Carotid stenosis and cryptogenic stroke.

OBJECTIVES: Cryptogenic stroke represents a type of ischemic stroke with an unknown origin, presenting a significant challenge in both stroke management and prevention. According to the Trial of Org 10,172 in Acute Stroke Treatment criteria, a stroke is categorized as being caused by large artery atherosclerosis only when there is >50% luminal narrowing of the ipsilateral internal carotid artery. However, nonstenosing carotid artery plaques can be an underlying cause of ischemic stroke. Indeed, emerging evidence documents that some features of plaque vulnerability may act as an independent risk factor, regardless of the degree of stenosis, in precipitating cerebrovascular events. This review, drawing from an array of imaging-based studies, explores the predictive values of carotid imaging modalities in the detection of nonstenosing carotid plaque (<50%), that could be the cause of a cerebrovascular event when some features of vulnerability are present. METHODS: Google Scholar, Scopus, and PubMed were searched for articles on cryptogenic stroke and those reporting the association between cryptogenic stroke and imaging features of carotid plaque vulnerability. RESULTS: Despite extensive diagnostic evaluations, the etiology of a considerable proportion of strokes remains undetermined, contributing to the recurrence rate and persistent morbidity in affected individuals. Advances in imaging modalities, such as magnetic resonance imaging, computed tomography scans, and ultrasound examination, facilitate more accurate detection of nonstenosing carotid artery plaque and allow better stratification of stroke risk, leading to a more tailored treatment strategy. CONCLUSIONS: Early detection of nonstenosing carotid plaque with features of vulnerability through carotid imaging techniques impacts the clinical management of cryptogenic stroke, resulting in refined stroke subtype classification and improved patient management. Additional research is required to validate these findings and recommend the integration of these state-of-the-art imaging methodologies into standard diagnostic protocols to improve stroke management and prevention.

UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review.

Cardiovascular disease (CVD) diagnosis and treatment are challenging since symptoms appear late in the disease's progression. Despite clinical risk scores, cardiac event prediction is inadequate, and many at-risk patients are not adequately categorised by conventional risk factors alone. Integrating genomic-based biomarkers (GBBM), specifically those found in plasma and/or serum samples, along with novel non-invasive radiomic-based biomarkers (RBBM) such as plaque area and plaque burden can improve the overall specificity of CVD risk. This review proposes two hypotheses: (i) RBBM and GBBM biomarkers have a strong correlation and can be used to detect the severity of CVD and stroke precisely, and (ii) introduces a proposed artificial intelligence (AI)-based preventive, precision, and personalized ( aiP 3 ) CVD/Stroke risk model. The PRISMA search selected 246 studies for the CVD/Stroke risk. It showed that using the RBBM and GBBM biomarkers, deep learning (DL) modelscould be used for CVD/Stroke risk stratification in the aiP 3 framework. Furthermore, we present a concise overview of platelet function, complete blood count (CBC), and diagnostic methods. As part of the AI paradigm, we discuss explainability, pruning, bias, and benchmarking against previous studies and their potential impacts. The review proposes the integration of RBBM and GBBM, an innovative solution streamlined in the DL paradigm for predicting CVD/Stroke risk in the aiP 3 framework. The combination of RBBM and GBBM introduces a powerful CVD/Stroke risk assessment paradigm. aiP 3 model signifies a promising advancement in CVD/Stroke risk assessment.

Effect of late gadolinium enhancement on left atrial impairment in myocarditis patients.

OBJECTIVE: The aims of our study were to investigate the effect of the extent and location of late gadolinium enhancement (LGE) on the left atrium (LA) function in patients with acute myocarditis (AM) using cardiovascular magnetic resonance (CMR). METHOD: This retrospective study performed CMR scans in 113 consecutive patients (89 males, 24 females; mean age 45.8 ± 17.3 years) with AM that met the updated Lake Louise criteria. Reservoir, conduit, and booster LA functions were analyzed by CMR feature tracking using dedicated software. Besides LA strain measurements, myocardial scar location and extent were assigned and quantified by LGE imaging. RESULTS: AM patients with septal LGE had impaired reservoir, conduit, and conduit strain rate function in comparison with AM patients with non-septal LGE (p = 0.001, for all). In fully adjusted multivariable linear regression, reservoir and conduit were significantly associated with left ventricle (LV) LGE location (ß coefficient = 8.205, p = 0.007; ß coefficient = 5.185, p = 0.026; respectively). In addition, LA parameters decreased according to the increase in the extent of LV fibrosis (LGE ≤ 10%; LGE 11-19%; LGE ≥ 20%). After adjustment in multivariable linear regression, the association with LV LGE extent was no longer statistically significant. CONCLUSION: In patients with acute myocarditis, LA function abnormalities are significantly associated with LV LGE location, but not with LGE extent. Septal LGE is paralleled by a deterioration of LA reservoir and conduit function. CLINICAL RELEVANCE STATEMENT: Left atrium dysfunction is associated with the presence of late gadolinium enhancement in the left ventricle septum and can be useful in the clinical prognostication of patients with acute myocarditis, allowing individually tailored treatment. KEY POINTS: • Myocardial fibrosis is related to atrial impairment. • The location of myocardial fibrosis is the main determinant of atrial dysfunction in myocarditis patients. • The quantification of atrial mechanisms may provide more in-depth insight into myocarditis pathophysiology.

Cine-cardiac magnetic resonance to distinguish between ischemic and non-ischemic cardiomyopathies: a machine learning approach.

OBJECTIVE: This work aimed to derive a machine learning (ML) model for the differentiation between ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM) on non-contrast cardiovascular magnetic resonance (CMR). METHODS: This retrospective study evaluated CMR scans of 107 consecutive patients (49 ICM, 58 NICM), including atrial and ventricular strain parameters. We used these data to compare an explainable tree-based gradient boosting additive model with four traditional ML models for the differentiation of ICM and NICM. The models were trained and internally validated with repeated cross-validation according to discrimination and calibration. Furthermore, we examined important variables for distinguishing between ICM and NICM. RESULTS: A total of 107 patients and 38 variables were available for the analysis. Of those, 49 were ICM (34 males, mean age 60 ± 9 years) and 58 patients were NICM (38 males, mean age 56 ± 19 years). After 10 repetitions of the tenfold cross-validation, the proposed model achieved the highest area under curve (0.82, 95% CI [0.47-1.00]) and lowest Brier score (0.19, 95% CI [0.13-0.27]), showing competitive diagnostic accuracy and calibration. At the Youden's index, sensitivity was 0.72 (95% CI [0.68-0.76]), the highest of all. Analysis of predictions revealed that both atrial and ventricular strain CMR parameters were important for the identification of ICM patients. CONCLUSION: The current study demonstrated that using a ML model, multi chamber myocardial strain, and function on non-contrast CMR parameters enables the discrimination between ICM and NICM with competitive diagnostic accuracy. CLINICAL RELEVANCE STATEMENT: A machine learning model based on non-contrast cardiovascular magnetic resonance parameters may discriminate between ischemic and non-ischemic cardiomyopathy enabling wider access to cardiovascular magnetic resonance examinations with lower costs and faster imaging acquisition. KEY POINTS: • The exponential growth in cardiovascular magnetic resonance examinations may require faster and more cost-effective protocols. • Artificial intelligence models can be utilized to distinguish between ischemic and non-ischemic etiologies. • Machine learning using non-contrast CMR parameters can effectively distinguish between ischemic and non-ischemic cardiomyopathies.

Atrial and ventricular strain using cardiovascular magnetic resonance in the prediction of outcomes of pericarditis patients: a pilot study.

OBJECTIVE: Our study aimed to explore with cardiovascular magnetic resonance (CMR) the impact of left atrial (LA) and left ventricular (LV) myocardial strain in patients with acute pericarditis and to investigate their possible prognostic significance in adverse outcomes. METHOD: This retrospective study performed CMR scans in 36 consecutive patients with acute pericarditis (24 males, age 52 [23-52]). The primary endpoint was the combination of recurrent pericarditis, constrictive pericarditis, and surgery for pericardial diseases defined as pericardial events. Atrial and ventricular strain function were performed on conventional cine SSFP sequences. RESULTS: After a median follow-up time of 16 months (interquartile range [13-24]), 12 patients with acute pericarditis reached the primary endpoint. In multivariable Cox regression analysis, LA reservoir and LA conduit strain parameters were all independent determinants of adverse pericardial diseases. Conversely, LV myocardial strain parameters did not remain an independent predictor of outcome. With receiving operating characteristics curve analysis, LA conduit and reservoir strain showed excellent predictive performance (area under the curve of 0.914 and 0.895, respectively) for outcome prediction at 12 months. CONCLUSION: LA reservoir and conduit mechanisms on CMR are independently associated with a higher risk of adverse pericardial events. Including atrial strain parameters in the management of acute pericarditis may improve risk stratification. CLINICAL RELEVANCE STATEMENT: Atrial strain could be a suitable non-invasive and non-contrast cardiovascular magnetic resonance parameter for predicting adverse pericardial complications in patients with acute pericarditis. KEY POINTS: • Myocardial strain is a well-validated CMR parameter for risk stratification in cardiovascular diseases. • LA reservoir and conduit functions are significantly associated with adverse pericardial events. • Atrial strain may serve as an additional non-contrast CMR parameter for stratifying patients with acute pericarditis.

Correlation of Cognitive Reappraisal and the Microstructural Properties of the Forceps Minor: A Deductive Exploratory Diffusion Tensor Imaging Study.

Cognitive reappraisal (CR) is a mechanism for emotion regulation, and the prefrontal cortex (PFC) plays a central role in the regulation of emotions. We tested the hypothesis of an association between CR function and microstructural properties of forceps minor (a commissural bundle within the PFC) in healthy subjects (HS). We analyzed a population of 65 young HS of a public dataset. The diffusion tensor imaging (DTI) sequence of every subject was analyzed to extract the derived shape (diameter and volume) and DTI metrics in terms of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) of the forceps minor. The CR subscale of the German version of the Emotion Regulation Questionnaire (ERQ) was used for CR assessment. The Shapiro-Wilk test was applied to test the assumption of normality in all these parameters, adopting a statistical threshold at p < 0.05. Whenever appropriate a non-parametric two-tailed partial correlation analysis was applied to test for correlations between the CR ERQ score and the derived shape and DTI metrics, including age and sex as confounders, adopting a statistical threshold at p < 0.05. The non-parametric two-tailed partial correlation analysis revealed a mildly significant correlation with FA (ρ = 0.303; p = 0.016), a weakly significant negative correlation with MD (ρ = - 0.269; p = 0.033), and a mildly significant negative correlation with RD (ρ = - 0.305; p = 0.015). These findings suggest a correlation between DTI microstructural properties of forceps minor and CR.

Hippocampus and olfactory impairment in Parkinson disease: a comparative exploratory combined volumetric/functional MRI study.

INTRODUCTION: Patients with Parkinson's Disease (PD) commonly experience Olfactory Dysfunction (OD). Our exploratory study examined hippocampal volumetric and resting-state functional magnetic resonance imaging (rs-fMRI) variations in a Healthy Control (HC) group versus a cognitively normal PD group, further categorized into PD with No/Mild Hyposmia (PD-N/MH) and PD with Severe Hyposmia (PD-SH). METHODS: We calculated participants' relative Total Hippocampal Volume (rTHV) and performed Spearman's partial correlations, controlled for age and gender, to examine the correlation between rTHV and olfactory performance assessed by the Odor Stick Identification Test for the Japanese (OSIT-J) score. Mann-Whitney U tests assessed rTHV differences across groups and subgroups, rejecting the null hypothesis for p < 0.05. Furthermore, a seed-based rs-fMRI analysis compared hippocampal connectivity differences using a one-way ANCOVA covariate model with controls for age and gender. RESULTS: Spearman's partial correlations indicated a moderate positive correlation between rTHV and OSIT-J in the whole study population (ρ = 0.406; p = 0.007), PD group (ρ = 0.493; p = 0.008), and PD-N/MH subgroup (ρ = 0.617; p = 0.025). Mann-Whitney U tests demonstrated lower rTHV in PD-SH subgroup compared to both HC group (p = 0.013) and PD-N/MH subgroup (p = 0.029). Seed-to-voxel rsfMRI analysis revealed reduced hippocampal connectivity in PD-SH subjects compared to HC subjects with a single cluster of voxels. CONCLUSIONS: Although the design of the study do not allow to make firm conclusions, it is reasonable to speculate that the progressive involvement of the hippocampus in PD patients is associated with the progression of OD.

Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography.

OBJECTIVES: While the link between carotid plaque composition and cerebrovascular vascular (CVE) events is recognized, the role of calcium configuration remains unclear. This study aimed to develop and validate a CT angiography (CTA)-based machine learning (ML) model that uses carotid plaques 6-type calcium grading, and clinical parameters to identify CVE patients with bilateral plaques. MATERIAL AND METHODS: We conducted a multicenter, retrospective diagnostic study (March 2013-May 2020) approved by the institutional review board. We included adults (18 +) with bilateral carotid artery plaques, symptomatic patients having recently experienced a carotid territory ischemic event, and asymptomatic patients either after 3 months from symptom onset or with no such event. Four ML models (clinical factors, calcium configurations, and both with and without plaque grading [ML-All-G and ML-All-NG]) and logistic regression on all variables identified symptomatic patients. Internal validation assessed discrimination and calibration. External validation was also performed, and identified important variables and causes of misclassifications. RESULTS: We included 790 patients (median age 72, IQR [61-80], 42% male, 64% symptomatic) for training and internal validation, and 159 patients (age 68 [63-76], 36% male, 39% symptomatic) for external testing. The ML-All-G model achieved an area-under-ROC curve of 0.71 (95% CI 0.58-0.78; p < .001) and sensitivity 80% (79-81). Performance was comparable on external testing. Calcified plaque, especially the positive rim sign on the right artery in older and hyperlipidemic patients, had a major impact on identifying symptomatic patients. CONCLUSION: The developed model can identify symptomatic patients using plaques calcium configuration data and clinical information with reasonable diagnostic accuracy. CLINICAL RELEVANCE: The analysis of the type of calcium configuration in carotid plaques into 6 classes, combined with clinical variables, allows for an effective identification of symptomatic patients. KEY POINTS: • While the association between carotid plaques composition and cerebrovascular events is recognized, the role of calcium configuration remains unclear. • Machine learning of 6-type plaque grading can identify symptomatic patients. Calcified plaques on the right artery, advanced age, and hyperlipidemia were the most important predictors. • Fast acquisition of CTA enables rapid grading of plaques upon the patient's arrival at the hospital, which streamlines the diagnosis of symptoms using ML.

Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review.

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.

Application of multi-spectral CT imaging in Crohn's disease: a systematic review.

BACKGROUND: No quantitative computed tomography (CT) biomarker is actually sufficiently accurate to assess Crohn's disease (CD) lesion activity, with adequate precision to guide clinical decisions. PURPOSE: To assess the available literature on the use of iodine concentration (IC), from multi-spectral CT acquisition, as a quantitative parameter able to distinguish healthy from affected bowel and assess CD bowel activity and heterogeneity of activity along the involved segments. MATERIAL AND METHODS: A literature search was conducted to identify original research studies published up to February 2022. The inclusion criteria were original research papers (>10 human participants), English language publications, focus on dual-energy CT (DECT) of CD with iodine quantification (IQ) as an outcome measure. The exclusion criteria were animal-only studies, languages other than English, review articles, case reports, correspondence, and study populations <10 patients. RESULTS: Nine studies were included in this review; all of which showed a strong correlation between IC measurements and CD activity markers, such as CD activity index (CDAI), endoscopy findings and simple endoscopic score for Crohn's disease (SES-CD), and routine CT enterography (CTE) signs and histopathologic score. Statistically significant differences in IC were reported between affected bowel segments and healthy ones (higher P value was P < 0.001), normal segments and those with active inflammation (P < 0.0001) as well as between patients with active disease and those in remission (P < 0.001). CONCLUSION: The mean normalized IC at DECTE could be a reliable tool in assisting radiologists in the diagnosis, classification and grading of CD activity.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

SBM vs VBM for highlighting similarities and differences between chronotype and Parkinson's MRI scans: a preliminary analysis.

OBJECTIVE: Voxel-Based Morphometry (VBM) and Source-Based Morphometry (SBM) are widely used techniques for analyzing structural Magnetic Resonance Imaging (MRI) data. VBM compares differences in gray and white matter volume, density, or concentration voxel-wise, while SBM identifies patterns of structural variation using independent component analysis. This study aims to compare the performance of VBM and SBM in detecting differences in brain structure across Parkinson's patients and healthy controls, grouped based on their chronotype. METHODS: Thirty-three subjects were divided into three groups: a Parkinson's Group (PG), an Early Chronotype Group (EG), and a Late Chronotype Group (LG). Circadian preference, daytime sleepiness, and sleep quality were assessed, and MRI data were acquired using a 3 T scanner. SBM and VBM were used to test differences and similarities in MRI scans and chronotypes. RESULTS: Results from SBM revealed significant clusters surviving the analysis, with the 1st component for the PG-EG and the 4th component for the PG-LG analysis showing the lowest p-value (< 0.05). Denser gray matter volume (GMV) or white matter volume (WMV) was observed in the Middle Frontal Gyrus and the Lentiform Nucleus through Talairach Coordinates analysis. CONCLUSIONS: This study emphasizes the importance of selecting appropriate methods for analyzing structural MRI data. VBM is effective in identifying local differences in brain structure, while SBM provides a more comprehensive view of structural variation, detecting patterns not captured by VBM. Future studies should consider utilizing both VBM and SBM to fully characterize brain structural differences in diverse clinical and cognitive populations. Further studies, with larger sample sizes and more balanced genders, genomic analysis, disease severity and duration, as well as medications' effect, are warranted.

Carotid Artery Plaque Calcifications: Lessons From Histopathology to Diagnostic Imaging.

The role of calcium in atherosclerosis is controversial and the relationship between vascular calcification and plaque vulnerability is not fully understood. Although calcifications are present in ≈50% to 60% of carotid plaques, their association with cerebrovascular ischemic events remains unclear. In this review, we summarize current understanding of carotid plaque calcification. We outline the role of calcium in atherosclerotic carotid disease by analyzing laboratory studies and histopathologic studies, as well as imaging findings to understand clinical implications of carotid artery calcifications. Differences in mechanism of calcium deposition express themselves into a wide range of calcification phenotypes in carotid plaques. Some patterns, such as rim calcification, are suggestive of plaques with inflammatory activity with leakage of the vasa vasourm and intraplaque hemorrhage. Other patterns such as dense, nodular calcifications may confer greater mechanical stability to the plaque and reduce the risk of embolization for a given degree of plaque size and luminal stenosis. Various distributions and patterns of carotid plaque calcification, often influenced by the underlying systemic pathological condition, have a different role in affecting plaque stability. Modern imaging techniques afford multiple approaches to assess geometry, pattern of distribution, size, and composition of carotid artery calcifications. Future investigations with these novel technologies will further improve our understanding of carotid artery calcification and will play an important role in understanding and minimizing stroke risk in patients with carotid plaques.

The emerging role of atrial strain assessed by cardiac MRI in different cardiovascular settings: an up-to-date review.

The left atrium (LA) has a crucial function in maintaining left ventricular filling, which is responsible for about one-third of all cardiac filling. A growing body of evidence shows that LA is involved in several cardiovascular diseases from a clinical and prognostic standpoint. LA enlargement has been recognized as a predictor of the outcomes of many diseases. However, LA enlargement itself does not explain the whole LA's function during the cardiac cycle. For this reason, the recently proposed assessment of atrial strain at advanced cardiac magnetic resonance (CMR) enables the usual limitations of the sole LA volumetric measurement to be overcome. Moreover, the left atrial strain impairment might allow several cardiovascular diseases to be detected at an earlier stage. While traditional CMR has a central role in assessing LA volume and, through cine sequences, a marginal role in evaluating LA function, feature tracking at advanced CMR (CMR-FT) has been increasingly confirmed as a feasible and reproducible technique for assessing LA function through strain. In comparison to atrial function evaluations via speckle tracking echocardiography, CMR-FT has a higher spatial resolution, larger field of view, and better reproducibility. In this literature review on atrial strain analysis, we describe the strengths, limitations, recent applications, and promising developments of studying atrial function using CMR-FT in clinical practice. KEY POINTS: • The left atrium has a crucial function in maintaining left ventricular filling; left atrial size has been recognized as a predictor of the outcomes of many diseases. • Left atrial strain has been confirmed as a marker of atrial functional status and demonstrated to be a sensitive tool in the subclinical phase of a disease. • A comprehensive evaluation of the three phases of atrial function by CMR-FT demonstrates an impairment before the onset of atrial enlargement, thus helping clinicians in their decision-making and improving patient outcomes.

The mid-term effects of carotid endarterectomy on cognition and regional neural activity analyzed with the amplitude of low frequency fluctuations technique.

PURPOSE: The study aims to evaluate the mid-term effects of carotid endarterectomy (CEA) on cognition and resting-state functional magnetic resonance imaging (rs-fMRI) using the Amplitude of Low Frequency Fluctuations (ALFF) technique. METHODS: In this observational study, patients eligible for CEA were prospectively included. On the same day, within 1 week of the CEA procedure performed and 12 months after the CEA procedure, all patients underwent (i) an MRI examination for rs-fMRI analysis and (ii) a cognitive evaluation using the Italian version of the Mini-Mental State Examination (MMSE) corrected for age and schooling. Pre-CEA and post-CEA MMSE scores were evaluated using paired sample t-tests, adopting a p-value < 0.05 as statistical threshold. The ALFF technique was used for analyzing the differences between pre-CEA and post-CEA rs-fMRI scans in terms of regional neural activation. This was accomplished by applying non-parametric statistics based on randomization/permutation for cluster-level inferences, adopting a cluster-mass p-value corrected for false discovery < 0.05 for cluster threshold, and a p-uncorrected < 0.01 for the voxel threshold. RESULTS: Twenty asymptomatic patients were enrolled. The mean MMSE score resulted improved following CEA procedure (p-value = 0.001). The ALFF analysis identified a single cluster of 6260 voxels of increased regional neural activity following CEA, and no cluster of reduced activity. The majority of voxels covered the right precentral gyrus, the right middle frontal gyrus, and the anterior division of the cingulate gyrus. CONCLUSION: Mid-term cognitive improvements observed after CEA are associated to increased regional neural activity of several cerebral regions.

Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients.

The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitus, and/or arterial hypertension, using conventional or office-based, laboratory-based blood biomarkers and carotid/femoral ultrasound image-based phenotypes. Two kinds of data (CVD risk factors and presence of CVD-defined as stroke, or myocardial infarction, or coronary artery syndrome, or peripheral artery disease, or coronary heart disease) as ground truth, were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3 years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) to study their effect on the ML classifiers. Three kinds of ML classifiers (Random Forest, Support Vector Machine, and Linear Discriminant Analysis) were applied in a two-fold cross-validation framework using the data augmented by synthetic minority over-sampling technique (SMOTE) strategy. The performance of the ML classifiers was recorded. In this cohort with overall 46 CVD risk factors (covariates) implemented in an online cardiovascular framework, that requires calculation time less than 1 s per patient, a mean accuracy and area-under-the-curve (AUC) of 98.40% and 0.98 (p < 0.0001) for CVD presence detection at visit 1, and 98.39% and 0.98 (p < 0.0001) at visit 2, respectively. The performance of the cardiovascular framework was significantly better than the classical CVD risk score. The ML paradigm proved to be powerful for CVD prediction in individuals at medium to high cardiovascular risk.

Optimal Management of Asymptomatic Carotid Stenosis in 2021: The Jury is Still Out. An International, Multispecialty, Expert Review and Position Statement.

OBJECTIVES: The recommendations of international guidelines for the management of asymptomatic carotid stenosis (ACS) often vary considerably and extend from a conservative approach with risk factor modification and best medical treatment (BMT) alone, to a more aggressive approach with a carotid intervention plus BMT. The aim of the current multispecialty position statement is to reconcile the conflicting views on the topic. MATERIALS AND METHODS: A literature review was performed with a focus on data from recent studies. RESULTS: Several clinical and imaging high-risk features have been identified that are associated with an increased long-term ipsilateral ischemic stroke risk in patients with ACS. Such high-risk clinical/imaging features include intraplaque hemorrhage, impaired cerebrovascular reserve, carotid plaque echolucency/ulceration/ neovascularization, a lipid-rich necrotic core, a thin or ruptured fibrous cap, silent brain infarction, a contralateral transient ischemic attack/stroke episode, male patients < 75 years and microembolic signals on transcranial Doppler. There is growing evidence that 80-99% ACS indicate a higher stroke risk than 50-79% stenoses. CONCLUSIONS: Although aggressive risk factor control and BMT should be implemented in all ACS patients, several high-risk features that may increase the risk of a future cerebrovascular event are now documented. Consequently, some guidelines recommend a prophylactic carotid intervention in high-risk patients to prevent future cerebrovascular events. Until the results of the much-anticipated randomized controlled trials emerge, the jury is still out regarding the optimal management of ACS patients.

Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation.

Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, "COVLIAS 1.0-Unseen" proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10,000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations-two Unseen sets: (i) train-CRO:test-ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web-based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL.

Atrial Strain by Feature-Tracking Cardiac Magnetic Resonance Imaging in Takotsubo Cardiomyopathy. Features, Feasibility, and Reproducibility.

OBJECTIVES: The purpose of this study was to investigate whether there may be a bi-atrial dysfunction in Takotsubo syndrome (TS) during the transient course of the disease, using cardiac magnetic resonance imaging feature tracking (CMR-FT) in analyzing bi-atrial strain. METHOD: Eighteen TS patients and 13 healthy controls were studied. Reservoir, conduit, and booster bi-atrial functions were analyzed by CMR-FT. The correlation between LA and RA strain parameters was assessed. Intra- and inter-observer reproducibility was evaluated for all strain and strain rate (SR) parameters using intraclass correlation coefficients (ICCs) and Bland-Altman analysis. RESULTS: Atrial strain were feasible in all patients and controls. Takotsubo patients showed an impaired LA Reservoir strain (∊s), LA Reservoir strain rate (SRs), LA and RA Conduit strain(∊e), LA and RA conduit strain rate (SRe) in comparison with controls (P < 0.001 for all of them), while no differences were found as to LA and RA booster deformation parameters (∊a and SRa). Analysis of correlation showed that LA ∊s, SRs, ∊e, and SRe were positively correlated with corresponding RA strain measurements (P < 0.001, r = 0.61 and P = 0,03, r = 0,54, respectively). Reproducibility was good to excellent for all atrial strain and strain rate parameters (ICCs ranging from 0,50 to 0,96). CONCLUSION: Atrial strain analysis using CMR-FT may be a useful tool to reveal new pathophysiological insights in Takotsubo cardiomyopathy. Additional studies, with a larger number of patients, are needed to confirm the possible role of these advanced CMR tools in characterizing TS patients.

The association between white matter hyperintensities, cognition and regional neural activity in healthy subjects.

White matter hyperintensities (WMH) are common findings that can be found in physiological ageing. Several studies suggest that the disruption of white matter tracts included in WMH could induce abnormal functioning of the respective linked cortical structures, with consequent repercussion on the cerebral functions, included the cognitive sphere. In this cross-sectional research, we analysed the effects of the total WMH burden (tWMHb) on resting-state functional magnetic resonance imaging (rs-fMRI) and cognition. Functional and structural MR data, as well as the scores of the trail making test subtests A (TMT-A) and B (TMT-B) of 75 healthy patients, were extracted from the public available Leipzig Study for Mind-Body-Emotion Interactions dataset. tWMHb was extracted from structural data. Spearman's correlation analyses were made for investigating correlations between WMHb and the scores of the cognitive tests. The fractional amplitude of low-frequency fluctuations (fALFF) method was applied for analysing the rs-fMRI data, adopting a multiple regression model for studying the effects of tWMHb on brain activity. Three different subanalyses were conducted using different statistical methods. We observed statistically significant correlations between WMHb and the scores of the cognitive tests. The fALFF analysis revealed that tWMHb is associated with the reduction of regional neural activity of several brain areas (in particular the prefrontal cortex, precuneus and cerebellar crus I/II). We conclude that our findings clarify better the relationships between WMH and cognitive impairment, evidencing that tWMHb is associated with impairments of the neurocognitive function in healthy subjects by inducing a diffuse reduction of the neural activity.