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This case report is about an 84-year-old female patient with a history of high-grade serous ovarian carcinoma who was diagnosed with a renal pseudotumor. Initial imaging in February 2023 showed signs of a renal cell carcinoma and possible lung metastases. A CT-guided biopsy and histopathological analysis ruled out malignancy and confirmed a benign inflammatory pseudotumor. This case underlines the importance of a biopsy to avoid unnecessary treatments such as nephrectomy and emphasizes the worth of comprehensive diagnostic evaluation. These steps are important to prevent unnecessary surgical interventions, overtreatment and to focus on effective, targeted therapy for the actual underlying pathology.

Introduction: The number of incidental renal lesions identified in CT scans of the abdomen is increasing. Objective: The aim of this study was to determine whether hyperdense renal lesions without solid components in a portal venous CT scan can be clearly classified as vascular or non-vascular by material decomposition into iodine and water. Methods: This retrospective single-center study included 26 patients (mean age 72 years ± 9; 16 male) with 42 hyperdense renal lesions (>20 HU) in a contrast-enhanced Photon-Counting Detector CT scan (PCD-CT) between May and December 2022. Spectral decomposition into virtual non-contrast (VNC) images and iodine quantification maps was performed, and HU values were quantified within the lesions. Further imaging and histopathological reports served as reference standards. Results: Mean VNC values were 55.7 (±24.2) HU for non-vascular and 32.2 (±11.1) HU for vascular renal lesions. Mean values in the iodine maps were 5.7 (±7.8) HU for non-vascular and 33.3 (±19.0) HU for vascular renal lesions. Using a threshold of >20.3 HU in iodine maps, a total of 7/8 (87.5%) vascular lesions were correctly identified. Conclusion: This proof-of-principle study suggests that the routine use of spectral information acquired in PCD-CT scans might be able to reduce the necessary workup for hyperdense renal lesions without solid components. Further studies with larger patient cohorts are necessary to validate the results of this study and to determine the usefulness of this method in clinical routine.

Objectives: Virtual non-contrast (VNC) series reconstructed from contrast-enhanced cardiac scans acquired with photon counting detector CT (PCD-CT) systems have the potential to replace true non-contrast (TNC) series. However, a quantitative comparison of the image characteristics of TNC and VNC data is necessary to determine to what extent they are interchangeable. This work quantitatively evaluates the image similarity between VNC and TNC reconstructions by measuring the stability of multi-class radiomics features extracted in intra-patient TNC and VNC reconstructions. Methods: TNC and VNC series of 84 patients were retrospectively collected. For each patient, the myocardium and epicardial adipose tissue (EAT) were semi-automatically segmented in both VNC and TNC reconstructions, and 105 radiomics features were extracted in each mask. Intra-feature correlation scores were computed using the intraclass correlation coefficient (ICC). Stable features were defined with an ICC higher than 0.75. Results: In the myocardium, 41 stable features were identified, and the three with the highest ICC were glrlm_GrayLevelVariance with ICC3 of 0.98 [0.97, 0.99], ngtdm_Strength with ICC3 of 0.97 [0.95, 0.98], firstorder_Variance with ICC3 of 0.96 [0.94, 0.98]. For the epicardial fat, 40 stable features were found, and the three highest ranked are firstorder_Median with ICC3 of 0.96 [0.93, 0.97], firstorder_RootMeanSquared with ICC3 of 0.95 [0.92, 0.97], firstorder_Mean with ICC3 of 0.95 [0.92, 0.97]. A total of 24 features (22.8%; 24/105) showed stability in both anatomical structures. Conclusions: The significant differences in the correlation of radiomics features in VNC and TNC volumes of the myocardium and epicardial fat suggested that the two reconstructions may differ more than initially assumed. This indicates that they may not be interchangeable, and such differences could have clinical implications. Therefore, care should be given when selecting VNC as a substitute for TNC in radiomics research to ensure accurate and reliable analysis. Moreover, the observed variations may impact clinical workflows, where precise tissue characterization is critical for diagnosis and treatment planning.

Abstract Background Aim of this study was to analyze long-term trends of hospitalizations, treatment regimen and in-hospital mortality of in-patients with acute mesenteric ischemia (AMI) over the past decade and effects of the SARS-CoV2-pandemic. Methods We analyzed fully anonymized data from the German Federal Statistical Office of patients with AMI between 2010 and 2020. Besides descriptive analyses of age, gender, in-hospital mortality, comorbidity burden and treatment regimen, multivariable logistic regression analyses were performed to identify independent variables associated with in-hospital mortality and different treatment. Results A total of 278,121 hospitalizations (120,667 male [43.4%], mean age 72.1 years) with AMI were included in this study. The total number of hospitalizations increased from 2010 (n = 24,172) to 2019 (n = 26,684) (relative increase 10.4%). In-hospital mortality decreased over the past decade from 36.6% to 2010 to 31.1% in 2019 (rel. decrease 15.2%). Independent risk factors for in-hospital mortality were older age (OR = 1.03 per year), higher comorbidity burden (OR = 1.06 per point in van Walraven score [vWs]), male gender (OR = 1.07), AMI as a secondary diagnosis (OR = 1.44), and the need for surgical (visceral surgery: OR = 1.38, vascular surgery: OR = 3.33) and endovascular treatment (OR = 1.21). We report a decline in hospitalizations during the first wave of infection in spring 2020 (rel. decrease 9.7%). Conclusion In-hospital mortality rate has declined over the past decade, but remains high at above 30%. Older age, increased comorbidity and male gender are independent factors for in-hospital mortality. Hospitalizations requiring vascular surgery are associated with high in-hospital mortality, followed by visceral surgery and endovascular approaches. The first wave of the SARS-CoV2-pandemic in spring 2020 implied a decrease in hospital admissions.

PURPOSERe-entry devices contribute to the high success rate of subintimal recanalization of chronic total occlusions (CTO). However, to date, there are no studies comparing the available conventional re-entry devices concerning the impact of their technical success on economic aspects, as these devices differ greatly in their acquisition costs. This prospective observational study intends to contribute to this question.METHODSPrior to the start of the prospective study, all previous applications of the Outback® in femoro-popliteal CTO since its introduction to our hospital were analyzed retrospectively (n = 31). From June 2018 until January 2020, all patients with femoro-popliteal CTO treated with clear subintimal recanalization were included (n = 109). In the case of failed spontaneous re-entry, either the OffRoad® (study arm I, n = 20) or the Enteer® catheter (study arm II, n = 20) was used. If assisted re-entry failed, the Outback® device was used as a bailout. Baseline demographic and clinical data, morphologic characteristics, and technical success were documented. Additional per-patient costs due to the use of re-entry devices were analyzed.RESULTSA retrospective evaluation of all Outback® applications revealed a technical success rate of 97% (30/31). In the prospective study, 63% (68/109) were successfully treated without using re-entry devices. The overall procedural success was 95% (103/109). In study arm I, the OffRoad® achieved a success rate of 45% (9/20), with a subsequent successful application of the Outback® in 80% (8/10) of the failed cases. In study arm II, the Enteer® was successfully employed in 60% (12/20) of cases, and the Outback® was then used successfully in a further 62% (5/8) of cases. Too large a distance between the device and the target lumen was a knockout criterion for all tested devices, leading to a subgroup analysis with the exclusion of three cases, resulting in a success rate of 47% for the OffRoad® and 67% for the Enteer® device. Furthermore, in severe calcification, only the Outback® reliably enabled revascularization. Significant savings of almost €600 were only achieved in study arm II according to German prices.CONCLUSIONWith proper patient selection, a gradual approach with the Enteer® as the primarily used device, with the Outback® used additionally in case of failure, leads to significant savings and can be recommended. In severe calcification, the Outback® should be used as the primary device.

In the early diagnostic workup of acute pancreatitis (AP), the role of contrast-enhanced CT is to establish the diagnosis in uncertain cases, assess severity, and detect potential complications like necrosis, fluid collections, bleeding or portal vein thrombosis. The value of texture analysis/radiomics of medical images has rapidly increased during the past decade, and the main focus has been on oncological imaging and tumor classification. Previous studies assessed the value of radiomics for differentiating between malignancies and inflammatory diseases of the pancreas as well as for prediction of AP severity. The aim of our study was to evaluate an automatic machine learning model for AP detection using radiomics analysis. Patients with abdominal pain and contrast-enhanced CT of the abdomen in an emergency setting were retrospectively included in this single-center study. The pancreas was automatically segmented using TotalSegmentator and radiomics features were extracted using PyRadiomics. We performed unsupervised hierarchical clustering and applied the random-forest based Boruta model to select the most important radiomics features. Important features and lipase levels were included in a logistic regression model with AP as the dependent variable. The model was established in a training cohort using fivefold cross-validation and applied to the test cohort (80/20 split). From a total of 1012 patients, 137 patients with AP and 138 patients without AP were included in the final study cohort. Feature selection confirmed 28 important features (mainly shape and first-order features) for the differentiation between AP and controls. The logistic regression model showed excellent diagnostic accuracy of radiomics features for the detection of AP, with an area under the curve (AUC) of 0.932. Using lipase levels only, an AUC of 0.946 was observed. Using both radiomics features and lipase levels, we showed an excellent AUC of 0.933 for the detection of AP. Automated segmentation of the pancreas and consecutive radiomics analysis almost achieved the high diagnostic accuracy of lipase levels, a well-established predictor of AP, and might be considered an additional diagnostic tool in unclear cases. This study provides scientific evidence that automated image analysis of the pancreas achieves comparable diagnostic accuracy to lipase levels and might therefore be used in the future in the rapidly growing era of AI-based image analysis.

Purpose: Coronary artery disease (CAD) and peripheral artery disease (PAD) are highly prevalent in society. This nationwide analysis aimed to evaluate the trends of in-hospital treatment of patients admitted due to PAD with and without concomitant CAD, to determine the prevalence and risk factors of concomitant CAD in patients with PAD. Methods: Using data from the German Federal Statistical Office, we included all admissions for PAD (with and without concomitant CAD) in Germany between 2009 and 2018. Baseline patient characteristics, outcomes and comorbidities were analyzed. Elixhauser comorbidity groups and the linear van Walraven comorbidity score (vWs) were calculated to assess the comorbidity burden. Results: Of all 1,793,517 patients hospitalized for PAD, a total of 21.8% (390,259) had concomitant CAD, increasing from 18.6% in 2009 to 24.4% in 2018. Patients with accompanying CAD showed higher in-hospital mortality (3.7 vs. 2.6%), more major amputations (9.0 vs. 7.7%) and more comorbidities (Elixhauser score: 4.2 vs. 3.2 and vWs: 9.1 vs. 6.1), resulting in higher costs (median: EUR 4541 vs. EUR 4268 per case). More advanced stages of PAD were associated with multi-vessel CAD (10% of all patients with PAD Fontaine IV showed 3-vessel CAD) and the prevalence of multi-vessel CAD increased predominantly in patients with advanced PAD. Conclusion: One in four patients hospitalized for PAD had concomitant CAD, showing an increase over time with an additional medical and economic burden for hospitals compared with patients without CAD.

Artificial intelligence is gaining increasing relevance in the field of radiology. This study retrospectively evaluates how a commercially available deep learning algorithm can detect pneumonia in chest radiographs (CR) in emergency departments. The chest radiographs of 948 patients with dyspnea between 3 February and 8 May 2020, as well as 15 October and 15 December 2020, were used. A deep learning algorithm was used to identify opacifications associated with pneumonia, and the performance was evaluated by using ROC analysis, sensitivity, specificity, PPV and NPV. Two radiologists assessed all enrolled images for pulmonal infection patterns as the reference standard. If consolidations or opacifications were present, the radiologists classified the pulmonal findings regarding a possible COVID-19 infection because of the ongoing pandemic. The AUROC value of the deep learning algorithm reached 0.923 when detecting pneumonia in chest radiographs with a sensitivity of 95.4%, specificity of 66.0%, PPV of 80.2% and NPV of 90.8%. The detection of COVID-19 pneumonia in CR by radiologists was achieved with a sensitivity of 50.6% and a specificity of 73%. The deep learning algorithm proved to be an excellent tool for detecting pneumonia in chest radiographs. Thus, the assessment of suspicious chest radiographs can be purposefully supported, shortening the turnaround time for reporting relevant findings and aiding early triage.

D-dimer might be correlated with prognosis in pulmonary embolism (PE). The predictive value of plasma D-dimer for disease severity and survival was investigated in the lowest and highest D-dimer quartile among 200 patients with PE. Patients with high D-dimers were significantly more often hypotensive ( P = .001), tachycardic ( P = .016), or hypoxemic ( P = .001). Pulmonary arterial obstruction index (PAOI) values were significantly higher in the high D-dimer quartile ( P < .001). Elevated troponin I (TNI) levels ( P < .001), simplified PE severity indices ≥1 ( P < .001), right-to-left ventricular (RV/LV) diameter ratios ≥1 ( P < .001), and thrombolysis ( P = .001) were more frequent in the high D-dimer quartile. D-dimer was associated with RV/LV ratios ≥1 ( P = .021), elevated PAOI ( P < .001) or TNI levels ( P < .001), hypotension ( P < .001), tachycardia ( P = .003), and hypoxemia ( P < .001), but not with long-term all-cause mortality. D-dimer predicts disease severity but not long-term prognosis in acute PE, possibly due to a more aggressive treatment strategy in severely affected patients.