Incremental value of 18F-FDG cardiac PET imaging over dobutamine stress echocardiography in predicting myocardial ischemia in patients with suspected coronary artery disease

Stress-only versus rest-stress SPECT MPI in the detection and diagnosis of myocardial ischemia and infarction by machine learning

BACKGROUND

Rest-stress SPECT myocardial perfusion imaging (MPI) is widely used to evaluate coronary artery disease (CAD). We aim to evaluate stress-only versus rest-stress MPI in diagnosing CAD by machine learning (ML).

METHODS

A total of 276 patients with suspected CAD were randomly divided into training (184 patients) and validation (92 patients) cohorts. Variables extracted from clinical, physiological, and rest-stress SPECT MPI were screened. Stress-only and rest-stress MPI using ML were established and compared using the training cohort. Then the diagnostic performance of two models in diagnosing myocardial ischemia and infarction was evaluated in the validation cohort.

RESULTS

Six ML models based on stress-only MPI selected summed stress score, summed wall thickness score of stress%, and end-diastolic volume of stress as key variables and performed equally good as rest-stress MPI in detecting CAD [area under the curve (AUC): 0.863 versus 0.877, P  = 0.519]. Furthermore, stress-only MPI showed a reasonable prediction of reversible deficit, as shown by rest-stress MPI (AUC: 0.861). Subsequently, nomogram models using the above-stated stress-only MPI variables showed a good prediction of CAD and reversible perfusion deficit in training and validation cohorts.

CONCLUSION

Stress-only MPI demonstrated similar diagnostic performance compared with rest-stress MPI using 6 ML algorithms. Stress-only MPI with ML models can diagnose CAD and predict ischemia from scar.

Ultra-High-Resolution Electrocardiography Enables Earlier Detection of Transmural and Subendocardial Myocardial Ischemia Compared to Conventional Electrocardiography

The sensitivity of exercise ECG is marginally sufficient for the detection of mild reduction of coronary blood flow in patients with early coronary atherosclerosis. Here, we describe the application of a new technique of ECG registration/analysis-ultra-high-resolution ECG (UHR ECG)-for early detection of myocardial ischemia (MIS). The utility of UHR ECG vs. conventional ECG (C ECG) was tested in anesthetized rats and pigs. Transmural MIS was induced in rats by the ligation of the left coronary artery (CA). In pigs, subendocardial ischemia of a variable extent was produced by stepwise inflation of a balloon within the right CA, causing a 25-100% reduction of its lumen. In rats, a reduction in power spectral density (PSD) in the high-frequency (HF) channel of UHR ECG was registered at 60 s after ischemia (power 0.81 ± 0.14 vs. 1.25 ± 0.12 mW at baseline, p < 0.01). This was not accompanied by any ST segment elevation on C ECG. In pigs, PSD in the HF channel of UHR ECG was significantly decreased at a 25% reduction of CA lumen, while the ST segment on C ECG remained unchanged. In conclusion, UHR ECG enabled earlier detection of transmural MIS compared to C ECG. PSD in the HF channel of UHR ECG demonstrated greater sensitivity in the settings of subendocardial ischemia.

Tales from the future-nuclear cardio-oncology, from prediction to diagnosis and monitoring

Cancer and cardiovascular diseases (CVD) often share common risk factors, and patients with CVD who develop cancer are at high risk of experiencing major adverse cardiovascular events. Additionally, cancer treatment can induce short- and long-term adverse cardiovascular events. Given the improvement in oncological patients' prognosis, the burden in this vulnerable population is slowly shifting towards increased cardiovascular mortality. Consequently, the field of cardio-oncology is steadily expanding, prompting the need for new markers to stratify and monitor the cardiovascular risk in oncological patients before, during, and after the completion of treatment. Advanced non-invasive cardiac imaging has raised great interest in the early detection of CVD and cardiotoxicity in oncological patients. Nuclear medicine has long been a pivotal exam to robustly assess and monitor the cardiac function of patients undergoing potentially cardiotoxic chemotherapies. In addition, recent radiotracers have shown great interest in the early detection of cancer-treatment-related cardiotoxicity. In this review, we summarize the current and emerging nuclear cardiology tools that can help identify cardiotoxicity and assess the cardiovascular risk in patients undergoing cancer treatments and discuss the specific role of nuclear cardiology alongside other non-invasive imaging techniques.

Artificial intelligence for disease diagnosis and risk prediction in nuclear cardiology

Artificial intelligence (AI) techniques have emerged as a highly efficient approach to accurately and rapidly interpret diagnostic imaging and may play a vital role in nuclear cardiology. In nuclear cardiology, there are many clinical, stress, and imaging variables potentially available, which need to be optimally integrated to predict the presence of obstructive coronary artery disease (CAD) or predict the risk of cardiovascular events. In spite of clinical awareness of a large number of potential variables, it is difficult for physicians to integrate multiple features consistently and objectively. Machine learning (ML) is particularly well suited to integrating this vast array of information to provide patient-specific predictions. Deep learning (DL), a branch of ML characterized by a multi-layered convolutional model architecture, can extract information directly from images and identify latent image features associated with a specific prediction. This review will discuss the latest AI applications to disease diagnosis and risk prediction in nuclear cardiology with a focus on potential clinical applications.