Global longitudinal strain by cardiac magnetic resonance is associated with cardiac iron and complications in beta-thalassemia major patients.

BACKGROUND: The aim of this cross-sectional study was to investigate the association of left ventricular (LV) strain parameters with demographics, clinical data, cardiovascular magnetic resonance (CMR) findings, and cardiac complications (heart failure and arrhythmias) in patients with ß-thalassemia major (ß-TM). METHOD: We considered 266 ß-TM patients (134 females, 37.08 ± 11.60 years) consecutively enrolled in the Extension-Myocardial Iron Overload in Thalassemia (E-MIOT) project and 80 healthy controls (50 females, mean age 39.77 ± 11.29 years). The CMR protocol included cine images for the assessment of global longitudinal strain (GLS), global circumferential strain (GCS), and global radial strain (GRS) using feature tracking (FT) and for the quantification of LV function parameters, the T2* technique for the assessment of myocardial iron overload, and late gadolinium enhancement (LGE) technique. RESULTS: In comparison to the healthy control group, ß-TM patients showed impaired GLS, GCS, and GRS values. Among ß-TM patients, sex was identified as the sole independent determinant of all LV strain parameters. All LV strain parameters displayed a significant correlation with LV end-diastolic volume index, end-systolic volume index, mass index, and ejection fraction, and with the number of segments exhibiting LGE. Only GLS exhibited a significant correlation with global heart T2* values and the number of segments with T2* < 20 ms. Patients with cardiac complications exhibited significantly impaired GLS compared to those without cardiac complications. CONCLUSION: In patients with ß-TM, GLS, GCS, and GRS were impaired in comparison with control subjects. Among LV strain parameters, only GLS demonstrated a significant association with cardiac iron levels and complications.

COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography.

Background and novelty: When RT-PCR is ineffective in early diagnosis and understanding of COVID-19 severity, Computed Tomography (CT) scans are needed for COVID diagnosis, especially in patients having high ground-glass opacities, consolidations, and crazy paving. Radiologists find the manual method for lesion detection in CT very challenging and tedious. Previously solo deep learning (SDL) was tried but they had low to moderate-level performance. This study presents two new cloud-based quantized deep learning UNet3+ hybrid (HDL) models, which incorporated full-scale skip connections to enhance and improve the detections. Methodology: Annotations from expert radiologists were used to train one SDL (UNet3+), and two HDL models, namely, VGG-UNet3+ and ResNet-UNet3+. For accuracy, 5-fold cross-validation protocols, training on 3,500 CT scans, and testing on unseen 500 CT scans were adopted in the cloud framework. Two kinds of loss functions were used: Dice Similarity (DS) and binary cross-entropy (BCE). Performance was evaluated using (i) Area error, (ii) DS, (iii) Jaccard Index, (iii) Bland-Altman, and (iv) Correlation plots. Results: Among the two HDL models, ResNet-UNet3+ was superior to UNet3+ by 17 and 10% for Dice and BCE loss. The models were further compressed using quantization showing a percentage size reduction of 66.76, 36.64, and 46.23%, respectively, for UNet3+, VGG-UNet3+, and ResNet-UNet3+. Its stability and reliability were proved by statistical tests such as the Mann-Whitney, Paired t-Test, Wilcoxon test, and Friedman test all of which had a p < 0.001. Conclusion: Full-scale skip connections of UNet3+ with VGG and ResNet in HDL framework proved the hypothesis showing powerful results improving the detection accuracy of COVID-19.

Rapid Plaque Progression Is Independently Associated With Hyperglycemia and Low HDL Cholesterol in Patients With Stable Coronary Artery Disease: A PARADIGM Study.

BACKGROUND: We assessed whether combinations of cardiometabolic risk factors independently predict coronary plaque progression (PP) and major adverse cardiovascular events in patients with stable coronary artery disease. METHODS: Patients with known or suspected stable coronary artery disease (60.9±9.3 years, 55.4% male) undergoing serial coronary computed tomography angiographies (≥2 years apart), with clinical characterization and follow-up (N=1200), were analyzed from the PARADIGM study (Progression of Atherosclerotic Plaque Determined by Computed Tomographic Angiography Imaging). Plaque volumes measured in coronary segments (≥2 mm in diameter) were summed to provide whole heart plaque volume (mm3) and percent atheroma volume (plaque volume/vessel volume×100; %) per patient at baseline and follow-up. Rapid PP was defined as a percent atheroma volume increase of ≥1.0%/y. Major adverse cardiovascular events included nonfatal myocardial infarction, death, and unplanned coronary revascularization. RESULTS: In an interscan period of 3.2 years (interquartile range, 1.9), rapid PP occurred in 341 patients (28%). At multivariable analysis, the combination of cardiometabolic risk factors defined as metabolic syndrome predicted rapid PP (odds ratio, 1.51 [95% CI, 1.12-2.03]; P=0.007) together with older age, smoking habits, and baseline percent atheroma volume. Among single cardiometabolic variables, high fasting plasma glucose (diabetes or fasting plasma glucose >100 mg/dL) and low HDL-C (high-density lipoprotein cholesterol; <40 mg/dL in males and <50 mg/dL in females) were independently associated with rapid PP, in particular when combined (odds ratio, 2.37 [95% CI, 1.56-3.61]; P<0.001). In a follow-up of 8.23 years (interquartile range, 5.92-9.53), major adverse cardiovascular events occurred in 201 patients (17%). At multivariable Cox analysis, the combination of high fasting plasma glucose with high systemic blood pressure (treated hypertension or systemic blood pressure >130/85 mm Hg) was an independent predictor of events (hazard ratio, 1.79 [95% CI, 1.10-2.90]; P=0.018) together with family history, baseline percent atheroma volume, and rapid PP. CONCLUSIONS: In patients with stable coronary artery disease, the combination of hyperglycemia with low HDL-C is associated with rapid PP independently of other risk factors, baseline plaque burden, and treatment. The combination of hyperglycemia with high systemic blood pressure independently predicts the worse outcome beyond PP. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT02803411.

Unlocking the potential of photon counting detector CT for paediatric imaging: a pictorial essay.

Recent advancements in CT technology have introduced a revolutionary innovation to practice known as the Photon-Counting detector (PCD) CT imaging. The pivotal hardware enhancement of the PCD-CT scanner lies in its detectors, which consist of smaller pixels than standard detectors and allow direct conversion of individual X-rays to electrical signals. As a result, CT images are reconstructed at higher spatial resolution (as low as 0.2 mm) and reduced overall noise, at no expense of an increased radiation dose. These features are crucial for paediatric imaging, especially for infants and young children, where anatomical structures are notably smaller than in adults and in whom keeping dose as low as possible is especially relevant. Since January 2022, our hospital has had the opportunity to work with PCD-CT technology for paediatric imaging. This pictorial review will showcase clinical examples of PCD-CT imaging in children. The aim of this pictorial review is to outline the potential paediatric applications of PCD-CT across different anatomical regions, as well as to discuss the benefits in utilizing PCD-CT in comparison to conventional standard energy integrating detector CT.

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.

Photon-Counting Computed Tomography in Atherosclerotic Plaque Characterization.

Atherosclerotic plaque buildup in the coronary and carotid arteries is pivotal in the onset of acute myocardial infarctions or cerebrovascular events, leading to heightened levels of illness and death. Atherosclerosis is a complex and multistep disease, beginning with the deposition of low-density lipoproteins in the arterial intima and culminating in plaque rupture. Modern technology favors non-invasive imaging techniques to assess atherosclerotic plaque and offer insights beyond mere artery stenosis. Among these, computed tomography stands out for its widespread clinical adoption and is prized for its speed and accessibility. Nonetheless, some limitations persist. The introduction of photon-counting computed tomography (PCCT), with its multi-energy capabilities, enhanced spatial resolution, and superior soft tissue contrast with minimal electronic noise, brings significant advantages to carotid and coronary artery imaging, enabling a more comprehensive examination of atherosclerotic plaque composition. This narrative review aims to provide a comprehensive overview of the main concepts related to PCCT. Additionally, we aim to explore the existing literature on the clinical application of PCCT in assessing atherosclerotic plaque. Finally, we will examine the advantages and limitations of this recently introduced technology.

Prognostic role of cardiovascular magnetic resonance in Takotsubo syndrome: A systematic review.

BACKGROUND: Takotsubo syndrome (TS) is characterized by transient myocardial dysfunction with outcomes ranging from favorable to life-threatening. Cardiovascular magnetic resonance (CMR) has emerged as an essential tool in its diagnosis and management and is consistently recommended by current guidelines in the diagnostic work-up. However, the prognostic value of CMR in patients with TS remains undetermined. The aim of this study was to assess the prognostic value of CMR in managing patients with TS. METHOD: PubMed, MEDLINE via Ovid, Scopus, and the Cochrane Library were searched to identify studies reporting the prognostic role of multiparameteric CMR in patients with TS with a follow-up ≥ 12 months. The primary endpoint was major adverse cardiovascular and cerebrovascular events (MACCE), defined as all-cause mortality, cardiac death, heart failure, sudden cardiac death, recurrence of TS, and cerebrovascular events. RESULTS: Five studies with 564 patients were included for reporting correlation of CMR parameters with MACCE. Primary endpoint occurred in 69 (12%) patients. Among the CMR parameters assessed, myocardial strain parameters (including measurements of the left atrium, left and right ventricle), right ventricle involvement, and a CMR-based radiomics model demonstrated correlations with MACCE. Additionally, one study showed the predictive ability of a CMR score. CONCLUSION: The current systematic review suggests that CMR may offer prognostic insights in TS patients, underscoring its potential clinical utility for integration into clinical practice. However, scarce data are currently available; hence, further research is needed.

Neuroradiological manifestations in hospitalized patients with COVID-19: An Italian national multicenter study on behalf of AINR (Associazione Italiana di Neuroradiologia) and SIRM (Società Italiana di Radiologia Medica).

PURPOSE: This multicentric study aims to characterize and assess the occurrence of neuroradiological findings among patients with SARS-CoV-2 infection during the first Italian wave of the pandemic outbreak. MATERIALS AND METHODS: Patients' data were collected between May 2020 and June 2020. Clinical and laboratory data, chest imaging, brain CT, and MRI imaging were included. Acquired data were centralized and analyzed in two hospitals: ASST Spedali Civili, Brescia, and IRRCS San Raffaele Research Hospital, Milan, Italy. COVID-19 patients were classified into two different subgroups, vascular and nonvascular. The vascular pattern was further divided into ischemic and hemorrhagic stroke groups. RESULTS: Four hundred and fifteen patients from 20 different Italian Centers were enrolled in the study. The most frequent symptom was focal neurological deficit, found in 143 patients (34.5%). The most frequent neuroradiological finding was ischemic stroke in 122 (29.4%) patients. Forty-four (10.6%) patients presented a cerebral hemorrhage. Forty-seven patients had non-stroke neuroimaging lesions (11.3%). The most common was PRES-like syndrome (28%), SWI hypointensities (22%), and encephalitis (19%). The stroke group had higher CAD risk (37.5% vs 20%, p = .016) and higher D-dimer levels (1875 ng/mL vs 451 ng/mL, p < .001) compared to the negative group. CONCLUSION: Our study describes the biggest cohort study in Italy on brain imaging of COVID-19 patients and confirms that COVID-19 patients are at risk of strokes, possibly due to a pro-thrombotic microenvironment. Moreover, apart from stroke, the other neuroradiological patterns described align with the ones reported worldwide.

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.

Machine Learning Detects Symptomatic Plaques in Patients With Carotid Atherosclerosis on CT Angiography.

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

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.

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.

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.

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.

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.

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.

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.