Saba, Luca

BACKGROUND: This study aimed to develop and validate a computed tomography angiography based machine learning model that uses plaque composition data and degree of carotid stenosis to detect symptomatic carotid plaques in patients with carotid atherosclerosis. METHODS: The machine learning based model was trained using degree of stenosis and the volumes of 13 computed tomography angiography derived intracarotid plaque subcomponents (eg, lipid, intraplaque hemorrhage, calcium) to identify plaques associated with cerebrovascular events. The model was internally validated through repeated 10-fold cross-validation and tested on a dedicated testing cohort according to discrimination and calibration. RESULTS: This retrospective, single-center study evaluated computed tomography angiography scans of 268 patients with both symptomatic and asymptomatic carotid atherosclerosis (163 for the derivation set and 106 for the testing set) performed between March 2013 and October 2019. The area-under-receiver-operating characteristics curve by machine learning on the testing cohort (0.89) was significantly higher than the areas under the curve of traditional logit analysis based on the degree of stenosis (0.51, P <0.001), presence of intraplaque hemorrhage (0.69, P <0.001), and plaque composition (0.78, P <0.001), respectively. Comparable performance was obtained on internal validation. The identified plaque components and associated cutoff values that were significantly associated with a higher likelihood of symptomatic status after adjustment were the ratio of intraplaque hemorrhage to lipid volume (≥50%, 38.5 [10.1–205.1]; odds ratio, 95% CI) and percentage of intraplaque hemorrhage volume (≥10%, 18.5 [5.7–69.4]; odds ratio, 95% CI). CONCLUSIONS: This study presented an interpretable machine learning model that accurately identifies symptomatic carotid plaques using computed tomography angiography derived plaque composition features, aiding clinical decision-making.

Introduction In the current paper, the “carotid artery calcium score” method is presented with the target to offer a metric method to quantify the amount of calcification in the carotid artery. Model and Definition The Volume of Interest (VOI) should be extracted and those voxels, with a Hounsfield Unit (HU) value ≥130, should be considered. The total weight value is determined by calculating the sum of the HU attenuation values of all voxels with values ≥130 HU. This value should be multiplied by the conversion factor (“or voxel size”) and divided by a weighting factor, the attenuation threshold to consider a voxel as calcified (and therefore 130 HU): this equation determines the Carotid Artery Calcium Score (CACS). Results In order to provide the demonstration of the potential feasibility of the model, the CACS was calculated in 131 subjects (94 males; mean age 72.7 years) for 235 carotid arteries (in 27 subjects, unilateral plaque was present) considered. The CACS value ranged from 0.67 to 11716. A statistically significant correlation was found (rho value = 0.663, p value = .0001) between the CACS in the right and left carotid plaques. Moreover, a statistically significant correlation between the age and the total CACS was present (rho value = 0.244, p value = .005), whereas no statistically significant difference was found in the distribution of CACS by gender ( p = .148). The CACS was also tested at baseline and after contrast and no statistically significant difference was found. Conclusion In conclusion, this method is of easy application, and it weights at the same time the volume and the degree of calcification in a unique parameter. This method needs to be tested to verify its potential utility, similar to the coronary artery calcium score, for the risk stratification of the occurrence of cerebrovascular events of the anterior circulation. Further studies using this new diagnostic tool to determine the prognostic value of carotid calcium quantification are needed.

Coronary artery calcification (CAC) accompanies the development of advanced atherosclerosis. Its role in atherosclerosis holds great interest because the presence and burden of coronary calcification provide direct evidence of the presence and extent of coronary artery disease; furthermore, CAC predicts future events independently of concomitant conventional cardiovascular risk factors and to a greater extent than any other noninvasive biomarker of this disease. Nevertheless, the relationship between CAC and the susceptibility of a plaque to provoke a thrombotic event remains incompletely understood. This review summarizes the current understanding and literature on CAC. It outlines the pathophysiology of CAC and reviews laboratory, histopathological, and genetic studies, as well as imaging findings, to characterize different types of calcification and to elucidate their implications. Some patterns of calcification such as microcalcification portend increased risk of rupture and cardiovascular events and may improve prognosis assessment noninvasively. However, contemporary computed tomography cannot assess early microcalcification. Limited spatial resolution and blooming artifacts may hinder estimation of degree of coronary artery stenosis. Technical advances such as photon counting detectors and combination with nuclear approaches (eg, NaF imaging) promise to improve the performance of cardiac computed tomography. These innovations may speed achieving the ultimate goal of providing noninvasively specific and clinically actionable information.

Background Distal medium vessel occlusions (DMVOs) and minor strokes represent emerging frontiers in mechanical thrombectomy (MT). Although several randomized clinical trials (RCTs) are underway, the design characteristics of these trials and the specific questions they aim to address have not been extensively explored. This current study sought to investigate the design and data elements reported in active prospective DMVO and minor stroke studies. Methods The ClinicalTrials.gov database was searched for ongoing prospective studies assessing the role of MT in patients with DMVOs or minor strokes. The Nested Knowledge AutoLit platform was utilized to categorize reported outcomes and inclusion/exclusion criteria. Frequencies of reported data elements were extracted from study protocols. Results A total of 10 (8 DMVO and 2 minor stroke) studies enrolling 3520 patients were included. All DMVO studies employ different criteria regarding target occlusion locations. Five DMVO studies use stent retrievers as the first-line thrombectomy technique (62.5%, 5/8), while three studies allow any MT techniques, generally at the operator’s discretion. Four DMVO studies permit intravenous thrombolysis (IVT) utilization in both intervention and control arms (50%, 4/8). The DISTALS trial excludes patients receiving IVT, while the DUSK trial and Tigertriever registry only enroll patients who are ineligible for IVT or for whom IVT failed to achieve reperfusion. DMVO studies exhibit notable heterogeneity in symptom onset duration thresholds for inclusion (<6 hours: 2 studies; <12 hours: 2 studies; <24 hours: 3 studies). Minor stroke trials employ similar inclusion criteria and outcome measures except for symptom duration thresholds for inclusion (8 hours for ENDOLOW and 23 hours for MOSTE). Conclusions There is considerable heterogeneity among active DMVO trials regarding potential target DMVO locations and time thresholds for inclusion based on the last known well time. Furthermore, our review indicates that the utility of aspiration thrombectomy in DMVOs and the advantages of MT without IVT over IVT alone will remain largely unexplored even after completion of active DMVO trials.

Carotid artery stenosis is a major cause of morbidity and mortality. The journey to understanding carotid disease has developed over time and radiology has a pivotal role in diagnosis, risk stratification and therapeutic management. This paper reviews the history of diagnostic imaging in carotid disease, its evolution towards its current applications in the clinical and research fields, and the potential of new technologies to aid clinicians in identifying the disease and tailoring medical and surgical treatment.

Introduction: The carotid artery rim sign on CTA is characterized by soft-plaque components surrounded by a rind of thin, adventitial calcification and its role in plaque instability is not established yet. Intraplaque hemorrhage (IPH) and the inflammatory microenvironment in carotid atheroma are considered features of atherosclerotic plaque vulnerability leading to cerebrovascular events. Hypothesis: The association between rim sign on CTA and carotid IPH and inflammation remains scantly investigated. In this study, we aimed to determine whether rim sign is associated with IPH on MRA and with the extent of IPH and inflammation on histology. We hypothesized that rim sign may be a radiological predictor of carotid IPH and inflammation. Methods: We retrospectively enrolled 100 consecutive patients who underwent carotid endarterectomy (CEA) and neck CTA before CEA. On CTA, the ipsilateral rim sign was defined as the presence of adventitial calcifications with internal soft-tissue plaque of ≥2 mm in maximum thickness. Of the included patients, 25 had MRA with 3D magnetization-prepared rapid acquisition with gradient echo (MPRAGE) sequence. Carotid plaque specimens were collected and stained with H&E to quantify IPH (%). Histologically, we defined IPH as an area of carotid plaque with blood elements, and inflammatory microenvironment as the presence of lymphocytes and plasma cells (chronic inflammation) and foam cells. Results: The rim sign on CTA was present in 56 (56%) of patients. IPH on MRA was present in 11 (44%) patients. The rim sign was significantly associated with the presence of IPH on MRA [9 (81.8%) vs 2 (14.3%); p=0.003]. On histology, the rim sign was associated with a higher extent of IPH [5.0 % (IQR 2.0-25.0) vs 5.0% (IQR 0.0-10.0); p=0.017], with chronic inflammation [44 (78.6%) vs 12 (21.4%); p=0.003], and foam cells [51 (91.1%) vs 5 (8.9%); p<0.001]. Conclusions: These findings suggest that rim sign on CTA may be proposed as a potential imaging marker of IPH as well as a histologic predictor of IPH extent and carotid plaque inflammatory microenvironment.

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.

Abstract Background Carotid intraplaque hemorrhage (IPH) is considered a strong marker of histologically defined plaque vulnerability leading to cerebrovascular ischemic events. Cerebral microbleeds (CMBs) represent hemorrhage-prone small vessel disease and they are a common finding on brain MRI in patients with cerebrovascular disease. However, the potential mechanism and the risk for CMBs are not clear. The characteristics of carotid lesions have been considered relevant in the relationship between carotid artery atherosclerosis and the presence of CMBs. The potential association between carotid IPH histologically defined and CMBs has not been investigated yet. Purpose We have previously reported the prevalence of IPH in patients with non-obstructive carotid disease in patients who underwent carotid endarterectomy (CEA). In this study, we aimed to investigate whether the presence and the extent of carotid IPH are related to the existence of CMBs. We hypothesized that patients with carotid IPH would have a higher risk of CMBs. Methods This retrospective study enrolled 101 consecutive patients undergoing CEA with symptomatic (including ischemic stroke, TIA, and amaurosis fugax) or asymptomatic ipsilateral carotid artery disease. Carotid plaque specimens were collected at CEA from all the patients and stained with Movat Pentachrome to identify the presence and the extent (%) of IPH. Neck CTA was obtained to measure the degree of carotid stenosis. Brain MRI was pre-surgically performed and CMBs were studied using T2*-weighted gradient-recalled echo (GRE) or susceptibility-weighted imaging (SWI) sequence. The CMBs were counted and localized. Clinical and biochemical data, comorbidities, and medications were recorded. The association between carotid IPH and CMBs was examined adjusted for other risk factors. Results The presence of carotid IPH was in 57 (56.4%) patients. CMBs were more observed in patients with carotid IPH compared to those without IPH [19 (33.3%) vs 5 (11.4%); p=0.010]. Logistic regression analysis demonstrated an association between the extent of IPH in the carotid atheroma and the presence of CMBs [OR 1.051 (95% CI 1.012–1.090); p=0.009]. Moreover, the carotid IPH extent was associated with the number of CMBs (p=0.004). In patients with CMBs, the median degree of ipsilateral carotid stenosis was 40% (35–65%) and it was 70% (50–80%) in those without CMBs, with a significant difference between the two groups (p=0.049). Conclusions In patients undergoing CEA, the histologically defined presence of carotid IPH and its extent are associated with CMBs on brain MR imaging. CMBs may be a potential mechanism for cerebrovascular events in patients with carotid atherosclerotic IPH and they may be an imaging marker that can distinguish the severity of the carotid artery disease. Funding Acknowledgement Type of funding sources: Other. Main funding source(s): Mayo Clinic Foundation

Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.