Computed tomography in patients with sepsis presenting to the emergency department: exploring its role in light of patient outcomes.

OBJECTIVES: This study aimed to explore the role of CT in septic patients presenting to the emergency department (ED). MATERIALS AND METHODS: We performed a retrospective secondary analysis of 192 septic patients from a prospective observational study, i.e., the "LIFE POC" study. Sepsis was diagnosed in accordance with the Sepsis-3 definition. Clinical and radiological data were collected from the hospital administration and radiological systems. Information on mortality and morbidity was collected. Time-to-CT between CT scan and sepsis diagnosis (ttCTsd) was calculated. Diagnostic accuracy was assessed with the final sepsis source as reference standard. The reference standard was established through the treating team of the patient based on all available clinical, imaging, and microbiological data. RESULTS: Sixty-two of 192 patients underwent a CT examination for sepsis focus detection. The final septic source was identified by CT in 69.4% (n = 43). CT detected septic foci with 81.1% sensitivity (95% CI, 68.0-90.6%) and 55.6% specificity (95% CI, 21.2-86.3%). Patients with short versus long ttCTsd did not differ in terms of mortality (16.1%, n = 5 vs 9.7, n = 3; p = 0.449), length of hospital stay (median 16 d, IQR 9 d 12 h-23 d 18 h vs median 13 d, IQR 10 d 00 h-24 d 00 h; p = 0.863), or duration of intensive care (median 3d 12 h, IQR 2 d 6 h-7 d 18 h vs median 5d, IQR 2 d-11 d; p = 0.800). CONCLUSIONS: Our findings show a high sensitivity of CT in ED patients with sepsis, confirming its relevance in guiding treatment decisions. The low specificity suggests that a negative CT requires further ancillary diagnostic tests for focus detection. The timing of CT did not affect morbidity or mortality outcomes. CLINICAL RELEVANCE STATEMENT: In patients with sepsis who present to the ED, CT can be used to identify infectious foci on the basis of clinical suspicion, but should not be used as a rule-out test. Scientific evidence for the optimal timing of CT beyond clinical decision-making is currently missing, as potential mortality benefits are clouded by differences in clinical severity at the time of ED presentation. KEY POINTS: • In patients with sepsis who present to the ED, CT for focus identification has a high sensitivity and can thereby be valuable for patient management. • As the specificity is considerably lower, a thorough microbiological assessment is important in these cases. • The timing of CT did not affect morbidity and mortality outcomes in this study, which might be due to variability in clinical severity at the time of ED presentation.

An exemplary reanalysis of coronary computed tomography angiography diagnostic meta-analyses shows insufficient data sharing and incorrect sensitivity and specificity estimates.

OBJECTIVES: To systematically evaluate the reproducibility of primary data and, the reproducibility and correctness of pooled sensitivity and specificity estimates reported in a sample of diagnostic meta-analyses. STUDY DESIGN AND SETTING: We conducted an exemplary systematic review of diagnostic meta-analyses comparing coronary computed tomography angiography to invasive coronary angiography in patients with suspected coronary artery disease. The objectives were to assess 1) the reproducibility of contingency tables, 2) the reproducibility of pooled sensitivity and specificity, and 3) differences to reported results when applying a recommended bivariate binomial model for pooling sensitivity and specificity. Therefore, we reproduced the contingency tables and recalculated sensitivity and specificity by utilizing both the pooling method of each meta-analysis and a bivariate binomial model. We used linear trends to assess the improvement of these objectives over time. RESULTS: We identified 38 diagnostic meta-analyses, each including on average 19 primary studies (range: 3 to 89 studies; total: 715-including duplicates) with an average of approximately 1800 patients per meta-analysis (range: 118 to 7516 patients). For 31 meta-analyses (82%, 95% CI: 65%, 91%), the contingency tables were reproducible; however, only 15 published them. Using the pooling method of each meta-analysis, we obtained comparable recalculated sensitivities/specificities for 28 meta-analyses (74% [57%, 86%]). Only 11 meta-analyses pooled sensitivity/specificity using a bivariate binomial model (29% [16%, 46%]). When all meta-analyses were pooled with this model, published sensitivities/specificities were confirmed for 19 of 38 meta-analyses (50% [34%, 66%]). There was only marginal improvement in data availability and application of recommended pooling methods over time. CONCLUSION: Data sharing should become standard practice along with the use of appropriate pooling methods. Journal publication requirements may play a key role in enhancing the quality of scientific reporting and methodological standards which may lead to more reliable and consistent outcomes. The ability to reproduce sensitivity and specificity estimates in diagnostic imaging meta-analyses is dependent on the availability of contingency tables and the explicit reporting of pooling methods and software used.

Automated graded prognostic assessment for patients with hepatocellular carcinoma using machine learning.

BACKGROUND: Accurate mortality risk quantification is crucial for the management of hepatocellular carcinoma (HCC); however, most scoring systems are subjective. PURPOSE: To develop and independently validate a machine learning mortality risk quantification method for HCC patients using standard-of-care clinical data and liver radiomics on baseline magnetic resonance imaging (MRI). METHODS: This retrospective study included all patients with multiphasic contrast-enhanced MRI at the time of diagnosis treated at our institution. Patients were censored at their last date of follow-up, end-of-observation, or liver transplantation date. The data were randomly sampled into independent cohorts, with 85% for development and 15% for independent validation. An automated liver segmentation framework was adopted for radiomic feature extraction. A random survival forest combined clinical and radiomic variables to predict overall survival (OS), and performance was evaluated using Harrell's C-index. RESULTS: A total of 555 treatment-naïve HCC patients (mean age, 63.8 years ± 8.9 [standard deviation]; 118 females) with MRI at the time of diagnosis were included, of which 287 (51.7%) died after a median time of 14.40 (interquartile range, 22.23) months, and had median followed up of 32.47 (interquartile range, 61.5) months. The developed risk prediction framework required 1.11 min on average and yielded C-indices of 0.8503 and 0.8234 in the development and independent validation cohorts, respectively, outperforming conventional clinical staging systems. Predicted risk scores were significantly associated with OS (p < .00001 in both cohorts). CONCLUSIONS: Machine learning reliably, rapidly, and reproducibly predicts mortality risk in patients with hepatocellular carcinoma from data routinely acquired in clinical practice. CLINICAL RELEVANCE STATEMENT: Precision mortality risk prediction using routinely available standard-of-care clinical data and automated MRI radiomic features could enable personalized follow-up strategies, guide management decisions, and improve clinical workflow efficiency in tumor boards. KEY POINTS: • Machine learning enables hepatocellular carcinoma mortality risk prediction using standard-of-care clinical data and automated radiomic features from multiphasic contrast-enhanced MRI. • Automated mortality risk prediction achieved state-of-the-art performances for mortality risk quantification and outperformed conventional clinical staging systems. • Patients were stratified into low, intermediate, and high-risk groups with significantly different survival times, generalizable to an independent evaluation cohort.

Patient satisfaction with coronary CT angiography versus invasive coronary angiography: results of a single-center randomized trial.

OBJECTIVE: Because there is evidence for a clinical benefit of using coronary computed tomography (CT) angiography instead of invasive coronary angiography (ICA) in patients with suspected coronary artery disease (CAD), we ascertained if patient satisfaction could represent an important barrier to implementation of coronary CT in clinical practice. MATERIALS AND METHODS: A total of 329 patients with suspected CAD and clinical indication for ICA were randomly assigned to undergo either CT or ICA for guiding treatment. Satisfaction for both groups was assessed by patient questionnaire completed twice, ≥24 h after CT or ICA, and at follow-up after a median of 3.7 years. Assessment included preparation, concern, comfort, helplessness, pain, willingness to undergo tests again, overall satisfaction, and preference. Pearson's chi-square test and Wilcoxon rank-sum test were used. RESULTS: Overall, 91% of patients undergoing CT (152/167) and 86% undergoing ICA completed assessment (140/162, p = 0.19). Patients reported being significantly better prepared for CT, less concerned about the test, and felt less helpless than during ICA (all: p < 0.001). Subjective pain (horizontal nonmarked visual analogue scale) was significantly lower for CT (6.9 ± 14.7) than for ICA (17.1 ± 22.7; p < 0.001). At follow-up, significantly more patients in the CT group reported very good satisfaction with communication of findings compared with the ICA group (p < 0.001) and 92% would recommend the institution to someone referred for the same examination. CONCLUSIONS: Results from our single-center randomized study show very good satisfaction with coronary CT compared to ICA. Thus, superior acceptance of CT should be considered in shared decision-making. CLINICAL RELEVANCE STATEMENT: This evaluation of patient satisfaction in a randomized study shows that patients' preference is in line with the clinical benefit provided by CT and also suggests to prefer a CT-first strategy in suspected coronary artery disease. KEY POINTS: • Subjective pain was significantly lower for coronary CT angiography than for invasive coronary angiography and patients felt better prepared and less concerned about CT. • Patients were overall more satisfied with coronary CT angiography than invasive coronary angiography in a randomized controlled trial. • After a median follow-up of 3.7 years, more patients in the CT group indicated very good satisfaction with the communication of findings and with the examination itself.

Automated MRI liver segmentation for anatomical segmentation, liver volumetry, and the extraction of radiomics.

OBJECTIVES: To develop and evaluate a deep convolutional neural network (DCNN) for automated liver segmentation, volumetry, and radiomic feature extraction on contrast-enhanced portal venous phase magnetic resonance imaging (MRI). MATERIALS AND METHODS: This retrospective study included hepatocellular carcinoma patients from an institutional database with portal venous MRI. After manual segmentation, the data was randomly split into independent training, validation, and internal testing sets. From a collaborating institution, de-identified scans were used for external testing. The public LiverHccSeg dataset was used for further external validation. A 3D DCNN was trained to automatically segment the liver. Segmentation accuracy was quantified by the Dice similarity coefficient (DSC) with respect to manual segmentation. A Mann-Whitney U test was used to compare the internal and external test sets. Agreement of volumetry and radiomic features was assessed using the intraclass correlation coefficient (ICC). RESULTS: In total, 470 patients met the inclusion criteria (63.9±8.2 years; 376 males) and 20 patients were used for external validation (41±12 years; 13 males). DSC segmentation accuracy of the DCNN was similarly high between the internal (0.97±0.01) and external (0.96±0.03) test sets (p=0.28) and demonstrated robust segmentation performance on public testing (0.93±0.03). Agreement of liver volumetry was satisfactory in the internal (ICC, 0.99), external (ICC, 0.97), and public (ICC, 0.85) test sets. Radiomic features demonstrated excellent agreement in the internal (mean ICC, 0.98±0.04), external (mean ICC, 0.94±0.10), and public (mean ICC, 0.91±0.09) datasets. CONCLUSION: Automated liver segmentation yields robust and generalizable segmentation performance on MRI data and can be used for volumetry and radiomic feature extraction. CLINICAL RELEVANCE STATEMENT: Liver volumetry, anatomic localization, and extraction of quantitative imaging biomarkers require accurate segmentation, but manual segmentation is time-consuming. A deep convolutional neural network demonstrates fast and accurate segmentation performance on T1-weighted portal venous MRI. KEY POINTS: • This deep convolutional neural network yields robust and generalizable liver segmentation performance on internal, external, and public testing data. • Automated liver volumetry demonstrated excellent agreement with manual volumetry. • Automated liver segmentations can be used for robust and reproducible radiomic feature extraction.

Combination of computed tomography angiography with coronary artery calcium score for improved diagnosis of coronary artery disease: a collaborative meta-analysis of stable chest pain patients referred for invasive coronary angiography.

OBJECTIVES: Coronary computed tomography angiography (CCTA) has higher diagnostic accuracy than coronary artery calcium (CAC) score for detecting obstructive coronary artery disease (CAD) in patients with stable chest pain, while the added diagnostic value of combining CCTA with CAC is unknown. We investigated whether combining coronary CCTA with CAC score can improve the diagnosis of obstructive CAD compared with CCTA alone. METHODS: A total of 2315 patients (858 women, 37%) aged 61.1 ± 10.2 from 29 original studies were included to build two CAD prediction models based on either CCTA alone or CCTA combined with the CAC score. CAD was defined as at least 50% coronary diameter stenosis on invasive coronary angiography. Models were built by using generalized linear mixed-effects models with a random intercept set for the original study. The two CAD prediction models were compared by the likelihood ratio test, while their diagnostic performance was compared using the area under the receiver-operating-characteristic curve (AUC). Net benefit (benefit of true positive versus harm of false positive) was assessed by decision curve analysis. RESULTS: CAD prevalence was 43.5% (1007/2315). Combining CCTA with CAC improved CAD diagnosis compared with CCTA alone (AUC: 87% [95% CI: 86 to 89%] vs. 80% [95% CI: 78 to 82%]; p < 0.001), likelihood ratio test 236.3, df: 1, p < 0.001, showing a higher net benefit across almost all threshold probabilities. CONCLUSION: Adding the CAC score to CCTA findings in patients with stable chest pain improves the diagnostic performance in detecting CAD and the net benefit compared with CCTA alone. CLINICAL RELEVANCE STATEMENT: CAC scoring CT performed before coronary CTA and included in the diagnostic model can improve obstructive CAD diagnosis, especially when CCTA is non-diagnostic. KEY POINTS: • The combination of coronary artery calcium with coronary computed tomography angiography showed significantly higher AUC (87%, 95% confidence interval [CI]: 86 to 89%) for diagnosis of coronary artery disease compared to coronary computed tomography angiography alone (80%, 95% CI: 78 to 82%, p < 0.001). • Diagnostic improvement was mostly seen in patients with non-diagnostic C. • The improvement in diagnostic performance and the net benefit was consistent across age groups, chest pain types, and genders.

Differentiating Macrovascular and Microvascular Ischemia Using Fractal Analysis of Dynamic Myocardial Perfusion Stress-CT.

OBJECTIVES: Fractal analysis of dynamic myocardial stress computed tomography perfusion imaging (4D-CTP) has shown potential to noninvasively differentiate obstructive coronary artery disease (CAD) and coronary microvascular disease (CMD). This study validates fractal analysis of 4D-CTP in a multicenter setting and assesses its diagnostic accuracy in subgroups with ischemia and nonobstructed coronary arteries (INOCA) and with mild to moderate stenosis. MATERIALS AND METHODS: From the AMPLIFiED multicenter trial, patients with suspected or known chronic myocardial ischemia and an indication for invasive coronary angiography were included. Patients underwent dual-source CT angiography, 4D-CTP, and CT delayed-enhancement imaging. Coronary artery disease, CMD, and normal perfusion were defined by a combined reference standard comprising invasive coronary angiography with fractional flow reserve, and absolute or relative CT-derived myocardial blood flow. Nonobstructed coronary arteries were defined as ≤25% stenosis and mild to moderate stenosis as 26%-80%. RESULTS: In 127 patients (27% female), fractal analysis accurately differentiated CAD (n = 61, 23% female), CMD (n = 23, 30% female), and normal perfusion (n = 34, 35% female) with a multiclass area under the receiver operating characteristic curve (AUC) of 0.92 and high agreement (multiclass κ = 0.89). In patients with ischemia (n = 84), fractal analysis detected CAD (n = 61) over CMD (n = 23) with sensitivity of 95%, specificity of 74%, accuracy of 89%, and AUC of 0.83. In patients with nonobstructed coronary arteries (n = 33), INOCA (n = 15) was detected with sensitivity of 100%, specificity of 78%, accuracy of 88%, and AUC of 0.94. In patients with mild to moderate stenosis (n = 27), fractal analysis detected CAD (n = 19) over CMD with sensitivity of 84%, specificity of 100%, accuracy of 89%, and AUC of 0.95. CONCLUSIONS: In this multicenter study, fractal analysis of 4D-CTP accurately differentiated CAD and CMD including subgroups with INOCA and with mild to moderate stenosis.

Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries.

Artificial intelligence (AI) is likely to revolutionize the way medical images are analysed and has the potential to improve the identification and analysis of vulnerable or high-risk atherosclerotic plaques in coronary arteries, leading to advances in the treatment of coronary artery disease. However, coronary plaque analysis is challenging owing to cardiac and respiratory motion, as well as the small size of cardiovascular structures. Moreover, the analysis of coronary imaging data is time-consuming, can be performed only by clinicians with dedicated cardiovascular imaging training, and is subject to considerable interreader and intrareader variability. AI has the potential to improve the assessment of images of vulnerable plaque in coronary arteries, but requires robust development, testing and validation. Combining human expertise with AI might facilitate the reliable and valid interpretation of images obtained using CT, MRI, PET, intravascular ultrasonography and optical coherence tomography. In this Roadmap, we review existing evidence on the application of AI to the imaging of vulnerable plaque in coronary arteries and provide consensus recommendations developed by an interdisciplinary group of experts on AI and non-invasive and invasive coronary imaging. We also outline future requirements of AI technology to address bias, uncertainty, explainability and generalizability, which are all essential for the acceptance of AI and its clinical utility in handling the anticipated growing volume of coronary imaging procedures.

Final-year medical students' perspective: a survey on the use of computed tomography in sepsis.

OBJECTIVES: To determine the perspective of final-year medical students on the use of computed tomography (CT) in patients with sepsis. METHODS: A total of 207 questionnaires were distributed to final-year medical students at a large university medical center, and 113 returned questionnaires met the criteria for inclusion in the analysis. Questions referred to sepsis guidelines, CT indications, and the use of contrast agents. Control variables included a level of practical experience as a final-year student (trimester of student's practical year) and previous radiological experience. Statistical hypothesis tests such as the Mann-Whitney U test and chi-square test were performed. RESULTS: The majority of participating students, 85% (n = 91/107), considered a Systemic Organ Failure Assessment (SOFA) score ≥ 2 as a diagnostic criterion for sepsis. The presence of ≥ 2 positive systemic inflammatory response syndrome (SIRS) criteria was considered relevant for diagnosing sepsis by 34% (n = 34/100). Ninety-nine percent (n = 64/65) of the participants who fully agreed with a SOFA score ≥ 2 being relevant for diagnosing sepsis would also use it as an indication for a CT scan. Seventy-six percent (n = 78/103) of the students rated a known severe allergic reaction to contrast agents as an absolute contraindication for its administration. Ninety-five percent (n = 78/82) considered radiation exposure as problematic in CT examinations, especially in repeat CTs. CONCLUSION: Most final-year medical students were familiar with the sepsis criteria. Still, some referred to outdated diagnostic criteria. Participants saw the ability to plan further patient management based on CT as a major benefit. Most participants were aware of radiation as a risk of CT. CRITICAL RELEVANCE STATEMENT: More detailed knowledge of CT in septic patients should be implemented in the medical curriculum. Retraining of medical students could help increase student confidence potentially improving patient care. KEY POINTS: 1. Whereas the majority of final-year medical students were familiar with sepsis criteria, some referred to outdated diagnostic criteria. 2. Participants saw the ability to plan further patient management based on CT as a major benefit. 3. Most participants were aware of radiation as a risk of CT.

Coronary artery disease grading by cardiac CT for predicting outcome in patients with stable angina.

BACKGROUND: The coronary atheroma burden drives major adverse cardiovascular events (MACE) in patients with suspected coronary heart disease (CHD). However, a consensus on how to grade disease burden for effective risk stratification is lacking. The purpose of this study was to compare the effectiveness of common CHD grading tools to risk stratify symptomatic patients. METHODS: We analyzed the 5-year outcome of 381 prospectively enrolled patients in the CORE320 international, multicenter study using baseline clinical and cardiac computer-tomography (CT) imaging characteristics, including coronary artery calcium score (CACS), percent atheroma volume, "high-risk" plaque, disease severity grading using the CAD-RADS, and two simplified CAD staging systems. We applied Cox proportional hazard models and area under the curve (AUC) analysis to predict MACE or hard MACE, defined as death, myocardial infarction, or stroke. Analyses were stratified by a history of CHD. Additional forward selection analysis was performed to evaluate incremental value of metrics. RESULTS: Clinical characteristics were the strongest predictors of MACE in the overall cohort. In patients without history of CHD, CACS remained the only independent predictor of MACE yielding an AUC of 73 (CI 67-79) vs. 64 (CI 57-70) for clinical characteristics. Noncalcified plaque volume did not add prognostic value. Simple CHD grading schemes yielded similar risk stratification as the CAD-RADS classification. Forward selection analysis confirmed prominent role of CACS and revealed usefulness of functional testing in subgroup with known CHD. CONCLUSION: In patients referred for invasive angiography, a history of CHD was the strongest predictor of MACE. In patients without history of CHD, a coronary calcium score yielded at least equal risk stratification vs. more complex CHD grading.

Interdisciplinary perspectives on computed tomography in sepsis: survey among medical doctors at a large university medical center.

OBJECTIVES: This study aims to describe physicians' perspectives on the use of computed tomography (CT) in patients with sepsis. METHODS: In January 2022, physicians of a large European university medical center were surveyed using a web-based questionnaire asking about their views on the role of CT in sepsis. A total of 371 questionnaires met the inclusion criteria and were analyzed using work experience, workplace, and medical specialty of physicians as variables. Chi-square tests were performed. RESULTS: Physicians considered the ability to detect an unknown focus as the greatest benefit of CT scans in sepsis (70.9%, n = 263/371). Two clinical criteria - "signs of decreased vigilance" (89.2%, n = 331/371) and "increased catecholamine demand" (84.7%, n = 314/371) - were considered highly relevant for a CT request. Elevated procalcitonin (82.7%, n = 307/371) and lactate levels (83.6%, n = 310/371) were consistently found to be critical laboratory values to request a CT. As long as there is evidence of infection in one organ region, most physicians (42.6%, n = 158/371) would order a CT scan based on clinical assessment. Combined examination of the chest, abdomen, and pelvis was favored (34.8%, n = 129/371) in cases without clinical clues of an infection source. A time window of ≥ 1-6 h was preferred for both CT examinations (53.9%, n = 200/371) and CT-guided interventions (59.3%, n = 220/371) in patients with sepsis. CONCLUSION: Despite much consensus, there are significant differences in attitudes towards the use of CT in septic patients among physicians from different workplaces and medical specialties. Knowledge of these perspectives may improve patient management and interprofessional communication. KEY POINTS: Despite interdisciplinary consensus on the use of CT in sepsis, statistically significant differences in the responses are apparent among physicians from different workplaces and medical specialties. The detection of a previously unknown source of infection and the ability to plan interventions and/or surgery based on CT findings are considered key advantages of CT in septic patients. Timing of CT reflects the requirements of specific disciplines.

Clinical quantitative coronary artery stenosis and coronary atherosclerosis imaging: a Consensus Statement from the Quantitative Cardiovascular Imaging Study Group.

The detection and characterization of coronary artery stenosis and atherosclerosis using imaging tools are key for clinical decision-making in patients with known or suspected coronary artery disease. In this regard, imaging-based quantification can be improved by choosing the most appropriate imaging modality for diagnosis, treatment and procedural planning. In this Consensus Statement, we provide clinical consensus recommendations on the optimal use of different imaging techniques in various patient populations and describe the advances in imaging technology. Clinical consensus recommendations on the appropriateness of each imaging technique for direct coronary artery visualization were derived through a three-step, real-time Delphi process that took place before, during and after the Second International Quantitative Cardiovascular Imaging Meeting in September 2022. According to the Delphi survey answers, CT is the method of choice to rule out obstructive stenosis in patients with an intermediate pre-test probability of coronary artery disease and enables quantitative assessment of coronary plaque with respect to dimensions, composition, location and related risk of future cardiovascular events, whereas MRI facilitates the visualization of coronary plaque and can be used in experienced centres as a radiation-free, second-line option for non-invasive coronary angiography. PET has the greatest potential for quantifying inflammation in coronary plaque but SPECT currently has a limited role in clinical coronary artery stenosis and atherosclerosis imaging. Invasive coronary angiography is the reference standard for stenosis assessment but cannot characterize coronary plaques. Finally, intravascular ultrasonography and optical coherence tomography are the most important invasive imaging modalities for the identification of plaques at high risk of rupture. The recommendations made in this Consensus Statement will help clinicians to choose the most appropriate imaging modality on the basis of the specific clinical scenario, individual patient characteristics and the availability of each imaging modality.

Has the quality of reporting improved since it became mandatory to use the Standards for Reporting Diagnostic Accuracy?

OBJECTIVES: To investigate whether making the Standards for Reporting Diagnostic Accuracy (STARD) mandatory by the leading journal 'Radiology' in 2016 improved the quality of reporting of diagnostic accuracy studies. METHODS: A validated search term was used to identify diagnostic accuracy studies published in Radiology in 2015 and 2019. STARD adherence was assessed by two independent reviewers. Each item was scored as yes (1 point) if adequately reported or as no (0 points) if not. The total STARD score per article was calculated. Wilcoxon-Mann-Whitney tests were used to evaluate differences of the total STARD scores between 2015 and 2019. In addition, the total STARD score was compared between studies stratified by study design, citation rate, and data collection. RESULTS: The median number of reported STARD items for the total of 66 diagnostic accuracy studies from 2015 to 2019 was 18.5 (interquartile range [IQR] 17.5-20.0) of 29. Adherence to the STARD checklist significantly improved the STARD score from a median of 18.0 (IQR 15.5-19.5) in 2015 to a median of 19.5 (IQR 18.5-21.5) in 2019 (p < 0.001). No significant differences were found between studies stratified by mode of data collection (prospective vs. retrospective studies, p = 0.68), study design (cohort vs. case-control studies, p = 0.81), and citation rate (two groups divided by median split [< 0.56 citations/month vs. ≥ 0.56 citations/month], p = 0.54). CONCLUSIONS: Making use of the STARD checklist mandatory significantly increased the adherence with reporting standards for diagnostic accuracy studies and should be considered by editors and publishers for widespread implementation. CRITICAL RELEVANCE STATEMENT: Editors may consider making reporting guidelines mandatory to improve the scientific quality.

Has the STARD statement improved the quality of reporting of diagnostic accuracy studies published in European Radiology?

OBJECTIVES: To investigate whether encouraging authors to follow the Standards for Reporting Diagnostic Accuracy (STARD) guidelines improves the quality of reporting of diagnostic accuracy studies. METHODS: In mid-2017, European Radiology started encouraging its authors to follow the STARD guidelines. Our MEDLINE search identified 114 diagnostic accuracy studies published in European Radiology in 2015 and 2019. The quality of reporting was evaluated by two independent reviewers using the revised STARD statement. Item 11 was excluded because a meaningful decision about adherence was not possible. Student's t test for independent samples was used to analyze differences in the mean number of reported STARD items between studies published in 2015 and in 2019. In addition, we calculated differences related to the study design, data collection, and citation rate. RESULTS: The mean total number of reported STARD items for all 114 diagnostic accuracy studies analyzed was 15.9 ± 2.6 (54.8%) of 29 items (range 9.5-22.5). The quality of reporting of diagnostic accuracy studies was significantly better in 2019 (mean ± standard deviation (SD), 16.3 ± 2.7) than in 2015 (mean ± SD, 15.1 ± 2.3; p < 0.02). No significant differences in the reported STARD items were identified in relation to study design (p = 0.13), data collection (p = 0.87), and citation rate (p = 0.09). CONCLUSION: The quality of reporting of diagnostic accuracy studies according to the STARD statement was moderate with a slight improvement since European Radiology started to recommend its authors to follow the STARD guidelines. KEY POINTS: • The quality of reporting of diagnostic accuracy studies was moderate with a mean total number of reported STARD items of 15.9 ± 2.6. • The adherence to STARD was significantly better in 2019 than in 2015 (16.3 ± 2.7 vs. 15.1 ± 2.3; p = 0.016). • No significant differences in the reported STARD items were identified in relation to study design (p = 0.13), data collection (p = 0.87), and citation rate (p = 0.09).

State-of-the-art CT and MR imaging and assessment of atherosclerotic carotid artery disease: the reporting-a consensus document by the European Society of Cardiovascular Radiology (ESCR).

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. KEY POINTS: • CT and MR imaging-based evaluation of carotid artery disease provides essential information for risk stratification and prediction of stroke. • The information in the report must cover vessel morphology, description of stenosis, and plaque imaging features. • A structured approach to reporting ensures that all essential information is delivered in a standardized and consistent way to the referring clinician.