Structural Brain MR Imaging Alterations in Patients with Systemic Lupus Erythematous with and without Neuropsychiatric Events

BACKGROUND AND PURPOSE

Systemic lupus erythematosus is a complex autoimmune disease known for its diverse clinical manifestations, including neuropsychiatric systemic lupus erythematosus, which impacts a patient's quality of life. Our aim was to explore the relationships among brain MR imaging morphometric findings, neuropsychiatric events, and laboratory values in patients with systemic lupus erythematosus, shedding light on potential volumetric biomarkers and diagnostic indicators for neuropsychiatric systemic lupus erythematosus.

MATERIALS AND METHODS

Twenty-seven patients with systemic lupus erythematosus (14 with neuropsychiatric systemic lupus erythematosus, 13 with systemic lupus erythematosus), 24 women and 3 men (average age, 43 years, ranging from 21 to 62 years) were included in this cross-sectional study, along with 10 neuropsychiatric patients as controls. An MR imaging morphometric analysis, with the VolBrain online platform, to quantitatively assess brain structural features and their differences between patients with neuropsychiatric systemic lupus erythematosus and systemic lupus erythematosus, was performed. Correlations and differences between MR imaging morphometric findings and laboratory values, including disease activity scores, such as the Systemic Lupus Erythematosus Disease Activity Index and the Systemic Lupus International Collaborating Clinics Damage Index, were explored. An ordinary least squares regression analysis further explored the Systemic Lupus Erythematosus Disease Activity Index and Systemic Lupus International Collaborating Clinics Damage Index relationship with MR imaging features.

RESULTS

For neuropsychiatric systemic lupus erythematosus and non-neuropsychiatric systemic lupus erythematosus, the brain regions with the largest difference in volumetric measurements were the insular central operculum volume (P value = .003) and the occipital cortex thickness (P = .003), which were lower in neuropsychiatric systemic lupus erythematosus. The partial correlation analysis showed that the most correlated morphometric features with neuropsychiatric systemic lupus erythematosus were subcallosal area thickness asymmetry (P < .001) and temporal pole thickness asymmetry (P = .011). The ordinary least squares regression analysis yielded an R 2 of 0.725 for the Systemic Lupus Erythematosus Disease Activity Index score, with calcarine cortex volume as a significant predictor, and an R 2 of 0.715 for the Systemic Lupus International Collaborating Clinics Damage Index score, with medial postcentral gyrus volume as a significant predictor.

CONCLUSIONS

The MR imaging volumetric analysis, along with the correlation study and the ordinary least squares regression analysis, revealed significant differences in brain regions and their characteristics between patients with neuropsychiatric systemic lupus erythematosus and those with systemic lupus erythematosus, as well as between patients with different Systemic Lupus Erythematosus Disease Activity Index and Systemic Lupus International Collaborating Clinics Damage Index scores.

Nutritional ecology of a prototypical generalist predator, the red fox (Vulpes vulpes)

Generalist species, which exploit a wide range of food resources, are expected to be able to combine available resources as to attain their specific macronutrient ratio (percentage of caloric intake of protein, lipids and carbohydrates). Among mammalian predators, the red fox Vulpes vulpes is a widespread, opportunistic forager: its diet has been largely studied, outlining wide variation according to geographic and climatic factors. We aimed to check if, throughout the species' European range, diets vary widely in macronutrient composition or foxes can combine complementary foods to gain the same nutrient intake. First, we assessed fox's intake target in the framework of nutritional geometry. Secondly, we aimed to highlight the effects of unbalanced diets on fox density, which was assumed as a proxy for Darwinian fitness, as assessed in five areas of the western Italian Alps. Unexpectedly, the target macronutrient ratio of the fox (52.4% protein-, 38.7% lipid- and 8.9% carbohydrate energy) was consistent with that of hypercarnivores, such as wolves and felids, except for carbohydrate intakes in urban and rural habitats. The inverse relation between density and the deviation of observed macronutrient ratios from the intake target suggests that fox capability of surviving in a wide range of habitats may not be exempt from fitness costs and that nutrient availability should be regarded among the biotic factors affecting animal abundance and distribution.

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.

Cardiovascular disease/stroke risk stratification in deep learning framework: a review

The global mortality rate is known to be the highest due to cardiovascular disease (CVD). Thus, preventive, and early CVD risk identification in a non-invasive manner is vital as healthcare cost is increasing day by day. Conventional methods for risk prediction of CVD lack robustness due to the non-linear relationship between risk factors and cardiovascular events in multi-ethnic cohorts. Few recently proposed machine learning-based risk stratification reviews without deep learning (DL) integration. The proposed study focuses on CVD risk stratification by the use of techniques mainly solo deep learning (SDL) and hybrid deep learning (HDL). Using a PRISMA model, 286 DL-based CVD studies were selected and analyzed. The databases included were Science Direct, IEEE Xplore, PubMed, and Google Scholar. This review is focused on different SDL and HDL architectures, their characteristics, applications, scientific and clinical validation, along with plaque tissue characterization for CVD/stroke risk stratification. Since signal processing methods are also crucial, the study further briefly presented Electrocardiogram (ECG)-based solutions. Finally, the study presented the risk due to bias in AI systems. The risk of bias tools used were (I) ranking method (RBS), (II) region-based map (RBM), (III) radial bias area (RBA), (IV) prediction model risk of bias assessment tool (PROBAST), and (V) risk of bias in non-randomized studies-of interventions (ROBINS-I). The surrogate carotid ultrasound image was mostly used in the UNet-based DL framework for arterial wall segmentation. Ground truth (GT) selection is vital for reducing the risk of bias (RoB) for CVD risk stratification. It was observed that the convolutional neural network (CNN) algorithms were widely used since the feature extraction process was automated. The ensemble-based DL techniques for risk stratification in CVD are likely to supersede the SDL and HDL paradigms. Due to the reliability, high accuracy, and faster execution on dedicated hardware, these DL methods for CVD risk assessment are powerful and promising. The risk of bias in DL methods can be best reduced by considering multicentre data collection and clinical evaluation.

The human carotid atherosclerotic plaque: an observational review of histological scoring systems

OBJECTIVE

The atherosclerotic plaque is a complex dynamic pathological lesion of the arterial wall, characterized by multiple elementary lesions of different diagnostic and prognostic significance. Fibrous cap thickness, lipid necrotic core dimension, inflammation, intra-plaque hemorrhage (IPH), plaque neovascularization and endothelial dysfunction (erosions) are generally considered the most relevant morphological details of plaque morphology. In this review, the most relevant features able to discriminate between stable and vulnerable plaques at histological level are discussed.

SUBJECTS AND METHODS

Retrospectively, we have evaluated the laboratory results from one hundred old histological samples from patients treated with carotid endarterectomy. These results were analyzed to assess elementary lesions that characterize stable and unstable plaques.

RESULTS

A thin fibrous cap (<65 micron), loss of smooth muscle cells, collagen depletion, a large lipid-rich necrotic core, infiltrating macrophages, IPH and intra-plaque vascularization are identified as the most important risk factors associated with plaque rupture.

CONCLUSIONS

Immunohistochemistry for smooth muscle actin (smooth muscle cell marker) and for CD68 (marker of monocytes/macrophages) and glycophorin (marker of red blood cells) are suggested as useful tools for an in deep characterization of any carotid plaque and for distinguishing plaque phenotypes at histology. Since patients with a carotid vulnerable plaque are at higher risk of developing vulnerable plaques in other arteries as well, the definition of the vulnerability index is underlined, in order to stratify patients at higher risk for undergoing cardiovascular events.

Epicardial fat volume assessed with cardiac magnetic resonance imaging in patients with Takotsubo cardiomyopathy

PURPOSE

The aims of our study were to investigate with cardiovascular magnetic resonance (CMR) the role of Epicardial Fat Volume (EFV) and distribution in patients with Takotsubo cardiomyopathy (TTC). Moreover, we explored EFV in patients with TTC and related this to comorbidities, cardiac biomarkers, and cardiac function.

METHODS

This retrospective study performed CMR scans in 30 consecutive TTC patients and 20 healthy controls. The absolute amount of EFV was quantified in consecutive short-axis cine stacks through the modified Simpson's rule. In addition, the left atrio-ventricular groove (LV) and right ventricle (RV) Epicardial Fat Thickness (EFT) were measured as well. Besides epicardial fat, LV myocardial strain parameters and T2 mapping measurements were obtained.

RESULTS

TTC patients and controls were of comparable age, sex, and body mass index. Compared to healthy controls, patients with TTC demonstrated a significantly increased EFV, epicardial fat mass, and EFV indexed for body 7surface area (p = 0.005; p = 0.003; p = 0.008; respectively). In a multiple regression model including age, sex, BMI, atrial fibrillation, and dyslipidemia, TTC remained an independent association with EFV (p = 0.008). Global T2 mapping and Global longitudinal strain in patients with TTC were correlated with EFV (r = 0.63, p = 0.001, and r = 0.44, p = 0.02, respectively).

CONCLUSION

Patients with TTC have increased EFV compared to healthy controls, despite a similar body mass index. The amount of epicardial fat was associated with CMR markers of myocardial inflammation and subclinical contractile dysfunction.

A new system of authorship best assessment

Purpose

The standard bibliometric indexes ("m-quotient "H-," "H2-," "g-," "a-," "m-," and "r-" index) do not considered the research' position in the author list of the paper. We proposed a new methodology, System of Authorship Best Assessment (SABA), to characterize the scientific output based on authors' position.

Material and Methods

Four classes S1A, S1B, S2A, and S2B include only papers where the researcher is in first, first/last, first/second/last, and first/second/second-last/last position respectively were used for the calculation of H-index and number of citations The system was tested with Noble prize winners controlled with researchers matched for H-index. The different in percentage between standard bibliometric index and S2B was calculated and compared.

Results

The percentage differences in Noble prize winners between S2B-H-index versus Global H-index and number of citations is very lower comparing with control group (median 4.15% [adjusted 95% CI, 2.54-5.30] vs 9.00 [adjusted 95% CI, 7.16-11.84], p < 0.001; average difference 8.7% vs 20.3%). All different in percentage between standard bibliometric index and S2B except two (H2- and m-index) were significantly lower among Noble prize compared with control group.

Conclusion

The SABA methodology better weight the research impact by showing that for excellent profiles the S2B is similar to global values whereas for other researchers there is a significant difference.

Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

Brain tumor characterization (BTC) is the process of knowing the underlying cause of brain tumors and their characteristics through various approaches such as tumor segmentation, classification, detection, and risk analysis. The substantial brain tumor characterization includes the identification of the molecular signature of various useful genomes whose alteration causes the brain tumor. The radiomics approach uses the radiological image for disease characterization by extracting quantitative radiomics features in the artificial intelligence (AI) environment. However, when considering a higher level of disease characteristics such as genetic information and mutation status, the combined study of "radiomics and genomics" has been considered under the umbrella of "radiogenomics". Furthermore, AI in a radiogenomics' environment offers benefits/advantages such as the finalized outcome of personalized treatment and individualized medicine. The proposed study summarizes the brain tumor's characterization in the prospect of an emerging field of research, i.e., radiomics and radiogenomics in an AI environment, with the help of statistical observation and risk-of-bias (RoB) analysis. The PRISMA search approach was used to find 121 relevant studies for the proposed review using IEEE, Google Scholar, PubMed, MDPI, and Scopus. Our findings indicate that both radiomics and radiogenomics have been successfully applied aggressively to several oncology applications with numerous advantages. Furthermore, under the AI paradigm, both the conventional and deep radiomics features have made an impact on the favorable outcomes of the radiogenomics approach of BTC. Furthermore, risk-of-bias (RoB) analysis offers a better understanding of the architectures with stronger benefits of AI by providing the bias involved in them.

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0

BACKGROUND

COVLIAS 1.0: an automated lung segmentation was designed for COVID-19 diagnosis. It has issues related to storage space and speed. This study shows that COVLIAS 2.0 uses pruned AI (PAI) networks for improving both storage and speed, wiliest high performance on lung segmentation and lesion localization.

METHOD

ology: The proposed study uses multicenter ∼9,000 CT slices from two different nations, namely, CroMed from Croatia (80 patients, experimental data), and NovMed from Italy (72 patients, validation data). We hypothesize that by using pruning and evolutionary optimization algorithms, the size of the AI models can be reduced significantly, ensuring optimal performance. Eight different pruning techniques (i) differential evolution (DE), (ii) genetic algorithm (GA), (iii) particle swarm optimization algorithm (PSO), and (iv) whale optimization algorithm (WO) in two deep learning frameworks (i) Fully connected network (FCN) and (ii) SegNet were designed. COVLIAS 2.0 was validated using "Unseen NovMed" and benchmarked against MedSeg. Statistical tests for stability and reliability were also conducted.

RESULTS

Pruning algorithms (i) FCN-DE, (ii) FCN-GA, (iii) FCN-PSO, and (iv) FCN-WO showed improvement in storage by 92.4%, 95.3%, 98.7%, and 99.8% respectively when compared against solo FCN, and (v) SegNet-DE, (vi) SegNet-GA, (vii) SegNet-PSO, and (viii) SegNet-WO showed improvement by 97.1%, 97.9%, 98.8%, and 99.2% respectively when compared against solo SegNet. AUC > 0.94 (p < 0.0001) on CroMed and > 0.86 (p < 0.0001) on NovMed data set for all eight EA model. PAI <0.25 s per image. DenseNet-121-based Grad-CAM heatmaps showed validation on glass ground opacity lesions.

CONCLUSIONS

Eight PAI networks that were successfully validated are five times faster, storage efficient, and could be used in clinical settings.

COVLIAS 1.0Lesion vs. MedSeg: An Artificial Intelligence Framework for Automated Lesion Segmentation in COVID-19 Lung Computed Tomography Scans

Background: COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world. Methodology: Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models—namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet—were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals. Results: The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests—namely, the Mann−Whitney test, paired t-test, and Wilcoxon test—demonstrated its stability and reliability, with p < 0.0001. The online system for each slice was <1 s. Conclusions: The AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0Lesion lesion locator passed the intervariability test.

The role of fetal programming in human carcinogenesis - May the Barker hypothesis explain interindividual variability in susceptibility to cancer insurgence and progression?

The growing incidence of cancers is pushing oncologists to find out new explanations other than the somatic mutation theory, based on the accumulation of DNA mutations. In particular, the embryo-fetal exposure to an increasing number of environmental factors during gestation might represent a trigger able to influence the susceptibility of the newborn to develop cancer later in life. This idea agrees with the fetal programming theory, also known as the Barker hypothesis. Here the role of insulin-like growth factors, thymosin beta-4, and epigenome are discussed as mediators of cancer in prenatal human development. The role of epigenetic factors that during gestation increase the predisposition to develop cancer and the similarities in the gene expression (like MMP9, OPN, TP53 and CDKN2A) between embryonic development and cancer are key factors. Likewise, maternal obesity might be able to re-program embryo-fetal development with long-term changes, including an increased risk to develop neuroblastoma and acute leukemia. Birth weight alone and birth weight corrected for gestational age are proposed as important variables capable of predicting the vulnerability to develop cancers. According to the findings here reported, we hypothesize that cancer prevention should start during gestation by improving the quality of maternal diet. In conclusion, the Barker hypothesis should be applied to cancer as well. Therefore, the identification of the epigenetic factors of cancer appears mandatory, so that the cancer prevention might start in the womb before birth.

Long-COVID diagnosis: from diagnostic to advanced AI-driven models

SARS-COV 2 is recognized to be responsible for a multi-organ syndrome. In most patients, symptoms are mild. However, in certain subjects, COVID-19 tends to progress more severely.

Most of the patients infected with SARS-COV2 fully recovered within some weeks. In a considerable number of patients, like many other viral infections, various long-lasting symptoms have been described, now defined as “long COVID-19 syndrome”.

Given the high number of contagious over the world, it is necessary to understand and comprehend this emerging pathology to enable early diagnosis and improve patents outcomes.

In this scenario, AI-based models can be applied in long-COVID-19 patients to assist clinicians and at the same time, to reduce the considerable impact on the care and rehabilitation unit.

The purpose of this manuscript is to review different aspects of long-COVID-19 syndrome from clinical presentation to diagnosis, highlighting the considerable impact that AI can have.

COVLIAS 1.0 vs. MedSeg: Artificial Intelligence-Based Comparative Study for Automated COVID-19 Computed Tomography Lung Segmentation in Italian and Croatian Cohorts

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPoint^TM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.

Fetal programming of atherosclerosis: may the barker hypothesis explain the susceptibility of a subset of patients to develop stroke or cardiac infarct?

The risk stratification of young adults between subjects who will develop a mild form of atherosclerosis and subjects who will undergo a severe disease remains inaccurate. In the eighties of the previous century, David JP Barker has demonstrated the relationship between fetal conditions and occurrence of pathologies in adulthood. In this paper, the multiple evidence that might explain the increased susceptibility to severe forms of atherosclerosis, including stroke and cardiac infarct, in subjects who underwent intrauterine growth restriction (IUGR) will be analyzed. Specifically, we will review those inter-connected data indicating an association between a low weight at birth and an adult phenotype which might favor a severe outcome of atherosclerosis. Young and adult subjects born too small (IUGR) or too early (pre-terms) might represent a subgroup of "at risk subjects", more susceptible toward severe forms of atherosclerosis. Given that low birth weight (LBW) may be considered a surrogate of IUGR, this phenotypic feature could be considered among those indispensable clinical data collected in every patient presenting with atherosclerosis, irrespectively of age. According to the hypothesis that structural arterial changes might represent the link between LBW and susceptibility to atherosclerosis later in life, we suggest that the prevention of atherosclerosis should begin at birth. Regenerative and physiological substances such as thymosin Beta-4 could be challenged for a new "arterial regenerative medicine" in the perinatal period. The goal of this new approach should be the reinforcement of the structure of the arterial wall, allowing LBW newborns to avoid the most severe complications of atherosclerosis later in life: a dream that our research could contribute to bringing to life.

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.

Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

Thrombotic sinusoiditis and local diffuse intrasinusoidal coagulation in the liver of subjects affected by COVID-19: the evidence from histology and scanning electron microscopy

OBJECTIVE

Liver injury has been reported in patients with COVID-19. This condition is characterized by severe outcome and could be related with the ability of SARS-CoV-2 to activate cytotoxic T cells. The purpose of this study is to show the histological and scanning electron microscopy features of liver involvement in COVID-19 to characterize the liver changes caused by the activation of multiple molecular pathways following this infection.

PATIENTS AND METHODS

Liver biopsies from 4 patients (3 post-mortems and 1 in vivo) with COVID-19 were analyzed with histology and by scanning electron microscopy.

RESULTS

The liver changes showed significant heterogeneity. The first case showed ground glass hepatocytes and scattered fibrin aggregates in the sinusoidal lumen. The second evidenced intra-sinusoidal thrombi. The third was characterized by sinusoidal dilatation, atrophy of hepatocytes, Disse's spaces dilatation and intra-sinusoidal aggregates of fibrin and red blood cells. The fourth case exhibited diffuse fibrin aggregates in the dilated Disse spaces and microthrombi in the sinusoidal lumen.

CONCLUSIONS

In COVID-19-related liver injury, a large spectrum of pathological changes was observed. The most peculiar features were very mild inflammation, intra-sinusoidal changes, including sinusoidal dilatation, thrombotic sinusoiditis and diffuse intra-sinusoidal fibrin deposition. These findings suggested that a thrombotic sinusoiditis followed by a local diffuse intra-vascular (intra-sinusoidal) coagulation could be the typical features of the SARS-CoV-2-related liver injury.

Imbalanced MMP-3 and MMP-12 serum levels in systemic lupus erythematosus patients with Jaccoud's arthropathy and a distinctive MRI pattern

OBJECTIVE

Metalloproteinase (MMP)-3 and MMP-12 are proteolytic enzymes especially implicated in joint inflammation. This study aims to evaluate their association with arthritis features and hand MRI abnormalities in patients with SLE.

METHODS

Fifty SLE patients, with a mean (s.d.) age of 48.1 (14.6) years were tested for MMP-3 and MMP-12 serum levels, then further classified according to the presence of X-ray erosions and joint deformities. Eighteen RA patients aged 47.9 (11.8) and 14 healthy people aged 46.0 (11.0) were enrolled as control groups. A subgroup of 28 SLE patients underwent a dominant-hand MRI; the detected changes were classified and semi-quantitatively scored as capsular swelling, synovitis, edematous or proliferative tenosynovitis, bone oedema, bone erosions. Statistical analysis was performed using multiple regression models.

RESULTS

MMP-3 were significantly higher in patients with Jaccoud's arthropathy (JA) (22.1 ng/ml, P < 0.05) and independently associated with hsCRP serum levels (B-coeff 0.50; r = 0.30; P < 0.05). MMP-12 serum levels were significantly lower in patients with JA (0.18 ng/ml, P < 0.05) and inversely associated with the prednisone daily dose (B-coeff -0.03; r = -0.44; P < 0.01). Capsular swelling and edematous tenosynovitis, the most prevalent hand MRI changes in patients with JA, associated with higher MMP-3 (B-coeff 0.12; r = 0.66; P < 0.01 and B-coeff 0.08; r = 0.59; P < 0.01, respectively) and lower MMP-12 serum levels (B-coeff -7.4; r = -0.50; P < 0.05 and B-coeff -5.2; r = -0.44; P = 0.05, respectively).

CONCLUSION

Imbalanced MMP-3 and MMP-12 serum levels are influenced by inflammation and glucocorticoids in SLE patients and associated with JA and distinctive hand MRI changes.

COVLIAS 1.0: Lung Segmentation in COVID-19 Computed Tomography Scans Using Hybrid Deep Learning Artificial Intelligence Models

BACKGROUND

COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation.

METHODOLOGY

The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT.

RESULTS

Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value < 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image.

CONCLUSIONS

The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

Volume of White Matter Hyperintensities, and Cerebral Micro-Bleeds

PURPOSE

In the past years the significance of white matter hyperintensities (WMH) has gained raising attention because it is considered a marker of severity of different pathologies. Another condition that in the last years has been assessed in the neuroradiology field is cerebral microbleeds (CMB). The purpose of this work was to evaluate the association between the volume of WMH and the presence and characteristics of CMB.

MATERIAL AND METHODS

Sixty-five consecutive (males 45; median age 70) subjects were retrospectively analyzed with a 1.5 Tesla scanner. WMH volume was quantified with a semi-automated procedure considering the FLAIR MR sequences whereas the CMB were studied with the SWI technique and CMBs were classified as absent (grade 1), mild (grade 2; total number of CMBs: 1-2), moderate (grade 3; total number of CMBs: 3-10), and severe (grade 4; total number of CMBs: >10). Moreover, overall number of CMBs and the maximum diameter were registered.

RESULTS

Prevalence of CMBs was 30.76% whereas WMH 81.5%. Mann-Whitney test showed a statistically significant difference in WMH volume between subjects with and without CMBs (p < 0.001). Pearson analysis showed significant correlation between CMB grade, number and maximum diameter and WMH. The better ROC area under the curve (Az) was obtained by the hemisphere volume with a 0.828 (95% CI from 0.752 to 0,888; SD = 0.0427; p value = 0.001). The only parameters that showed a statistically significant association in the logistic regression analysis were Hemisphere volume of WMH (p = 0.001) and Cholesterol LDL (p = 0.0292).

CONCLUSION

In conclusion, the results of this study suggest the presence of a significant correlation between CMBs and volume of WMH. No differences were found between the different vascular territories.

Trace elements and the carotid plaque: the GOOD (Mg, Zn, Se), the UGLY (Fe, Cu), and the BAD (P, Ca)?

Multiple epidemiological studies have suggested that industrialization and progressive urbanization should be considered one of the main factors responsible for the rising of atherosclerosis in the developing world. In this scenario, the role of trace metals in the insurgence and progression of atherosclerosis has not been clarified yet. In this paper, the specific role of selected trace elements (magnesium, zinc, selenium, iron, copper, phosphorus, and calcium) is described by focusing on the atherosclerotic prevention and pathogenesis plaque. For each element, the following data are reported: daily intake, serum levels, intra/extracellular distribution, major roles in physiology, main effects of high and low levels, specific roles in atherosclerosis, possible interactions with other trace elements, and possible influences on plaque development. For each trace element, the correlations between its levels and clinical severity and outcome of COVID-19 are discussed. Moreover, the role of matrix metalloproteinases, a family of zinc-dependent endopeptidases, as a new medical therapeutical approach to atherosclerosis is discussed. Data suggest that trace element status may influence both atherosclerosis insurgence and plaque evolution toward a stable or an unstable status. However, significant variability in the action of these traces is evident: some - including magnesium, zinc, and selenium - may have a protective role, whereas others, including iron and copper, probably have a multi-faceted and more complex role in the pathogenesis of the atherosclerotic plaque. Finally, calcium and phosphorus are implicated in the calcification of atherosclerotic plaques and in the progression of the plaque toward rupture and severe clinical complications. In particular, the role of calcium is debated. Focusing on the COVID-19 pandemia, optimized magnesium and zinc levels are indicated as important protective tools against a severe clinical course of the disease, often related to the ability of SARS-CoV-2 to cause a systemic inflammatory response, able to transform a stable plaque into an unstable one, with severe clinical complications.

Volume of White Matter Hyperintensities, and Cerebral Micro-Bleeds

PURPOSE

In the past years the significance of white matter hyperintensities (WMH) has gained raising attention because it is considered a marker of severity of different pathologies. Another condition that in the last years has been assessed in the neuroradiology field is cerebral microbleeds (CMB). The purpose of this work was to evaluate the association between the volume of WMH and the presence and characteristics of CMB.

MATERIAL AND METHODS

Sixty-five consecutive (males 45; median age 70) subjects were retrospectively analyzed with a 1.5 Tesla scanner. WMH volume was quantified with a semi-automated procedure considering the FLAIR MR sequences whereas the CMB were studied with the SWI technique and CMBs were classified as absent (grade 1), mild (grade 2; total number of CMBs: 1-2), moderate (grade 3; total number of CMBs: 3-10), and severe (grade 4; total number of CMBs: >10). Moreover, overall number of CMBs and the maximum diameter were registered.

RESULTS

Prevalence of CMBs was 30.76% whereas WMH 81.5%. Mann-Whitney test showed a statistically significant difference in WMH volume between subjects with and without CMBs (p < 0.001). Pearson analysis showed significant correlation between CMB grade, number and maximum diameter and WMH. The better ROC area under the curve (Az) was obtained by the hemisphere volume with a 0.828 (95% CI from 0.752 to 0,888; SD = 0.0427; p value = 0.001). The only parameters that showed a statistically significant association in the logistic regression analysis were Hemisphere volume of WMH (p = 0.001) and Cholesterol LDL (p = 0.0292).

CONCLUSION

In conclusion, the results of this study suggest the presence of a significant correlation between CMBs and volume of WMH. No differences were found between the different vascular territories.

A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework

Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi-automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from "ground truth" images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound.

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings.

Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs

Background

COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive cases through quick and accurate testing. Since artificial intelligence (AI) offers the powerful mechanism to automatically extract the tissue features and characterise the disease, we therefore hypothesise that AI-based strategies can provide quick detection and classification, especially for radiological computed tomography (CT) lung scans.

Methodology

Six models, two traditional machine learning (ML)-based (k-NN and RF), two transfer learning (TL)-based (VGG19 and InceptionV3), and the last two were our custom-designed deep learning (DL) models (CNN and iCNN), were developed for classification between COVID pneumonia (CoP) and non-COVID pneumonia (NCoP). K10 cross-validation (90% training: 10% testing) protocol on an Italian cohort of 100 CoP and 30 NCoP patients was used for performance evaluation and bispectrum analysis for CT lung characterisation.

Results

Using K10 protocol, our results showed the accuracy in the order of DL > TL > ML, ranging the six accuracies for k-NN, RF, VGG19, IV3, CNN, iCNN as 74.58 ± 2.44%, 96.84 ± 2.6, 94.84 ± 2.85%, 99.53 ± 0.75%, 99.53 ± 1.05%, and 99.69 ± 0.66%, respectively. The corresponding AUCs were 0.74, 0.94, 0.96, 0.99, 0.99, and 0.99 (p-values < 0.0001), respectively. Our Bispectrum-based characterisation system suggested CoP can be separated against NCoP using AI models. COVID risk severity stratification also showed a high correlation of 0.7270 (p < 0.0001) with clinical scores such as ground-glass opacities (GGO), further validating our AI models.

Conclusions

We prove our hypothesis by demonstrating that all the six AI models successfully classified CoP against NCoP due to the strong presence of contrasting features such as ground-glass opacities (GGO), consolidations, and pleural effusion in CoP patients. Further, our online system takes < 2 s for inference.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11548-021-02317-0.

Multinational Survey of Current Practice from Imaging to Treatment of Atherosclerotic Carotid Stenosis

BACKGROUND

In the last 20-30 years, there have been many advances in imaging and therapeutic strategies for symptomatic and asymptomatic individuals with carotid artery stenosis. Our aim was to examine contemporary multinational practice standards.

METHODS

Departmental Review Board approval for this study was obtained, and 3 authors prepared the 44 multiple choice survey questions. Endorsement was obtained by the European Society of Neuroradiology, American Society of Functional Neuroradiology, and African Academy of Neurology. A link to the online questionnaire was sent to their respective members and members of the Faculty Advocating Collaborative and Thoughtful Carotid Artery Treatments (FACTCATS). The questionnaire was open from May 16 to July 16, 2019.

RESULTS

The responses from 223 respondents from 46 countries were included in the analyses including 65.9% from academic university hospitals. Neuroradiologists/radiologists comprised 68.2% of respondents, followed by neurologists (15%) and vascular surgeons (12.9%). In symptomatic patients, half (50.4%) the respondents answered that the first exam they used to evaluate carotid bifurcation was ultrasound, followed by computed tomography angiography (CTA, 41.6%) and then magnetic resonance imaging (MRI 8%). In asymptomatic patients, the first exam used to evaluate carotid bifurcation was ultrasound in 88.8% of respondents, CTA in 7%, and MRA in 4.2%. The percent stenosis upon which carotid endarterectomy or stenting was recommended was reduced in the presence of imaging evidence of "vulnerable plaque features" by 66.7% respondents for symptomatic patients and 34.2% for asymptomatic patients with a smaller subset of respondents even offering procedural intervention to patients with <50% symptomatic or asymptomatic stenosis.

CONCLUSIONS

We found heterogeneity in current practices of carotid stenosis imaging and management in this worldwide survey with many respondents including vulnerable plaque imaging into their decision analysis despite the lack of proven benefit from clinical trials. This study highlights the need for new clinical trials using vulnerable plaque imaging to select high-risk patients despite maximal medical therapy who may benefit from procedural intervention.

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors.

Cardiac computed tomography radiomics: an emerging tool for the non-invasive assessment of coronary atherosclerosis

In the last decades, significant advances have been made in the preventive approaches to cardiovascular disease. Even so, coronary artery disease remains one of the main causes of morbidity and mortality worldwide. Invasive imaging modalities, such as intravascular ultrasound or optical coherence tomography, have played a key role in the comprehension of the pathological processes underlying myocardial infarction and cerebrovascular disease. These imaging techniques have contributed greatly to the identification and phenotyping of the culprit lesion, the so-called vulnerable plaque. Coronary computed tomographic angiography (CCTA) has emerged in more recent years as the non-invasive modality of choice in the study of coronary atherosclerosis, showing in many studies a diagnostic yield comparable to invasive approaches. Moreover, being able to describe extra-luminal characteristics of the affected vessel, CCTA has greatly contributed towards shifting the attention of researchers from the mere quantification of luminal stenosis to the identification of adverse plaque features, which appear to have a stronger prognostic value. However, the identification of some of the hallmarks of vulnerable plaques is qualitative in nature and, therefore, subject to some degree of inter-reader variability. Moreover, CCTA is still unable to identify some fine markers of plaque vulnerability which can be detected by invasive techniques, such as neovascularization and plaque erosion, among others. Nonetheless, radiological images can be viewed as vast 3-D datasets which, via the use of recent technology, allow for the extraction of numerous quantitative features that may be used to accurately phenotype a given lesion. Radiomics is the process of extrapolating innumerable parameters from a given region of interest, with the goal of establishing correlations between quantitative variables and clinical data. These datasets can then be manipulated to create predictive models via the use of automated algorithms in a process called machine learning. As a result of these approaches, radiological images may offer information regarding the characterization of a plaque which can go much beyond the boundaries of what can be qualitatively asserted by the human eye, contributing to expanding the knowledge of the disease and ultimately assist clinical decisions. Thus far, radiomics has found its more consistent area of application in the field of oncology; to present date, the amount of clinical data regarding coronary artery disease is still relatively small, partly due to the technical difficulties associated with the implementation of such techniques to the study of a small and geometrically complex lesion such as the coronary plaque. The present review, after a summary of the imaging modalities most commonly used nowadays in the study of coronary plaques, will provide a perspective on the application of radiomic analysis to coronary artery disease.

Molecular pathways triggered by COVID-19 in different organs: ACE2 receptor-expressing cells under attack? A review

OBJECTIVE

In human pathology, SARS-CoV-2 utilizes multiple molecular pathways to determine structural and biochemical changes within the different organs and cell types. The clinical picture of patients with COVID-19 is characterized by a very large spectrum. The reason for this variability has not been clarified yet, causing the inability to make a prognosis on the evolution of the disease.

MATERIALS AND METHODS

PubMed search was performed focusing on the role of ACE 2 receptors in allowing the viral entry into cells, the role of ACE 2 downregulation in triggering the tissue pathology or in accelerating previous disease states, the role of increased levels of Angiotensin II in determining endothelial dysfunction and the enhanced vascular permeability, the role of the dysregulation of the renin angiotensin system in COVID-19 and the role of cytokine storm.

RESULTS

The pathological changes induced by SARS-CoV-2 infection in the different organs, the correlations between the single cell types targeted by the virus in the different human organs and the clinical consequences, COVID-19 chronic pathologies in liver fibrosis, cardiac fibrosis and atrial arrhythmias, glomerulosclerosis and pulmonary fibrosis, due to the systemic fibroblast activation induced by angiotensin II are discussed.

CONCLUSIONS

The main pathways involved showed different pathological changes in multiple tissues and the different clinical presentations. Even if ACE2 is the main receptor of SARS-CoV-2 and the main entry point into cells for the virus, ACE2 expression does not always explain the observed marked inter-individual variability in clinical presentation and outcome, evidencing the complexity of this disorder. The proper interpretation of the growing data available might allow to better classifying COVID-19 in human pathology.

White-matter hyperintensities in patients with carotid artery stenosis: An exploratory connectometry study

BACKGROUND

White-matter lesions (WMLs) are frequently found in magnetic resonance imaging (MRi), and the WML load tends to be higher in patients affected by cervical internal carotid artery (cICA) stenosis.

PURPOSE

This study aimed to investigate whether and how WMLs influence cerebral networking in patients with asymptomatic cICA stenosis eligible for carotid endarterectomy (CEA) by exploiting the connectometry technique.

METHODS

The study was designed as a cross-sectional exploratory investigation, and 28 patients with cICA stenosis eligible for CEA were enrolled. All patients received an MRI scan, including a T1-weighted, a FLAIR and a diffusion-weighted (DW) sequence. The T1 and FLAIR sequences were analysed for quantification of WML burden (WMLB) and total number of WMLs (TNWMLs). The DW data were reconstructed in the MNI space using q-space diffeomorphic reconstruction, and were grouped to create a connectometry database. The connectometry analysis evaluated the influence of both the WMLB and TNWMLs to local connectivity in a multiple regression model that included age, WMLB and TNWMLs, adopting three different T-score thresholds (1, 2 and 3). A p-value corrected for false discovery rate of <0.05 was adopted as a threshold to identify statistically significant results.

RESULTS

The connectometry analysis identified several white-matter bundles negatively correlated with WMLB; no statistically significant correlation was found for TNWMLs.

CONCLUSION

Results of our study suggest that WMLs influence brain connectivity measured by the connectometry technique in patients with cICA stenosis eligible for CEA. Further studies are warranted to understand the role of WMLs better as a marker of disease in patients with cICA stenosis.

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients-specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.

Insight from imaging on plaque vulnerability: similarities and differences between coronary and carotid arteries-implications for systemic therapies

Nowadays it is widely accepted that the rupture of the atherosclerotic plaque in coronary and carotid arteries plays a fundamental role in the development of acute myocardial infarctions or cerebrovascular events. In recent years, imaging techniques have explored, with a new level of detail, the atherosclerotic disease generating new evidences that some plaque characteristics are significantly associated to the risk of rupture and subsequent thrombosis or embolization. Moreover, the recent evidence of the anti-atherosclerotic effects determined by lipid-lowering and anti-inflammatory therapies poses a challenge for the choice of therapeutic approaches (best/optimal medical therapy vs. revascularization), maximized by the evidence that coronary and carotid atherosclerosis share common patterns but also differ regarding some important features. In this Review, we discuss the similarities and differences between coronary and carotid artery vulnerable plaque from the imaging point of view and the potential implications for systemic therapies according to the emerging evidence.

Plaque imaging volume analysis: technique and application

The prevention and management of atherosclerosis poses a tough challenge to public health organizations worldwide. Together with myocardial infarction, stroke represents its main manifestation, with up to 25% of all ischemic strokes being caused by thromboembolism arising from the carotid arteries. Therefore, a vast number of publications have focused on the characterization of the culprit lesion, the atherosclerotic plaque. A paradigm shift appears to be taking place at the current state of research, as the attention is gradually moving from the classically defined degree of stenosis to the identification of features of plaque vulnerability, which appear to be more reliable predictors of recurrent cerebrovascular events. The present review will offer a perspective on the present state of research in the field of carotid atherosclerotic disease, focusing on the imaging modalities currently used in the study of the carotid plaque and the impact that such diagnostic means are having in the clinical setting.

Perivascular Fat Density and Contrast Plaque Enhancement: Does a Correlation Exist?

BACKGROUND AND PURPOSE

Inflammatory changes in the fat tissue surrounding the coronary arteries have been associated with coronary artery disease and high-risk vulnerable plaques. Our aim was to investigate possible correlations between the presence and degree of perivascular fat density and a marker of vulnerable carotid plaque, namely contrast plaque enhancement on CTA.

MATERIALS AND METHODS

One-hundred patients (76 men, 24 women; mean age, 69 years) who underwent CT angiography for investigation of carotid artery stenosis were retrospectively analyzed. Contrast plaque enhancement and perivascular fat density were measured in 100 carotid arteries, and values were stratified according to symptomatic (ipsilateral-to-cerebrovascular symptoms)/asymptomatic status (carotid artery with the most severe degree of stenosis). Correlation coefficients (Pearson ρ product moment) were calculated between the contrast plaque enhancement and perivascular fat density. The differences among the correlation ρ values were calculated using the Fisher r-to-z transformation. Mann-Whitney analysis was also calculated to test differences between the groups.

RESULTS

There was a statistically significant positive correlation between contrast plaque enhancement and perivascular fat density (ρ value = 0.6582, P value = .001). The correlation was stronger for symptomatic rather than asymptomatic patients (ρ value = 0.7052, P value = .001 versus ρ value = 0.4092, P value = .001).

CONCLUSIONS

There was a positive association between perivascular fat density and contrast plaque enhancement on CTA. This correlation was stronger for symptomatic rather than asymptomatic patients. Our results suggest that perivascular fat density could be used as an indirect marker of plaque instability.

Early diagnosis of chemotherapy-induced cardiotoxicity by cardiac MRI

Survival rate in cancer patients has improved over the course of the years. In cancer survivors, cardiovascular disease is the second leading cause of mortality and early detection and serial monitoring of cardiotoxicity are key factors towards the improvement of patients' outcomes. This review article will provide an overview of the existing literature regarding the tools that MRI can offer in the early diagnosis of myocardial damage.

Erdheim-Chester disease presenting with cough, abdominal pain, and headache

Erdheim-Chester disease (ECD) is a rare non-Langerhans cell histiocytic disorder. The diagnosis was based on the relationship between radiologic findings, clinical manifestations, and pathologic features of the bone biopsy. We report a case of ECD with unusual presenting symptoms: a 56 year-old man presented with cough, abdominal pain, and recurrent episodes of headache associated without any seizures. Peculiar computer tomography (CT) findings were key for the diagnostic suspicion. Bone biopsy and other radiological investigations confirmed the diagnosis. CT findings can help raise the suspicion of ECD. CT is easy to perform and widely available in comparison with kinetic cardiac magnetic resonance imaging and nuclear imaging. Therefore, CT findings of ECD can reduce the therapeutic delay between diagnosis and therapy prescription.

Carotid Intraplaque-Hemorrhage Volume and Its Association with Cerebrovascular Events

BACKGROUND AND PURPOSE

Our aim was to assess the relationship between volume and percentage of intraplaque hemorrhage measured using CT and the occurrence of cerebrovascular events at the time of CT.

MATERIALS AND METHODS

One-hundred-twenty-three consecutive subjects (246 carotid arteries) with a mean age of 69 years who underwent CTA were included in this retrospective study. Plaque volume of components and subcomponents (including intraplaque hemorrhage volume) was quantified with dedicated software.

RESULTS

Forty-six arteries were excluded because no plaque was identified. In the remaining 200 carotid arteries, a statistically significant difference was found between presentation with cerebrovascular events and lipid volume (P = .002), intraplaque hemorrhage volume (P = .002), percentage of lipid (P = .002), percentage of calcium (P = .001), percentage of intraplaque hemorrhage (P = .001), percentage of lipid-intraplaque hemorrhage (P = .001), and intraplaque hemorrhage/lipid ratio (P = .001). The highest receiver operating characteristic area under the curve was obtained with the intraplaque hemorrhage volume with a value of 0.793 (P = .001), percentage of intraplaque hemorrhage with an area under the curve of 0.812 (P = .001), and the intraplaque hemorrhage/lipid ratio with an area under the curve value of 0.811 (P = .001).

CONCLUSIONS

Results of our study suggest that Hounsfield unit values <25 have a statistically significant association with the presence of cerebrovascular events and that the ratio intraplaque hemorrhage/lipid volume represents a strong parameter for the association of cerebrovascular events.

Does the clinical information play a role in the magnetic resonance diagnostic confidence analysis of ovarian and deep endometriosis?

OBJECTIVE:

Some recent studies have explored how the experience in the observers change their performance in the endometriosis detection using MRI but the effects of the clinical information remains uncertain. The purpose of this study was to assess the effect of the clinical information in the diagnostic confidence in the MRI diagnosis of endometriosis.

METHODS AND MATERIALS:

Institutional Review Board was obtained. This study is compliant to STARD method. 80 patients (mean age 32 years; range 19 - 46 years) who had undergone MRI study and surgery for suspected endometriosis were retrospectively evaluated. MRI exams were performed with a 1.5 T scanner and the following five locations were assessed: ovary, anterior compartment, vaginal fornix, utero-sacral ligaments, and Rectum\Sigmoid\Pouch of Douglas. Data sets were evaluated twice on a 5-point scale by four radiologists with different level of expertise; the first time blinded to the clinical information and the second time, after 3 months together with the clinical chart. Statistical analysis included receiver operating characteristics curve analysis, the Cohen weighted test and sensitivity, specificity, positive predictive value, negative predictive value, accuracy, LR+ and LR-.

RESULTS:

A total of 140 localization of endometriosis (47 endometriomas and 93 endometriotic nodules) were found. The pairwise comparison demonstrated that in all cases the presence of clinical information improved the Az value. The concordance analysis indicated a mixed pattern from modest agreement (weighted κ value 0.556 for anterior compartment) to excellent agreement values (weighted κ value 0.867 for ovarian endometriomas).

CONCLUSION:

The results of our study suggest that clinical information is useful in diagnosing endometriosis in general anterior compartment, but not in other locations. Less experienced radiologists (resident) may benefit from it at utero-sacral ligaments or Rectum\Sigmoid\Pouch of Douglas.

ADVANCES IN KNOWLEDGE:

In this era of sometimes indiscriminate use of diagnostic methods, it is important to emphasis the context for interpretation of diagnostic results. Our paper confirms that clinical information is useful in diagnosing endometriosis.

Resting-state functional connectivity MRI analysis in Human Immunodeficiency Virus and Hepatitis C Virus co-infected subjects. A pilot study

BACKGROUND AND PURPOSE

Hepatitis C virus (HCV) co-infection's role on cognitive impairment of human immunodeficiency virus (HIV) positive patients is still debated and functional neuroimaging evaluation on this matter is lacking. To provide further insight about HCV's neuro-effects on HIV associated neurocognitive disorder (HAND), we performed a pilot resting state (RS) functional connectivity magnetic resonance imaging (fcMRI) study to find eventual functional connectivity alteration that could reflect HCV related cognitive performance degradation.

METHODS

Eighteen patients (8 HIV, 10 HIV + HCV), either impaired or not impaired, were assessed with RS fcMRI. A statistic model including cognitive testing results was elaborated during data processing to evaluate brain networks alteration related to actual cognitive status in patients.

RESULTS

Statistically significant different patterns of connectivity were found: HCV co-infection modified 17 ROIs' connectivity with 45 supra-threshold connections (p-FDR min 0.0022, max 0.0497). ROIs most involved were right pallidum, brainstem, vermian lobules 1-2 and right cerebellar lobule 10. Graph theory analysis did not demonstrate significant difference between networks, but HCV related modifications at ROI's local level were found, with particular involvement of ROIs of frontal lobe, basal ganglia and cerebellum. Increased fronto-striatal dysfunctions have been already reported as consequences of HCV infection and could reflect an additive effect. Cerebellar alterations are associated with HIV and HAND, but not with HCV infection, suggesting a synergic effect of HCV.

CONCLUSION

Our study demonstrates RS fcMRI can help to understand the interactions between HIV and HCV co-infection, and our preliminary results suggest synergic effects of HCV in HIV-related brain functional modification.