Intravascular optical coherence tomography method for automated detection of macrophage infiltration within atherosclerotic coronary plaques

Heavy macrophage infiltration identified by optical coherence tomography relates to plaque rupture

OBJECTIVE

Risk stratification plays a critical role in patients with asymptomatic carotid atherosclerotic stenosis. Heavy macrophage infiltration (HMC) is an important factor of plaque destabilization. However, in vivo imaging technologies and screening criteria for HMC remain limited. We aimed to (i) introduce algorithms for in vivo detection of macrophage infiltrations using optical coherence tomography (OCT) and (ii) to investigate the threshold of HMC and its association with plaque vulnerability.

METHODS

Ex vivo OCT images were co-registered with histopathology in 282 cross-sectional pairs from 19 carotid endarterectomy specimens. Of these, 197 randomly selected pairs were employed to define the parameters, and the remaining 85 pairs were used to evaluate the accuracy of the OCT-based algorithm in detecting macrophage infiltrations. Clinical analysis included 93 patients receiving carotid OCT evaluation. The prevalence and burden of macrophage infiltration were analyzed. Multivariable and subgroup analysis were performed to investigate the association between HMC and plaque rupture.

RESULTS

The sensitivity and specificity of algorithm for detecting macrophage infiltration were 88.0% and 74.9%, respectively. Of 93 clinical patients, ruptured plaques exhibited higher prevalence of macrophage infiltration than nonruptured plaques (83.7% [36/43] vs 32.0% [16/50], p < 0.001). HMC was identified when the macrophage index was greater than 60.2 (sensitivity = 74.4%, specificity = 84.0%). Multivariable analysis showed that HMC and multiple calcification were independent risk factors for non-lipid-rich plaque rupture.

INTERPRETATION

This study provides a novel approach and screening criteria for HMC, which might be valuable for atherosclerotic risk stratification.

Diagnostics of atherosclerosis: Overview of the existing methods

Atherosclerosis was and remains an extremely common and serious health problem. Since the elderly are most at risk of cardiovascular risk, and the average life expectancy is increasing, the spread of atherosclerosis and its consequences increases as well. One of the features of atherosclerosis is its asymptomaticity. This factor makes it difficult to make a timely diagnosis. This entails the lack of timely treatment and even prevention. To date, in the arsenal of physicians, there is only a limited set of methods to suspect and fully diagnose atherosclerosis. In this review, we have tried to briefly describe the most common and effective methods for diagnosing atherosclerosis.

Optical Coherence Tomography in Cerebrovascular Disease: Open up New Horizons

Optical coherence tomography (OCT), based on the backscattering or reflection of near-infrared light, enables an ultra-high resolution of up to 10 μm. The successful application of OCT in coronary artery diseases has sparked increasing interest in its implementation in cerebrovascular diseases. OCT has shown promising potential in the atherosclerotic plaque structure characterization, plaque rupture risk stratification, pre-stenting and post-stenting evaluation, and long-term follow-up in extracranial and intracranial atherosclerotic stenosis (ICAS). In hemorrhagic cerebrovascular diseases, OCT plays an important role in the structure evaluation, rupture risk stratification, and healing and occlusion evaluation following initial treatment in intracranial aneurysms (IAs). In this study, we summarized the applications of OCT in the diagnosis, treatment, and follow-up of cerebrovascular diseases, especially in ICAS and IAs. The current limitations and future directions of OCT in the endovascular treatment of cerebrovascular diseases were also discussed.

Unravelling the role of macrophages in cardiovascular inflammation through imaging: a state-of-the-art review

Cardiovascular disease remains the leading cause of death and disability for patients across the world. Our understanding of atherosclerosis as a primary cholesterol issue has diversified, with a significant dysregulated inflammatory component that largely remains untreated and continues to drive persistent cardiovascular risk. Macrophages are central to atherosclerotic inflammation, and they exist along a functional spectrum between pro-inflammatory and anti-inflammatory extremes. Recent clinical trials have demonstrated a reduction in major cardiovascular events with some, but not all, anti-inflammatory therapies. The recent addition of colchicine to societal guidelines for the prevention of recurrent cardiovascular events in high-risk patients with chronic coronary syndromes highlights the real-world utility of this class of therapies. A highly targeted approach to modification of interleukin-1-dependent pathways shows promise with several novel agents in development, although excessive immunosuppression and resulting serious infection have proven a barrier to implementation into clinical practice. Current risk stratification tools to identify high-risk patients for secondary prevention are either inadequately robust or prohibitively expensive and invasive. A non-invasive and relatively inexpensive method to identify patients who will benefit most from novel anti-inflammatory therapies is required, a role likely to be fulfilled by functional imaging methods. This review article outlines our current understanding of the inflammatory biology of atherosclerosis, upcoming therapies and recent landmark clinical trials, imaging modalities (both invasive and non-invasive) and the current landscape surrounding functional imaging including through targeted nuclear and nanobody tracer development and their application.

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10-20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients' arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016-2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.

Intravascular imaging of coronary artery: Bridging the gap between clinical needs and technical advances

Coronary artery disease is the leading cause of mortality worldwide. Almost seven million deaths are reported each year due to coronary disease. Coronary artery events in the adult are primarily due to atherosclerosis with seventy-five percent of the related mortality caused by plaque rupture. Despite significant progress made to improve intravascular imaging of coronary arteries, there is still a large gap between clinical needs and technical developments. The goal of this review is to identify the gap elements between clinical knowledge and recent advances in the domain of medical image analysis. Efficient image analysis computational models should be designed with respect to the exact clinical needs, and detailed features of the tissues under review. In this review, we discuss the detailed clinical features of the intracoronary plaques for mathematical and biomedical researchers. We emphasize the importance of integrating this clinical knowledge validated by clinicians to investigate the potentially effective models for proper features efficiency in the scope of leveraging the state-of-the-art of coronary image analyses.

A deep learning-based model for characterization of atherosclerotic plaque in coronary arteries using optical coherence tomography images

PURPOSE

Coronary artery events are mainly associated with atherosclerosis in adult population, which is recognized as accumulation of plaques in arterial wall tissues. Optical Coherence Tomography (OCT) is a light-based imaging system used in cardiology to analyze intracoronary tissue layers and pathological formations including plaque accumulation. This state-of-the-art catheter-based imaging system provides intracoronary cross-sectional images with high resolution of 10-15 µm. But interpretation of the acquired images is operator dependent, which is not only very time-consuming but also highly error prone from one observer to another. An automatic and accurate coronary plaque tagging using OCT image post-processing can contribute to wide adoption of the OCT system and reducing the diagnostic error rate.

METHOD

In this study, we propose a combination of spatial pyramid pooling module with dilated convolutions for semantic segmentation to extract atherosclerotic tissues regardless of their types and training a sparse auto-encoder to reconstruct the input features and enlarge the training data as well as plaque type characterization in OCT images.

RESULTS

The results demonstrate high precision of the proposed model with reduced computational complexity, which can be appropriate for real-time analysis of OCT images. At each step of the work, measured accuracy, sensitivity, specificity of more than 93% demonstrate high performance of the model.

CONCLUSION

The main focus of this study is atherosclerotic tissue characterization using OCT imaging. This contributes to wide adoption of the OCT imaging system by providing clinicians with a fully automatic interpretation of various atherosclerotic tissues. Future studies will be focused on analyzing atherosclerotic vulnerable plaques, those coronary plaques which are prone to rupture.