Review of imaging biomarkers for the vulnerable carotid plaque

Saba, Luca; Agarwal, Nivedita; Cau, Riccardo; Gerosa, Clara; Sanfilippo, Roberto; Porcu, Michele; Montisci, Roberto; Cerrone, Giulia; Yang, Qi; Balestrieri, Antonella; Lucatelli, Pierleone; Politi, Carola; Faa, Gavino; Suri, Jasjit S.

doi:10.1016/j.jvssci.2021.03.001

Cited by 42 publications

(34 citation statements)

References 115 publications

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“…stroke, which is a leading cause of disability and economic burden and is responsible for over 10% of mortality worldwide (34). Carotid plaque stenosis, one of the leading causes of ischemic stroke, is a multifactorial and complex process.…”

Section: Discussionmentioning

confidence: 99%

“…Atherosclerotic processes are a common denominator in a great number of pathological conditions, such as ischemic stroke, which is a leading cause of disability and economic burden and is responsible for over 10% of mortality worldwide ( 34 ). Carotid plaque stenosis, one of the leading causes of ischemic stroke, is a multifactorial and complex process.…”

Section: Discussionmentioning

confidence: 99%

“…Carotid plaque stenosis, one of the leading causes of ischemic stroke, is a multifactorial and complex process. It involves fundamental mechanisms of atherosclerosis, such as oxidative stress, inflammation, lipid accumulation, angiogenesis, apoptosis, proteolysis, and thrombotic processes which are among the main factors of carotid plaque vulnerability and rupture ( 34 , 35 ).…”

Section: Discussionmentioning

confidence: 99%

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Markers of Carotid Plaque Destabilization in Patients With Sleep-Disordered Breathing

2022

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Sleep-disordered breathing (SDB) is a nightly respiratory condition characterized by intermittent hypoxia, leading to oxidative stress, inflammation, and atherosclerosis. However, most cellular markers of human carotid plaques in SDB have not yet been assessed. We aimed at characterizing the cellular, inflammatory, and nitro-oxidative stress markers in carotid plaques obtained from 25 patients undergoing endarterectomy and screened for SDB. Sleep studies were performed during their preoperative hospitalization night using the Watch-PAT 100 device. Oxygen desaturation index (ODI) was used for dividing patients into two groups. Fourteen patients with ODI >5 were designated as SDB and 11 patients with ODI ≤ 5 as non-SDB. Demographics, comorbidities, cardiovascular risk factors, and medications were recorded. Cellular markers in plaques were analyzed by immunofluorescence using confocal microscopy. The expression of neutrophils was identified by CD66b+ and neutrophil elastase, macrophage-foam cells were identified by CD163+, and scavenger receptors by CD68+ and CD36+ expression. Additional markers included 3-nitrotyrosine, endothelial CD31, and smooth muscle cell-actin (SMC-actin). Plaques' lipids were determined by immunohistochemistry with Oil Red O staining. Notably, significantly higher values were found for SDB as compared to patients with non-SDB for 3-nitrotyrosine (p <0.004) and intracellular lipids' content (p <0.02), whereas SMC-actin was lower (p <0.006). There were no significant differences between patients with carotid-associated symptoms (symptomatic) and patients without carotid-associated symptoms (asymptomatic). However, a sub-group of symptomatic patients with co-existent SDB expressed the highest 3-nitrotyrosin, and intracellular lipids levels, and the lowest SMC-actin levels, whereas non-SDB/asymptomatic patients expressed the lowest 3-nitrotyrosin and lipids levels and the highest SMS-actin levels among all patients. Accordingly, ODI was lowest in non-SDB/asymptomatic patients and highest in SDB/symptomatic. In conclusion, plaques of patients with SDB were characterized by markedly increased levels of 3-nitrotyrosine and intracellular lipids content. Conversely, SMC-actin levels were significantly lower. These three markers, such as increased 3-nitrotyrosine and intracellular lipids and decreased SMC-actin are associated with plaque vulnerability and instability. These findings are in line with earlier reports demonstrating increased intima-media thickness in large cohorts of sleep apnea and patients with SDB, and thus, may indicate a higher susceptibility to plaque vulnerability and rapture in patients with SDB.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Markers of Carotid Plaque Destabilization in Patients With Sleep-Disordered Breathing

2022

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“…For patients with a high clinical suspicion of vaccine-induced immune thrombocytopenia, the guidelines suggest ordering imaging studied based on the location of symptoms to confirm the site of thrombosis, keeping in mind that VITT is associated with both arterial and venous thrombosis in a variety of sites [ 6 , 71 , 72 ]. Modern CT scans, thanks to constantly advancing technology with an increasingly better spatial and temporal resolution, can provide optimal and rapid imaging of the vessel lumen [ 73 , 74 ]. In addition, CT scan is able to rule out organ complications, including mesenteric ischemia, infarcted bowel, and solid organ infarcts [ 75 ].…”

Section: Vascular Complicationsmentioning

confidence: 99%

Role of imaging in rare COVID-19 vaccine multiorgan complications

et al. 2022

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As of September 18th, 2021, global casualties due to COVID-19 infections approach 200 million, several COVID-19 vaccines have been authorized to prevent COVID-19 infection and help mitigate the spread of the virus. Despite the vast majority having safely received vaccination against SARS-COV-2, the rare complications following COVID-19 vaccination have often been life-threatening or fatal. The mechanisms underlying (multi) organ complications are associated with COVID-19, either through direct viral damage or from host immune response (i.e., cytokine storm). The purpose of this manuscript is to review the role of imaging in identifying and elucidating multiorgan complications following SARS-COV-2 vaccination—making clear that, in any case, they represent a minute fraction of those in the general population who have been vaccinated. The authors are both staunch supporters of COVID-19 vaccination and vaccinated themselves as well.

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“…Various automated and semi-automated methods have been proposed to calculate imaging biomarkers in the past [ 25 ]. These biomarkers have their own relevance based on the study objective involved [ 26 ]. The measurement of the plaque area was proposed as an alternate method of stroke risk prediction [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Unseen Artificial Intelligence—Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study

et al. 2021

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Background: The early detection of carotid wall plaque is recommended in the prevention of cardiovascular disease (CVD) in moderate-risk patients. Previous techniques for B-mode carotid atherosclerotic wall plaque segmentation used artificial intelligence (AI) methods on monoethnic databases, where training and testing are from the “same” ethnic group (“Seen AI”). Therefore, the versatility of the system is questionable. This is the first study of its kind that uses the “Unseen AI” paradigm where training and testing are from “different” ethnic groups. We hypothesized that deep learning (DL) models should perform in 10% proximity between “Unseen AI” and “Seen AI”. Methodology: Two cohorts from multi-ethnic groups (330 Japanese and 300 Hong Kong (HK)) were used for the validation of our hypothesis. We used a four-layered UNet architecture for the segmentation of the atherosclerotic wall with low plaque. “Unseen AI” (training: Japanese, testing: HK or vice versa) and “Seen AI” experiments (single ethnicity or mixed ethnicity) were performed. Evaluation was conducted by measuring the wall plaque area. Statistical tests were conducted for its stability and reliability. Results: When using the UNet DL architecture, the “Unseen AI” pair one (Training: 330 Japanese and Testing: 300 HK), the mean accuracy, dice-similarity, and correlation-coefficient were 98.55, 78.38, and 0.80 (p < 0.0001), respectively, while for “Unseen AI” pair two (Training: 300 HK and Testing: 330 Japanese), these were 98.67, 82.49, and 0.87 (p < 0.0001), respectively. Using “Seen AI”, the same parameters were 99.01, 86.89 and 0.92 (p < 0.0001), respectively. Conclusion: We demonstrated that “Unseen AI” was in close proximity (<10%) to “Seen AI”, validating our DL model for low atherosclerotic wall plaque segmentation. The online system runs < 1 s.

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Review of imaging biomarkers for the vulnerable carotid plaque

Cited by 42 publications

References 115 publications

Markers of Carotid Plaque Destabilization in Patients With Sleep-Disordered Breathing

Markers of Carotid Plaque Destabilization in Patients With Sleep-Disordered Breathing

Role of imaging in rare COVID-19 vaccine multiorgan complications

Unseen Artificial Intelligence—Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study

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