Cooperative AI training for cardiothoracic segmentation in computed tomography: An iterative multi-center annotation approach.

PURPOSE: Radiological reporting is transitioning to quantitative analysis, requiring large-scale multi-center validation of biomarkers. A major prerequisite and bottleneck for this task is the voxelwise annotation of image data, which is time-consuming for large cohorts. In this study, we propose an iterative training workflow to support and facilitate such segmentation tasks, specifically for high-resolution thoracic CT data. METHODS: Our study included 132 thoracic CT scans from clinical practice, annotated by 13 radiologists. In three iterative training experiments, we aimed to improve and accelerate segmentation of the heart and mediastinum. Each experiment started with manual segmentation of 5-25 CT scans, which served as training data for a nnU-Net. Further iterations incorporated AI pre-segmentation and human correction to improve accuracy, accelerate the annotation process, and reduce human involvement over time. RESULTS: Results showed consistent improvement in AI model quality with each iteration. Resampled datasets improved the Dice similarity coefficients for both the heart (DCS 0.91 [0.88; 0.92]) and the mediastinum (DCS 0.95 [0.94; 0.95]). Our AI models reduced human interaction time by 50 % for heart and 70 % for mediastinum segmentation in the most potent iteration. A model trained on only five datasets achieved satisfactory results (DCS > 0.90). CONCLUSIONS: The iterative training workflow provides an efficient method for training AI-based segmentation models in multi-center studies, improving accuracy over time and simultaneously reducing human intervention. Future work will explore the use of fewer initial datasets and additional pre-processing methods to enhance model quality.

Pericardial Effusion Predicts Clinical Outcomes in Patients with COVID-19: A Nationwide Multicenter Study.

RATIONALE AND OBJECTIVES: The prognostic role of pericardial effusion (PE) in Covid 19 is unclear. The aim of the present study was to estimate the prognostic role of PE in patients with Covid 19 in a large multicentre setting. MATERIALS AND METHODS: This retrospective study is a part of the German multicenter project RACOON (Radiological Cooperative Network of the Covid 19 pandemic). The acquired sample comprises 1197 patients, 363 (30.3%) women and 834 (69.7%) men. In every case, chest computed tomography was analyzed for PE. Data about 30-day mortality, need for mechanical ventilation and need for intensive care unit (ICU) admission were collected. Data were evaluated by means of descriptive statistics. Group differences were calculated with Mann-Whitney test and Fisher exact test. Uni-and multivariable regression analyses were performed. RESULTS: Overall, 46.4% of the patients were admitted to ICU, mechanical lung ventilation was performed in 26.6% and 30-day mortality was 24%. PE was identified in 159 patients (13.3%). The presence of PE was associated with 30-day mortality: HR= 1.54, CI 95% (1.05; 2.23), p = 0.02 (univariable analysis), and HR= 1.60, CI 95% (1.03; 2.48), p = 0.03 (multivariable analysis). Furthermore, density of PE was associated with the need for intubation (OR=1.02, CI 95% (1.003; 1.05), p = 0.03) and the need for ICU admission (OR=1.03, CI 95% (1.005; 1.05), p = 0.01) in univariable regression analysis. The presence of PE was associated with 30-day mortality in male patients, HR= 1.56, CI 95%(1.01-2.43), p = 0.04 (multivariable analysis). In female patients, none of PE values predicted clinical outcomes. CONCLUSION: The prevalence of PE in Covid 19 is 13.3%. PE is an independent predictor of 30-day mortality in male patients with Covid 19. In female patients, PE plays no predictive role.

Possible Alterations of Imaging Patterns in Computed Tomography for Delta-VOC of SARS-CoV-2.

BACKGROUND: So far, typical findings for COVID-19 in computed tomography (CT) have been described as bilateral, multifocal ground glass opacities (GGOs) and consolidations, as well as intralobular and interlobular septal thickening. On the contrary, round consolidations with the halo sign are considered uncommon and are typically found in fungal infections, such as invasive pulmonary aspergillosis. The authors recently observed several patients with COVID-19 pneumonia presenting with round, multifocal consolidations accompanied by a halo sign. As this may indicate alterations of CT morphology based on the virus variant, the aim of this study was to investigate this matter in more detail. METHODS: 161 CT scans of patients with confirmed SARS-CoV-2 infection (RT-PCR within 2 days of CT) examined between January 2021 and September 15, 2021 were included. Follow-up examinations, patients with invasive ventilation at the time of CT, and patients with insufficient virus typing for variants of concern (VOC) were excluded. CT scans were assessed for vertical and axial distribution of pulmonary patterns, degree of involvement, uni- vs. bilaterality, reticulations, and other common findings. The mean density of representative lesions was assessed in Hounsfield units. Results were compared using Mann-Whitney U-tests, Student's t-rests, descriptive statistics, and Fisher's exact tests. RESULTS: 75 patients did not meet the inclusion criteria. Therefore, 86/161 CT scans of unique patients were analyzed. PCR VOC testing confirmed manifestation of the Delta-VOC SARS-CoV-2 in 22 patients, 39 patients with Alpha-VOC and the remaining 25 patients with Non-VOC SARS-CoV-2 infections. Three patients with the Delta-VOC demonstrated multiple pulmonary masses or nodules with surrounding halo sign, whereas no patients with either Alpha-VOC (p = 0.043) or non-VOC (p = 0.095) demonstrated these findings. All three patients were admitted to normal wards and had no suspicion of a pulmonary co-infection. Patients with Delta-VOC were less likely to have ground glass opacities compared to Alpha-VOC (7/22 or 31.8 % vs. 4/39 or 10.3 %; p < 0.001), whereas a significant difference has not been observed between Delta-VOC and non-VOC (5/25 or 20 %; p = 0.348). The mean representative density of lesions did not show significant differences between the studied cohorts. CONCLUSION: In this study 3 out of 22 patients (13.6 %) with Delta-VOC presented with bilateral round pulmonary masses or nodules with surrounding halo signs, which has not been established as a notable imaging pattern in COVID-19 pneumonia yet. Compared to the other cohorts, a lesser percentage of patients with Delta-VOC presented with ground glass opacities. Based on these results Delta-VOC might cause a divergence in CT-morphologic phenotype. KEY POINTS: · Until recently, CT-morphologic signs of COVID-19 pneumonia have been presumed to be uncontroversially understood. Yet, recently the authors observed diverging pulmonary alterations in patients infected with Delta-VOC.. · These imaging alterations included round pulmonary masses or nodules with surrounding halo sign.. · These imaging alterations have not yet been established as typical for COVID-19 pneumonia, yet.. · Based on these results, Delta-VOC could impose a divergence of CT-morphologic phenotype.. CITATION FORMAT: · Yüksel C, Sähn M, Kleines M et al. Possible Alterations of Imaging Patterns in Computed Tomography for Delta-VOC of SARS-CoV-2 . Fortschr Röntgenstr 2022; 194: 1229 - 1241.

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.

Accuracy of Chest CT for Differentiating COVID-19 from COVID-19 Mimics.

PURPOSE: To determine the performance of radiologists with different levels of expertise regarding the differentiation of COVID-19 from other atypical pneumonias. Chest CT to identify patients suffering from COVID-19 has been reported to be limited by its low specificity for distinguishing COVID-19 from other atypical pneumonias ("COVID-19 mimics"). Meanwhile, the understanding of the morphologic patterns of COVID-19 has improved and they appear to be fairly specific. MATERIALS AND METHODS: Between 02/2020 and 04/2020, 60 patients with COVID-19 pneumonia underwent chest CT in our department. Cases were matched with a comparable control group of 60 patients of similar age, sex, and comorbidities, who underwent chest CT prior to 01/2020 for atypical pneumonia caused by other pathogens. Included were other viral, fungal, and bacterial pathogens. All 120 cases were blinded to patient history and were reviewed independently by two radiologists and two radiology residents. Readers rated the probability of COVID-19 pneumonia according to the COV-RADS classification system. Results were analyzed using Clopper-Pearson 95 % confidence intervals, Youden's Index for test quality criteria, and Fleiss' kappa statistics. RESULTS: Overall, readers were able to correctly identify the presence of COVID-19 pneumonia in 219/240 (sensitivity: 91 %; 95 %-CI; 86.9 %-94.5 %), and to correctly attribute CT findings to COVID-19 mimics in 159/240 ratings (specificity: 66.3 %; 59.9 %-72.2 %), yielding an overall diagnostic accuracy of 78.8 % (378/480; 74.8 %-82.3 %). Individual reader accuracy ranged from 74.2 % (89/120) to 84.2 % (101/120) and did not correlate significantly with reader expertise. Youden's Index was 0.57. Between-reader agreement was moderate (κ = 0.53). CONCLUSION: In this enriched cohort, radiologists were able to distinguish COVID-19 from "COVID-19 mimics" with moderate diagnostic accuracy. Accuracy did not correlate with reader expertise. KEY POINTS: · In a scenario of direct comparison (no negative findings), CT allows the differentiation of COVID-19 from other atypical pneumonias ("COVID mimics") with moderate accuracy.. · Reader expertise did not significantly influence these results.. · Despite similar patterns and distributions of pulmonary findings, radiologists were able to estimate the probability of COVID-19 pneumonia using the COV-RADS classification in a standardized manner in the larger proportion of cases.. CITATION FORMAT: · Sähn M, Yüksel C, Keil S et al. Accuracy of Chest CT for Differentiating COVID-19 from COVID-19 Mimics. Fortschr Röntgenstr 2021; 193: 1081 - 1091.