Cardiac imaging to detect coronary artery disease in athletes aged 35 years and older. A scoping review.

Sudden cardiac death (SCD) is a devastating event in athletes. Screening efforts that were first directed at athletes younger than 35 years, are now focusing on the rapidly growing group of older sportspersons. Athletes aged ≥35 years have a 10-fold increased risk of exercise-related cardiac arrest, mostly due to coronary artery disease (CAD). Although cardiac imaging is pivotal in identifying CAD, the role of imaging modalities in screening asymptomatic older sportspersons remains unclear. We performed a scoping review to identify the role of cardiac imaging to detect CAD in older sportspersons and to identify gaps in the existing literature. We searched Medline, Embase and the Cochrane library for studies reporting data on cardiac imaging of CAD in sportspersons ≥35 years. The systematic search yielded 1737 articles and 14 were included in this scoping review. Imaging modalities included 2 echocardiography, 1 unenhanced Computed Tomography (CT) for coronary artery calcium scoring (CACS), 3 CACS and contrast-enhanced CT angiography (CCTA), 2 CACS and Cardiac Magnetic Resonance (CMR), 1 CCTA with CMR and echocardiography, 2 CCTA, 2 CMR, and 1 myocardial perfusion imaging article. The low number of relevant articles and the selection bias introduced by studying specific groups, like veteran marathon runners, indicate the need for future research. Cardiac CT (CACS and CCTA) probably has the highest potential for pre-participation screening, with high diagnostic value to detect CAD and low radiation dose. However, currently there is insufficient evidence for incorporating routine cardiac imaging in the pre-participation screening of asymptomatic sportspersons over 35 years

Prognostic value of heart valve calcifications for cardiovascular events in a lung cancer screening population

To assess the prognostic value of aortic valve and mitral valve/annulus calcifications for cardiovascular events in heavily smoking men without a history of cardiovascular disease. Heavily smoking men without a cardiovascular disease history who underwent non-contrast-enhanced low-radiation-dose chest CT for lung cancer screening were included. Non-imaging predictors (age, smoking status and pack-years) were collected and imaging-predictors (calcium volume of the coronary arteries, aorta, aortic valve and mitral valve/annulus) were obtained. The outcome was the occurrence of cardiovascular events. Multivariable Cox proportional-hazards regression was used to calculate hazard-ratios (HRs) with 95 % confidence interval (CI). Subsequently, concordance-statistics were calculated. In total 3111 individuals were included, of whom 186 (6.0 %) developed a cardiovascular event during a follow-up of 2.9 (Q1-Q3, 2.7-3.3) years. If aortic (n = 657) or mitral (n = 85) annulus/valve calcifications were present, cardiovascular event incidence increased to 9.0 % (n = 59) or 12.9 % (n = 11), respectively. HRs of aortic and mitral valve/annulus calcium volume for cardiovascular events were 1.46 (95 % CI, 1.09-1.84) and 2.74 (95 % CI, 0.92-4.56) per 500 mm(3). The c-statistic of a basic model including age, pack-years, current smoking status, coronary and aorta calcium volume was 0.68 (95 % CI, 0.63-0.72), which did not change after adding heart valve calcium volume. Aortic valve calcifications are predictors of future cardiovascular events. However, there was no added prognostic value beyond age, number of pack-years, current smoking status, coronary and aorta calcium volume for short term cardiovascular events

The effect of iterative model reconstruction on coronary artery calcium quantification

Coronary artery calcium (CAC) scoring with computed tomography (CT) is an established tool for quantifying calcified atherosclerotic plaque burden. Despite the widespread use of novel image reconstruction techniques in CT, the effect of iterative model reconstruction on CAC score remains unclear. We sought to assess the impact of iterative model based reconstruction (IMR) on coronary artery calcium quantification as compared to the standard filtered back projection (FBP) algorithm and hybrid iterative reconstruction (HIR). In addition, we aimed to simulate the impact of iterative reconstruction techniques on calcium scoring based risk stratification of a larger asymptomatic population. We studied 63 individuals who underwent CAC scoring. Images were reconstructed with FBP, HIR and IMR and CAC scores were measured. We estimated the cardiovascular risk reclassification rate of IMR versus HIR and FBP in a larger asymptomatic population (n = 504). The median CAC scores were 147.7 (IQR 9.6-582.9), 107.0 (IQR 5.9-526.6) and 115.1 (IQR 9.3-508.3) for FBP, HIR and IMR, respectively. The HIR and IMR resulted in lower CAC scores as compared to FBP (both p < 0.001), however there was no difference between HIR and IMR (p = 0.855). The CAC score decreased by 7.2 % in HIR and 7.3 % in IMR as compared to FBP, resulting in a risk reclassification rate of 2.4 % for both HIR and IMR. The utilization of IMR for CAC scoring reduces the measured calcium quantity. However, the CAC score based risk stratification demonstrated modest reclassification in IMR and HIR versus FBP

What scans we will read: imaging instrumentation trends in clinical oncology

Oncological diseases account for a significant portion of the burden on public healthcare systems with associated costs driven primarily by complex and long-lasting therapies. Through the visualization of patient-specific morphology and functional-molecular pathways, cancerous tissue can be detected and characterized non- invasively, so as to provide referring oncologists with essential information to support therapy management decisions. Following the onset of stand-alone anatomical and functional imaging, we witness a push towards integrating molecular image information through various methods, including anato-metabolic imaging (e.g., PET/ CT), advanced MRI, optical or ultrasound imaging. This perspective paper highlights a number of key technological and methodological advances in imaging instrumentation related to anatomical, functional, molecular medicine and hybrid imaging, that is understood as the hardware-based combination of complementary anatomical and molecular imaging. These include novel detector technologies for ionizing radiation used in CT and nuclear medicine imaging, and novel system developments in MRI and optical as well as opto-acoustic imaging. We will also highlight new data processing methods for improved non-invasive tissue characterization. Following a general introduction to the role of imaging in oncology patient management we introduce imaging methods with well-defined clinical applications and potential for clinical translation. For each modality, we report first on the status quo and point to perceived technological and methodological advances in a subsequent status go section. Considering the breadth and dynamics of these developments, this perspective ends with a critical reflection on where the authors, with the majority of them being imaging experts with a background in physics and engineering, believe imaging methods will be in a few years from now. Overall, methodological and technological medical imaging advances are geared towards increased image contrast, the derivation of reproducible quantitative parameters, an increase in volume sensitivity and a reduction in overall examination time. To ensure full translation to the clinic, this progress in technologies and instrumentation is complemented by progress in relevant acquisition and image-processing protocols and improved data analysis. To this end, we should accept diagnostic images as “data”, and – through the wider adoption of advanced analysis, including machine learning approaches and a “big data” concept – move to the next stage of non-invasive tumor phenotyping. The scans we will be reading in 10 years from now will likely be composed of highly diverse multi- dimensional data from multiple sources, which mandate the use of advanced and interactive visualization and analysis platforms powered by Artificial Intelligence (AI) for real-time data handling by cross-specialty clinical experts with a domain knowledge that will need to go beyond that of plain imaging