How to improve epidemiological trustworthiness concerning abdominal aortic aneurysms.

BACKGROUND: Research on degenerative abdominal aortic aneurysms (AAA) is hampered by complex pathophysiology, sub-optimal pre-clinical models, and lack of effective medical therapies. In addition, trustworthiness of existing epidemiological data is impaired by elements of ambiguity, inaccuracy, and inconsistency. Our aim is to foster debate concerning the trustworthiness of AAA epidemiological data and to discuss potential solutions. METHODS: We searched the literature from the last five decades for relevant epidemiological data concerning AAA development, rupture, and repair. We then discussed the main issues burdening existing AAA epidemiological figures and proposed suggestions potentially beneficial to AAA diagnosis, prognostication, and management. RESULTS: Recent data suggest a heterogeneous scenario concerning AAA epidemiology with rates markedly varying by country and study cohorts. Overall, AAA prevalence seems to be decreasing worldwide while mortality is apparently increasing regardless of recent improvements in aortic-repair techniques. Prevalence and mortality are decreasing in high-income countries, whereas low-income countries show an increase in both. However, several pieces of information are missing or outdated, thus systematic renewal is necessary. Current AAA definition and surgical criteria do not consider inter-individual variability of baseline aortic size, further decreasing their reliability. CONCLUSIONS: Switching from flat aortic-size thresholds to relative aortic indices would improve epidemiological trustworthiness regarding AAAs. Aortometry standardization focusing on simplicity, univocity, and accuracy is crucial. A patient-tailored approach integrating clinical data, multi-adjusted indices, and imaging parameters is desirable. Several novel imaging modalities boast promising profiles for investigating the aortic wall. New contrast agents, computational analyses, and artificial intelligence-powered software could provide further improvements.

Atherosclerotic carotid artery disease Radiomics: A systematic review with meta-analysis and radiomic quality score assessment.

BACKGROUND: Stroke, a leading global cause of mortality and neurological disability, is often associated with atherosclerotic carotid artery disease. Distinguishing between symptomatic and asymptomatic carotid artery disease is crucial for appropriate treatment decisions. Radiomics, a quantitative image analysis technique, and ML have emerged as promising tools in medical imaging, including neuroradiology. This systematic review and meta-analysis aimed to evaluate the methodological quality of studies employing radiomics for atherosclerotic carotid artery disease analysis and ML algorithms for culprit plaque identification using CT or MRI. MATERIALS AND METHODS: Pubmed, WoS and Scopus databases were searched for relevant studies published from January 2005 to May 2023. RQS assessed methodological quality of studies included in the review. QUADAS-2 assessed the risk of bias. A meta-analysis and three meta regressions were conducted on study performance based on model type, imaging modality and segmentation method. RESULTS: RQS assessed methodological quality, revealing an overall low score and consistent findings with other radiology domains. QUADAS-2 indicated an overall low risk, except for a single study with high bias. The meta-analysis demonstrated that radiomics-based ML models for predicting culprit plaques had a satisfactory performance, with an AUC of 0.85, surpassing clinical models. However, combining radiomics with clinical features yielded the highest AUC of 0.89. Meta-regression analyses confirmed these findings. MRI-based models slightly outperformed CT-based ones, but the difference was not significant. CONCLUSION: In conclusion, radiomics and ML hold promise for assessing carotid plaque vulnerability, aiding in early cerebrovascular event prediction. Combining radiomics with clinical data enhances predictive performance.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.

Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.

Radiomics and artificial intelligence: General notions and applications in the carotid vulnerable plaque.

Carotid atherosclerosis plays a substantial role in cardiovascular morbidity and mortality. Given the multifaceted impact of this disease, there has been increasing interest in harnessing artificial intelligence (AI) and radiomics as complementary tools for the quantitative analysis of medical imaging data. This integrated approach holds promise not only in refining medical imaging data analysis but also in optimizing the utilization of radiologists' expertise. By automating time consuming tasks, AI allows radiologists to focus on more pertinent responsibilities. Simultaneously, the capacity of AI in radiomics to extract nuanced patterns from raw data enhances the exploration of carotid atherosclerosis, advancing efforts in terms of (1) early detection and diagnosis, (2) risk stratification and predictive modeling, (3) improving workflow efficiency, and (4) contributing to advancements in research. This review provides an overview of general concepts related to radiomics and AI, along with their application in the field of carotid vulnerable plaque. It also offers insights into various research studies conducted on this topic across different imaging techniques.

Left and right atrioventricular coupling index in patients with beta-thalassemia major.

The aim of this cross-sectional study was to investigate the relationship of left atrioventricular coupling index (LACI) and right atrioventricular coupling index (RACI) with demographics, clinical data, cardiovascular magnetic resonance findings, and cardiac complications (heart failure, arrhythmias, and pulmonary hypertension) in a cohort of patients with beta-thalassemia major (ß-TM). We evaluated 292 ß-TM patients (151 females, 36.72 ± 11.76 years) consecutively enrolled in the Extension-Myocardial Iron Overload in Thalassemia (E-MIOT) project. Moreover, we assessed 32 sex- and age-matched healthy controls (12 females, mean age 40.78 ± 14.35 years). LACI was determined by calculating the ratio of the left atrium end-diastolic volume to the left ventricle end-diastolic volume, while RACI was defined by calculating the ratio of the right atrium end-diastolic volume to the right ventricle end-diastolic volume. Compared to healthy control, ß-TM demonstrated increased LACI (22.99 ± 13.58% vs. 16.05 ± 5.28%; p < 0.0001) and RACI (27.84 ± 10.30% vs. 17.06 ± 5.03%; p < 0.0001). Aging, diabetes, splenectomy, and the presence of late gadolinium enhancement (LGE) showed a significant positive association with both LACI and RACI. In stepwise regression analysis, the presence of LGE was found to be an independent predictor of both impaired LACI and RACI (ß coefficient = 0.244, p < 0.0001 and ß coefficient = 0.218, p = 0.003; respectively). LACI and RACI were not correlated with myocardial iron overload. Patients with cardiac complications had significantly higher LACI and RACI than patients without cardiac complications. In patients with ß-TM, LACI and RACI were significantly associated with the presence of LV LGE. In addition, patients with cardiac complications had impaired LACI and RACI.

Carotid artery calcium score: Definition, classification, application, and limits.

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

Lessons from autopsy: Topographical variability of atherosclerosis plaques.

Atherosclerosis is a complex disease characterized by the accumulation of plaques in arterial walls. Understanding its pathogenesis remains incomplete, with factors like inflammation, oxidative stress, and hypertension playing critical roles. The disease exhibits preferential localization of plaques, with variability observed even within the same individual. Genetic, environmental, and lifestyle factors contribute to its heterogeneity. Histological plaque phenotypes vary widely, prompting classification schemes focusing on systemic and local factors deteriorating fibrous caps. Recent research highlights differences in plaque histology among arterial systems, suggesting unique pathophysiological mechanisms. This study reports on multiple atherosclerotic plaques detected at autopsy in various vascular sites of a single subject, emphasizing their histological diversity and underscoring the systemic nature of atherosclerosis.

Segmental Mandibulectomy and Mandibular Reconstruction with Fibula-Free Flap Using a 3D Template.

INTRODUCTION: The present study evaluates the influence of virtual surgical planning with a preoperative 3D resin model on aesthetic and functional outcomes in patients treated by segmental mandibulectomy and reconstruction with fibula-free flap for oral cancer. METHODS: All consecutive patients who underwent segmental mandibulectomy and mandibular reconstruction with a fibula-free flap using a 3D template at our department from January 2021 to January 2023 were included in the study. "Patients control" were patients treated by reconstruction with a fibula-free flap without using a 3D template. Three-dimensional modeling was performed by converting from preoperative computed tomography to a stereolithography format to obtain the resin 3D models. Qualitative analysis of anatomical and aesthetic results consisted of the evaluation of the patients' aesthetic and functional satisfaction and the symmetry of the mandibular contour observed at clinical examination. Quantitative analysis was based on the assessment of the accuracy and precision of the reconstruction by comparing preoperative and postoperative computed tomograms as objective indicators. RESULTS: Seven patients (five males and two females, mean age of 65.1 years) were included in the study. All patients showed a symmetric mandibular contour based on the clinical examination. After recovery, six patients (85.7%) considered themselves aesthetically satisfied. The quantitative analysis (assessed in six/seven patients) showed that the mean difference between preoperative and postoperative intercondylar distance, intergonial angle distance, anteroposterior dimension, and gonial angle improved in the 3D template-assisted group. CONCLUSION: The 3D-printed template for mandibular reconstruction with microvascular fibula-free flap can improve aesthetic outcomes in comparison with standard approaches.

A multi-institutional meningioma MRI dataset for automated multi-sequence image segmentation.

Meningiomas are the most common primary intracranial tumors and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on brain MRI for diagnosis, treatment planning, and longitudinal treatment monitoring. However, automated, objective, and quantitative tools for non-invasive assessment of meningiomas on multi-sequence MR images are not available. Here we present the BraTS Pre-operative Meningioma Dataset, as the largest multi-institutional expert annotated multilabel meningioma multi-sequence MR image dataset to date. This dataset includes 1,141 multi-sequence MR images from six sites, each with four structural MRI sequences (T2-, T2/FLAIR-, pre-contrast T1-, and post-contrast T1-weighted) accompanied by expert manually refined segmentations of three distinct meningioma sub-compartments: enhancing tumor, non-enhancing tumor, and surrounding non-enhancing T2/FLAIR hyperintensity. Basic demographic data are provided including age at time of initial imaging, sex, and CNS WHO grade. The goal of releasing this dataset is to facilitate the development of automated computational methods for meningioma segmentation and expedite their incorporation into clinical practice, ultimately targeting improvement in the care of meningioma patients.

Prediction of Response to Anti-Angiogenic Treatment for Advanced Colorectal Cancer Patients: From Biological Factors to Functional Imaging.

Colorectal cancer (CRC) is a leading tumor worldwide. In CRC, the angiogenic pathway plays a crucial role in cancer development and the process of metastasis. Thus, anti-angiogenic drugs represent a milestone for metastatic CRC (mCRC) treatment and lead to significant improvement of clinical outcomes. Nevertheless, not all patients respond to treatment and some develop resistance. Therefore, the identification of predictive factors able to predict response to angiogenesis pathway blockade is required in order to identify the best candidates to receive these agents. Unfortunately, no predictive biomarkers have been prospectively validated to date. Over the years, research has focused on biologic factors such as genetic polymorphisms, circulating biomarkers, circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), and microRNA. Moreover, research efforts have evaluated the potential correlation of molecular biomarkers with imaging techniques used for tumor assessment as well as the application of imaging tools in clinical practice. In addition to functional imaging, radiomics, a relatively newer technique, shows real promise in the setting of correlating molecular medicine to radiological phenotypes.

Utility of Dual-Energy Computed Tomography in Clinical Conundra.

Advancing medical technology revolutionizes our ability to diagnose various disease processes. Conventional Single-Energy Computed Tomography (SECT) has multiple inherent limitations for providing definite diagnoses in certain clinical contexts. Dual-Energy Computed Tomography (DECT) has been in use since 2006 and has constantly evolved providing various applications to assist radiologists in reaching certain diagnoses SECT is rather unable to identify. DECT may also complement the role of SECT by supporting radiologists to confidently make diagnoses in certain clinically challenging scenarios. In this review article, we briefly describe the principles of X-ray attenuation. We detail principles for DECT and describe multiple systems associated with this technology. We describe various DECT techniques and algorithms including virtual monoenergetic imaging (VMI), virtual non-contrast (VNC) imaging, Iodine quantification techniques including Iodine overlay map (IOM), and two- and three-material decomposition algorithms that can be utilized to demonstrate a multitude of pathologies. Lastly, we provide our readers commentary on examples pertaining to the practical implementation of DECT's diverse techniques in the Gastrointestinal, Genitourinary, Biliary, Musculoskeletal, and Neuroradiology systems.

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.

Spectral Photon-Counting Computed Tomography: Technical Principles and Applications in the Assessment of Cardiovascular Diseases.

Spectral Photon-Counting Computed Tomography (SPCCT) represents a groundbreaking advancement in X-ray imaging technology. The core innovation of SPCCT lies in its photon-counting detectors, which can count the exact number of incoming x-ray photons and individually measure their energy. The first part of this review summarizes the key elements of SPCCT technology, such as energy binning, energy weighting, and material decomposition. Its energy-discriminating ability represents the key to the increase in the contrast between different tissues, the elimination of the electronic noise, and the correction of beam-hardening artifacts. Material decomposition provides valuable insights into specific elements' composition, concentration, and distribution. The capability of SPCCT to operate in three or more energy regimes allows for the differentiation of several contrast agents, facilitating quantitative assessments of elements with specific energy thresholds within the diagnostic energy range. The second part of this review provides a brief overview of the applications of SPCCT in the assessment of various cardiovascular disease processes. SPCCT can support the study of myocardial blood perfusion and enable enhanced tissue characterization and the identification of contrast agents, in a manner that was previously unattainable.

Embolic strokes of undetermined source: a clinical consensus statement of the ESC Council on Stroke, the European Association of Cardiovascular Imaging and the European Heart Rhythm Association of the ESC.

One in six ischaemic stroke patients has an embolic stroke of undetermined source (ESUS), defined as a stroke with unclear aetiology despite recommended diagnostic evaluation. The overall cardiovascular risk of ESUS is high and it is important to optimize strategies to prevent recurrent stroke and other cardiovascular events. The aim of clinicians when confronted with a patient not only with ESUS but also with any other medical condition of unclear aetiology is to identify the actual cause amongst a list of potential differential diagnoses, in order to optimize secondary prevention. However, specifically in ESUS, this may be challenging as multiple potential thromboembolic sources frequently coexist. Also, it can be delusively reassuring because despite the implementation of specific treatments for the individual pathology presumed to be the actual thromboembolic source, patients can still be vulnerable to stroke and other cardiovascular events caused by other pathologies already identified during the index diagnostic evaluation but whose thromboembolic potential was underestimated. Therefore, rather than trying to presume which particular mechanism is the actual embolic source in an ESUS patient, it is important to assess the overall thromboembolic risk of the patient through synthesis of the individual risks linked to all pathologies present, regardless if presumed causally associated or not. In this paper, a multi-disciplinary panel of clinicians/researchers from various backgrounds of expertise and specialties (cardiology, internal medicine, neurology, radiology and vascular surgery) proposes a comprehensive multi-dimensional assessment of the overall thromboembolic risk in ESUS patients through the composition of individual risks associated with all prevalent pathologies.

Intraprocedural continuous saline infusion lines significantly reduce the incidence of acute kidney injury during endovascular procedures for stroke and myocardial infarction: evidence from a systematic review and meta-regression.

BACKGROUND: Contrast media used in mechanical therapies for stroke and myocardial infarction represent a significant cause of acute kidney injury (AKI) in acute medical scenarios. Although the continuous saline infusion line (CSIL) is a standard procedure to prevent thrombus formation within the catheter during neurovascular interventions of mechanical thrombectomy (MT), it is not utilized in percutaneous coronary interventions (PCI). METHODS: A systematic review of the incidence of AKI after MT for stroke treatment was performed. These data were compared with those reported in the literature regarding the incidence of AKI after PCI for acute myocardial infarction. A random-effect model meta-regression was performed to explore the effects of CSIL on AKI incidence, using clinical details as covariates. RESULTS: A total of 18 and 33 studies on MT and PCI were included, respectively, with 69,464 patients (30,138 [43.4%] for MT and 39,326 [56.6%] for PCI). The mean age was 63.6 years±5.8 with male 66.6%±12.8. Chronic kidney disease ranged 2.0-50.3%. Diabetes prevalence spanned 11.1% to 53.0%. Smoking status had a prevalence of 7.5-72.0%. Incidence of AKI proved highly variable (I2=98%, Cochrane's Q 2985), and appeared significantly lower in the MT subgroup than in the PCI subgroups (respectively 8.3% [95% confidence interval: 4.7-11.9%] vs. 14.7 [12.6-16.8%], P<0.05). Meta-regression showed that CSIL was significantly associated with a decreased incidence of AKI (OR=0.93 [1.001-1.16]; P=0.03). CONCLUSIONS: Implementation of CSIL during endovascular procedures in acute settings was associated with a significant decrease in the risk of AKI, and its safety should be routinely considered in such interventions.

Structural Brain MR Imaging Alterations in Patients with Systemic Lupus Erythematosus 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.

The fascinating theory of fetal programming of adult diseases: A review of the fundamentals of the Barker hypothesis.

The theory of fetal programming of adult diseases was first proposed by David J.P. Barker in the eighties of the previous century, to explain the higher susceptibility of some people toward the development of ischemic heart disease. According to his hypothesis, poor maternal living conditions during gestation represent an important risk factor for the onset of atherosclerotic heart disease later in life. The analysis of the early phases of fetal development is a fundamental tool for the risk stratification of children and adults, allowing the identification of susceptible or resistant subjects to multiple diseases later in life. Here, we provide a narrative summary of the most relevant evidence supporting the Barker hypothesis in multiple fields of medicine, including neuropsychiatric disorders, such as Parkinson disease and Alzheimer disease, kidney failure, atherosclerosis, coronary heart disease, stroke, diabetes, cancer onset and progression, metabolic syndrome, and infectious diseases including COVID-19. Given the consensus on the role of body weight at birth as a practical indicator of the fetal nutritional status during gestation, every subject with a low birth weight should be considered an "at risk" subject for the development of multiple diseases later in life. The hypothesis of the "physiological regenerative medicine," able to improve fetal organs' development in the perinatal period is discussed, in the light of recent experimental data indicating Thymosin Beta-4 as a powerful growth promoter when administered to pregnant mothers before birth.

Coronary Artery Calcium Score Predicts Major Adverse Cardiovascular Events in Stable Chest Pain.

Background Coronary artery calcium (CAC) has prognostic value for major adverse cardiovascular events (MACE) in asymptomatic individuals, whereas its role in symptomatic patients is less clear. Purpose To assess the prognostic value of CAC scoring for MACE in participants with stable chest pain initially referred for invasive coronary angiography (ICA). Materials and Methods This prespecified subgroup analysis from the Diagnostic Imaging Strategies for Patients With Stable Chest Pain and Intermediate Risk of Coronary Artery Disease (DISCHARGE) trial, conducted between October 2015 and April 2019 across 26 centers in 16 countries, focused on adult patients with stable chest pain referred for ICA. Participants were randomly assigned to undergo either ICA or coronary CT. CAC scores from noncontrast CT scans were categorized into low, intermediate, and high groups based on scores of 0, 1-399, and 400 or higher, respectively. The end point of the study was the occurrence of MACE (myocardial infarction, stroke, and cardiovascular death) over a median 3.5-year follow-up, analyzed using Cox proportional hazard regression tests. Results The study involved 1749 participants (mean age, 60 years ± 10 [SD]; 992 female). The prevalence of obstructive coronary artery disease (CAD) at CT angiography rose from 4.1% (95% CI: 2.8, 5.8) in the CAC score 0 group to 76.1% (95% CI: 70.3, 81.2) in the CAC score 400 or higher group. Revascularization rates increased from 1.7% to 46.2% across the same groups (P < .001). The CAC score 0 group had a lower MACE risk (0.5%; HR, 0.08 [95% CI: 0.02, 0.30]; P < .001), as did the 1-399 CAC score group (1.9%; HR, 0.27 [95% CI: 0.13, 0.59]; P = .001), compared with the 400 or higher CAC score group (6.8%). No significant difference in MACE between sexes was observed (P = .68). Conclusion In participants with stable chest pain initially referred for ICA, a CAC score of 0 showed very low risk of MACE, and higher CAC scores showed increasing risk of obstructive CAD, revascularization, and MACE at follow-up. Clinical trial registration no. NCT02400229 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Hanneman and Gulsin in this issue.

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.

GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides.

Due to the intricate relationship between the small non-coding ribonucleic acid (miRNA) sequences, the classification of miRNA species, namely Human, Gorilla, Rat, and Mouse is challenging. Previous methods are not robust and accurate. In this study, we present AtheroPoint's GeneAI 3.0, a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep learning (EDL) frameworks. GeneAI 3.0 utilized five conventional (Entropy, Dissimilarity, Energy, Homogeneity, and Contrast), and three contemporary (Shannon entropy, Hurst exponent, Fractal dimension) features, to generate a composite feature set from given miRNA sequences which were then passed into our ML and DL classification framework. A set of 11 new classifiers was designed consisting of 5 EML and 6 EDL for binary/multiclass classification. It was benchmarked against 9 solo ML (SML), 6 solo DL (SDL), 12 hybrid DL (HDL) models, resulting in a total of 11 + 27 = 38 models were designed. Four hypotheses were formulated and validated using explainable AI (XAI) as well as reliability/statistical tests. The order of the mean performance using accuracy (ACC)/area-under-the-curve (AUC) of the 24 DL classifiers was: EDL > HDL > SDL. The mean performance of EDL models with CNN layers was superior to that without CNN layers by 0.73%/0.92%. Mean performance of EML models was superior to SML models with improvements of ACC/AUC by 6.24%/6.46%. EDL models performed significantly better than EML models, with a mean increase in ACC/AUC of 7.09%/6.96%. The GeneAI 3.0 tool produced expected XAI feature plots, and the statistical tests showed significant p-values. Ensemble models with composite features are highly effective and generalized models for effectively classifying miRNA sequences.