Search

Purpose: To determine the prevalence of clinically relevant extracardiac findings at cardiac CT and MRI examinations from a multicenter, multinational MR/CT registry and the relationship of prevalence with examination indications and patient characteristics. Materials and Methods: This was a retrospective analysis of data from the European Society of Cardiovascular Radiology MR/CT Registry. Data from 208 506 cardiac CT examinations (median patient age, 66 years [IQR, 55–77]; 121 617 [58.33%] male patients) and 228 462 cardiac MRI examinations (median patient age, 57 years [IQR, 42–69]; 145 792 [63.81%] male patients) entered into the registry between January 2011 and November 2023 were analyzed. Clinically relevant extracardiac findings were defined as findings requiring follow-up examinations or influencing clinical management. The association of examination indication and patient characteristics, including age, with prevalence of extracardiac findings was evaluated using incidence rate ratios (IRRs) derived from multivariable Poisson regression models. Results: The prevalence of clinically relevant extracardiac findings was 3.28% (6832 of 208 506) at cardiac CT and 1.50% (3421 of 228 462) at cardiac MRI examinations. Extracardiac findings were more common at CT examinations performed for transcatheter aortic valve replacement (IRR, 2.07; P < .001) and structural heart disease (IRR, 1.44; P < .001) compared with CT performed for coronary artery disease (IRR, 1; reference). Extracardiac findings were more common at MRI examinations performed for myocarditis (IRR, 1.36; P < .001) and structural heart disease (IRR, 1.16; P < .001) than for coronary artery disease. Older patient age was also significantly associated with higher prevalence of extracardiac findings, with an IRR for both CT and MRI examinations of 1.02 (P < .001). Conclusion: Data from the multicenter, multinational MR/CT registry indicate that clinically relevant extracardiac findings a

RATIONALE: To provide an overview of the current status of cardiac multimodality imaging practices in Europe and radiologist involvement using data from the European Society of Cardiovascular Radiology (ESCR) MRCT-registry. MATERIALS AND METHODS: Numbers on cardiac CT and MRI examinations were extracted from the MRCT-registry of the ESCR, entered between January 2011 and October 2023 (n = 432,265). Data collection included the total/annual numbers of examinations, indications, complications, and reporting habits. RESULTS: Thirty-two countries contributed to the MRCT-registry, including 29 European countries. Between 2011 and 2022, there was a 4.5-fold increase in annually submitted CT examinations, from 3368 to 15,267, and a 3.8-fold increase in MRI examinations, from 3445 to 13,183. The main indications for cardiac CT were suspected coronary artery disease (CAD) (59%) and transcatheter aortic valve replacement planning (21%). The number of patients with intermediate pretest probability who underwent CT for suspected CAD showed an increase from 61% in 2012 to 82% in 2022. The main MRI indications were suspected myocarditis (26%), CAD (21%), and suspected cardiomyopathy (19%). Adverse event rates were very low for CT (0.3%) and MRI (0.7%) examinations. Reporting of CT and MRI examinations was performed mainly by radiologists (respectively 76% and 71%) and, to a lesser degree, in consensus with non-radiologists (19% and 27%, respectively). The remaining examinations (4.9% CT and 1.7% MRI) were reported by non-radiological specialties or in separate readings of radiologists and non-radiologists. CONCLUSIONS: Real-life data on cardiac imaging in Europe using the largest available MRCT-registry demonstrate a considerable increase in examinations over the past years, the vast majority of which are read by radiologists. These findings indicate that radiologists contribute to meeting the increasing demands of competent and effective care in cardiac imaging to a relevant ext

Rationale: To provide an overview of the current status of cardiac multimodality imaging practices in Europe and radiologist involvement using data from the European Society of Cardiovascular Radiology (ESCR) MRCT-registry. Materials and methods: Numbers on cardiac CT and MRI examinations were extracted from the MRCT-registry of the ESCR, entered between January 2011 and October 2023 (n = 432,265). Data collection included the total/annual numbers of examinations, indications, complications, and reporting habits. Results: Thirty-two countries contributed to the MRCT-registry, including 29 European countries. Between 2011 and 2022, there was a 4.5-fold increase in annually submitted CT examinations, from 3368 to 15,267, and a 3.8-fold increase in MRI examinations, from 3445 to 13,183. The main indications for cardiac CT were suspected coronary artery disease (CAD) (59%) and transcatheter aortic valve replacement planning (21%). The number of patients with intermediate pretest probability who underwent CT for suspected CAD showed an increase from 61% in 2012 to 82% in 2022. The main MRI indications were suspected myocarditis (26%), CAD (21%), and suspected cardiomyopathy (19%). Adverse event rates were very low for CT (0.3%) and MRI (0.7%) examinations. Reporting of CT and MRI examinations was performed mainly by radiologists (respectively 76% and 71%) and, to a lesser degree, in consensus with non-radiologists (19% and 27%, respectively). The remaining examinations (4.9% CT and 1.7% MRI) were reported by non-radiological specialties or in separate readings of radiologists and non-radiologists. Conclusions: Real-life data on cardiac imaging in Europe using the largest available MRCT-registry demonstrate a considerable increase in examinations over the past years, the vast majority of which are read by radiologists. These findings indicate that radiologists contribute to meeting the increas

BackgroundParameters of body composition have prognostic potential in patients with oncologic diseases. The aim of the present study was to analyse the prognostic potential of radiomics-based parameters of the skeletal musculature and adipose tissues in patients with advanced hepatocellular carcinoma (HCC). MethodsRadiomics features were extracted from a cohort of 297 HCC patients as post hoc sub-study of the SORAMIC randomized controlled trial. Patients were treated with selective internal radiation therapy (SIRT) in combination with sorafenib or with sorafenib alone yielding two groups: (1) sorafenib monotherapy (n = 147) and (2) sorafenib and SIRT (n = 150). The main outcome was 1-year survival. Segmentation of muscle tissue and adipose tissue was used to retrieve 881 features. Correlation analysis and feature cleansing yielded 292 features for each patient group and each tissue type. We combined 9 feature selection methods with 10 feature set compositions to build 90 feature sets. We used 11 classifiers to build 990 models. We subdivided the patient groups into a train and validation cohort and a test cohort, that is, one third of the patient groups. ResultsWe used the train and validation set to identify the best feature selection and classification model and applied it to the test set for each patient group. Classification yields for patients who underwent sorafenib monotherapy an accuracy of 75.51% and area under the curve (AUC) of 0.7576 (95% confidence interval [CI]: 0.6376-0.8776). For patients who underwent treatment with SIRT and sorafenib, results are accuracy = 78.00% and AUC = 0.8032 (95% CI: 0.6930-0.9134). ConclusionsParameters of radiomics-based analysis of the skeletal musculature and adipose tissue predict 1-year survival in patients with advanced HCC. The prognostic value of radiomics-based parameters was higher in patients who were treated with SIRT and sorafenib.

Background:Body composition parameters have been reported to be prognostic factors in patients with oncologic diseases. However, the available data on patients with HCC are conflicting. The aim of this study was to assess the impact of body composition on survival in patients with HCC treated with sorafenib or selective internal radioembolization (SIRT) and sorafenib. Methods:This is an exploratory subanalysis of the prospective, randomized controlled SORAMIC trial. Within the palliative arm of the study, patients were selected if a baseline abdominal CT was available. A broad set of skeletal muscle and adipose tissue parameters were measured at the L3 level. Low skeletal muscle mass (LSMM) and density parameters were defined using published cutoffs. The parameters were correlated with overall survival. Results:Of 424 patients in the palliative study arm, 369 patients were included in the analysis. There were 192 patients in the combined sorafenib/SIRT and 177 patients in the sorafenib group. Median overall survival was 9.9 months for the entire cohort and 10.8 and 9.2 months for the SIRT/sorafenib and sorafenib groups, respectively. There was no relevant association of either body composition parameter with overall survival in either the overall cohort or in the SIRT/sorafenib or sorafenib subgroups. Conclusions:This subanalysis of the prospective SORAMIC trial does not suggest a relevant influence of body composition parameters of survival in patients with advanced HCC. Body composition parameters therefore do not serve in patient allocation in this palliative treatment cohort.

Abstract: Cardiac computed tomography (CT) and cardiac magnetic resonance imaging (MRI) are routine radiological examinations for diagnosis and prognosis of cardiac disease. The expected growth in cardiac radiology in the coming years will exceed the current scanner capacity and trained workforce. The European Society of Cardiovascular Radiology (ESCR) focuses on supporting and strengthening the role of cardiac cross-sectional imaging in Europe from a multi-modality perspective. Together with the European Society of Radiology (ESR), the ESCR has taken the initiative to describe the current status of, a vision for, and the required activities in cardiac radiology to sustain, increase and optimize the quality and availability of cardiac imaging and experienced radiologists across Europe. Key Points: • Providing adequate availability for performing and interpreting cardiac CT and MRI is essential, especially with expanding indications. • The radiologist has a central role in non-invasive cardiac imaging examinations which encompasses the entire process from selecting the best modality to answer the referring physician’s clinical question to long-term image storage. • Optimal radiological education and training, knowledge of the imaging process, regular updating of diagnostic standards, and close collaboration with colleagues from other specialties are essential.

Objectives To identify clinical and imaging parameters associated with progression of non-hypervascular hepatobiliary phase hypointense lesions during follow-up in patients who received treatment for hepatocellular carcinoma. Methods A total of 67 patients with 106 lesions were identified after screening 538 patients who underwent gadoxetic acid-enhanced MRI within the SORAMIC trial. All patients were allocated to the trial treatment according to the trial scheme, and 61 of 67 patients received systemic treatment with sorafenib (either alone or combined with locoregional therapies) during the trial period. Follow-up images after treatment according to trial scheme were reviewed for subsequent hypervascularization or > 1 cm size increase. The correlation between progression and several imaging and clinical parameters was assessed using univariable and multivariable analyses. Results On a median 178 (range, 48-1072) days follow-up period, progression was encountered in 18 (16.9%) lesions in 12 (17.9%) patients. In univariable analysis size > 12.6 mm (p = 0.070), ECOG-PS (p = 0.025), hypointensity at T1-weighted imaging (p = 0.028), hyperintensity at T2-weighted imaging (p < 0.001), hyperintensity at DWI images (p = 0.007), and cirrhosis (p = 0.065) were correlated with progression during follow-up. Hyperintensity at T2 images (p = 0.011) was an independent risk factor for progression in multivariable analysis, as well as cirrhosis (p = 0.033) and ECOG-PS (p = 0.030). Conclusions Non-hypervascular hepatobiliary phase hypointense lesions are associated with subsequent progression after treatment in patients with HCC. T2 hyperintensity, diffusion restriction, cirrhosis, and higher ECOG-PS could identify lesions with increased risk. These factors should be considered for further diagnostic evaluation or treatment of such lesions.

Cardiac computed tomography (CT) and cardiac magnetic resonance imaging (MRI) are routine radiological examinations for diagnosis and prognosis of cardiac disease. The expected growth in cardiac radiology in the coming years will exceed the current scanner capacity and trained workforce. The European Society of Cardiovascular Radiology (ESCR) focuses on supporting and strengthening the role of cardiac cross-sectional imaging in Europe from a multi-modality perspective. Together with the European Society of Radiology (ESR), the ESCR has taken the initiative to describe the current status of, a vision for, and the required activities in cardiac radiology to sustain, increase and optimize the quality and availability of cardiac imaging and experienced radiologists across Europe.

The European Society of Cardiovascular Radiology (ESCR) is the European specialist society of cardiac and vascular imaging. This society's highest priority is the continuous improvement, development, and standardization of education, training, and best medical practice, based on experience and evidence. The present intra-society consensus is based on the existing scientific evidence and on the individual experience of the members of the ESCR writing group on carotid diseases, the members of the ESCR guidelines committee, and the members of the executive committee of the ESCR. The recommendations published herein reflect the evidence-based society opinion of ESCR. The purpose of this second document is to discuss suggestions for standardized reporting based on the accompanying consensus document part I.

The European Society of Cardiovascular Radiology (ESCR) is the European specialist society of cardiac and vascular imaging. This society's highest priority is the continuous improvement, development, and standardization of education, training, and best medical practice, based on experience and evidence. The present intra-society consensus is based on the existing scientific evidence and on the individual experience of the members of the ESCR writing group on carotid diseases, the members of the ESCR guidelines committee, and the members of the executive committee of the ESCR. The recommendations published herein reflect the evidence-based society opinion of ESCR. We have produced a twin-papers consensus, indicated through the documents as respectively "Part I" and "Part II." The first document (Part I) begins with a discussion of features, role, indications, and evidence for CT and MR imaging-based diagnosis of carotid artery disease for risk stratification and prediction of stroke (Section I). It then provides an extensive overview and insight into imaging-derived biomarkers and their potential use in risk stratification (Section II). Finally, detailed recommendations about optimized imaging technique and imaging strategies are summarized (Section III). The second part of this consensus paper (Part II) is focused on structured reporting of carotid imaging studies with CT/MR.

Background Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. Methods A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. Results/conclusions Items with >= 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with <= 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of ope, Funding Agencies|European Union [826494, 952159, 952172, 101057699]; NIH/NCI Cancer Center Support Grant [P30 CA008748]; National Institute for Health Research University College London Hospitals Biomedical Research Centre; French government under management of the Agence Nationale de la Recherche as part of the "Investissements davenir" program [ANR19-P3IA-0001]

Background Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. Methods A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. Results/conclusions Items with >= 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with <= 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of ope, Funding Agencies|European Union [826494, 952159, 952172, 101057699]; NIH/NCI Cancer Center Support Grant [P30 CA008748]; National Institute for Health Research University College London Hospitals Biomedical Research Centre; French government under management of the Agence Nationale de la Recherche as part of the "Investissements davenir" program [ANR19-P3IA-0001]

Background Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. Methods A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. Results/conclusions Items with >= 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with <= 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of ope, Funding Agencies|European Union [826494, 952159, 952172, 101057699]; NIH/NCI Cancer Center Support Grant [P30 CA008748]; National Institute for Health Research University College London Hospitals Biomedical Research Centre; French government under management of the Agence Nationale de la Recherche as part of the "Investissements davenir" program [ANR19-P3IA-0001]

Objectives To compare the use of coronary computed tomography angiography (CCTA) between academic and non-academic sites across Europe over the last decade. Methods We analyzed a large multicenter registry (ESCR MR/CT Registry) of stable symptomatic patients who received CCTA 01/2010-01/2020 at 47 (22%) academic and 165 (78%) non-academic sites across 19 European countries. We compared image quality, radiation dose, contrast-media-related adverse events, patient characteristics, CCTA findings, and downstream testing between academic and non-academic sites. Results Among 64,317 included patients (41% female; 60 +/- 13 years), academic sites accounted for most cases in 2010-2014 (52%), while non-academic sites dominated in 2015-2020 (71%). Despite less contemporary technology, non-academic sites maintained low radiation doses (4.76 [2.46-6.85] mSv) with a 30% decline of high-dose scans (> 7 mSv) over time. Academic and non-academic sites both reported diagnostic image quality in 98% of cases and low rate of scan-related adverse events (0.4%). Academic and non-academic sites examined similar patient populations (41% females both; age: 61 +/- 14 vs. 60 +/- 12 years; pretest probability for obstructive CAD: low 21% vs. 23%, intermediate 73% vs. 72%, high 6% both, CAD prevalence on CCTA: 40% vs. 41%). Nevertheless, non-academic sites referred more patients to non-invasive ischemia testing (6.5% vs. 4.2%) and invasive coronary angiography/surgery (8.5% vs. 5.6%). Conclusions Non-academic and academic sites provide safe, high-quality CCTA across Europe, essential to successfully implement the recently updated guidelines for the diagnosis and management of chronic coronary syndromes. However, despite examining similar populations with comparable CAD prevalence, non-academic sites tend to refer more patients to downstream testing.

Background: To investigate whole-body contrast-enhanced CT and hepatobiliary contrast liver MRI for the detection of extrahepatic disease (EHD) in hepatocellular carcinoma (HCC) and to quantify the impact of EHD on therapy decision. Methods: In this post-hoc analysis of the prospective phase II open-label, multicenter, randomized controlled SORAMIC trial, two blinded readers independently analyzed the whole-body contrast-enhanced CT and gadoxetic acid-enhanced liver MRI data sets of 538 HCC patients. EHD (defined as tumor manifestation outside the liver) detection rates of the two imaging modalities were compared using multiparametric statistical tests. In addition, the most appropriate treatment recommendation was determined by a truth panel. Results: EHD was detected significantly more frequently in patients with portal vein infiltration (21% vs. 10%, p < 0.001), macrovascular infiltration (22% vs. 9%, p < 0.001), and bilobar liver involvement (18% vs. 9%, p = 0.006). Further on, the maximum lesion diameter in patients with EHD was significantly higher (8.2 cm vs. 5.8 cm, p = 0.002). CT detected EHD in significantly more patients compared to MRI in both reader groups (p < 0.001). Higher detection rates of EHD in CT led to a change in management only in one patient since EHD was predominantly present in patients with locally advanced HCC, in whom palliative treatment is the standard of care. Conclusions: Whole-body contrast-enhanced CT shows significantly higher EHD detection rates compared to hepatobiliary contrast liver MRI. However, the higher detection rate did not yield a significant impact on patient management in advanced HCC.

Background: The aim of this study was to explore the relationship between follow-up imaging characteristics and overall survival (OS) in advanced hepatocellular carcinoma (HCC) patients under sorafenib treatment. Methods: Associations between OS and objective response (OR) by mRECIST or early tumor shrinkage (ETS; ≥20% reduction in enhancing tumor diameter at the first follow-up imaging) were analyzed in HCC patients treated with sorafenib within a multicenter phase II trial (SORAMIC). 115 patients were included in this substudy. The relationship between survival and OR or ETS were explored. Landmark analyses were performed according to OR at fixed time points. Cox proportional hazards models with OR and ETS as a time-dependent covariate were used to compare survival with factors known to influence OS. Results: The OR rate was 29.5%. Responders had significantly better OS than non-responders (median 30.3 vs. 11.4 months; HR, 0.38 [95% CI, 0.22-0.63], p < 0.001), and longer progression-free survival (PFS; median 10.1 vs. 4.3 months, p = 0.015). Patients with ETS ≥ 20% had longer OS (median 22.1 vs. 11.4 months, p = 0.002) and PFS (median 8.0 vs. 4.3 months, p = 0.034) than patients with ETS < 20%. Besides OR and ETS, male gender, lower bilirubin and ALBI grade were associated with improved OS in univariate analysis. Separate models of multivariable analysis confirmed OR and ETS as independent predictors of OS. Conclusion: OR according to mRECIST and ETS in patients receiving sorafenib treatment are independent prognostic factors for OS. These parameters can be used for assessment of treatment benefit and optimal treatment sequencing in patients with advanced HCC.

Objectives To evaluate the correlation between liver enhancement on hepatobiliary phase and liver function parameters in a multicenter, multivendor study. Methods A total of 359 patients who underwent gadoxetic acid-enhanced MRI using a standardized protocol with various scanners within a prospective multicenter phase II trial (SORAMIC) were evaluated. The correlation between liver enhancement on hepatobiliary phase normalized to the spleen (liver-to-spleen ratio, LSR) and biochemical laboratory parameters, clinical findings related to liver functions, liver function grading systems (Child-Pugh and Albumin-Bilirubin [ALBI]), and scanner characteristics were analyzed using uni- and multivariate analyses. Results There was a significant positive correlation between LSR and albumin (rho = 0.193; p < 0.001), platelet counts (rho = 0.148; p = 0.004), and sodium (rho = 0.161; p = 0.002); and a negative correlation between LSR and total bilirubin (rho = -0.215; p < 0.001) and AST (rho = -0.191; p < 0.001). Multivariate analysis confirmed independent significance for each of albumin (p = 0.022), total bilirubin (p = 0.045), AST (p = 0.031), platelet counts (p = 0.012), and sodium (p = 0.006). The presence of ascites (1.47 vs. 1.69, p < 0.001) and varices (1.55 vs. 1.69, p = 0.006) was related to significantly lower LSR. Similarly, patients with ALBI grade 1 had significantly higher LSR than patients with grade 2 (1.74 +/- 0.447 vs. 1.56 +/- 0.408, p < 0.001); and Child-Pugh A patients had a significantly higher LSR than Child-Pugh B (1.67 +/- 0.44 vs. 1.49 +/- 0.33, p = 0.021). Also, LSR was negatively correlated with MELD-Na scores (rho = -0.137; p = 0.013). However, one scanner brand was significantly associated with lower LSR (p < 0.001). Conclusions The liver enhancement on the hepatobiliary phase of gadoxetic acid-enhanced MRI is correlated with biomarkers of liver functions in a multicenter cohort. However, this correlation shows variations between s

Background Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. Methods A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. Results/conclusions Items with >= 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with <= 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of ope, Funding Agencies|European Union [826494, 952159, 952172, 101057699]; NIH/NCI Cancer Center Support Grant [P30 CA008748]; National Institute for Health Research University College London Hospitals Biomedical Research Centre; French government under management of the Agence Nationale de la Recherche as part of the "Investissements davenir" program [ANR19-P3IA-0001]

Purpose: To compare the treatment response and progression-free survival (PFS) in advanced hepatocellular carcinoma (HCC) patients who received sorafenib treatment either alone or combined with radioembolization (RE). Methods: Follow-up images of the patients treated within a multicenter phase II trial (SORAMIC) were assessed by mRECIST. A total of 177 patients (73 combination arm [RE + sorafenib] and 104 sorafenib arm) were included in this post-hoc analysis. Response and progression characteristics were compared between treatment arms. Survival analyses were done to compare PFS and post-progression survival between treatment arms. Multivariate Cox regression analysis was used to compare survival with factors known to influence PFS in patients with HCC. Results: The combination arm had significantly higher objective response rate (61.6% vs. 29.8%, p < 0.001), complete response rate (13.7% vs. 3.8%, p = 0.022), and a trend for higher disease control rate (79.2% vs. 72.1%, p = 0.075). Progression was encountered in 116 (65.5%) patients and was more common in the sorafenib arm (75% vs. 52.0%, p = 0.001). PFS (median 8.9 vs. 5.4 months, p = 0.022) and hepatic PFS were significantly better in the combination arm (9.0 vs. 5.7 months, p = 0.014). Multivariate analysis confirmed the treatment arm as an independent predictor of PFS. Conclusion: In advanced HCC patients receiving sorafenib, combination with RE has an additive anticancer effect on sorafenib treatment resulting in a higher and longer tumor response. However, the enhanced response did not translate into prolonged survival. Better patient selection and superselective treatment could improve outcomes after combination therapy.

Background Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. Methods A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2-4. Subsequent rounds were informed by responses of previous rounds. Results/conclusions Items with >= 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60-74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with <= 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of ope, Funding Agencies|European Union [826494, 952159, 952172, 101057699]; NIH/NCI Cancer Center Support Grant [P30 CA008748]; National Institute for Health Research University College London Hospitals Biomedical Research Centre; French government under management of the Agence Nationale de la Recherche as part of the "Investissements davenir" program [ANR19-P3IA-0001]

Background: Lesion/tissue segmentation on digital medical images enables biomarker extraction, image-guided therapy delivery, treatment response measurement, and training/validation for developing artificial intelligence algorithms and workflows. To ensure data reproducibility, criteria for standardised segmentation are critical but currently unavailable. Methods: A modified Delphi process initiated by the European Imaging Biomarker Alliance (EIBALL) of the European Society of Radiology (ESR) and the European Organisation for Research and Treatment of Cancer (EORTC) Imaging Group was undertaken. Three multidisciplinary task forces addressed modality and image acquisition, segmentation methodology itself, and standards and logistics. Devised survey questions were fed via a facilitator to expert participants. The 58 respondents to Round 1 were invited to participate in Rounds 2–4. Subsequent rounds were informed by responses of previous rounds. Results/conclusions: Items with ≥ 75% consensus are considered a recommendation. These include system performance certification, thresholds for image signal-to-noise, contrast-to-noise and tumour-to-background ratios, spatial resolution, and artefact levels. Direct, iterative, and machine or deep learning reconstruction methods, use of a mixture of CE marked and verified research tools were agreed and use of specified reference standards and validation processes considered essential. Operator training and refreshment were considered mandatory for clinical trials and clinical research. Items with a 60–74% agreement require reporting (site-specific accreditation for clinical research, minimal pixel number within lesion segmented, use of post-reconstruction algorithms, operator training refreshment for clinical practice). Items with ≤ 60% agreement are outside current recommendations for segmentation (frequency of system performance tests, use of only CE-marked tools, board certification of operators, frequency of operator refr

Objectives To compare the use of coronary computed tomography angiography (CCTA) between academic and non-academic sites across Europe over the last decade.Methods We analyzed a large multicenter registry (ESCR MR/CT Registry) of stable symptomatic patients who received CCTA 01/2010-01/2020 at 47 (22%) academic and 165 (78%) non-academic sites across 19 European countries. We compared image quality, radiation dose, contrast-media-related adverse events, patient characteristics, CCTA findings, and downstream testing between academic and non-academic sites.Results Among 64,317 included patients (41% female; 60 +/- 13 years), academic sites accounted for most cases in 2010-2014 (52%), while non-academic sites dominated in 2015-2020 (71%). Despite less contemporary technology, non-academic sites maintained low radiation doses (4.76 [2.46-6.85] mSv) with a 30% decline of high-dose scans (> 7 mSv) over time. Academic and non-academic sites both reported diagnostic image quality in 98% of cases and low rate of scan-related adverse events (0.4%). Academic and non-academic sites examined similar patient populations (41% females both; age: 61 +/- 14 vs. 60 +/- 12 years; pretest probability for obstructive CAD: low 21% vs. 23%, intermediate 73% vs. 72%, high 6% both, CAD prevalence on CCTA: 40% vs. 41%). Nevertheless, non-academic sites referred more patients to non-invasive ischemia testing (6.5% vs. 4.2%) and invasive coronary angiography/surgery (8.5% vs. 5.6%).Conclusions Non-academic and academic sites provide safe, high-quality CCTA across Europe, essential to successfully implement the recently updated guidelines for the diagnosis and management of chronic coronary syndromes. However, despite examining similar populations with comparable CAD prevalence, non-academic sites tend to refer more patients to downstream testing.

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the da

A Correction to this paper has been published

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. KEY POINTS: • Data-driven processes like radiomics risk false discoveries due to high-dimensionality of the da

A Correction to this paper has been published

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials.

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials.

A Correction to this paper has been published

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials.

Machine learning offers great opportunities to streamline and improve clinical care from the perspective of cardiac imagers, patients, and the industry and is a very active scientific research field. In light of these advances, the European Society of Cardiovascular Radiology (ESCR), a non-profit medical society dedicated to advancing cardiovascular radiology, has assembled a position statement regarding the use of machine learning (ML) in cardiovascular imaging. The purpose of this statement is to provide guidance on requirements for successful development and implementation of ML applications in cardiovascular imaging. In particular, recommendations on how to adequately design ML studies and how to report and interpret their results are provided. Finally, we identify opportunities and challenges ahead. While the focus of this position statement is ML development in cardiovascular imaging, most considerations are relevant to ML in radiology in general.

Abstract: Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials. Key Points: • Data-driven processes like radiomics risk false discoveries due to high-dimensional

A Correction to this paper has been published

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials.

A Correction to this paper has been published

Existing quantitative imaging biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials.

Machine learning offers great opportunities to streamline and improve clinical care from the perspective of cardiac imagers, patients, and the industry and is a very active scientific research field. In light of these advances, the European Society of Cardiovascular Radiology (ESCR), a non-profit medical society dedicated to advancing cardiovascular radiology, has assembled a position statement regarding the use of machine learning (ML) in cardiovascular imaging. The purpose of this statement is to provide guidance on requirements for successful development and implementation of ML applications in cardiovascular imaging. In particular, recommendations on how to adequately design ML studies and how to report and interpret their results are provided. Finally, we identify opportunities and challenges ahead. While the focus of this position statement is ML development in cardiovascular imaging, most considerations are relevant to ML in radiology in general. KEY POINTS: • Development and clinical implementation of machine learning in cardiovascular imaging is a multidisciplinary pursuit. • Based on existing study quality standard frameworks such as SPIRIT and STARD, we propose a list of quality criteria for ML studies in radiology. • The cardiovascular imaging research community should strive for the compilation of multicenter datasets for the development, evaluation, and benchmarking of ML algorithms.

Machine learning offers great opportunities to streamline and improve clinical care from the perspective of cardiac imagers, patients, and the industry and is a very active scientific research field. In light of these advances, the European Society of Cardiovascular Radiology (ESCR), a non-profit medical society dedicated to advancing cardiovascular radiology, has assembled a position statement regarding the use of machine learning (ML) in cardiovascular imaging. The purpose of this statement is to provide guidance on requirements for successful development and implementation of ML applications in cardiovascular imaging. In particular, recommendations on how to adequately design ML studies and how to report and interpret their results are provided. Finally, we identify opportunities and challenges ahead. While the focus of this position statement is ML development in cardiovascular imaging, most considerations are relevant to ML in radiology in general.

The severe acute respiratory syndrome coronavirus 2019 (SARS-CoV-2) pandemic currently constitutes a significant burden on worldwide health care systems, with important implications on many levels, including radiology departments. Given the established fundamental role of cardiovascular imaging in modern healthcare, and the specific value of cardiopulmonary radiology in COVID-19 patients, departmental organisation and imaging programs need to be restructured during the pandemic in order to provide access to modern cardiovascular services to both infected and non-infected patients while ensuring safety for healthcare professionals. The uninterrupted availability of cardiovascular radiology services remains, particularly during the current pandemic outbreak, crucial for the initial evaluation and further follow-up of patients with suspected or known cardiovascular diseases in order to avoid unnecessary complications. Suspected or established COVID-19 patients may also have concomitant cardiovascular symptoms and require further imaging investigations. This statement by the European Society of Cardiovascular Radiology (ESCR) provides information on measures for safety of healthcare professionals and recommendations for cardiovascular imaging during the pandemic in both non-infected and COVID-19 patients.

BACKGROUND: Left ventricular (LV) dilatation is a key compensatory feature in patients with chronic aortic regurgitation (AR). However, sex-differences in LV remodeling and outcomes in chronic AR have been poorly investigated so far. METHODS: We performed cardiovascular magnetic resonance imaging (CMR) including phase-contrast velocity-encoded imaging for the measurement of regurgitant fraction (RegF) at the sinotubular junction, in consecutive patients with at least mild AR on echocardiography. We assessed LV size (end-diastolic volume indexed to body surface area, LVEDV/BSA) and investigated sex differences between LV remodeling and increasing degrees of AR severity. Cox-regression models were used to test differences in outcomes between men and women using a composite of heart failure hospitalization, unscheduled AR intervention, and cardiovascular death. RESULTS: 270 consecutive patients (59.6% male, 59.8 ± 20.8 y/o, 59.6% with at least moderate AR on echocardiography) were included. On CMR, mean RegF was 18.1 ± 17.9% and a total of 65 (24.1%) had a RegF ≥ 30%. LVEDV/BSA was markedly closer related with AR severity (RegF) in men compared to women. Each 1-SD increase in LVEDV/BSA (mL/m$^{2}$) was associated with a 9.7% increase in RegF in men and 5.9% in women, respectively (p-value for sex-interaction < 0.001). Based on previously published reference values, women-in contrast to men-frequently had a normal LV size despite severe AR (e.g., for LVEDV/BSA on CMR: 35.3% versus 8.7%, p < 0.001). In a Cox-regression model adjusted for age, LVEDV/BSA and RegF, women were at significantly higher risk for the composite endpoint when compared to men (adj. HR 1.81 (95%CI 1.09-3.03), p = 0.022). CONCLUSION: In patients with chronic AR, LV remodeling is a hallmark feature in men but not in women. Severity of AR may be underdiagnosed in female patients in the absence of LV dilatation. Future studies need to address the dismal prognosis in female patients with chronic AR.

The severe acute respiratory syndrome coronavirus 2019 (SARS-CoV-2) pandemic currently constitutes a significant burden on worldwide health care systems, with important implications on many levels, including radiology departments. Given the established fundamental role of cardiovascular imaging in modern healthcare, and the specific value of cardiopulmonary radiology in COVID-19 patients, departmental organisation and imaging programs need to be restructured during the pandemic in order to provide access to modern cardiovascular services to both infected and non-infected patients while ensuring safety for healthcare professionals. The uninterrupted availability of cardiovascular radiology services remains, particularly during the current pandemic outbreak, crucial for the initial evaluation and further follow-up of patients with suspected or known cardiovascular diseases in order to avoid unnecessary complications. Suspected or established COVID-19 patients may also have concomitant cardiovascular symptoms and require further imaging investigations. This statement by the European Society of Cardiovascular Radiology (ESCR) provides information on measures for safety of healthcare professionals and recommendations for cardiovascular imaging during the pandemic in both non-infected and COVID-19 patients.

Transcatheter aortic valve replacement (TAVR) is a minimally invasive alternative to conventional aortic valve replacement in symptomatic patients with severe aortic stenosis and contraindications to surgery. The procedure has shown to improve patient's quality of life and prolong short- and mid-term survival in high-risk individuals, becoming a widely accepted therapeutic option which has been integrated into current clinical guidelines for the management of valvular heart disease. Nevertheless, not every patient at high-risk for surgery is a good candidate for TAVR. Besides clinical selection, which is usually established by the Heart Team, certain technical and anatomic criteria must be met as, unlike in surgical valve replacement, annular sizing is not performed under direct surgical evaluation but on the basis of non-invasive imaging findings. Present consensus document was outlined by a working group of researchers from the European Society of Cardiovascular Radiology (ESCR) and aims to provide guidance on the utilisation of CT and MR imaging prior to TAVR. Particular relevance is given to the technical requirements and standardisation of the scanning protocols which have to be tailored to the remarkable variability of the scanners currently utilised in clinical practice; recommendations regarding all required pre-procedural measurements and medical reporting standardisation have been also outlined, in order to ensure quality and consistency of reported data and terminology.

Objective: Pregnancy has been reported to be an independent risk factor for 50% of acute aortic dissections recorded in women younger than 45 years of age. The present epidemiologic study aimed to identify whether this putative association of pregnancy and acute type A dissection could be an artifact of selective reporting. Methods: This population-based study was conducted in the City of Vienna, Austria, Europe, in an average female population of 341381 women in the age range of 15-45 years who were followed up between 1994 and 2004 (total of 3755.195 person-years of observation). During this study, the incidence, management, and outcome of acute type A dissection were determined. Results: Fifteen patients (mean age: 38.8 years, SD: 4.8) with acute aortic dissection were identified, and an overall incidence of 0.4 case per 100000 person-years was estimated. The prehospital mortality rate was recorded to be 53%. Six patients, including two women in late pregnancy (incidence: 0.05 cases per 100000 person-years), were treated successfully by surgical repair during deep hypothermic circulatory arrest (in-hospital mortality rate: 6.6%). Pregnancy and aortic dissection were identified as events that were not related (RR: 3.27; 95% confidence interval (CI): 0.82-12.95; P=0.14). Observation during long-term follow-up was uneventful. Conclusions: Acute aortic dissection represents a rare pathology in women younger than 45 years of age; however, it is associated with a high rate of sudden death. Pregnancy may not be a risk factor for this life-threatening vascular emergency. Immediate referral to surgery, even during pregnancy, will result in a prognosis of favorable outcome

Since the implementation of the congestion charge in Gothenburg on the first of January 2013, there has been a debate whether or not the congestion charge would harm the retail business. This concerns, in particular the retail stores located in the charging zone. The general aim of this study was to investigate attitudes and consumer behavior related to the recently introduced congestion charge in Gothenburg and to illustrate the possible impact the congestion charge may have had on retail business performance. In order to do this we conducted a questionnaire survey of visitors to Bäckebol shopping center (n=335). The questionnaire consisted of 24 questions and the response rate was 76 %. In addition, we performed two extensive interviews with key representatives for the local retail business in order to add context and background to the questionnaire findings. In the questionnaire, consumer behavior was investigated by questions on time of visit, destination, and means of transport. In the analyses we assessed associations between changes in consumer behavior, acceptance and demographic factors. The results of this study showed a low level of acceptance of the congestion charge in the study population. Furthermore, we found a high proportion of subjects reporting a behavioral change related to the introduction of the congestion charge. This was associated to but not explained by a low level of acceptance. It was also related to residency outside the inner parts of Gothenburg. In this way our results support the theory of an association between behavioral change, degree of acceptance as well as demographic factors. However, in a comparison of certain aspects of behavioral change to a previous study conducted prior to the introduction of the congestion charge we found no difference. It is likely that behavioral changes related to the congestion charge have taken place but they may have been exaggerated in the questionnaire responses. The interviews implied that the