Molecular Imaging of Fibroblast Activation in Rabbit Atherosclerotic Plaques: a Preclinical PET/CT Study

AIM

Atherosclerosis remains the pathological basis of myocardial infarction and ischemic stroke. Early and accurate identification of plauqes is crucial to improve clinical outcomes of atherosclerosis patients. Our study aims to evaluate the potential value of fibroblast activation protein inhibitor (FAPI)-04 PET/CT in identifying plaques via a preclinical rabbit model of atherosclerosis.

METHODS

New Zealand white rabbits were fed high-fat diet (HFD), and randomly divided into the model group injured by the balloon, and the sham group only with incisions. Ultrasound was performed to detect plaques, and FAPI-avid was determined through Al18F-NOTA-FAPI-04 PET/CT. Mean standardized uptake values (SUVmean) in lesions were compared, and biodistribution of Al18F-NOTA-FAPI-04 and target-to-background ratios (TBRs) were calculated. Histological staining was performed to display arterial plaques, and autoradiography (ARG) was employed to measure the in vitro intensity of Al18F-NOTA-FAPI-04. At last, the correlation among FAP levels, plaque area, SUVmean values and fibrous cap thickness was assessed.

RESULTS

The rabbit carotid and abdominal atherosclerosis model was established. Al18F-NOTA-FAPI-04 showed a higher uptake in carotid plaques (SUVmean 1.32 ± 0.11) and abdominal plaques (SUVmean 0.73 ± 0.13) compared to corresponding controls (SUVmean 1.07 ± 0.06; 0.46 ± 0.03) (P < 0.05). Biodistribution analysis of Al18F-NOTA-FAPI-04 revealed that the bigger plaques were delineated with higher TBRs. Pathological staining showed the formation of arterial plaques, and ARG staining exhibited a higher intensity of Al18F-NOTA-FAPI-04 in the bigger plaques. Lastly, plaque area was found to be positively correlated to FAP expression and SUVmean, while FAP expression was negatively correlated to fibrous cap thickness of plaques.

CONCLUSIONS

We successfully achieve molecular imaging of fibroblast activation in atherosclerotic lesions of rabbits, suggesting Al18F-NOTA-FAPI-04 PET/CT may be a potentially valuable tool to identify plaques.

CMR-based cardiac phenotyping in different forms of heart failure

Heart failure (HF) is a heterogenous disease requiring precise diagnostics and knowledge of pathophysiological processes. Since structural and functional imaging data are scarce we hypothesized that cardiac magnetic resonance (CMR)-based analyses would provide accurate characterization and mechanistic insights into different HF groups comprising preserved (HFpEF), mid-range (HFmrEF) and reduced ejection fraction (HFrEF). 22 HFpEF, 17 HFmrEF and 15 HFrEF patients as well as 19 healthy volunteers were included. CMR image assessment contained left atrial (LA) and left ventricular (LV) volumetric evaluation as well as left atrioventricular coupling index (LACI). Furthermore, CMR feature-tracking included LV and LA strain in terms of reservoir (Es), conduit (Ee) and active boosterpump (Ea) function. CMR-based tissue characterization comprised T1 mapping as well as late-gadolinium enhancement (LGE) analyses. HFpEF patients showed predominant atrial impairment (Es 20.8%vs.25.4%, p = 0.02 and Ee 8.3%vs.13.5%, p = 0.001) and increased LACI compared to healthy controls (14.5%vs.23.3%, p = 0.004). Patients with HFmrEF showed LV enlargement but mostly preserved LA function with a compensatory increase in LA boosterpump (LA Ea: 15.0%, p = 0.049). In HFrEF LA and LV functional impairment was documented (Es: 14.2%, Ee: 5.4% p < 0.001 respectively; Ea: 8.8%, p = 0.02). This was paralleled by non-invasively assessed progressive fibrosis (T1 mapping and LGE; HFrEF > HFmrEF > HFpEF). CMR-imaging reveals insights into HF phenotypes with mainly atrial affection in HFpEF, ventricular affection with atrial compensation in HFmrEF and global impairment in HFrEF paralleled by progressive LV fibrosis. These data suggest a necessity for a personalized HF management based on imaging findings for future optimized patient management.

Prognostic Value of Left Atrial Volume and Late Gadolinium Enhancement on Cardiac Magnetic Resonance Imaging in Patients with Coronary Artery Disease and Severe Left Ventricular Dysfunction Underwent CABG

RATIONALE AND OBJECTIVES

This study aims to investigate whether the combination of Left atrial volume (LAV) and late gadolinium enhancement (LGE) is helpful in stratifying the risk in CABG patients with CAD with EF≤ 35%.

MATERIALS AND METHODS

We conducted a retrospective analysis involving 205 CAD patients with EF≤ 35% who underwent CABG. All patients underwent gadolinium-enhanced CMR before surgery. The CMR images were analyzed for LAV, biventricular function, LGE, and left ventricular myocardial strain. Primary endpoint events included all-cause mortality, revascularization, re-hospitalization due to myocardial infarction or heart failure, and stroke after CABG. Multivariable Cox analysis was performed to identify independent risk factors for adverse outcomes. Kaplan-Meier curve analysis with the log-rank test was employed to evaluate survival estimates.

RESULTS

A total of 55 patients reached the primary endpoints. Univariate Cox proportional hazard regression analysis showed that LAV index (LAVi), left ventricular EF (LVEF), right ventricular EF, LGE percent, and global longitudinal strain were significantly associated with the primary outcome (all P < 0.05). Multivariable analysis showed that LAVi (hazard ratio [HR] 1.05, [95% confidence interval (CI) 1.02-1.07], P < 0.001) and LGE percent (HR 1.10, [95% CI 1.06-1.15], P < 0.001) were independently associated with the primary outcome. Kaplan-Meier analysis indicated a significant increase in the risk of endpoint occurrence when patients exhibited LAVi≥ 51.0 mL/m2 and LGE≥ 11.6% (both P < 0.05).

CONCLUSION

For CAD patients with LVEF≤ 35%, the combination of LAVi and LGE percent demonstrated good predictive value for adverse events after CABG. CMR is a helpful tool to risk-stratify patients with severe left ventricular dysfunction undergoing CABG.

Cardiovascular multimodality imaging in women: a scientific statement of the European Association of Cardiovascular Imaging of the European Society of Cardiology

Cardiovascular diseases (CVD) represent an important cause of mortality and morbidity in women. It is now recognized that there are sex differences regarding the prevalence and the clinical significance of the traditional cardiovascular (CV) risk factors as well as the pathology underlying a range of CVDs. Unfortunately, women have been under-represented in most CVD imaging studies and trials regarding diagnosis, prognosis, and therapeutics. There is therefore a clear need for further investigation of how CVD affects women along their life span. Multimodality CV imaging plays a key role in the diagnosis of CVD in women as well as in prognosis, decision-making, and monitoring of therapeutics and interventions. However, multimodality imaging in women requires specific consideration given the differences in CVD between the sexes. These differences relate to physiological changes that only women experience (e.g. pregnancy and menopause) as well as variation in the underlying pathophysiology of CVD and also differences in the prevalence of certain conditions such as connective tissue disorders, Takotsubo, and spontaneous coronary artery dissection, which are all more common in women. This scientific statement on CV multimodality in women, an initiative of the European Association of Cardiovascular Imaging of the European Society of Cardiology, reviews the role of multimodality CV imaging in the diagnosis, management, and risk stratification of CVD, as well as highlights important gaps in our knowledge that require further investigation.

Cardiac Troponin Serum Concentration Measurement Is Useful Not Only in the Diagnosis of Acute Cardiovascular Events

Cardiac troponin serum concentration is the primary marker used for the diagnosis of acute coronary syndrome. Moreover, the measurement of cardiac troponin concentration is important for risk stratification in patients with pulmonary embolism. The cardiac troponin level is also a general marker of myocardial damage, regardless of etiology. The purpose of this study is to conduct a literature review and present the most important information regarding the current state of knowledge on the cardiac troponin serum concentration in patients with chronic cardiovascular disease (CVD), as well as on the relationships between cardiac troponin serum concentration and features of subclinical cardiovascular dysfunction. According to research conducted to date, patients with CVDs, such as chronic coronary syndrome, chronic lower extremities' ischemia, and cerebrovascular disease, are characterized by higher cardiac troponin concentrations than people without a CVD. Moreover, the literature data indicate that the concentration of cardiac troponin is correlated with markers of subclinical dysfunction of the cardiovascular system, such as the intima-media thickness, pulse wave velocity, ankle-brachial index, coronary artery calcium index (the Agatston score), and flow-mediated dilation. However, further research is needed in various patient subpopulations and in different clinical contexts.

Automated T1 and T2 mapping segmentation on cardiovascular magnetic resonance imaging using deep learning

Introduction

Structural and functional heart abnormalities can be examined non-invasively with cardiac magnetic resonance imaging (CMR). Thanks to the development of MR devices, diagnostic scans can capture more and more relevant information about possible heart diseases. T1 and T2 mapping are such novel technology, providing tissue specific information even without the administration of contrast material. Artificial intelligence solutions based on deep learning have demonstrated state-of-the-art results in many application areas, including medical imaging. More specifically, automated tools applied at cine sequences have revolutionized volumetric CMR reporting in the past five years. Applying deep learning models to T1 and T2 mapping images can similarly improve the efficiency of post-processing pipelines and consequently facilitate diagnostic processes.

Methods

In this paper, we introduce a deep learning model for myocardium segmentation trained on over 7,000 raw CMR images from 262 subjects of heterogeneous disease etiology. The data were labeled by three experts. As part of the evaluation, Dice score and Hausdorff distance among experts is calculated, and the expert consensus is compared with the model's predictions.

Results

Our deep learning method achieves 86% mean Dice score, while contours provided by three experts on the same data show 90% mean Dice score. The method's accuracy is consistent across epicardial and endocardial contours, and on basal, midventricular slices, with only 5% lower results on apical slices, which are often challenging even for experts.

Conclusions

We trained and evaluated a deep learning based segmentation model on 262 heterogeneous CMR cases. Applying deep neural networks to T1 and T2 mapping could similarly improve diagnostic practices. Using the fine details of T1 and T2 mapping images and high-quality labels, the objective of this research is to approach human segmentation accuracy with deep learning.

Approach to Imaging Ischemia in Women

Approach to imaging ischemia in women Coronary artery disease in women tends to have a worse short- and long-term prognosis relative to men and remains the leading cause of mortality worldwide. Both clinical symptoms and diagnostic approach remain challenging in women due to lesser likelihood of women presenting with classic anginal symptoms on one hand and underperformance of conventional exercise treadmill testing in women on the other. Moreover, a higher proportion of women with signs and symptoms suggestive of ischemia are more likely to have nonobstructive coronary artery disease (CAD) that requires additional imaging and therapeutic considerations. New imaging techniques such as coronary computed tomography (CT) angiography, CT myocardial perfusion imaging, CT functional flow reserve assessment, and cardiac magnetic resonance imaging carry substantially better sensitivity and specificity for the detection of ischemia and coronary artery disease in women. Familiarity with various clinical subtypes of ischemic heart disease in women and with the major advantages and disadvantages of advanced imaging tests to ensure the decision to select one modality over another is one of the keys to successful diagnosis of CAD in women. This review compares the 2 major types of ischemic heart disease in women - obstructive and nonobstructive, while focusing on sex-specific elements of its pathophysiology.

The Role of Molecular Imaging in Personalized Medicine

The concept of personalized medicine refers to the tailoring of medical treatment to each patient's unique characteristics. Scientific advancements have led to a better understanding of how a person's unique molecular and genetic profile makes them susceptible to certain diseases. It provides individualized medical treatments that will be safe and effective for each patient. Molecular imaging modalities play an essential role in this aspect. They are used widely in screening, detection and diagnosis, treatment, assessing disease heterogeneity and progression planning, molecular characteristics, and long-term follow-up. In contrast to conventional imaging approaches, molecular imaging techniques approach images as the knowledge that can be processed, allowing for the collection of relevant knowledge in addition to the evaluation of enormous patient groups. This review presents the fundamental role of molecular imaging modalities in personalized medicine.

Reduced functional capacity is associated with the proportion of impaired myocardial deformation assessed in heart failure patients by CMR

Aims

Heart failure (HF) does not only reduce the life expectancy in patients, but their life is also often limited by HF symptoms leading to a reduced quality of life (QoL) and a diminished exercise capacity. Novel parameters in cardiac imaging, including both global and regional myocardial strain imaging, promise to contribute to better patient characterization and ultimately to better patient management. However, many of these methods are not part of clinical routine yet, their associations with clinical parameters have been poorly studied. An imaging parameters that also indicate the clinical symptom burden of HF patients would make cardiac imaging more robust toward incomplete clinical information and support the clinical decision process.

Methods and results

This prospective study conducted at two centers in Germany between 2017 and 2018 enrolled stable outpatient subjects with HF [n = 56, including HF with reduced ejection fraction (HFrEF), HF with mid-range ejection fraction (HFmrEF), and HF with preserved ejection fraction (HFpEF)] and a control cohort (n = 19). Parameters assessed included measures for external myocardial function, for example, cardiac index and myocardial deformation measurements by cardiovascular magnetic resonance imaging, left ventricular global longitudinal strain (GLS), the global circumferential strain (GCS), and the regional distribution of segment deformation within the LV myocardium, as well as basic phenotypical characteristics including the Minnesota Living with Heart Failure Questionnaire (MLHFQ) and the 6-minute walk test (6MWT). If less than 80% of the LV segments are preserved in their deformation capacity the functional capacity by 6MWT (6 minutes walking distance: MyoHealth ≥ 80%: 579.8 ± 177.6 m; MyoHealth 60-<80%: 401.3 ± 121.7 m; MyoHealth 40-<60%: 456.4 ± 68.9 m; MyoHealth < 40%: 397.6 ± 125.9 m, overall p-value: 0.03) as well as the symptom burden are significantly impaired (NYHA class: MyoHealth ≥ 80%: 0.6 ± 1.1 m; MyoHealth 60-<80%: 1.7 ± 1.2 m; MyoHealth 40-<60%: 1.8 ± 0.7 m; MyoHealth < 40%: 2.4 ± 0.5 m; overall p-value < 0.01). Differences were also observed in the perceived exertion assessed by on the Borg scale (MyoHealth ≥ 80%: 8.2 ± 2.3 m; MyoHealth 60-<80%: 10.4 ± 3.2 m; MyoHealth 40-<60%: 9.8 ± 2.1 m; MyoHealth < 40%: 11.0 ± 2.9 m; overall p-value: 0.20) as well as quality of life measures (MLHFQ; MyoHealth ≥ 80%: 7.5 ± 12.4 m; MyoHealth 60-<80%: 23.4 ± 23.4 m; MyoHealth 40-<60%: 20.5 ± 21.2 m; MyoHealth < 40%: 27.4 ± 24.4 m; overall p-value: 0.15)-while these differences were not significant.

Conclusion

The share of LV segments with preserved myocardial contraction promises to discriminate between symptomatic and asymptomatic subjects based on the imaging findings, even when the LV ejection fraction is preserved. This finding is promising to make imaging studies more robust toward incomplete clinical information.

Recognized and Unrecognized Value of Echocardiography in Guideline and Consensus Documents Regarding Patients With Chest Pain

Guideline and consensus documents have recently been published on the important topic of the noninvasive evaluation of patients presenting with chest pain (CP) or patients with known acute or chronic coronary syndromes. Authors for these documents have included members representing multispecialty imaging societies, yet the process of generating consensus and the need to produce concise written documents have led to a situation where the particular advantages of echocardiography are overlooked. Broad guidelines such as these can be helpful when it comes to "when to do" noninvasive cardiac testing, but they do not pretend to offer nuances on "how to do" noninvasive cardiac testing. This report details the particular value of echocardiography and potential explanations for its understated role in recent guidelines. This report is categorized into the following sections: (1) impact of the level of evidence on guideline creation; (2) versatility of echocardiography in the assessment of CP and the inimitable role for echo Doppler echocardiography in the assessment of dyspnea; (3) value of point-of-care ultrasound in assessing CP and dyspnea; and (4) the future role of echocardiography in ischemic heart disease.

Molecular imaging research in atherosclerosis: A 23-year scientometric and visual analysis

Background: Cardiovascular and cerebrovascular diseases are major global health problems, and the main cause is atherosclerosis. Recently, molecular imaging has been widely employed in the diagnosis and therapeutic applications of a variety of diseases, including atherosclerosis. Substantive facts have announced that molecular imaging has broad prospects in the early diagnosis and targeted treatment of atherosclerosis. Objective: We conducted a scientometric analysis of the scientific publications over the past 23 years on molecular imaging research in atherosclerosis, so as to identify the key progress, hotspots, and emerging trends. Methods: Original research and reviews regarding molecular imaging in atherosclerosis were retrieved from the Web of Science Core Collection database. Microsoft Excel 2021 was used to analyze the main findings. CiteSpace, VOSviewer, and a scientometric online platform were used to perform visualization analysis of the co-citation of journals and references, co-occurrence of keywords, and collaboration between countries/regions, institutions, and authors. Results: A total of 1755 publications were finally included, which were published by 795 authors in 443 institutions from 59 countries/regions. The United States was the top country in terms of the number and centrality of publications in this domain, with 810 papers and a centrality of 0.38, and Harvard University published the largest number of articles (182). Fayad, ZA was the most productive author, with 73 papers, while LIBBY P had the most co-citations (493). CIRCULATION was the top co-cited journal with a frequency of 1,411, followed by ARTERIOSCL THROM VAS (1,128). The co-citation references analysis identified eight clusters with a well-structured network (Q = 0.6439) and highly convincing clustering (S = 0.8865). All the studies calculated by keyword co-occurrence were divided into five clusters: "nanoparticle," "magnetic resonance imaging," "inflammation," "positron emission tomography," and "ultrasonography". Hot topics mainly focused on cardiovascular disease, contrast media, macrophage, vulnerable plaque, and microbubbles. Sodium fluoride ⁃PET, targeted drug delivery, OCT, photoacoustic imaging, ROS, and oxidative stress were identified as the potential trends. Conclusion: Molecular imaging research in atherosclerosis has attracted extensive attention in academia, while the challenges of clinical transformation faced in this field have been described in this review. The findings of the present research can inform funding agencies and researchers toward future directions.

Imaging of heart disease in women: review and case presentation

Cardiovascular diseases (CVD) remain the leading cause of mortality worldwide. Although major diagnostic and therapeutic advances have significantly improved the prognosis of patients with CVD in the past decades, these advances have less benefited women than age-matched men. Noninvasive cardiac imaging plays a key role in the diagnosis of CVD. Despite shared imaging features and strategies between both sexes, there are critical sex disparities that warrant careful consideration, related to the selection of the most suited imaging techniques, to technical limitations, and to specific diseases that are overrepresented in the female population. Taking these sex disparities into consideration holds promise to improve management and alleviate the burden of CVD in women. In this review, we summarize the specific features of cardiac imaging in four of the most common presentations of CVD in the female population including coronary artery disease, heart failure, pregnancy complications, and heart disease in oncology, thereby highlighting contemporary strengths and limitations. We further propose diagnostic algorithms tailored to women that might help in selecting the most appropriate imaging modality.

Clinician's guide to trustworthy and responsible artificial intelligence in cardiovascular imaging

A growing number of artificial intelligence (AI)-based systems are being proposed and developed in cardiology, driven by the increasing need to deal with the vast amount of clinical and imaging data with the ultimate aim of advancing patient care, diagnosis and prognostication. However, there is a critical gap between the development and clinical deployment of AI tools. A key consideration for implementing AI tools into real-life clinical practice is their "trustworthiness" by end-users. Namely, we must ensure that AI systems can be trusted and adopted by all parties involved, including clinicians and patients. Here we provide a summary of the concepts involved in developing a "trustworthy AI system." We describe the main risks of AI applications and potential mitigation techniques for the wider application of these promising techniques in the context of cardiovascular imaging. Finally, we show why trustworthy AI concepts are important governing forces of AI development.

Machine Learning Algorithm to Predict Obstructive Coronary Artery Disease: Insights from the CorLipid Trial

Developing risk assessment tools for CAD prediction remains challenging nowadays. We developed an ML predictive algorithm based on metabolic and clinical data for determining the severity of CAD, as assessed via the SYNTAX score. Analytical methods were developed to determine serum blood levels of specific ceramides, acyl-carnitines, fatty acids, and proteins such as galectin-3, adiponectin, and APOB/APOA1 ratio. Patients were grouped into: obstructive CAD (SS > 0) and non-obstructive CAD (SS = 0). A risk prediction algorithm (boosted ensemble algorithm XGBoost) was developed by combining clinical characteristics with established and novel biomarkers to identify patients at high risk for complex CAD. The study population comprised 958 patients (CorLipid trial (NCT04580173)), with no prior CAD, who underwent coronary angiography. Of them, 533 (55.6%) suffered ACS, 170 (17.7%) presented with NSTEMI, 222 (23.2%) with STEMI, and 141 (14.7%) with unstable angina. Of the total sample, 681 (71%) had obstructive CAD. The algorithm dataset was 73 biochemical parameters and metabolic biomarkers as well as anthropometric and medical history variables. The performance of the XGBoost algorithm had an AUC value of 0.725 (95% CI: 0.691−0.759). Thus, a ML model incorporating clinical features in addition to certain metabolic features can estimate the pre-test likelihood of obstructive CAD.