Chest computed tomography findings typical of COVID-19 pneumonia in Germany as early as 30 December 2019: a case report.

BACKGROUND: The first cases of coronavirus disease 2019 were officially confirmed in Germany and its European neighbors in late January 2020. In France and Italy, there is evidence that coronavirus disease 2019 was spreading as early as December 2019. CASE PRESENTATION: We report on a 71-year-old male patient from Germany who was admitted to our hospital on 30 December 2019 with pneumonia of unclear etiology and chest computed tomography findings typical of COVID-19 pneumonia. CONCLUSION: This case may indicate that coronavirus disease 2019 was already spreading in Germany as early as December 2019.

Two-Stage Deep Learning Model for Automated Segmentation and Classification of Splenomegaly.

Splenomegaly is a common cross-sectional imaging finding with a variety of differential diagnoses. This study aimed to evaluate whether a deep learning model could automatically segment the spleen and identify the cause of splenomegaly in patients with cirrhotic portal hypertension versus patients with lymphoma disease. This retrospective study included 149 patients with splenomegaly on computed tomography (CT) images (77 patients with cirrhotic portal hypertension, 72 patients with lymphoma) who underwent a CT scan between October 2020 and July 2021. The dataset was divided into a training (n = 99), a validation (n = 25) and a test cohort (n = 25). In the first stage, the spleen was automatically segmented using a modified U-Net architecture. In the second stage, the CT images were classified into two groups using a 3D DenseNet to discriminate between the causes of splenomegaly, first using the whole abdominal CT, and second using only the spleen segmentation mask. The classification performances were evaluated using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Occlusion sensitivity maps were applied to the whole abdominal CT images, to illustrate which regions were important for the prediction. When trained on the whole abdominal CT volume, the DenseNet was able to differentiate between the lymphoma and liver cirrhosis in the test cohort with an AUC of 0.88 and an ACC of 0.88. When the model was trained on the spleen segmentation mask, the performance decreased (AUC = 0.81, ACC = 0.76). Our model was able to accurately segment splenomegaly and recognize the underlying cause. Training on whole abdomen scans outperformed training using the segmentation mask. Nonetheless, considering the performance, a broader and more general application to differentiate other causes for splenomegaly is also conceivable.

CODEX Meets RACOON - A Concept for Collaborative Documentation of Clinical and Radiological COVID-19 Data.

Within the scope of the two NUM projects CODEX and RACOON we developed a preliminary technical concept for documenting clinical and radiological COVID-19 data in a collaborative approach and its preceding findings of a requirement analysis. At first, we provide an overview of NUM and its two projects CODEX and RACOON including the GECCO data set. Furthermore, we demonstrate the foundation for the increased collaboration of both projects, which was additionally supported by a survey conducted at University Hospital Frankfurt. Based on the survey results mint Lesion™, developed by Mint Medical and used at all project sites within RACOON, was selected as the "Electronic Data Capture" (EDC) system for CODEX. Moreover, to avoid duplicate entry of GECCO data into both EDC systems, an early effort was made to consider a collaborative and efficient technical approach to reduce the workload for the medical documentalists. As a first effort we present a preliminary technical concept representing the current and possible future data workflow of CODEX and RACOON. This concept includes a software component to synchronize GECCO data sets between the two EDC systems using the HL7 FHIR standard. Our first approach of a collaborative use of an EDC system and its medical documentalists could be beneficial in combination with the presented synchronization component for all participating project sites of CODEX and RACOON with regard to an overall reduced documentation workload.

Changes of radiological examination volumes over the course of the COVID-19 pandemic: a comprehensive analysis of the different waves of infection.

OBJECTIVES: Data from radiological departments provide important information on overall quantities of medical care provided. With this study we used a comprehensive analysis of radiological examinations as a surrogate marker to quantify the effect of the different COVID-19 waves on medical care provided. METHODS: Radiological examination volumes during the different waves of infection were compared among each other as well as to time-matched control periods from pre-pandemic years using a locally weighted scatterplot smoothing as well as negative binominal regression models. RESULTS: A total of 1,321,119 radiological examinations were analyzed. Examination volumes were reduced by about 10% over the whole study period (IRR = 0.90; 95% CI 0.89-0.92), with a focus on acute medical care (0.84; 0.83-0.85) and outpatients (0.93: 0.90-0.97). When compared to wave 1, examination volumes were about 17% higher during wave 2 (1.17; 1.10-1.25), and 33% higher in wave 3 of the pandemic (1.33; 1.24-1.42). CONCLUSIONS: This study shows the severe effect of COVID-19 pandemic and related shutdown measures on overall provided medical care as measured by radiological examinations. When compared, the decrease of medical care was more pronounced in the earlier waves of the pandemic.

Impact of the COVID-19 Pandemic on Radiology in Inpatient and Outpatient Care in Germany: A Nationwide Survey Regarding the First and Second Wave.

OBJECTIVES: To find out the opinion of radiological inpatient and outpatient medical staff regarding the measures taken in relation to the COVID-19 pandemic during the first and second waves and to identify the measures that are still perceived as needing improvement. MATERIALS AND METHODS: We conducted an anonymous online survey among more than 10 000 radiologists/technicians in Germany from January 5 to January 31, 2021. A total of 862 responses (head physicians, n = 225 [inpatient doctors, n = 138; outpatient doctors, n = 84; N/A, n = 3]; radiologic personnel, n = 637 [inpatient doctor, n = 303; outpatient doctor, n = 50; inpatient technician, n = 217; outpatient technician, n = 26; N/A, n = 41]) were received. Questions of approximation, yes/no questions, and Likert scales were used. RESULTS: During the first/second wave, 70 % (86/123)/43 % (45/104) of inpatient and 26 % (17/66)/10 % (5/52) of outpatient head physicians agreed that they received financial support from the authorities but the majority rated the financial support as insufficient. During the first and second wave, 33 % (8/24) and 80 % (16/20) of outpatient technicians agreed that they were adequately provided with personal protective equipment. The perceived lack of personal protective equipment improved for all participants during the second wave. Inpatient [outpatient] technicians perceived an increased workload in the first and second wave: 72 % (142/198) [79 % (19/24)] and 84 % (146/174) [80 % (16/20)]. CONCLUSION: Technicians seem increasingly negatively affected by the COVID-19 pandemic in Germany. Financial support by the competent authorities seems to be in need of improvement. KEY POINTS: · The accessibility of personal protective equipment resources improved in the second wave.. · In particular, radiology technicians seem increasingly negatively affected by the COVID-19 pandemic.. · Financial and consulting support from the government could be improved.. CITATION FORMAT: · Bernatz S, Afat S, Othman AE et al. Impact of the COVID-19 Pandemic on Radiology in Inpatient and Outpatient Care in Germany: A Nationwide Survey Regarding the First and Second Wave. Fortschr Röntgenstr 2022; 194: 70 - 82.

Point-of-care lung ultrasound in COVID-19 patients: inter- and intra-observer agreement in a prospective observational study.

With an urgent need for bedside imaging of coronavirus disease 2019 (COVID-19), this study's main goal was to assess inter- and intraobserver agreement in lung ultrasound (LUS) of COVID-19 patients. In this single-center study we prospectively acquired and evaluated 100 recorded ten-second cine-loops in confirmed COVID-19 intensive care unit (ICU) patients. All loops were rated by ten observers with different subspeciality backgrounds for four times by each observer (400 loops overall) in a random sequence using a web-based rating tool. We analyzed inter- and intraobserver variability for specific pathologies and a semiquantitative LUS score. Interobserver agreement for both, identification of specific pathologies and assignment of LUS scores was fair to moderate (e.g., LUS score 1 Fleiss' κ = 0.27; subpleural consolidations Fleiss' κ = 0.59). Intraobserver agreement was mostly moderate to substantial with generally higher agreement for more distinct findings (e.g., lowest LUS score 0 vs. highest LUS score 3 (median Fleiss' κ = 0.71 vs. 0.79) or air bronchograms (median Fleiss' κ = 0.72)). Intraobserver consistency was relatively low for intermediate LUS scores (e.g. LUS Score 1 median Fleiss' κ = 0.52). We therefore conclude that more distinct LUS findings (e.g., air bronchograms, subpleural consolidations) may be more suitable for disease monitoring, especially with more than one investigator and that training material used for LUS in point-of-care ultrasound (POCUS) should pay refined attention to areas such as B-line quantification and differentiation of intermediate LUS scores.

A reporting and analysis framework for structured evaluation of COVID-19 clinical and imaging data.

The COVID-19 pandemic has worldwide individual and socioeconomic consequences. Chest computed tomography has been found to support diagnostics and disease monitoring. A standardized approach to generate, collect, analyze, and share clinical and imaging information in the highest quality possible is urgently needed. We developed systematic, computer-assisted and context-guided electronic data capture on the FDA-approved mint LesionTM software platform to enable cloud-based data collection and real-time analysis. The acquisition and annotation include radiological findings and radiomics performed directly on primary imaging data together with information from the patient history and clinical data. As proof of concept, anonymized data of 283 patients with either suspected or confirmed SARS-CoV-2 infection from eight European medical centers were aggregated in data analysis dashboards. Aggregated data were compared to key findings of landmark research literature. This concept has been chosen for use in the national COVID-19 response of the radiological departments of all university hospitals in Germany.

Decreased Medical Care During the COVID-19 Pandemic - A Comprehensive Analysis of Radiological Examinations.

OBJECTIVES: As a cross-section discipline within the hospital infrastructure, radiological departments might be able to provide important information regarding the impact of the COVID-19 pandemic on healthcare. The goal of this study was to quantify changes in medical care during the first wave of the pandemic using radiological examinations as a comprehensive surrogate marker and to determine potential future workload. METHODS: A retrospective analysis of all radiological examinations during the first wave of the pandemic was performed. The number of examinations was compared to time-matched control periods. Furthermore, an in-depth analysis of radiological examinations attributed to various medical specialties was conducted and postponed examinations were extrapolated to calculate additional workload in the near future. RESULTS: A total of 596,760 examinations were analyzed. Overall case volumes decreased by an average of 41 % during the shutdown compared to the control period. The most affected radiological modalities were sonography (-54 %), X-ray (-47 %) followed by MRI (-42 %). The most affected medical specialty was trauma and orthopedics (-60 % case volume) followed by general surgery (-49 %). Examination numbers increased during the post-shutdown period leading to a predicted additional workload of up to 22 %. CONCLUSION: This study shows a marked decrease in radiological examinations in total and among several core medical specialties, indicating a significant reduction in medical care during the first COVID-19 shutdown. KEY POINTS: · Number of radiological examinations decreased by 41 % during the first wave of the COVID-19 pandemic.. · Several core medical specialties were heavily affected with a reduction of case volumes up to 60 %.. · When extrapolating postponed examinations to the near future, the overall workload for radiological departments might increase up to 22 %.. CITATION FORMAT: · Fleckenstein FN, Maleitzke T, Böning G et al. Decreased Medical Care During the COVID-19 Pandemic - A Comprehensive Analysis of Radiological Examinations. Fortschr Röntgenstr 2021; 193: 937 - 946.

The role of visceral adiposity in the severity of COVID-19: Highlights from a unicenter cross-sectional pilot study in Germany.

BACKGROUND AND AIMS: Overall obesity has recently been established as an independent risk factor for critical illness in patients with coronavirus disease 2019 (COVID-19). The role of fat distribution and especially that of visceral fat, which is often associated with metabolic syndrome, remains unclear. Therefore, this study aims at investigating the association between fat distribution and COVID-19 severity. METHODS: Thirty patients with COVID-19 and a mean age of 65.6 ±â€¯13.1 years from a level-one medical center in Berlin, Germany, were included in the present cross-sectional analysis. COVID-19 was confirmed by polymerase chain reaction (PCR) from nasal and throat swabs. A severe clinical course of COVID-19 was defined by hospitalization in the intensive care unit (ICU) and/or invasive mechanical ventilation. Fat was measured at the level of the first lumbar vertebra on routinely acquired low-dose chest computed tomography (CT). RESULTS: An increase in visceral fat area (VFA) by ten square centimeters was associated with a 1.37-fold higher likelihood of ICU treatment and a 1.32-fold higher likelihood of mechanical ventilation (adjusted for age and sex). For upper abdominal circumference, each additional centimeter of circumference was associated with a 1.13-fold higher likelihood of ICU treatment and a 1.25-fold higher likelihood of mechanical ventilation. CONCLUSIONS: Our proof-of-concept study suggests that visceral adipose tissue and upper abdominal circumference specifically increase the likelihood of COVID-19 severity. CT-based quantification of visceral adipose tissue and upper abdominal circumference in routine chest CTs may therefore be a simple tool for risk assessment in COVID-19 patients.

Diagnostic Value of Initial Chest CT Findings for the Need of ICU Treatment/Intubation in Patients with COVID-19.

Computed tomography (CT) plays an important role in the diagnosis of COVID-19. The aim of this study was to evaluate a simple, semi-quantitative method that can be used for identifying patients in need of subsequent intensive care unit (ICU) treatment and intubation. We retrospectively analyzed the initial CT scans of 28 patients who tested positive for SARS-CoV-2 at our Level-I center. The extent of lung involvement on CT was classified both subjectively and with a simple semi-quantitative method measuring the affected area at three lung levels. Competing risks Cox regression was used to identify factors associated with the time to ICU admission and intubation. Their potential diagnostic ability was assessed with receiver operating characteristic (ROC)/area under the ROC curves (AUC) analysis. A 10% increase in the affected lung parenchyma area increased the instantaneous risk of intubation (hazard ratio (HR) = 2.00) and the instantaneous risk of ICU admission (HR 1.73). The semi-quantitative measurement outperformed the subjective assessment diagnostic ability (AUC = 85.6% for ICU treatment, 71.9% for intubation). This simple measurement of the involved lung area in initial CT scans of COVID-19 patients may allow early identification of patients in need of ICU treatment/intubation and thus help make optimal use of limited ICU/ventilation resources in hospitals.

Submillisievert chest CT in patients with COVID-19 - experiences of a German Level-I center.

PURPOSE: Computed tomography (CT) is used for initial diagnosis and therapy monitoring of patients with coronavirus disease 2019 (COVID-19). As patients of all ages are affected, radiation dose is a concern. While follow-up CT examinations lead to high cumulative radiation doses, the ALARA principle states that the applied dose should be as low as possible while maintaining adequate image quality. The aim of this study was to evaluate parameter settings for two commonly used CT scanners to ensure sufficient image quality/diagnostic confidence at a submillisievert dose. MATERIALS AND METHODS: We retrospectively analyzed 36 proven COVID-19 cases examined on two different scanners. Image quality was evaluated objectively as signal-to-noise ratio (SNR)/contrast-to-noise ratio (CNR) measurement and subjectively by two experienced, independent readers using 3-point Likert scales. CT dose index volume (CTDIvol) and dose-length product (DLP) were extracted from dose reports, and effective dose was calculated. RESULTS: With the tested parameter settings we achieved effective doses below 1 mSv (median 0.5 mSv, IQR: 0.2 mSv, range: 0.3-0.9 mSv) in all 36 patients. Thirty-four patients had typical COVID-19 findings. Both readers were confident regarding the typical COVID-19 CT-characteristics in all cases (3 ± 0). Objective image quality parameters were: SNRnormal lung: 17.0 ± 5.9, CNRGGO/normal lung: 7.5 ± 5.0, and CNRconsolidation/normal lung: 15.3 ± 6.1. CONCLUSION: With the tested parameters, we achieved applied doses in the submillisievert range, on two different CT scanners without sacrificing diagnostic confidence regarding COVID-19 findings.

Comparing different deep learning architectures for classification of chest radiographs.

Chest radiographs are among the most frequently acquired images in radiology and are often the subject of computer vision research. However, most of the models used to classify chest radiographs are derived from openly available deep neural networks, trained on large image datasets. These datasets differ from chest radiographs in that they are mostly color images and have substantially more labels. Therefore, very deep convolutional neural networks (CNN) designed for ImageNet and often representing more complex relationships, might not be required for the comparably simpler task of classifying medical image data. Sixteen different architectures of CNN were compared regarding the classification performance on two openly available datasets, the CheXpert and COVID-19 Image Data Collection. Areas under the receiver operating characteristics curves (AUROC) between 0.83 and 0.89 could be achieved on the CheXpert dataset. On the COVID-19 Image Data Collection, all models showed an excellent ability to detect COVID-19 and non-COVID pneumonia with AUROC values between 0.983 and 0.998. It could be observed, that more shallow networks may achieve results comparable to their deeper and more complex counterparts with shorter training times, enabling classification performances on medical image data close to the state-of-the-art methods even when using limited hardware.

Clinical and Imaging Characteristics in Patients with SARS-CoV-2 Infection and Acute Intracranial Hemorrhage.

BACKGROUND AND PURPOSE: Intracranial hemorrhage has been observed in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (COVID-19), but the clinical, imaging, and pathophysiological features of intracranial bleeding during COVID-19 infection remain poorly characterized. This study describes clinical and imaging characteristics of patients with COVID-19 infection who presented with intracranial bleeding in a European multicenter cohort. METHODS: This is a multicenter retrospective, observational case series including 18 consecutive patients with COVID-19 infection and intracranial hemorrhage. Data were collected from February to May 2020 at five designated European special care centers for COVID-19. The diagnosis of COVID-19 was based on laboratory-confirmed diagnosis of SARS-CoV-2. Intracranial bleeding was diagnosed on computed tomography (CT) of the brain within one month of the date of COVID-19 diagnosis. The clinical, laboratory, radiologic, and pathologic findings, therapy and outcomes in COVID-19 patients presenting with intracranial bleeding were analyzed. RESULTS: Eighteen patients had evidence of acute intracranial bleeding within 11 days (IQR 9-29) of admission. Six patients had parenchymal hemorrhage (33.3%), 11 had subarachnoid hemorrhage (SAH) (61.1%), and one patient had subdural hemorrhage (5.6%). Three patients presented with intraventricular hemorrhage (IVH) (16.7%). CONCLUSION: This study represents the largest case series of patients with intracranial hemorrhage diagnosed with COVID-19 based on key European countries with geospatial hotspots of SARS-CoV-2. Isolated SAH along the convexity may be a predominant bleeding manifestation and may occur in a late temporal course of severe COVID-19.