Progression of atherosclerotic plaque in coronary arteries is characterized by complex cellular and non-cellular molecular interactions. Within recent years, atherosclerosis has been recognized as inflammation-driven disease condition, where progressive stages are characterized by morphological changes in plaque composition but also relevant molecular processes resulting in increased plaque vulnerability. While existing intravascular imaging modalities are able to resolve key morphological features during plaque progression, they lack capability to characterize the molecular profile of advanced atherosclerotic plaque. Because hybrid imaging modalities may provide incremental information related to plaque biology, they are expected to provide synergistic effects in detecting high risk patients and lesions. The aim of this article is to review existing literature on intravascular molecular imaging approaches, and to provide clinically oriented proposals of their application. In addition, we assembled an overview of future developments in this field geared towards detection of patients at risk for cardiovascular events.
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.
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