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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 Background Colorectal cancer is the third most common tumour entity in the world and up to 50% of the patients develop liver metastases (CRLM) within five years. To improve and personalize therapeutic strategies, new diagnostic tools are urgently needed. For instance, biomechanical tumour properties measured by magnetic resonance elastography (MRE) could be implemented as such a diagnostic tool. We postulate that ex vivo MRE combined with histological and radiological evaluation of CRLM could provide biomechanics-based diagnostic markers for cell viability in tumours. Methods 34 CRLM specimens from patients who had undergone hepatic resection were studied using ex vivo MRE in a frequency range from 500 Hz to 5300 Hz with increments of 400 Hz. Single frequency evaluation of shear wave speed and wave penetration rate as proxies for stiffness and viscosity was performed, along with rheological model fitting based on the spring-pot model and powerlaw exponent α, ranging between 0 (complete solid behaviour) and 1 (complete fluid behaviour). For histological analysis, samples were stained with H&E and categorized according to the degree of regression. Quantitative histologic analysis was performed to analyse nucleus size, aspect ratio, and density. Radiological response was assessed according to RECIST-criteria. Results Five samples showed major response to chemotherapy, six samples partial response and 23 samples no response. For higher frequencies (> 2100 Hz), shear wave speed correlated significantly with the degree of regression (p ≤ 0.05) indicating stiffer properties with less viable tumour cells. Correspondingly, rheological analysis of α revealed more elastic-solid tissue properties at low cell viability and major response (α = 0.43 IQR 0.36, 0.47) than at higher cell viability and no response (α = 0.51 IQR 0.48, 0.55; p = 0.03). Quantitative histological analysis showed a decreased nuclear area and density as well as a higher nuclear aspect ratio in patients with major response to treatment compared to patients with no response (all p

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

Serious complications after pancreaticoduodenectomy include rupture of pseudoaneurysms arising from pancreatic fistula and pancreatojejunostomy leakage. We report a case of successful endovascular minimally invasive treatment using a covered stent endoprosthesis of a right hepatic artery stump bleeding following pylorus-preserving pancreaticoduodenectomy that was not suitable for coil or glue embolization due to an insufficiently short neck.

We evaluated a simple semi-quantitative (SSQ) method for determining pulmonary involvement in computed tomography (CT) scans of COVID-19 patients. The extent of lung involvement in the first available CT was assessed with the SSQ method and subjectively. We identified risk factors for the need of invasive ventilation, intensive care unit (ICU) admission and for time to death after infection. Additionally, the diagnostic performance of both methods was evaluated. With the SSQ method, a 10% increase in the affected lung area was found to significantly increase the risk for need of ICU treatment with an odds ratio (OR) of 1.68 and for invasive ventilation with an OR of 1.35. Male sex, age, and pre-existing chronic lung disease were also associated with higher risks. A larger affected lung area was associated with a higher instantaneous risk of dying (hazard ratio (HR) of 1.11) independently of other risk factors. SSQ measurement was slightly superior to the subjective approach with an AUC of 73.5% for need of ICU treatment and 72.7% for invasive ventilation. SSQ assessment of the affected lung in the first available CT scans of COVID-19 patients may support early identification of those with higher risks for need of ICU treatment, invasive ventilation, or death.