Validation and application of OCT tissue attenuation index for the detection of neointimal foam cells

Neoatherosclerosis: A Distinctive Pathological Mechanism of Stent Failure

With the development of drug-eluting stents, intimal re-endothelialisation is significantly inhibited by antiproliferative drugs, and stent restenosis transforms from smooth muscle cell proliferation to neoatherosclerosis (NA). As a result of the development of intravascular imaging technology, the incidence and characteristics of NA can be explored in vivo, with some progress made in illustrating the mechanisms of NA. Experimental studies have shed light on the molecular characteristics of NA. More critically, sufficient evidence proves NA as a significant cause of late stent failure. Treatments for NA are still being explored. In this review, we summarise the histopathological characteristics of different types of stent NA, explore the potential relationship of NA with native atherosclerosis and discuss the clinical significance of NA in late stent failure and the promising present and future prevention and treatment strategies.

HClO-triggered interventional probe enabled early detection and intervention of atherosclerosis

Foam cell formation and further accumulation in the subendothelial space of the vascular wall is a hallmark of early atherosclerosis (AS). Targeting foam cell formation can be a promising approach for the early detection and prevention of AS. However, only a few studies have actually examined foam cells in vivo, and most methods combined nanotechnology with angiography, which is complex and could cause further damage to the endothelium. Herein, based on methylene blue, a biosafe NIR dye approved by the FDA, an interventional probe (HMB-NA@Mp) triggered by hypochlorous acid (HClO) was designed for imaging foam cells easily, safely, and effectively in the early stage of AS. Here, encapsulation of the probe by foam cells targeted platelet membrane (Mp) increased probe targeting and reduced toxicity. Cell and animal experimental results showed that the probe could accumulate at the lesion site and significantly enhance fluorescence in the early AS model group. Remarkably, at the same time, it could also release the metabolite niacin, which played a role in inhibiting atherosclerosis. Thus, HMB-NA@Mp is expected to be a powerful means for the early detection and timely intervention of early AS in the absence of clinical symptoms.

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.

Histopathology-Based Deep-Learning Predicts Atherosclerotic Lesions in Intravascular Imaging

Background: Optical coherence tomography is a powerful modality to assess atherosclerotic lesions, but detecting lesions in high-resolution OCT is challenging and requires expert knowledge. Deep-learning algorithms can be used to automatically identify atherosclerotic lesions, facilitating identification of patients at risk. We trained a deep-learning algorithm (DeepAD) with co-registered, annotated histopathology to predict atherosclerotic lesions in optical coherence tomography (OCT). Methods: Two datasets were used for training DeepAD: (i) a histopathology data set from 7 autopsy cases with 62 OCT frames and co-registered histopathology for high quality manual annotation and (ii) a clinical data set from 51 patients with 222 OCT frames in which manual annotations were based on clinical expertise only. A U-net based deep convolutional neural network (CNN) ensemble was employed as an atherosclerotic lesion prediction algorithm. Results were analyzed using intersection over union (IOU) for segmentation. Results: DeepAD showed good performance regarding the prediction of atherosclerotic lesions, with a median IOU of 0.68 ± 0.18 for segmentation of atherosclerotic lesions. Detection of calcified lesions yielded an IOU = 0.34. When training the algorithm without histopathology-based annotations, a performance drop of >0.25 IOU was observed. The practical application of DeepAD was evaluated retrospectively in a clinical cohort (n = 11 cases), showing high sensitivity as well as specificity and similar performance when compared to manual expert analysis. Conclusion: Automated detection of atherosclerotic lesions in OCT is improved using a histopathology-based deep-learning algorithm, allowing accurate detection in the clinical setting. An automated decision-support tool based on DeepAD could help in risk prediction and guide interventional treatment decisions.