PURPOSE:Differences in severity and complexity among polytrauma patients remain a major challenge for clinicians and radiologists worldwide. We aimed to provide a descriptive analysis of patient characteristics, trauma mechanisms, and severity in connection with the need for intensive care in polytrauma patients undergoing computed tomography (CT). METHODS:This retrospective monocentric analysis consecutively included 1993 patients (1305 males, mean age 50.67±23.40 years) with 2002 emergency room cases between 12/2015 and 06/2021. Nine patients were examined twice during the study period. CT was performed in 1897/2002 cases (94.8%). Additional magnetic resonance imaging (MRI) was obtained in 438/2002 cases. For subgroup analysis, trauma mechanisms were compared. Besides conventional trauma causes also critically ill non-trauma patients (CINT) were included. Admission time, day of the week, and month were also analyzed. RESULTS:Traffic accidents constituted the largest trauma group, accounting for 847/2002 cases (42.3%). CINT with non-traumatic emergency died significantly more often (p < 0.001) than patients within the other trauma subgroups. In patients with an injury at the neck area, significantly more often an intubation was registered (p < 0.001). MRI was significantly more frequently performed in cases involving injuries to the cranium (p < 0.001), face (p < 0.001), or neck (p < 0.001). Mean time spent in the hospital was 9.96±13.49 days, and the mean time in the intensive care unit (ICU) was 6.58±9.67 days. Overall, 1171/2002 patients (58.5%) required at least one night in the ICU. A total of 233 patients died following trauma (11.6%), with a mean age of 69.92±18.67years. CONCLUSION:Imaging strategies in polytrauma patients vary depending on the mechanism of injury and the affected body region. Especially additional MRI was more often needed when brain, face, or neck trauma was present.

Background:Software-guided semi-quantitative analysis of coronavirus disease 2019 (COVID-19) pneumonia in lung computed tomography (CT) datasets for severity assessment. Further to correlate imaging findings with the need of intensive care medicine and clinical parameters. Methods:This single-center retrospective study analyzed 66 consecutive patients (31 females, mean age 64.6±16.2 years) with lung CT datasets from 12/2020 to 05/2021 and confirmed COVID-19 pneumonia. Lung CT datasets were evaluated using a semi-quantitative software for segmentation and quantification. Correlation with underlying diseases, laboratory parameters and further course were assessed, including intubation and need for intensive care. Results:Total lung volume was 3,903.65±1,185.67 mL, mean volume of opacities was 866.52±829.29 mL, reflecting 23.54%±21.92% of total lung volume. Volume of high opacities was 186.88±208.15 mL reflecting 0.06%±0.07% of total lung volume. Overall, 12 patients died (18.2%), 10 patients (15.2%) required intubation and in 27 cases (40.9%) intensive care was necessary. In patients who died volume of opacities and high opacities were significantly higher (P<0.05). Significant differences with a risk for needing intensive care medicine were extensive pulmonary opacities, volume of high opacities, and percentage of high opacities (P<0.001 each). Conclusions:COVID-19 pneumonia may be semi-quantified using an artificial intelligence (AI)-based software approach. Quantitative methods could provide precise information on the volume of opacities and may allow detecting connections to patient therapy, including the need for intensive care.

CT-based fractional flow reserve (CT-FFR) is a promising noninvasive method for the functional assessment of coronary stenosis. It expands the diagnostic capabilities of coronary CT angiography (cCTA) by providing hemodynamic information and potentially reducing unnecessary invasive coronary angiography examinationsThis review summarizes current technological developments, study results, and clinical applications of CT-FFR. It also discusses the advantages and disadvantages of various software solutions, including artificial intelligence (AI)-based on-site analyses, and their potential integration into the clinical routine.Studies show that CT-FFR improves diagnostic accuracy compared to cCTA and can optimize patient management. Advances in artificial intelligence and new imaging techniques such as photon-counting CT could further refine CT-FFR and expand its applicability. Despite promising results, further research is needed regarding long-term validation, standardized workflows, and economic feasibility.CT-FFR is a promising complementary tool for assessing the hemodynamic relevance of coronary stenoses. CT-FFR is particularly helpful in complex, long-segment, or consecutive stenosis, because a purely anatomical visual examination is not always sufficient. The combination of technical innovations and AI-assisted image analysis could have the potential to transform noninvasive coronary diagnostics. · CT-FFR increases specificity and diagnostic accuracy compared to cCTA alone.. · Technological advances could further refine CT-FFR and expand its applicability.. · The increasing adoption and improved applicability of CT-FFR in routine clinical practice is promising.. · Kloth C, Brendel JM, Kübler J et al. CT-FFR: How a new technology could transform cardiovascular diagnostic imaging. Rofo 2025; DOI 10.1055/a-2697-5413.

Purpose To investigate the segmental distribution of hepatic fat fraction, determined with MRI (MR proton density fat fraction, short MR-PDFF) in patients suspected of having liver iron overload. Methods The liver of 44 patients examined with MRI using a 3D multi-echo gradient-echo sequence was segmented semiautomatically and subdivided into nine segments (segment 4 divided in 4a and 4b). Segmental fat content was determined on MR-PDFF maps. Whole-liver steatosis grades were compared to those found in individual segments. Segmental MR-PDFF differences were tested for statistical significance. Results The most common diseases were thalassemia, various forms of anemia, and hereditary hemochromatosis. No patients suffered from fat metabolism disease. Iron overload was present in 37/44 (84 %) patients. For the whole liver, 22 patients showed a steatosis grade of 0, 21 patients were graded S1, and one patient had a steatosis grade of 2. The grade of steatosis was underestimated in 5 of 21 patients (24 %) in segment 8 and in 8 of 21 patients (38 %) in segment 7. Highly significant segmental MR-PDFF differences were detected with p < 0.00 001, e. g., comparing segment 2 to 5. Segments 1 to 3 had the highest fat content, segments 7 and 8 had the lowest. Conclusion Our results suggest that the storage of fat in the liver is inhomogeneous, so that segment-wise differing fat concentrations were found. Fat distribution in patients with suspected hepatic iron overload was similar to living liver donors. However, it showed significant differences compared with the values published for NAFLD patients, which were less pronounced in the group with high average hepatic MR-PDFF values than in the group with normal lipid content. In patients suspected of having iron overload, segment 8, which is mainly targeted for biopsy, and segment 7 may underestimate steatosis grade. Key Points:

Background Trauma and shock often severely affect the kidneys. This can lead to trauma-related acute kidney injury (TRAKI), which significantly increases the risk of adverse outcomes. Methods To study the pathophysiology of TRAKI, we developed a murine model of combined blunt thoracic trauma and pressure-controlled hemorrhage that induces mild transient TRAKI. Results The mice showed early and transient increased plasma creatinine, urea, NGAL, and urine albumin, resolving 5 days after TRAKI induction. Despite normal kidney morphology, significant damage to proximal tubular cells and a loss of the brush border was observed. This included kidney stress responses, e.g., with induced heme oxygenase-1 expression in tubules. The upregulation of inflammatory mediators and kidney injury markers was followed by elevated leukocyte numbers, mainly consisting of monocytes/macrophages. Proteomic analyses revealed a distinct time course of intrarenal processes after trauma. 3D x-ray-based whole-organ histology by contrast-enhanced microcomputed tomography showed significant impairment of capillary blood flow, especially during the first day post THS, which was partly resolved by day 5. Conclusions Our novel model of murine TRAKI has revealed previously unknown aspects of the complex temporal pathophysiological response of the kidney along the nephron after trauma and hemorrhage, which may provide mechanistic starting points for future therapeutic approaches. ### Competing Interest Statement The authors have declared no competing interest.

Purpose Technical feasibility of CT-based calculation of fractional flow reserve (cFFR) using a 128-row computed tomography scanner in an everyday routine setting. Post-processing and everyday practicability should be analyzed on the scanner on-site in connection with clinical parameters. Materials and Methods This single-center retrospective analysis included 230 patients (74 female; mean age 63.8 years) with CCTA within 21 months between 01/2018 and 09/2019 without non-pathological examinations. cFFR values were obtained using a deep learning-based non-commercial research prototype (cFFR Version3.5.0; Siemens Healthineers GmbH, Erlangen). cFFR values were evaluated at two points: at the maximum point of the stenosis and 1.0 cm distal to the stenosis. Comparison with invasive coronary angiography in 57/230 patients (24.7 %) was performed. CT parameters and quality were evaluated. Further subgroup classification concerning criteria of technical postprocessing was performed: no changes necessary, minor corrections necessary, major corrections necessary, and no evaluation was possible. The required time from starting the software to the final result was evaluated. Results A total of 116/448 (25.9 %) mild, 223/448 (49.8 %) moderate, and 109/448 (24.3 %) obstructive stenoses was found. The mean cFFR at the maximum point of the stenosis was 0.92 ± 0.09 and significantly higher than the cFRR value of 0.89 ± 0.13 distal to the stenosis (p < 0.001*). The mean degree of stenosis was 44.02 ± 26.99 % (range: 1–99 %) with an area of 5.39 ± 3.30 mm2. In a total of 45 patients (19.1 %), a relevant reduction in cFFR below 0.80 was determined. Overall, in 57/230 patients (24.8 %), catheter angiography was performed. No significant difference in the degree of maximal stenosis (CAD-RADS 0–2/3/4) was detected between the classification of CCTA and ICA (p = 0.171). The mean post-processing time varied significantly with 8.34 ± 4.66 min. in single-vessel CAD vs. 12.91 ± 3.92 min. in two-vessel CAD vs. 21.80 ± 5.94 min. in three-vessel CAD (each p < 0.001). Conclusion Noninvasive onsite quantification of cFFR is feasible with minimal observer interaction in a routine real-world setting on a 128-row scanner. Deep learning-based algorithms allow a robust and semi-automatic on-site determination of cFFR based on data from standard CT scanners. Key Points:

Accurate retroperitoneal lymph node metastasis (LNM) prediction in early-stage testicular germ cell tumours (TGCTs) harbours the potential to significantly reduce over- or undertreatment and treatment-related morbidity in this group of young patients as an important survivorship imperative. We investigated the role of computed tomography (CT) radiomics models integrating clinical predictors for the individualised prediction of LNM in early-stage TGCT. Ninety-one patients with surgically proven testicular germ cell tumours and contrast-enhanced CT were included in this retrospective study. Dedicated radiomics software was used to segment 273 retroperitoneal lymph nodes and extract features. After feature selection, radiomics-based machine learning models were developed to predict LN metastasis. The robustness of the procedure was controlled by 10-fold cross-validation. Using multivariable logistic regression modelling, we developed three prediction models: a radiomics-only model, a clinical-only model, and a combined radiomics–clinical model. The models’ performances were evaluated using the area under the receiver operating characteristic curve (AUC). Finally, decision curve analysis was performed to estimate the clinical usefulness of the predictive model. The radiomics-only model for predicting lymph node metastasis reached a greater discrimination power than the clinical-only model, with an AUC of 0.87 (±0.04; 95% CI) vs. 0.75 (±0.08; 95% CI) in our study cohort. The combined model integrating clinical risk factors and selected radiomics features outperformed the clinical-only and the radiomics-only prediction models, and showed good discrimination with an area under the curve of 0.89 (±0.03; 95% CI). The decision curve analysis demonstrated the clinical usefulness of our proposed combined model. The presented combined CT-based radiomics–clinical model represents an exciting non-invasive tool for individualised LN metastasis prediction in testicular germ cell tumours. Multi-centre validation is required to generate high-quality evidence for its clinical application.

Radiologische Verfahren spielen eine entscheidende Rolle in der Diagnostik von Dünndarmerkrankungen. Aufgrund eines breiten und oft unspezifischen Symptomspektrums ist die klinische Beurteilung häufig schwierig und endoskopische Verfahren sind personal-, zeit- und kostenintensiv. Dagegen kann die radiologische Bildgebung wichtige Informationen über morphologische und funktionelle Veränderungen des Dünndarms liefern und helfen, verschiedene Krankheitsentitäten wie Entzündungen, Tumoren, vaskuläre Probleme und Obstruktionen zu erkennen. Zu den gebräuchlichsten radiologischen Modalitäten in der Dünndarmdiagnostik gehören der Ultraschall (US), die Computertomographie (CT), die Magnetresonanztomographie (MRT) sowie Durchleuchtungsuntersuchungen (DL). Jede dieser Methoden hat ihre eigenen Vorteile und Grenzen, wobei die Wahl des bildgebenden Verfahrens neben der Verfügbarkeit von der jeweiligen klinischen Symptomatik und Verdachtsdiagnose abhängt. In den letzten Jahren konnten durch technische Neu- und Weiterentwicklungen erhebliche Fortschritte vor allem der schnittbildgebenden Modalitäten erzielt werden. Die technischen Möglichkeiten reichen von einer zunehmenden Detailauflösung bis hin zu funktionellen und molekularen Bildgebungstechniken, die weit über die reine Morphologie hinausgehen. Zudem spielen IT-Anwendungen, wie z. B. künstliche Intelligenz (KI) oder Radiomics, eine zunehmende Rolle. Viele der genannten Methoden sind noch im Anfangsstadium und müssen für die tägliche Praxis noch weiterentwickelt werden, einige haben jedoch bereits Einzug in die klinische Routine gehalten. Diese Arbeit soll eine Übersicht über die wichtigsten Krankheitsentitäten des Dünndarms liefern und dabei auch neue, innovative diagnostische Ansätze beleuchten.

Background Sarcopenia is an age-related syndrome characterized by a loss of muscle mass and strength. As a result, the independence of the elderly is reduced and the hospitalization rate and mortality increase. The onset of sarcopenia often begins in middle age due to an unbalanced diet or malnutrition in association with a lack of physical activity. This effect is intensified by concomitant diseases such as obesity or metabolic diseases including diabetes mellitus. Method With effective preventative diagnostic procedures and specific therapeutic treatment of sarcopenia, the negative effects on the individual can be reduced and the negative impact on health as well as socioeconomic effects can be prevented. Various diagnostic options are available for this purpose. In addition to basic clinical methods such as measuring muscle strength, sarcopenia can also be detected using imaging techniques like dual X-ray absorptiometry (DXA), computed tomography (CT), magnetic resonance imaging (MRI), and sonography. DXA, as a simple and cost-effective method, offers a low-dose option for assessing body composition. With cross-sectional imaging techniques such as CT and MRI, further diagnostic possibilities are available, including MR spectroscopy (MRS) for noninvasive molecular analysis of muscle tissue. CT can also be used in the context of examinations performed for other indications to acquire additional parameters of the skeletal muscles (opportunistic secondary use of CT data), such as abdominal muscle mass (total abdominal muscle area – TAMA) or the psoas as well as the pectoralis muscle index. The importance of sarcopenia is already well studied for patients with various tumor entities and also infections such as SARS-COV2. Results and Conclusion Sarcopenia will become increasingly important, not least due to demographic changes in the population. In this review, the possibilities for the diagnosis of sarcopenia, the clinical significance, and therapeutic options are described. In particular, CT examinations, which are repeatedly performed on tumor patients, can be used for diagnostics. This opportunistic use can be supported by the use of artificial intelligence. Key Points: Citation Format

Radiomics refers to the extraction and analysis of a wide range of medical imaging features in a non-invasive and cost-effective manner to comprehensively characterise tumours. In this study, machine learning models combining radiomics and clinical factors were developed to predict retroperitoneal lymph node metastasis in testicular germ cell tumours (TGCTs), with the aim of reducing unnecessary treatment in this group of young patients. Ninety-one patients with surgically proven testicular germ cell tumours and contrast-enhanced CT were included in this retrospective study. After segmenting 273 retroperitoneal lymph nodes using dedicated radiomics software, we developed machine-learning prediction models using Random Forest (RF), Light Gradient Boosting Machine (LGBM), Support Vector Machine Classifier (SVC), and K-Nearest Neighbours (KNN). For each classifier, we developed a radiomics-only, clinical-only, and combined radiomics-clinical prediction model. The models’ performances were evaluated using the area under the receiver operating characteristic curves (AUCs). The RF-based combined clinical and radiomic model showed the most robust performance in predicting LNM with an area under the curve (AUC) of 0.95 (±0.03; 95% CI), accuracy 87%, precision 89%, recall 86% and F1 score 87%, followed by the LGBM model with an area under the curve (AUC) of 0.93 (±0.05; 95% CI), accuracy 83%, precision 87%, recall 80% and F1 score 82%. Decision curve analysis demonstrated the clinical utility of our proposed RF-based combined clinical–radiomics model. Our study has identified reliable and predictive machine-learning techniques for predicting lymph node metastasis in early-stage testicular cancer. Identifying the most effective machine-learning approaches for predictive analysis based on radiomics integrating clinical risk factors can expand the applicability of radiomics in precision oncology and cancer treatment. Multi-centre validation is required to provide high-quality evidence for clinical application.

Purpose MR transverse relaxation rate R-2* has been shown to be useful for monitoring liver iron overload. A sequence enabling acquisition of the whole liver in a single breath hold is now available, thus allowing volumetric hepatic R-2* distribution studies. We evaluated the feasibility of computer-assisted whole liver segmentation of 3 D multi-gradient-echo MRI data, and compared whole liver R-2* determination to analyzing only a single slice. Also, segmental R-2* differences were studied. Materials and Methods The liver of 44 patients, investigated by multi-gradient echo MRI at 1.5 T, was segmented and divided into nine segments. Segmental R-2* values were examined for all patients together and with respect to two criteria: average R-2* values, and reason for iron overload. Correlation of single-slice and volumetric data was tested with Spearman's rank test, segmental and group differences were evaluated by analysis of variance. Results Whole-liver R-2* values correlated excellent to single slice data (p < 0.001). The lowest R-2* occurred in segment 1 (S1), differences of S1 with regard to other segments were significant in five cases and highly significant in two cases. Patients with high average R-2* showed significant differences between S1 and segments 2, 6, and 7. Disease-related differences with respect to S1 were significant in segments 3 to 5 and 7. Conclusion Our results suggest inhomogeneous hepatic iron distribution. Low R-2* in S1 may be explained by its special vascularization.

Accurate prediction of lymph node metastasis (LNM) in patients with testicular cancer is highly relevant for treatment decision-making and prognostic evaluation. Our study aimed to develop and validate clinical radiomics models for individual preoperative prediction of LNM in patients with testicular cancer. We enrolled 91 patients with clinicopathologically confirmed early-stage testicular cancer, with disease confined to the testes. We included five significant clinical risk factors (age, preoperative serum tumour markers AFP and B-HCG, histotype and BMI) to build the clinical model. After segmenting 273 retroperitoneal lymph nodes, we then combined the clinical risk factors and lymph node radiomics features to establish combined predictive models using Random Forest (RF), Light Gradient Boosting Machine (LGBM), Support Vector Machine Classifier (SVC), and K-Nearest Neighbours (KNN). Model performance was assessed by the area under the receiver operating characteristic (ROC) curve (AUC). Finally, the decision curve analysis (DCA) was used to evaluate the clinical usefulness. The Random Forest combined clinical lymph node radiomics model with the highest AUC of 0.95 (±0.03 SD; 95% CI) was considered the candidate model with decision curve analysis, demonstrating its usefulness for preoperative prediction in the clinical setting. Our study has identified reliable and predictive machine learning techniques for predicting lymph node metastasis in early-stage testicular cancer. Identifying the most effective machine learning approaches for predictive analysis based on radiomics integrating clinical risk factors can expand the applicability of radiomics in precision oncology and cancer treatment.

Abstract Purpose  Environmental aspects and sustainability are becoming increasingly important. In addition to energy consumption, the consumption and environmental discharge of contrast agents pose a particular challenge. Because of their desired stability, X-ray contrast agents (XCAs) are deposited in surface water at a rate of up to 400 tons per year. Materials and Methods  In a pilot project, a set of measures (installation of specific separation toilets, the establishment of feedback systems, interviews, questionnaires, and observation) was implemented to sensitize patients and staff to the problem of XCAs during outpatient CT examinations and a retention and recovery system for XCAs was evaluated. Results  In the initial baseline phase, a separation toilet with an additional collection system and a feedback/button system was installed. The built-in feedback system indicated that the separation toilets were used by approx. 16 % of patients without measures. In two subsequent intervention phases, accompanying measures significantly (p < 0.01) increased the use of these separation toilets to 21 % and 25 %, respectively. The measures to reduce the discharge of XCAs were positively assessed by both staff and patients. Conclusion  Measures to reduce the discharge of XCAs into the environment have a high acceptance among staff and patients. The subsequent installation of separation toilets is one possibility to achieve on-site retention of XCAs. However, this measure is likely to be of high value only if patients stay on site for a correspondingly long time, as is the case in cardiology, for example. Key points:   The input of X-ray contrast agents into the environment is relevant in light of the quantity Measures to reduce the discharge of X-ray contrast agents into the environment have been investigated in pilot projects The (subsequent) installation of separation toilets is possible and allows retention of X-ray contrast agents This measure is considered useful by patients and staff The financing of these measures needs to be clarified Citation Format Beer M, Schuler J, Kraus E et al. Discharge of iodine-containing contrast media into the environment – problem analysis and implementation of measures to reduce discharge by means of separation toilets – experience from a pilot project. Fortschr Röntgenstr 2023; 195: 1122 – 1127 Zusammenfassung Ziel  Umweltaspekte und Nachhaltigkeit spielen eine zunehmend größere Rolle. Neben dem Energieverbrauch stellt der Verbrauch und der damit verbundene Umwelteintrag von Kontrastmitteln eine besondere Herausforderung dar. Röntgenkontrastmittel (RKM) reichern sich angesichts ihrer gewünschten Stabilität mit bis zu 400 Tonnen pro Jahr vor allem im Oberflächenwasser an. Material und Methoden  In einem Pilotprojekt wurde ein Maßnahmenbündel (Einbau von spezifischen Trenntoiletten, die Einrichtung von Feedback-Systemen, Interviews, Fragebögen und Beobachtungen) zur Sensibilisierung von Patient:innen und Personal bei ambulanten CT-Untersuchungen für die RKM-Problematik evaluiert sowie ein Rückhalte- und Gewinnungssystem für RKM implementiert. Ergebnisse  In einer Basisphase wurde ein Trenntoilettensystem mit einem zusätzlichen Auffangsystem eingebaut. Das eingebaute Feedback-System zeigte an, dass die Trenntoiletten ohne Maßnahmen von ca. 16 % der Patient:innen genutzt wurde. In zwei darauffolgenden Interventionsphasen konnte mit flankierenden Maßnahmen die Verwendung dieser Trenntoiletten signifikant (p < 0.01) auf 21 % bzw. 25 % gesteigert werden. Die Maßnahmen zur Reduktion des Eintrags von RKM wurden sowohl von Personal als auch Patienten positiv beurteilt. Schlussfolgerung  Maßnahmen zur Reduktion des Eintrags von RKM in die Umwelt haben bei Personal und PatientInnen eine hohe Akzeptanz. Der nachträgliche Einbau von Trenntoiletten ist eine Möglichkeit, ein Zurückhalten der RKMs vor Ort zu erreichen. Allerdings zeigt diese Maßnahme voraussichtlich nur bei entsprechend langer Verweildauer der PatientInnen vor Ort einen relevanten Effekt, wie dies z. B. in der Kardiologie gegeben ist. Kernaussagen: der Eintrag von Röntgenkontrastmitteln (RKM) in die Umwelt ist im Hinblick auf die Menge relevant Maßnahmen zur Reduzierung des Eintrags von RKM wurden und werden in Pilotprojekten untersucht der (nachträgliche) Einbau von Trenntoiletten ist möglich und erlaubt eine Rückhaltung von RKM diese Maßnahme wird von Patient:innen und Personal als sinnvoll erachtet die Finanzierung dieser Maßnahme ist zu klären Zitierweise Beer M, Schuler J, Kraus E et al. Discharge of iodine-containing contrast media into the environment – problem analysis and implementation of measures to reduce discharge by means of separation toilets – experience from a pilot project. Fortschr Röntgenstr 2023; 195: 1122 – 1128

Einleitung und Studienziele Die Messung der Größe kolorektaler Polypen während der Endoskopie wird hauptsächlich visuell durchgeführt. In dieser Arbeit schlagen wir ein neuartiges System zur Messung der Polypengröße (Poseidon) vor, das auf künstlicher Intelligenz (KI) basiert und den Wasserstrahl als Messreferenz verwendet.

Due to the increasing use of cross-sectional imaging techniques and new technical possibilities, the number of incidentally detected cystic lesions of the pancreas is rapidly increasing in everyday radiological routines. Precise and rapid classification, including targeted therapeutic considerations, is of essential importance. The new European guideline should also support this. This review article provides information on the spectrum of cystic pancreatic lesions, their appearance, and a comparison of morphologic and histologic characteristics. This is done in the context of current literature and clinical value. The recommendations of the European guidelines include statements on conservative management as well as relative and absolute indications for surgery in cystic lesions of the pancreas. The guidelines suggest surgical resection for mucinous cystic neoplasm (MCN) ≥ 40 mm; furthermore, for symptomatic MCN or imaging signs of malignancy, this is recommended independent of its size (grade IB recommendation). For main duct IPMNs (intraductal papillary mucinous neoplasms), surgical therapy is always recommended; for branch duct IPMNs, a number of different risk criteria are applicable to evaluate absolute or relative indications for surgery. Based on imaging characteristics of the most common cystic pancreatic lesions, a precise diagnostic classification of the tumor, as well as guidance for further treatment, is possible through radiology.

Gastroenteropancreatic neuroendocrine neoplasia (GEP-NEN) is a heterogeneous and complex group of tumors that are often difficult to classify due to their heterogeneity and varying locations. As standard radiological methods, ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography–computed tomography (PET/CT) are available for both localization and staging of NEN. Nuclear medical imaging methods with somatostatin analogs are of great importance since radioactively labeled receptor ligands make tumors visible with high sensitivity. CT and MRI have high detection rates for GEP-NEN and have been further improved by developments such as diffusion-weighted imaging. However, nuclear medical imaging methods are superior in detection, especially in gastrointestinal NEN. It is important for radiologists to be familiar with NEN, as it can occur ubiquitously in the abdomen and should be identified as such. Since GEP-NEN is predominantly hypervascularized, a biphasic examination technique is mandatory for contrast-enhanced cross-sectional imaging. PET/CT with somatostatin analogs should be used as the subsequent method.

PURPOSE: To implement the technical feasibility of an AI-based software prototype optimized for the detection of COVID-19 pneumonia in CT datasets of the lung and the differentiation between other etiologies of pneumonia. METHODS: This single-center retrospective case-control-study consecutively yielded 144 patients (58 female, mean age 57.72 ± 18.25 y) with CT datasets of the lung. Subgroups including confirmed bacterial (n = 24, 16.6%), viral (n = 52, 36.1%), or fungal (n = 25, 16.6%) pneumonia and (n = 43, 30.7%) patients without detected pneumonia (comparison group) were evaluated using the AI-based Pneumonia Analysis prototype. Scoring (extent, etiology) was compared to reader assessment. RESULTS: The software achieved an optimal sensitivity of 80.8% with a specificity of 50% for the detection of COVID-19; however, the human radiologist achieved optimal sensitivity of 80.8% and a specificity of 97.2%. The mean postprocessing time was 7.61 ± 4.22 min. The use of a contrast agent did not influence the results of the software (p = 0.81). The mean evaluated COVID-19 probability is 0.80 ± 0.36 significantly higher in COVID-19 patients than in patients with fungal pneumonia (p < 0.05) and bacterial pneumonia (p < 0.001). The mean percentage of opacity (PO) and percentage of high opacity (PHO ≥ -200 HU) were significantly higher in COVID-19 patients than in healthy patients. However, the total mean HU in COVID-19 patients was -679.57 ± 112.72, which is significantly higher than in the healthy control group (p < 0.001). CONCLUSION: The detection and quantification of pneumonia beyond the primarily trained COVID-19 datasets is possible and shows comparable results for COVID-19 pneumonia to an experienced reader. The advantages are the fast, automated segmentation and quantification of the pneumonia foci.