Automated Comprehensive CT Assessment of the Risk of Diabetes and Associated Cardiometabolic Conditions.

Background CT performed for various clinical indications has the potential to predict cardiometabolic diseases. However, the predictive ability of individual CT parameters remains underexplored. Purpose To evaluate the ability of automated CT-derived markers to predict diabetes and associated cardiometabolic comorbidities. Materials and Methods This retrospective study included Korean adults (age ≥ 25 years) who underwent health screening with fluorine 18 fluorodeoxyglucose PET/CT between January 2012 and December 2015. Fully automated CT markers included visceral and subcutaneous fat, muscle, bone density, liver fat, all normalized to height (in meters squared), and aortic calcification. Predictive performance was assessed with area under the receiver operating characteristic curve (AUC) and Harrell C-index in the cross-sectional and survival analyses, respectively. Results The cross-sectional and cohort analyses included 32166 (mean age, 45 years ± 6 [SD], 28833 men) and 27 298 adults (mean age, 44 years ± 5 [SD], 24 820 men), respectively. Diabetes prevalence and incidence was 6% at baseline and 9% during the 7.3-year median follow-up, respectively. Visceral fat index showed the highest predictive performance for prevalent and incident diabetes, yielding AUC of 0.70 (95% CI: 0.68, 0.71) for men and 0.82 (95% CI: 0.78, 0.85) for women and C-index of 0.68 (95% CI: 0.67, 0.69) for men and 0.82 (95% CI: 0.77, 0.86) for women, respectively. Combining visceral fat, muscle area, liver fat fraction, and aortic calcification improved predictive performance, yielding C-indexes of 0.69 (95% CI: 0.68, 0.71) for men and 0.83 (95% CI: 0.78, 0.87) for women. The AUC for visceral fat index in identifying metabolic syndrome was 0.81 (95% CI: 0.80, 0.81) for men and 0.90 (95% CI: 0.88, 0.91) for women. CT-derived markers also identified US-diagnosed fatty liver, coronary artery calcium scores greater than 100, sarcopenia, and osteoporosis, with AUCs ranging from 0.80 to 0.95. Conclusion Automated multiorgan CT analysis identified individuals at high risk of diabetes and other cardiometabolic comorbidities. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Pickhardt in this issue.

Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics.

BACKGROUND: Timely prevention of major adverse cardiovascular events (MACEs) is imperative for reducing cardiovascular diseases-related mortality. Perivascular adipose tissue (PVAT), the adipose tissue surrounding coronary arteries, has attracted increased amounts of attention. Developing a model for predicting the incidence of MACE utilizing machine learning (ML) integrating clinical and PVAT features may facilitate targeted preventive interventions and improve patient outcomes. METHODS: From January 2017 to December 2019, we analyzed a cohort of 1077 individuals who underwent coronary CT scanning at our facility. Clinical features were collected alongside imaging features, such as coronary artery calcium (CAC) scores and perivascular adipose tissue (PVAT) characteristics. Logistic regression (LR), Framingham Risk Score, and ML algorithms were employed for MACE prediction. RESULTS: We screened seven critical features to improve the practicability of the model. MACE patients tended to be older, smokers, and hypertensive. Imaging biomarkers such as CAC scores and PVAT characteristics differed significantly between patients with and without a 3-year MACE risk in a population that did not exhibit disparities in laboratory results. The ensemble model, which leverages multiple ML algorithms, demonstrated superior predictive performance compared with the other models. Finally, the ensemble model was used for risk stratification prediction to explore its clinical application value. CONCLUSIONS: The developed ensemble model effectively predicted MACE incidence based on clinical and imaging features, highlighting the potential of ML algorithms in cardiovascular risk prediction and personalized medicine. Early identification of high-risk patients may facilitate targeted preventive interventions and improve patient outcomes.

[4D flow MRI: value and clinical perspectives in patients with pathology of the heart and great vessels (part 2): A review].

The study of blood flow is becoming a new trend in cardiology and cardiovascular surgery. Based on the literature and our own data, a review is presented on the use of 4D flow in diseases of the heart and blood vessels. The main state of the question about the features of the application of the technique in various pathologies of the cardiovascular system is described in detail, the priorities, limitations and promising directions of the technique application are considered taking into account the goals of practical medicine. The review consists of two parts. The first is devoted to general issues, limitations of the technique, and issues of 4D flow mapping in patients with lesions of the great vessels. In the second part, the emphasis is on the use of 4D flow MRI in the study of intraventricular blood flow and the application of the technique in congenital heart and vascular diseases.

Ultrasound enhancing agents in cardiovascular imaging: expanding horizons beyond coronary arteries.

From its inception as a two-dimensional snapshot of the beating heart, echocardiography has become an indelible part of cardiovascular diagnostics. The integration of ultrasound enhancing agents (UEAs) marks a pivotal transition, enhancing its diagnostic acumen beyond myocardial perfusion. These agents have refined echocardiography's capacity to visualize complex cardiac anatomy and pathology with unprecedented clarity, especially in non-coronary artery disease contexts. UEAs aid in detailed assessments of myocardial viability, endocardial border delineation in left ventricular opacification, and identification of intracardiac masses. Recent innovations in UEAs, accompanied by advancements in echocardiographic technology, offer clinicians a more nuanced view of cardiac function and blood flow dynamics. This review explores recent developments in these applications and future contemplated studies.

Myocardial Strain Measured by Cardiac Magnetic Resonance Predicts Cardiovascular Morbidity and Death.

BACKGROUND: Myocardial strain using cardiac magnetic resonance (CMR) is a sensitive marker for predicting adverse outcomes in many cardiac disease states, but the prognostic value in the general population has not been studied conclusively. OBJECTIVES: The goal of this study was to assess the independent prognostic value of CMR feature tracking (FT)-derived LV global longitudinal (GLS), circumferential (GCS), and radial strain (GRS) metrics in predicting adverse outcomes (heart failure, myocardial infarction, stroke, and death). METHODS: Participants from the UK Biobank population imaging study were included. Univariable and multivariable Cox models were used for each outcome and each strain marker (GLS, GCS, GRS) separately. The multivariable models were tested with adjustment for prognostically important clinical features and conventional global LV imaging markers relevant for each outcome. RESULTS: Overall, 45,700 participants were included in the study (average age 65 ± 8 years), with a median follow-up period of 3 years. All univariable and multivariable models demonstrated that lower absolute GLS, GCS, and GRS were associated with increased incidence of heart failure, myocardial infarction, stroke, and death. All strain markers were independent predictors (incrementally above some respective conventional LV imaging markers) for the morbidity outcomes, but only GLS predicted death independently: (HR: 1.18; 95% CI: 1.07-1.30). CONCLUSIONS: In the general population, LV strain metrics derived using CMR-FT in radial, circumferential, and longitudinal directions are strongly and independently predictive of heart failure, myocardial infarction, and stroke, but only GLS is independently predictive of death in an adult population cohort.

Multisystem Imaging Manifestations of Kidney Failure.

Kidney failure (KF) refers to a progressive decline in glomerular filtration rate to below 15 ml/min per 1.73 m2, necessitating renal replacement therapy with dialysis or renal transplant. The hemodynamic and metabolic alterations in KF combined with a proinflammatory and coagulopathic state leads to complex multisystemic complications. The imaging hallmark of systemic manifestations of KF is bone resorption caused by secondary hyperparathyroidism. Other musculoskeletal complications include brown tumor, osteosclerosis, calcinosis, soft-tissue calcification, and amyloid arthropathy. Cardiovascular complications and infections are the leading causes of death in KF. Cardiovascular complications include accelerated atherosclerosis, cardiomyopathy, pericarditis, myocardial calcinosis, and venous thromboembolism. Neurologic complications such as encephalopathy, osmotic demyelination, cerebrovascular disease, and opportunistic infections are also frequently encountered. Pulmonary complications include edema and calcifications. Radiography and CT are used in assessing musculoskeletal and thoracic complications, while MRI plays a key role in assessing neurologic and cardiovascular complications. CT iodinated contrast material is generally avoided in patients with KF except in situations where the benefit of contrast-enhanced CT outweighs the risks and in patients already undergoing maintenance dialysis. At MRI, group II gadolinium-based contrast material can be safely administered in patients with KF. The authors discuss the extrarenal systemic manifestations of KF, the choice of imaging modality in their assessment, and imaging findings of complications. ©RSNA, 2024 Supplemental material is available for this article.

Cardiovascular Imaging, Climate Change, and Environmental Sustainability.

Environmental exposures including poor air quality and extreme temperatures are exacerbated by climate change and are associated with adverse cardiovascular outcomes. Concomitantly, the delivery of health care generates substantial atmospheric greenhouse gas (GHG) emissions contributing to the climate crisis. Therefore, cardiac imaging teams must be aware not only of the adverse cardiovascular health effects of climate change, but also the downstream environmental ramifications of cardiovascular imaging. The purpose of this review is to highlight the impact of climate change on cardiovascular health, discuss the environmental impact of cardiovascular imaging, and describe opportunities to improve environmental sustainability of cardiac MRI, cardiac CT, echocardiography, cardiac nuclear imaging, and invasive cardiovascular imaging. Overarching strategies to improve environmental sustainability in cardiovascular imaging include prioritizing imaging tests with lower GHG emissions when more than one test is appropriate, reducing low-value imaging, and turning equipment off when not in use. Modality-specific opportunities include focused MRI protocols and low-field-strength applications, iodine contrast media recycling programs in cardiac CT, judicious use of US-enhancing agents in echocardiography, improved radiopharmaceutical procurement and waste management in nuclear cardiology, and use of reusable supplies in interventional suites. Finally, future directions and research are highlighted, including life cycle assessments over the lifespan of cardiac imaging equipment and the impact of artificial intelligence tools. Keywords: Heart, Safety, Sustainability, Cardiovascular Imaging Supplemental material is available for this article. © RSNA, 2024.

MRI-based adrenal gland volume is associated with cardiovascular alterations in individuals without prior cardiovascular disease.

Aim of this study was to analyse the associations of cardiovascular health and adrenal gland volume as a rather new imaging biomarker of chronic hypothalamic-pituitary-adrenal (HPA) axis activation. The study population originates from the KORA population-based cross-sectional prospective cohort. 400 participants without known cardiovascular disease underwent a whole-body MRI. Manual segmentation of adrenal glands was performed on VIBE-Dixon gradient-echo sequence. MRI based evaluation of cardiac parameters was achieved semi-automatically. Cardiometabolic risk factors were obtained through standardized interviews and medical examination. Univariate and multivariate associations were derived. Bi-directional causal mediation analysis was performed. 351 participants were eligible for analysis (56 ± 9.1 years, male 58.7%). In multivariate analysis, significant associations were observed between adrenal gland volume and hypertension (outcome hypertension: Odds Ratio = 1.11, 95% CI [1.01, 1.21], p = 0.028), left ventricular remodelling index (LVRI) (outcome LVRI: ß = 0.01, 95% CI [0.00, 0.02], p = 0.011), and left ventricular (LV) wall thickness (outcome LV wall thickness: ß = 0.06, 95% CI [0.02, 0.09], p = 0.005). In bi-directional causal mediation analysis adrenal gland volume had a borderline significant mediating effect on the association between hypertension and LVRI (p = 0.052) as well as wall thickness (p = 0.054). MRI-based assessment of adrenal gland enlargement is associated with hypertension and LV remodelling. Adrenal gland volume may serve as an indirect cardiovascular imaging biomarker.

Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging.

Cardiovascular diseases remain a significant health burden, with imaging modalities like echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging playing a crucial role in diagnosis and prognosis. However, the inherent heterogeneity of these diseases poses challenges, necessitating advanced analytical methods like radiomics and artificial intelligence. Radiomics extracts quantitative features from medical images, capturing intricate patterns and subtle variations that may elude visual inspection. Artificial intelligence techniques, including deep learning, can analyze these features to generate knowledge, define novel imaging biomarkers, and support diagnostic decision-making and outcome prediction. Radiomics and artificial intelligence thus hold promise for significantly enhancing diagnostic and prognostic capabilities in cardiac imaging, paving the way for more personalized and effective patient care. This review explores the synergies between radiomics and artificial intelligence in cardiac imaging, following the radiomics workflow and introducing concepts from both domains. Potential clinical applications, challenges, and limitations are discussed, along with solutions to overcome them.

Association of Cardiovascular Health With Brain Age Estimated Using Machine Learning Methods in Middle-Aged and Older Adults.

BACKGROUND AND OBJECTIVES: Cardiovascular health (CVH) has been associated with cognitive decline and dementia, but the extent to which CVH affects brain health remains unclear. We investigated the association of CVH, assessed using Life's Essential 8 (LE8), with neuroimaging-based brain age and brain-predicted age difference (brain-PAD). METHODS: This longitudinal community-based study was based on UK Biobank participants aged 40-69 years who were free from dementia and other neurologic diseases at baseline. LE8 score at baseline was assessed with 8 measures and tertiled as low, moderate, and high CVH. Structural and functional brain MRI scans were performed approximately 9 years after baseline, and 1,079 measures from 6 neuroimaging modalities were used to model brain age. A Least Absolute Shrinkage and Selection Operator regression model was trained in 4,355 healthy participants and then used to calculate brain age and brain-PAD in the whole population. Data were analyzed using linear regression models. RESULTS: The study included 32,646 participants (mean age at baseline 54.74 years; 53.44% female; mean LE8 score: 71.90). In multivariable-adjusted linear regression, higher LE8 score was associated with younger brain age (ß [95% CI] -0.037 [-0.043 to -0.031]) and more negative brain-PAD (ß [95% CI] -0.043 [-0.048 to -0.038]) (brain looks younger for chronological age). Compared with high CVH, low/moderate CVH was associated with older brain age (ß [95% CI] 1.030 [0.852-1.208]/0.475 [0.303-0.647]) and increased brain-PAD (ß [95% CI] 1.193 [1.029-1.357]/0.528 [0.370-0.686]). The associations between low CVH and older brain age/brain-PAD remained similar and significant in both middle-aged (ß [95% CI] 1.199 [0.992-1.405]/1.351 [1.159-1.542]) and older adults (ß [95% CI] 0.764 [0.417-1.110]/0.948 [0.632-1.263]). DISCUSSION: Low CVH is associated with older brain age and greater brain-PAD, even among middle-aged adults. Our findings suggest that optimizing CVH could support brain health. The main limitation of our study is that the study sample was healthier than the general population, thus caution is required when generalizing our findings to other populations.

The role of echocardiography in sports cardiology: An expert opinion statement of the Italian Society of Sports Cardiology (SIC sport).

Transthoracic echocardiography (TTE) is routinely required during pre-participation screening in the presence of symptoms, family history of sudden cardiac death or cardiomyopathies <40-year-old, murmurs, abnormal ECG findings or in the follow-up of athletes with a history of cardiovascular disease (CVD). TTE is a cost-effective first-line imaging modality to evaluate the cardiac remodeling due to long-term, intense training, previously known as the athlete's heart, and to rule out the presence of conditions at risk of sudden cardiac death, including cardiomyopathies, coronary artery anomalies, congenital, aortic and heart valve diseases. Moreover, TTE is useful for distinguishing physiological cardiac adaptations during intense exercise from pathological behavior due to an underlying CVD. In this expert opinion statement endorsed by the Italian Society of Sports Cardiology, we discussed common clinical scenarios where a TTE is required and conditions falling in the grey zone between the athlete's heart and underlying cardiomyopathies or other CVD. In addition, we propose a minimum dataset that should be included in the report for the most common indications of TTE in sports cardiology clinical practice.

Trial of Cardiovascular Risk Factor Assessment and Transthoracic Echocardiography Results in Patients with Primary Antibody Deficiency.

The life expectancy and the risk of developing cardiovascular diseases in patients with inborn errors of immunity are systematically increasing. The aim of the study was to assess cardiovascular risk factors and to evaluate the heart in echocardiography in patients with primary antibody deficiency (PAD). Cardiac echography and selected cardiovascular risk factors, including body mass index, sedentary lifestyle, nicotine, glucose, C-reactive protein, lipid profile, uric acid level, certain chronic diseases, and glucocorticoid use, were analyzed in 94 patients >18 years of age with PAD. Of the patients,25.5% had a cardiovascular disease (mostly hypertension, 18%), 10.5% smoked, 17% were overweight, 14% were obese, and 15% were underweight. Abnormal blood pressure was found in 6.5% of the patients. Lipid metabolism disorders were found in 72.5% of in the studied cohort, increased total cholesterol (45.5%), non-high-density lipoprotein (HDL) (51%), low-density lipoprotein (LDL) (47%), and triglycerides (32%) were observed. Furthermore, 28.5% had a decrease in HDL and 9.5% had a history of hyperuricemia. The average number of risk factors was 5 ± 3 for the entire population and 4 ± 2 for those under 40 years of age. Elevated uric acid levels were found de novo in 4% of participants. In particular, 74.5% of the patients had never undergone an echocardiogram with a successful completion rate of 87% among those tested. Among them, 30% showed parameters within normal limits, primarily regurgitation (92.5%). New pathologies were identified in 28% of patients. Prevention in patients with PAD, aimed at reducing cardiovascular risk, should be a priority.

The beating heart: artificial intelligence for cardiovascular application in the clinic.

Artificial intelligence (AI) integration in cardiac magnetic resonance imaging presents new and exciting avenues for advancing patient care, automating post-processing tasks, and enhancing diagnostic precision and outcomes. The use of AI significantly streamlines the examination workflow through the reduction of acquisition and postprocessing durations, coupled with the automation of scan planning and acquisition parameters selection. This has led to a notable improvement in examination workflow efficiency, a reduction in operator variability, and an enhancement in overall image quality. Importantly, AI unlocks new possibilities to achieve spatial resolutions that were previously unattainable in patients. Furthermore, the potential for low-dose and contrast-agent-free imaging represents a stride toward safer and more patient-friendly diagnostic procedures. Beyond these benefits, AI facilitates precise risk stratification and prognosis evaluation by adeptly analysing extensive datasets. This comprehensive review article explores recent applications of AI in the realm of cardiac magnetic resonance imaging, offering insights into its transformative potential in the field.

Optimizing cardiovascular image segmentation through integrated hierarchical features and attention mechanisms.

BACKGROUND: Cardiovascular diseases are the top cause of death in China. Manual segmentation of cardiovascular images, prone to errors, demands an automated, rapid, and precise solution for clinical diagnosis. OBJECTIVE: The paper highlights deep learning in automatic cardiovascular image segmentation, efficiently identifying pixel regions of interest for auxiliary diagnosis and research in cardiovascular diseases. METHODS: In our study, we introduce innovative Region Weighted Fusion (RWF) and Shape Feature Refinement (SFR) modules, utilizing polarized self-attention for significant performance improvement in multiscale feature integration and shape fine-tuning. The RWF module includes reshaping, weight computation, and feature fusion, enhancing high-resolution attention computation and reducing information loss. Model optimization through loss functions offers a more reliable solution for cardiovascular medical image processing. RESULTS: Our method excels in segmentation accuracy, emphasizing the vital role of the RWF module. It demonstrates outstanding performance in cardiovascular image segmentation, potentially raising clinical practice standards. CONCLUSIONS: Our method ensures reliable medical image processing, guiding cardiovascular segmentation for future advancements in practical healthcare and contributing scientifically to enhanced disease diagnosis and treatment.