Objectives: Lymph node metastases (LNM) are frequent in patients with intrahepatic cholangiocarcinoma (iCC) and worsen their prognosis even after surgery. Our aim was to investigate the predictive value of lymph node (LN) short axis, the most common discriminator for identifying LNM in tumor-imaging and to develop a predictive model for regional LNM in iCC taking computed tomography (CT) features of extranodal disease into account. Materials and methods: We enrolled 102 patients with pathologically proven iCC who underwent CT prior to hepatic resection and hilar lymph node dissection (LND) from 2005 to 2021. Two blinded radiologists assessed various imaging characteristics and LN diameters, which were analyzed by bivariate and multivariate logistic regression to develop a prediction model for LNM. Results: Prevalence of LNM was high (42.4 %) and estimated survival was shorter in LN-positive patients (p = 0.07). An LN short axis diameter of ≥ 9 mm demonstrated the highest predictive power for LNM. Three additional, statistically significant imaging features, presence of intrahepatic metastasis (p = 0.003), hilar tumor infiltration (p = 0.003), and tumor growth along the liver capsule (p = 0.004), were integrated into a prediction model, which substantially outperformed use of LN axis alone in ROC analysis (AUC 0.856 vs 0.701). Conclusions: LN diameter alone proved to be a relevant but unreliable imaging-marker for LNM prediction in iCC. Our proposed prognostic model, which additionally considers intrahepatic metastases and hilar and capsular infiltration, significantly improves discriminatory power. Hilar and capsular involvement might indicate direct tumor extension to lymphatic liver structures.

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.

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. 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. Intermittent BCA revealed significantly higher long-term PMA loss rates of 2.62% (vs. 1.16%, p < 0.001) and maximum muscle decay of 5.48% (vs. 3.66%, p = 0.039) per day in non-survivors. The first available decay rate did not significantly differ between survival groups but showed significant associations with survival in Cox regression (p = 0.011). In ROC analysis, PMA loss averaged over the stay had the greatest discriminatory power (AUC = 0.777) for survival. A long-term PMA decline per day of 1.84% was defined as a threshold; muscle loss beyond this cut-off proved to be a significant BCA-derived predictor of mortality. Muscle wasting in critically ill COVID-19 patients is severe and correlates with survival. Intermittent BCA derived from clinically indicated CT scans proved to be a valuable monitoring tool, which allows identification of individuals at risk for adverse outcomes and has great potential to support critical care decision-making.

Objectives: Radiography remains the mainstay of diagnostic and follow-up imaging. In view of the risks and the increasing use of ionizing radiation, dose reduction is a key issue for research and development. The introduction of digital radiography and the associated access to image postprocessing have opened up new opportunities to minimize the radiation dosage. These advances are contingent upon quality controls to ensure adequate image detail and maintenance of diagnostic confidence. The purpose of this study was to investigate the clinical applicability of postprocessed low-dose images in skeletal radiography. Methods: In our study setting, the median radiation dose for full dose X-rays was 9.61 dGy*cm2 for pelvis, 1.20 dGy*cm2 for shoulder and 18.64 dGy*cm2 for lumbar spine exams. Based on these values, we obtained 200 radiographs for each anatomic region in four consecutive steps, gradually reducing the dose to 84%, 71%, 60% and 50% of the baseline using an automatic exposure control (AEC). 549 patients were enrolled for a total of 600 images. All X-rays were postprocessed with a spatial noise reduction algorithm. Two radiologists assessed the diagnostic value of the radiographs by rating the visualization of anatomical landmarks and image elements on a five-point Likert scale. A mean-sum score was calculated by averaging the two reader’s total scores. Given the non-parametric distribution, we used the Mann-Whitney U test to evaluate the scores. Results: Median dosage at full dose accounted for 38.4%, 48 and 53.2% of the German reference dose area product for shoulder, pelvis and lumbar spine, respectively. The applied radiation was incrementally reduced to 21.5%, 18.4% and 18.7% of the respective reference value for shoulder, pelvis and lumbar spine. Throughout the study, we observed an estimable tendency of superior quality at higher dosage in overall image quality. Statistically significant differences in image quality were restricted to the 50% dose groups in shoulder and lumbar spine images. Regardless of the applied dosage, 598 out of 600 images were of sufficient diagnostic value. Conclusion: In digital radiography image postprocessing allows for extensive reduction of radiation dosage. Despite a trend of superior image detail at higher dose levels, overall quality and, more importantly, diagnostic utility of low-dose images was not significantly affected. Therefore, our results not only confirm the clinical utility of postprocessed low-dose radiographs, but also suggest a widespread deployment of this advanced technology to ensure further dose limitations in clinical practice. Advances in knowledge: The diagnostic image quality of postprocessed skeletal radiographs is not significantly impaired even after extensive dose reduction by up to 20% of the reference value.

Objective: Muscle wasting in critically ill patients, particularly those with prolonged hospitalization, poses a significant challenge to recovery and long-term outcomes. The aim of this study was to characterize long-term muscle wasting trajectories in ICU patients with acute respiratory distress syndrome (ARDS) due to COVID-19 and acute pancreatitis (AP), to evaluate correlations between muscle wasting and patient outcomes, and to identify clinically feasible thresholds that have the potential to enhance patient care strategies. Materials and methods: A collective of 154 ICU patients (100 AP and 54 COVID-19 ARDS) with a minimum ICU stay of 10 days and at least three abdominal CT scans were retrospectively analyzed. AI-driven segmentation of CT scans quantified changes in psoas muscle area (PMA). A mixed model analysis was used to assess the correlation between mortality and muscle wasting, Cox regression was applied to identify potential predictors of survival. Muscle loss rates, survival thresholds and outcome correlations were assessed using Kaplan-Meier and receiver operating characteristic (ROC) analyses. Results: Muscle loss in ICU patients was most pronounced in the first two weeks, peaking at -2.42% and − 2.39% psoas muscle area (PMA) loss per day in weeks 1 and 2, respectively, followed by a progressive decline. The median total PMA loss was 48.3%, with significantly greater losses in non-survivors. Mixed model analysis confirmed correlation of muscle wasting with mortality. Cox regression identified visceral adipose tissue (VAT), sequential organ failure assessment (SOFA) score and muscle wasting as significant risk factors, while increased skeletal muscle area (SMA) was protective. ROC and Kaplan-Meier analyses showed strong correlations between PMA loss thresholds and survival, with daily loss > 4% predicting the worst survival (39.7%). Conclusions: To our knowledge, This is the first study to highlight the substantial progression of muscle wasting in prolonged hospitalized ICU patients. The mortality-related thresholds for muscle wasting rates identified in this study may provide a basis for clinical risk stratification. Future research should validate these findings in larger cohorts and explore strategies to mitigate muscle loss. Clinical trial number: Not applicable.

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.