or a related syndrome require frequent aorta monitoring using imaging techniques like transthoracic echocardiography (TTE) and computed tomography (CT). Accurate aortic measurement is crucial, as even slight enlargement (>2 mm) often necessitates surgical intervention. The 2022 ACC/AHA guideline for Aortic Disease Diagnosis and Management includes updated imaging recommendations. We aimed to compare these with the 2010 guideline. Methods: This retrospective study involved 137 patients with Marfan syndrome or a related disorder, undergoing TTE and ECG-triggered CT. Aortic diameter measurements were taken based on the old 2010 guideline (TTE: inner edge to inner edge, CT: external diameter) and the new 2022 guideline (TTE: leading edge to leading edge, CT: internal diameter). Bland-Altman plots compared measurement differences. Results: Using the 2022 guideline significantly reduced differences outside the clinical agreement limit from 49% to 26% for the aortic sinus and from 41% to 29% for the ascending aorta. Mean differences were –0.30 mm for the aortic sinus and +1.12 mm for the ascending aorta using the 2022 guideline, compared to –2.66 mm and +1.21 mm using the 2010 guideline. Conclusion: This study demonstrates for the first time that the 2022 ACC/AHA guideline improves concordance between ECG-triggered CT and TTE measurements in Marfan syndrome patients, crucial for preventing life-threatening aortic complications. However, the frequency of differences >2 mm remains high. Clinical Relevance/Application: Accurate aortic diameter measurement is vital for patients at risk of fatal aortic complications. While the 2022 guideline enhances concordance between imaging modalities, frequent differences >2 mm persist, potentially impacting decisions on aortic repair. The risk of repeat radiation exposure from ECG-triggered CT, considered the ‘gold standard’, continues to be justified.

Purpose: To evaluate a commercially available mobile device for the highly specialized task of detection of intracranial arterial aneurysm in telemedicine. Methods: Six radiologists with three different levels of experience retrospectively interpreted 60 computed tomography (CT) angiographies for the presence of intracranial arterial aneurysm, among them 30 cases with confirmed positive findings. Each radiologist reviewed the angiography datasets twice: once on a dedicated medical-grade workstation and on a commercially available mobile consumer-grade tablet with an interval of 3 months. Diagnostic performance, reading efficiency and subjective scorings including diagnostic confidence were analyzed and compared. Results: Diagnostic performance was comparable on both devices regardless of readers’ experience, and no significant differences in sensitivity (66–87.5%) and specificity (79.4–87%) were found. Results obtained with tablets and medical workstations were also comparable in terms of subjective assessment across all reader groups. Conclusions: There was no significant difference between tablet and workstation readings of angiography datasets for the presence of intracranial arterial aneurysm. Sensitivity, specificity, efficiency and subjective scorings were similar with the two devices for all three reader groups. While medical workstations are 10 times more expensive, tablets allow higher mobility especially for radiologists on call.

Background: Bronchopulmonary dysplasia (BPD) is a lung disease of preterm born infants, characterized by alveolar simplification. MicroRNA (miR) are known to be involved in many biological and pathological processes in the lung. Although a changed expression has been described for several miR in BPD, a causal role remains to be established. Results: Our results showed that the expression level of miR-154 increases during lung development and decreases postnatally. Further, hyperoxia treatment maintains high levels of miR-154 in alveolar type 2 cells (AT2). We hypothesized that the decrease in miR-154 expression in AT2 cells is required for normal alveologenesis. To test this hypothesis, we generated a novel transgenic mouse allowing doxycycline-based miR-154 overexpression. Maintenance of miR-154 expression in the postnatal distal lung epithelium under normoxia conditions is sufficient to reproduce the hypoalveologenesis phenotype triggered by hyperoxia. Using a pull-down assay, we identified Caveolin1 as a key downstream target of miR-154. Caveolin1 protein is downregulated in response to overexpression of miR-154. This is associated with increased phosphorylation of Smad3 and Tgf-ß signaling. We found that AT2 cells overexpressing miR-154 display decreased expression of AT2 markers and increased expression of AT1 markers. Conclusion: Our results suggest that down-regulation of miR-154 in postnatal lung may function as an important physiological switch that permits the induction of the correct alveolar developmental program, while conversely, failure to down-regulate miR-154 suppresses alveolarization, leading to the common clinically observed phenotype of alveolar simplification.