Non-invasive Multimodality Imaging of Coronary Vulnerable Patient

SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach

Background

Worldwide, coronary artery disease (CAD) is a leading cause of mortality and morbidity and remains to be a top health priority in many countries. A non-invasive imaging modality for diagnosis of CAD such as single photon emission computed tomography-myocardial perfusion imaging (SPECT-MPI) is usually requested by cardiologists as it displays radiotracer distribution in the heart reflecting myocardial perfusion. The interpretation of SPECT-MPI is done visually by a nuclear medicine physician and is largely dependent on his clinical experience and showing significant inter-observer variability.

Objective

The aim of the study is to apply a deep learning approach in the classification of SPECT-MPI for perfusion abnormalities using convolutional neural networks (CNN).

Methods

A publicly available anonymized SPECT-MPI from a machine learning repository (https://www.kaggle.com/selcankaplan/spect-mpi) was used in this study involving 192 patients who underwent stress-test-rest Tc99m MPI. An exploratory approach of CNN hyperparameter selection to search for optimum neural network model was utilized with particular focus on various dropouts (0.2, 0.5, 0.7), batch sizes (8, 16, 32, 64), and number of dense nodes (32, 64, 128, 256). The base CNN model was also compared with the commonly used pre-trained CNNs in medical images such as VGG16, InceptionV3, DenseNet121 and ResNet50. All simulations experiments were performed in Kaggle using TensorFlow 2.6.0., Keras 2.6.0, and Python language 3.7.10.

Results

The best performing base CNN model with parameters consisting of 0.7 dropout, batch size 8, and 32 dense nodes generated the highest normalized Matthews Correlation Coefficient at 0.909 and obtained 93.75% accuracy, 96.00% sensitivity, 96.00% precision, and 96.00% F1-score. It also obtained higher classification performance as compared to the pre-trained architectures.

Conclusions

The results suggest that deep learning approaches through the use of CNN models can be deployed by nuclear medicine physicians in their clinical practice to further augment their decision skills in the interpretation of SPECT-MPI tests. These CNN models can also be used as a dependable and valid second opinion that can aid physicians as a decision-support tool as well as serve as teaching or learning materials for the less-experienced physicians particularly those still in their training career. These highlights the clinical utility of deep learning approaches through CNN models in the practice of nuclear cardiology.

Advancements in non-invasive imaging of atherosclerosis: Future perspectives

Atherosclerosis is a chronic inflammatory disease characterized by the buildup of plaques in arterial walls, leading to cardiovascular diseases and high morbidity and mortality rates worldwide. Non-invasive imaging techniques play a crucial role in evaluating patients with suspected or established atherosclerosis. However, there is a growing body of evidence suggesting the need to visualize the underlying processes of plaque progression and rupture to enhance risk stratification. This review explores recent advancements in non-invasive assessment of atherosclerosis, focusing on computed tomography, magnetic resonance imaging, and nuclear imaging. These advancements provide valuable insights into the assessment and management of atherosclerosis, potentially leading to better risk stratification and improved patient outcomes.

Vulnerable Atherosclerotic Plaque: Is There a Molecular Signature?

Atherosclerosis and its clinical manifestations, coronary and cerebral artery diseases, are the most common cause of death worldwide. The main pathophysiological mechanism for these complications is the rupture of vulnerable atherosclerotic plaques and subsequent thrombosis. Pathological studies of the vulnerable lesions showed that more frequently, plaques rich in lipids and with a high level of inflammation, responsible for mild or moderate stenosis, are more prone to rupture, leading to acute events. Identifying the vulnerable plaques helps to stratify patients at risk of developing acute vascular events. Traditional imaging methods based on plaque appearance and size are not reliable in prediction the risk of rupture. Intravascular imaging is a novel technique able to identify vulnerable lesions, but it is invasive and an operator-dependent technique. This review aims to summarize the current data from literature regarding the main biomarkers involved in the attempt to diagnose vulnerable atherosclerotic lesions. These biomarkers could be the base for risk stratification and development of the new therapeutic drugs in the treatment of patients with vulnerable atherosclerotic plaques.

Detecting vulnerable carotid plaque and its component characteristics: Progress in related imaging techniques

Carotid atherosclerotic plaque rupture and thrombosis are independent risk factors for acute ischemic cerebrovascular disease. Timely identification of vulnerable plaque can help prevent stroke and provide evidence for clinical treatment. Advanced invasive and non-invasive imaging modalities such as computed tomography, magnetic resonance imaging, intravascular ultrasound, optical coherence tomography, and near-infrared spectroscopy can be employed to image and classify carotid atherosclerotic plaques to provide clinically relevant predictors used for patient risk stratification. This study compares existing clinical imaging methods, and the advantages and limitations of different imaging techniques for identifying vulnerable carotid plaque are reviewed to effectively prevent and treat cerebrovascular diseases.