A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography

Obesity and cardiovascular disease: an ESC clinical consensus statement

The global prevalence of obesity has more than doubled over the past four decades, currently affecting more than a billion individuals. Beyond its recognition as a high-risk condition that is causally linked to many chronic illnesses, obesity has been declared a disease per se that results in impaired quality of life and reduced life expectancy. Notably, two-thirds of obesity-related excess mortality is attributable to cardiovascular disease. Despite the increasingly appreciated link between obesity and a broad range of cardiovascular disease manifestations including atherosclerotic disease, heart failure, thromboembolic disease, arrhythmias, and sudden cardiac death, obesity has been underrecognized and sub-optimally addressed compared with other modifiable cardiovascular risk factors. In the view of major repercussions of the obesity epidemic on public health, attention has focused on population-based and personalized approaches to prevent excess weight gain and maintain a healthy body weight from early childhood and throughout adult life, as well as on comprehensive weight loss interventions for persons with established obesity. This clinical consensus statement by the European Society of Cardiology discusses current evidence on the epidemiology and aetiology of obesity; the interplay between obesity, cardiovascular risk factors and cardiac conditions; the clinical management of patients with cardiac disease and obesity; and weight loss strategies including lifestyle changes, interventional procedures, and anti-obesity medications with particular focus on their impact on cardiometabolic risk and cardiac outcomes. The document aims to raise awareness on obesity as a major risk factor and provide guidance for implementing evidence-based practices for its prevention and optimal management within the context of primary and secondary cardiovascular disease prevention.

Radiomics parameters of epicardial adipose tissue predict mortality in acute pulmonary embolism

BACKGROUND

Accurate prediction of short-term mortality in acute pulmonary embolism (APE) is very important. The aim of the present study was to analyze the prognostic role of radiomics values of epicardial adipose tissue (EAT) in APE.

METHODS

Overall, 508 patients were included into the study, 209 female (42.1%), mean age, 64.7 ± 14.8 years. 4.6%and 12.4% died (7- and 30-day mortality, respectively). For external validation, a cohort of 186 patients was further analysed. 20.2% and 27.7% died (7- and 30-day mortality, respectively). CTPA was performed at admission for every patient before any previous treatment on multi-slice CT scanners. A trained radiologist, blinded to patient outcomes, semiautomatically segmented the EAT on a dedicated workstation using ImageJ software. Extraction of radiomic features was applied using the pyradiomics library. After correction for correlation among features and feature cleansing by random forest and feature ranking, we implemented feature signatures using 247 features of each patient. In total, 26 feature combinations with different feature class combinations were identified. Patients were randomly assigned to a training and a validation cohort with a ratio of 7:3. We characterized two models (30-day and 7-day mortality). The models incorporate a combination of 13 features of seven different image feature classes.

FINDINGS

We fitted the characterized models to a validation cohort (n = 169) in order to test accuracy of our models. We observed an AUC of 0.776 (CI 0.671-0.881) and an AUC of 0.724 (CI 0.628-0.820) for the prediction of 30-day mortality and 7-day mortality, respectively. The overall percentage of correct prediction in this regard was 88% and 79% in the validation cohorts. Lastly, the AUC in an independent external validation cohort was 0.721 (CI 0.633-0.808) and 0.750 (CI 0.657-0.842), respectively.

INTERPRETATION

Radiomics parameters of EAT are strongly associated with mortality in patients with APE.

CLINICAL TRIAL NUMBER

Not applicable.

Coronary CTA-based vascular radiomics predicts atherosclerosis development proximal to LAD myocardial bridging

AIMS

Cardiac cycle morphological changes can accelerate plaque growth proximal to myocardial bridging (MB) in the left anterior descending artery (LAD). To assess coronary computed tomography angiography (CCTA)-based vascular radiomics for predicting proximal plaque development in LAD MB.

METHODS AND RESULTS

Patients with repeated CCTA scans showing LAD MB without proximal plaque in index CCTA were included from Jinling Hospital as a development set. They were divided into training and internal testing in an 8:2 ratio. Patients from four other tertiary hospitals were set as external validation set. The endpoint was proximal plaque development of LAD MB in follow-up CCTA. Four vascular radiomics models were built: MB centreline (MB CL), proximal MB CL (pMB CL), MB cross-section (MB CS), and proximal MB CS (pMB CS), whose performances were evaluated using area under the receiver operating characteristic curve (AUC), integrated discrimination improvement (IDI), and net reclassification improvement (NRI). In total, 295 patients were included in the development (n = 192; median age, 54 ± 11 years; 137 men) and external validation sets (n = 103; median age, 57 ± 9 years; 57 men). The pMB CS vascular radiomics model exhibited higher AUCs in training, internal test, and external sets (AUC = 0.78, 0.75, 0.75) than the clinical and anatomical model (all P < 0.05). Integration of the pMB CS vascular radiomics model significantly raised the AUC of the clinical and anatomical model from 0.56 to 0.75 (P = 0.002), along with enhanced NRI [0.76 (0.37-1.14), P < 0.001] and IDI [0.17 (0.07-0.26), P < 0.001] in the external validation set.

CONCLUSION

The CCTA-based pMB CS vascular radiomics model can predict plaque development in LAD MB.

Assessing abdominal aortic aneurysm growth using radiomic features of perivascular adipose tissue after endovascular repair

OBJECTIVES

The study aimed to investigate the relationship between the radiomic features of perivascular adipose tissue (PVAT) and abdominal aortic aneurysm (AAA) growth after endovascular aneurysm repair (EVAR).

METHODS

Patients with sub-renal AAA who underwent regular follow-up after EVAR between March 2014 and March 2024 were retrospectively collected. Two radiologists segmented aneurysms and PVAT. Patients were categorised into growing and non-growing groups based on volumetric changes observed in two follow-up computed tomography examinations. One hundred seven radiomic features were automatically extracted from the PVAT region. Univariable and multivariable logistic regression was performed to analyse radiomic features and clinical characteristics. Furthermore, the performance of the integrated clinico-radiological model was compared with models using only radiomic features or clinical characteristics separately.

RESULTS

A total of 79 patients (68 ± 9 years, 89% men) were enroled in this study, 19 of whom had a growing aneurysm. Compared to the non-growing group, PVAT of growing AAA showed a higher surface area to volume ratio (non-growing vs growing, 0.63 vs 0.70, p = 0.04), and a trend of low dependence and high dispersion manifested by texture features (p < 0.05). The area under the curve of the integrated clinico-radiological model was 0.78 (95% confidence intervals 0.65-0.91), with a specificity of 87%. The integrated model outperformed models using only radiomic or clinical features separately (0.78 vs 0.69 vs 0.69).

CONCLUSIONS

Higher surface area to volume ratio and more heterogeneous texture presentation of PVAT were associated with aneurysm dilation after EVAR. Radiomic features of PVAT have the potential to predict AAA progression.

CLINICAL RELEVANCE STATEMENT

Radiomic features of PVAT are associated with AAA progression and can be an independent risk factor for aneurysm dilatation to assist clinicians in postoperative patient surveillance and management.

KEY POINTS

After EVAR for AAA, patients require monitoring for progression. PVAT surrounding growing AAA after EVAR exhibits a more heterogeneous texture. Integrating PVAT-related features and clinical features results in better predictive performance.

Predictive value of CCTA-based pericoronary adipose tissue imaging for major adverse cardiovascular events

RATIONALE AND OBJECTIVE

To evaluate the ability of the radiomic characteristics of pericoronary adipose tissue (PCAT) as determined by coronary computed tomography angiography (CCTA) to predict the likelihood of major adverse cardiovascular events (MACEs) within the next five years.

MATERIALS AND METHODS

In this retrospective casecontrol study, the case group consisted of 210 patients with coronary artery disease (CAD) who developed MACEs within five years, and the control group consisted of 210 CAD patients without MACEs who were matched with the case group patients according to baseline characteristics. Both groups were divided into training and testing cohorts at an 8:2 ratio. After data standardization and the exclusion of features with Pearson correlation coefficients of |r| ≥ 0.9, independent logistic regression models were constructed using selected radiomics features of the proximal PCAT of the left anterior descending (LAD) artery, left circumflex (LCX) artery, and right coronary artery (RCA) via least absolute shrinkage and selection operator (LASSO) techniques. An integrated PCAT radiomics model including all three coronary arteries was also developed. Five models, including individual PCAT radiomics models for the LAD artery, LCX artery, and RCA; an integrated radiomics model; and a fat attenuation index (FAI) model, were assessed for diagnostic accuracy via receiver operating characteristic (ROC) curves, calibration curves, and decision curves.

RESULTS

Compared with the FAI model (AUC=0.564 in training, 0.518 in testing), the integrated radiomics model demonstrated superior diagnostic performance (area under the curve [AUC]=0.923 in training, 0.871 in testing). The AUC values of the integrated model were greater than those of the individual coronary radiomics models, with all the models showing goodness of fit (P > 0.05). The decision curves indicated greater clinical utility of the radiomics models than the FAI model.

CONCLUSION

PCAT radiomics models derived from CCTA data are highly valuable for predicting future MACE risk and significantly outperform the FAI model.

Progression prediction of coronary artery lesions by echocardiography-based ultrasomics analysis in Kawasaki disease

BACKGROUND

Echocardiography-based ultrasomics analysis aids Kawasaki disease (KD) diagnosis but its role in predicting coronary artery lesions (CALs) progression remains unknown. We aimed to develop and validate a predictive model combining echocardiogram-based ultrasomics with clinical parameters for CALs progression in KD.

METHODS

Total 371 KD patients with CALs at baseline were enrolled from a retrospective cohort (cohort 1, n = 316) and a prospective cohort (cohort 2, n = 55). CALs progression was defined by increased Z scores in any coronary artery branch at the 1-month follow-up. Patients in cohort 1 were split randomly into training and validation set 1 at the ratio of 6:4, while cohort 2 comprised validation set 2. Clinical parameters and ultrasomics features at baseline were analyzed and selected for models construction. Model performance was evaluated by area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) and decision curve analysis (DCA) in the training and two validation sets.

RESULTS

At the 1-month follow-ups, 65 patients presented with CALs progression. Three clinical parameters and six ultrasomics features were selected to construct the model. The clinical-ultrasomics model exhibited a good predictive capability in the training, validation set 1 and set 2, achieving AUROCs of 0.83 (95% CI, 0.75-0.90), 0.84 (95% CI, 0.74-0.94), and 0.73 (95% CI, 0.40-0.86), respectively. Moreover, the AUPRC values and DCA of three model demonstrated that the clinical-ultrasomics model consistently outperformed both the clinical model and the ultrasomics model across all three sets, including the training set and the two validation sets.

CONCLUSIONS

Our study demonstrated the effective predictive capacity of a prediction model combining echocardiogram-based ultrasomics features and clinical parameters in predicting CALs progression in KD.

Artificial intelligence-enhanced patient evaluation: bridging art and science

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

Relationship between plaque burden and plaque vulnerability: Acute coronary syndromes versus chronic coronary syndrome

BACKGROUND

The relationship between plaque burden and microscopic characterization of plaque features as it pertains to clinical presentation has not been fully investigated. The aim of this study was to compare the relationship between plaque burden and plaque vulnerability in patients with acute coronary syndromes (ACS) versus chronic coronary syndrome (CCS).

METHODS

Patients who underwent both coronary computed tomography angiography (CTA) and optical coherence tomography (OCT) before coronary intervention were enrolled. All plaques were detected in culprit vessels using CTA, and total plaque volume (TPV) and OCT features were assessed at the corresponding sites. All plaques were divided into three groups according to the tertile levels of TPV (low TPV: <96.5 ​mm3, moderate TPV: 96.5-164.7 ​mm3, high TPV: ≥164.8 ​mm3).

RESULTS

A total of 990 plaques were imaged by OCT in 419 patients: 445 plaques in 190 (45.3%) patients with ACS and 545 in 229 (54.7%) with CCS. Macrophage was more prevalent in plaques with greater TPV in patients who presented with ACS but not in those who presented with CCS (low vs. moderate vs. high TPV group: macrophage 57.4% vs. 71.8% vs. 82.4% in ACS; 63.4% vs. 67.8% vs. 66.7% in CCS; interaction P ​= ​0.004). Lipid arc increased as TPV increased, especially in patients who presented with ACS. Conversely, the layer index increased as TPV increased in patients with CCS.

CONCLUSION

Greater plaque burden was closely related to higher levels of plaque vulnerability in ACS and greater volume of layered plaque in CCS.

TRIAL REGISTRATION

clinicaltrials.gov Identifier: NCT04523194.

Artificial intelligence-assisted measurements of coronary computed tomography angiography parameters such as stenosis, flow reserve, and fat attenuation for predicting major adverse cardiac events in patients with coronary arterial disease

Advancements in artificial intelligence (AI) offer promising tools for improving diagnostic accuracy and patient outcomes in cardiovascular medicine. This study explores the potential of AI-assisted measurements in enhancing the prediction of major adverse cardiac events (MACE) in patients with coronary artery disease (CAD). We conducted a retrospective cohort study involving patients diagnosed with CAD who underwent coronary computed tomography angiography (CCTA). Participants were classified into MACE and non-MACE groups based on their clinical outcomes. Clinical characteristics and AI-assisted measurements of CCTA parameters, including CT-derived fractional flow reserve (CT-FFR) and fat attenuation index (FAI), were collected. Both univariate and multivariable logistic regression analyses were performed to identify independent predictors of MACE, which were used to build predictive models. Statistical analyses revealed three independent predictors of MACE: severe stenosis, CT-FFR ≤ 0.8, and mean FAI (P < 0.05). Seven predictive models incorporating various combinations of these predictors were developed. The model combining all three predictors demonstrated superior performance, as evidenced by the receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.811 (95% confidence interval [CI] 0.774 - 0.847), a sensitivity of 0.776, and a specificity of 0.726. Our findings suggest that AI-assisted CCTA analysis, particularly using fractional flow reserve (FFR) and FAI, could significantly improve the prediction of MACE in patients with CAD, thereby potentially aiding clinical decision making.

A Scoping Review of Machine-Learning Derived Radiomic Analysis of CT and PET Imaging to Investigate Atherosclerotic Cardiovascular Disease

BACKGROUND

Cardiovascular disease affects the carotid arteries, coronary arteries, aorta and the peripheral arteries. Radiomics involves the extraction of quantitative data from imaging features that are imperceptible to the eye. Radiomics analysis in cardiovascular disease has largely focused on CT and MRI modalities. This scoping review aims to summarise the existing literature on radiomic analysis techniques in cardiovascular disease.

METHODS

MEDLINE and Embase databases were searched for eligible studies evaluating radiomic techniques in living human subjects derived from CT, MRI or PET imaging investigating atherosclerotic disease. Data on study population, imaging characteristics and radiomics methodology were extracted.

RESULTS

Twenty-nine studies consisting of 5753 patients (3752 males) were identified, and 78.7% of patients were from coronary artery studies. Twenty-seven studies employed CT imaging (19 CT carotid angiography and 6 CT coronary angiography (CTCA)), and two studies studied PET/CT. Manual segmentation was most frequently undertaken. Processing techniques included voxel discretisation, voxel resampling and filtration. Various shape, first-order, second-order and higher-order radiomic features were extracted. Logistic regression was most commonly used for machine learning.

CONCLUSION

Most published evidence was feasibility/proof of concept work. There was significant heterogeneity in image acquisition, segmentation techniques, processing and analysis between studies. There is a need for the implementation of standardised imaging acquisition protocols, adherence to published reporting guidelines and economic evaluation.

Fully automated epicardial adipose tissue volume quantification with deep learning and relationship with CAC score and micro/macrovascular complications in people living with type 2 diabetes: the multicenter EPIDIAB study

BACKGROUND

The aim of this study (EPIDIAB) was to assess the relationship between epicardial adipose tissue (EAT) and the micro and macrovascular complications (MVC) of type 2 diabetes (T2D).

METHODS

EPIDIAB is a post hoc analysis from the AngioSafe T2D study, which is a multicentric study aimed at determining the safety of antihyperglycemic drugs on retina and including patients with T2D screened for diabetic retinopathy (DR) (n = 7200) and deeply phenotyped for MVC. Patients included who had undergone cardiac CT for CAC (Coronary Artery Calcium) scoring after inclusion (n = 1253) were tested with a validated deep learning segmentation pipeline for EAT volume quantification.

RESULTS

Median age of the study population was 61 [54;67], with a majority of men (57%) a median duration of the disease 11 years [5;18] and a mean HbA1c of7.8 ± 1.4%. EAT was significantly associated with all traditional CV risk factors. EAT volume significantly increased with chronic kidney disease (CKD vs no CKD: 87.8 [63.5;118.6] vs 82.7 mL [58.8;110.8], p = 0.008), coronary artery disease (CAD vs no CAD: 112.2 [82.7;133.3] vs 83.8 mL [59.4;112.1], p = 0.0004, peripheral arterial disease (PAD vs no PAD: 107 [76.2;141] vs 84.6 mL[59.2; 114], p = 0.0005 and elevated CAC score (> 100 vs  < 100 AU: 96.8 mL [69.1;130] vs 77.9 mL [53.8;107.7], p < 0.0001). By contrast, EAT volume was neither associated with DR, nor with peripheral neuropathy. We further evidenced a subgroup of patients with high EAT volume and a null CAC score. Interestingly, this group were more likely to be composed of young women with a high BMI, a lower duration of T2D, a lower prevalence of microvascular complications, and a higher inflammatory profile.

CONCLUSIONS

Fully-automated EAT volume quantification could provide useful information about the risk of both renal and macrovascular complications in T2D patients.

A systematic literature analysis of multi-organ cancer diagnosis using deep learning techniques

Cancer is becoming the most toxic ailment identified among individuals worldwide. The mortality rate has been increasing rapidly every year, which causes progression in the various diagnostic technologies to handle this illness. The manual procedure for segmentation and classification with a large set of data modalities can be a challenging task. Therefore, a crucial requirement is to significantly develop the computer-assisted diagnostic system intended for the initial cancer identification. This article offers a systematic review of Deep Learning approaches using various image modalities to detect multi-organ cancers from 2012 to 2023. It emphasizes the detection of five supreme predominant tumors, i.e., breast, brain, lung, skin, and liver. Extensive review has been carried out by collecting research and conference articles and book chapters from reputed international databases, i.e., Springer Link, IEEE Xplore, Science Direct, PubMed, and Wiley that fulfill the criteria for quality evaluation. This systematic review summarizes the overview of convolutional neural network model architectures and datasets used for identifying and classifying the diverse categories of cancer. This study accomplishes an inclusive idea of ensemble deep learning models that have achieved better evaluation results for classifying the different images into cancer or healthy cases. This paper will provide a broad understanding to the research scientists within the domain of medical imaging procedures of which deep learning technique perform best over which type of dataset, extraction of features, different confrontations, and their anticipated solutions for the complex problems. Lastly, some challenges and issues which control the health emergency have been discussed.

Obesity and cardiovascular disease: an ESC clinical consensus statement

The global prevalence of obesity has more than doubled over the past four decades, currently affecting more than a billion individuals. Beyond its recognition as a high-risk condition that is causally linked to many chronic illnesses, obesity has been declared a disease per se that results in impaired quality of life and reduced life expectancy. Notably, two-thirds of obesity-related excess mortality is attributable to cardiovascular disease. Despite the increasingly appreciated link between obesity and a broad range of cardiovascular disease manifestations including atherosclerotic disease, heart failure, thromboembolic disease, arrhythmias, and sudden cardiac death, obesity has been underrecognized and sub-optimally addressed compared with other modifiable cardiovascular risk factors. In the view of major repercussions of the obesity epidemic on public health, attention has focused on population-based and personalized approaches to prevent excess weight gain and maintain a healthy body weight from early childhood and throughout adult life, as well as on comprehensive weight loss interventions for persons with established obesity. This clinical consensus statement by the European Society of Cardiology discusses current evidence on the epidemiology and aetiology of obesity; the interplay between obesity, cardiovascular risk factors and cardiac conditions; the clinical management of patients with cardiac disease and obesity; and weight loss strategies including lifestyle changes, interventional procedures, and anti-obesity medications with particular focus on their impact on cardiometabolic risk and cardiac outcomes. The document aims to raise awareness on obesity as a major risk factor and provide guidance for implementing evidence-based practices for its prevention and optimal management within the context of primary and secondary cardiovascular disease prevention.

Intraindividual reproducibility of myocardial radiomic features between energy-integrating detector and photon-counting detector CT angiography

BACKGROUND

Radiomics is not yet used in clinical practice due to concerns regarding its susceptibility to technical factors. We aimed to assess the stability and interscan and interreader reproducibility of myocardial radiomic features between energy-integrating detector computed tomography (EID-CT) and photon-counting detector CT (PCD-CT) in patients undergoing coronary CT angiography (CCTA) on both systems.

METHODS

Consecutive patients undergoing clinically indicated CCTA on an EID-CT were prospectively enrolled for a PCD-CT CCTA within 30 days. Virtual monoenergetic images (VMI) at various keV levels and polychromatic images (T3D) were generated for PCD-CT, with image reconstruction parameters standardized between scans. Two readers performed myocardial segmentation and 110 radiomic features were compared intraindividually between EID-CT and PDC-CT series. The agreement of parameters was assessed using the intraclass correlation coefficient and paired t-test for the stability of the parameters.

RESULTS

Eighteen patients (15 males) aged 67.6 ± 9.7 years (mean ± standard deviation) were included. Besides polychromatic PCD-CT reconstructions, 60- and 70-keV VMIs showed the highest feature stability compared to EID-CT (96%, 90%, and 92%, respectively). The interscan reproducibility of features was moderate even in the most favorable comparisons (median ICC 0.50 [interquartile range 0.20-0.60] for T3D; 0.56 [0.33-0.74] for 60 keV; 0.50 [0.36-0.62] for 70 keV). Interreader reproducibility was excellent for the PCD-CT series and good for EID-CT segmentations.

CONCLUSION

Most myocardial radiomic features remain stable between EID-CT and PCD-CT. While features demonstrated moderate reproducibility between scanners, technological advances associated with PCD-CT may lead to greater reproducibility, potentially expediting future standardization efforts.

RELEVANCE STATEMENT

While the use of PCD-CT may facilitate reduced interreader variability in radiomics analysis, the observed interscanner variations in comparison to EID-CT should be taken into account in future research, with efforts being made to minimize their impact in future radiomics studies.

KEY POINTS

Most myocardial radiomic features resulted in being stable between EID-CT and PCD-CT on certain VMIs. The reproducibility of parameters between detector technologies was limited. PCD-CT improved interreader reproducibility of myocardial radiomic features.

Epicardial fat in heart failure-Friend, foe, or bystander

Epicardial adipose tissue (EAT) is a fat depot covering the heart. No physical barrier separates EAT from the myocardium, so EAT can easily affect the underlying cardiac muscle. EAT can participate in the development and progression of heart failure with preserved (HFpEF) and reduced ejection fraction (HFrEF). In healthy humans, excess EAT is associated with impaired cardiac function and worse outcomes. In HFpEF, this trend continues: EAT amount is usually increased, and excess EAT correlates with worse function/outcomes. However, in HFrEF, the opposite is true: reduced EAT amount correlates with worse cardiac function/outcomes. Surprisingly, although EAT has beneficial effects on cardiac function, it aggravates ventricular arrhythmias. Here, we dissect these phenomena, trying to explain these paradoxical findings to find a target for novel heart failure therapies aimed at EAT rather than the myocardium itself. However, the success of this approach depends on a thorough understanding of interactions between EAT and the myocardium.

Diagnosis of perimenopausal coronary heart disease patients using radiomics signature of pericoronary adipose tissue based on coronary computed tomography angiography

To assess whether the radiomics signature of pericoronary adipose tissue (PCAT) from coronary computed tomography angiography (CCTA) can distinguish between perimenopausal women with coronary heart disease (CHD) and those without coronary artery disease (CAD). This single-center retrospective case-control study comprised 140 perimenopausal women with CHD presenting with chest pain who underwent CCTA within 48 h of admission. They were matched with 140 control patients presenting with chest pain but without CAD, based on age, risk factors, radiation dose and CT tube voltage. For all participants, PCAT around the proximal right coronary artery was segmented, from which radiomics features and the fat attenuation index (FAI) were extracted and analyzed. Subsequently, corresponding models were developed and internally validated using Bootstrap methods. Model performance was assessed through measures of identification, calibration, and clinical utility. Using logistic regression analysis, an integrated model that combines clinical features, fat attenuation index and radiomics parameters demonstrated enhanced discrimination ability for perimenopausal CHD (area under the curve [AUC]: 0.80, 95% confidence interval [CI]:0.740-0.845). This model outperformed both the combination of clinical features and PCAT attenuation (AUC 0.67, 95% CI 0.602-0.727) and the use of clinical features alone (AUC 0.66, 95% CI 0.603-0.732). Calibration curves for the three predictive models indicated satisfactory fit (all p > 0.05). Moreover, decision curve analysis demonstrated that the integrated model offered greater clinical benefit compared to the other two models. The CCTA-based radiomics signature derived from the PCAT model outperforms the FAI model in differentiating perimenopausal CHD patients from non-CAD individuals. Integrating PCAT radiomics with the FAI could enhance the diagnostic accuracy for perimenopausal CHD.

Perivascular Fat: A Novel Risk Factor for Coronary Artery Disease

Perivascular adipose tissue (PVAT) interacts with the vascular wall and secretes bioactive factors which regulate vascular wall physiology. Vice versa, vascular wall inflammation affects the adjacent PVAT via paracrine signals, which induce cachexia-type morphological changes in perivascular fat. These changes can be quantified in pericoronary adipose tissue (PCAT), as an increase in PCAT attenuation in coronary computed tomography angiography images. Fat attenuation index (FAI), a novel imaging biomarker, measures PCAT attenuation around coronary artery segments and is associated with coronary artery disease presence, progression, and plaque instability. Beyond its diagnostic capacity, PCAT attenuation can also ameliorate cardiac risk stratification, thus representing an innovative prognostic biomarker of cardiovascular disease (CVD). However, technical, biological, and anatomical factors are weakly related to PCAT attenuation and cause variation in its measurement. Thus, to integrate FAI, a research tool, into clinical practice, a medical device has been designed to provide FAI values standardized for these factors. In this review, we discuss the interplay of PVAT with the vascular wall, the diagnostic and prognostic value of PCAT attenuation, and its integration as a CVD risk marker in clinical practice.

Long-term prognostic value of CT-based high-risk coronary lesion attributes and radiomic features of pericoronary adipose tissue in diabetic patients

AIMS

To investigate the long-term prognostic value of coronary computed tomography angiography (CCTA)-derived high-risk attributes and radiomic features of pericoronary adipose tissue (PCAT) in diabetic patients for predicting major adverse cardiac event (MACE).

METHODS AND RESULTS

Diabetic patients with intermediate pre-test probability of coronary artery disease were prospectively enrolled and referred for CCTA. Three models (model-1 with clinical parameters; model-2 with clinical factors + CCTA imaging parameters; model-3 with the above parameters and PCAT radiomic features) were developed in the training cohort (835 patients) and tested in the independent validation cohort (557 patients). 1392 patients were included and MACEs occurred in 108 patients (7.8%). Multivariable Cox regression analysis revealed that HbA1c, coronary calcium Agatston score, significant stenosis and high-risk plaque were independent predictors for MACE whereas none of PCAT radiomic features showed predictive value. In the training cohort, model-2 demonstrated higher predictive performance over model-1 (C-index = 0.79 vs. 0.68, p < 0.001) whereas model-3 did not show incremental value over model-2(C-index = 0.79 vs. 0.80, p = 0.408). Similar findings were found in the validation cohort.

CONCLUSIONS

The combined model (clinical and CCTA high-risk anatomical features) demonstrated high efficacy in predicting MACE in diabetes. PCAT radiomic features failed to show incremental value for risk stratification.

Artificial intelligence in cardiovascular medicine: clinical applications

Clinical medicine requires the integration of various forms of patient data including demographics, symptom characteristics, electrocardiogram findings, laboratory values, biomarker levels, and imaging studies. Decision-making on the optimal management should be based on a high probability that the envisaged treatment is appropriate, provides benefit, and bears no or little potential harm. To that end, personalized risk-benefit considerations should guide the management of individual patients to achieve optimal results. These basic clinical tasks have become more and more challenging with the massively growing data now available; artificial intelligence and machine learning (AI/ML) can provide assistance for clinicians by obtaining and comprehensively preparing the history of patients, analysing face and voice and other clinical features, by integrating laboratory results, biomarkers, and imaging. Furthermore, AI/ML can provide a comprehensive risk assessment as a basis of optimal acute and chronic care. The clinical usefulness of AI/ML algorithms should be carefully assessed, validated with confirmation datasets before clinical use, and repeatedly re-evaluated as patient phenotypes change. This review provides an overview of the current data revolution that has changed and will continue to change the face of clinical medicine radically, if properly used, to the benefit of physicians and patients alike.

Plaque Characteristics Derived from Intravascular Optical Coherence Tomography That Predict Cardiovascular Death

This study aimed to investigate whether plaque characteristics derived from intravascular optical coherence tomography (IVOCT) could predict a long-term cardiovascular (CV) death. This study was a single-center, retrospective study on 104 patients who had undergone IVOCT-guided percutaneous coronary intervention. Plaque characterization was performed using Optical Coherence TOmography PlaqUe and Stent (OCTOPUS) software developed by our group. A total of 31 plaque features, including lesion length, lumen, calcium, fibrous cap (FC), and vulnerable plaque features (e.g., microchannel), were computed from the baseline IVOCT images. The discriminatory power for predicting CV death was determined using univariate/multivariate logistic regressions. Of 104 patients, CV death was identified in 24 patients (23.1%). Univariate logistic regression revealed that lesion length, calcium angle, calcium thickness, FC angle, FC area, and FC surface area were significantly associated with CV death (p < 0.05). In the multivariate logistic analysis, only the FC surface area (OR 2.38, CI 0.98-5.83, p < 0.05) was identified as a significant determinant for CV death, highlighting the importance of the 3D lesion analysis. The AUC of FC surface area for predicting CV death was 0.851 (95% CI 0.800-0.927, p < 0.05). Patients with CV death had distinct plaque characteristics (i.e., large FC surface area) in IVOCT. Studies such as this one might someday lead to recommendations for pharmaceutical and interventional approaches.

Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics

BACKGROUND

Timely prevention of major adverse cardiovascular events (MACEs) is imperative for reducing cardiovascular diseases-related mortality. Perivascular adipose tissue (PVAT), the adipose tissue surrounding coronary arteries, has attracted increased amounts of attention. Developing a model for predicting the incidence of MACE utilizing machine learning (ML) integrating clinical and PVAT features may facilitate targeted preventive interventions and improve patient outcomes.

METHODS

From January 2017 to December 2019, we analyzed a cohort of 1077 individuals who underwent coronary CT scanning at our facility. Clinical features were collected alongside imaging features, such as coronary artery calcium (CAC) scores and perivascular adipose tissue (PVAT) characteristics. Logistic regression (LR), Framingham Risk Score, and ML algorithms were employed for MACE prediction.

RESULTS

We screened seven critical features to improve the practicability of the model. MACE patients tended to be older, smokers, and hypertensive. Imaging biomarkers such as CAC scores and PVAT characteristics differed significantly between patients with and without a 3-year MACE risk in a population that did not exhibit disparities in laboratory results. The ensemble model, which leverages multiple ML algorithms, demonstrated superior predictive performance compared with the other models. Finally, the ensemble model was used for risk stratification prediction to explore its clinical application value.

CONCLUSIONS

The developed ensemble model effectively predicted MACE incidence based on clinical and imaging features, highlighting the potential of ML algorithms in cardiovascular risk prediction and personalized medicine. Early identification of high-risk patients may facilitate targeted preventive interventions and improve patient outcomes.

Prediction of human epidermal growth factor receptor 2 (HER2) status in breast cancer by mammographic radiomics features and clinical characteristics: a multicenter study

OBJECTIVES

To preoperatively evaluate the human epidermal growth factor 2 (HER2) status in breast cancer using mammographic radiomics features and clinical characteristics on a multi-vendor and multi-center basis.

METHODS

This multi-center study included a cohort of 1512 Chinese female with invasive ductal carcinoma of no special type (IDC-NST) from two different hospitals and five devices (1332 from Institution A, used for training and testing the models, and 180 women from Institution B, as the external validation cohort). The Gradient Boosting Machine (GBM) was employed to establish radiomics and multiomics models. Model efficacy was evaluated by the area under the curve (AUC).

RESULTS

The number of HER2-positive patients in the training, testing, and external validation cohort were 245(26.3%), 105 (26.3.8%), and 51(28.3%), respectively, with no statistical differences among the three cohorts (p = 0.842, chi-square test). The radiomics model, based solely on the radiomics features, achieved an AUC of 0.814 (95% CI, 0.784-0.844) in the training cohort, 0.776 (95% CI, 0.727-0.825) in the testing cohort, and 0.702 (95% CI, 0.614-0.790) in the external validation cohort. The multiomics model, incorporated radiomics features with clinical characteristics, consistently outperformed the radiomics model with AUC values of 0.838 (95% CI, 0.810-0.866) in the training cohort, 0.788 (95% CI, 0.741-0.835) in the testing cohort, and 0.722 (95% CI, 0.637-0.811) in the external validation cohort.

CONCLUSIONS

Our study demonstrates that a model based on radiomics features and clinical characteristics has the potential to accurately predict HER2 status of breast cancer patients across multiple devices and centers.

CLINICAL RELEVANCE STATEMENT

By predicting the HER2 status of breast cancer reliably, the presented model built upon radiomics features and clinical characteristics on a multi-vendor and multi-center basis can help in bolstering the model's applicability and generalizability in real-world clinical scenarios.

KEY POINTS

• The mammographic presentation of breast cancer is closely associated with the status of human epidermal growth factor receptor 2 (HER2). • The radiomics model, based solely on radiomics features, exhibits sub-optimal performance in the external validation cohort. • By combining radiomics features and clinical characteristics, the multiomics model can improve the prediction ability in external data.

Developing a Radiomics Atlas Dataset of normal Abdominal and Pelvic computed Tomography (RADAPT)

Atlases of normal genomics, transcriptomics, proteomics, and metabolomics have been published in an attempt to understand the biological phenotype in health and disease and to set the basis of comprehensive comparative omics studies. No such atlas exists for radiomics data. The purpose of this study was to systematically create a radiomics dataset of normal abdominal and pelvic radiomics that can be used for model development and validation. Young adults without any previously known disease, aged > 17 and ≤ 36 years old, were retrospectively included. All patients had undergone CT scanning for emergency indications. In case abnormal findings were identified, the relevant anatomical structures were excluded. Deep learning was used to automatically segment the majority of visible anatomical structures with the TotalSegmentator model as applied in 3DSlicer. Radiomics features including first order, texture, wavelet, and Laplacian of Gaussian transformed features were extracted with PyRadiomics. A Github repository was created to host the resulting dataset. Radiomics data were extracted from a total of 531 patients with a mean age of 26.8 ± 5.19 years, including 250 female and 281 male patients. A maximum of 53 anatomical structures were segmented and used for subsequent radiomics data extraction. Radiomics features were derived from a total of 526 non-contrast and 400 contrast-enhanced (portal venous) series. The dataset is publicly available for model development and validation purposes.

Prediction value of pericoronary fat attenuation index for coronary in-stent restenosis

OBJECTIVES

As a novel imaging marker, pericoronary fat attenuation index (FAI) reflects the local coronary inflammation which is one of the major mechanisms for in-stent restenosis (ISR). We aimed to validate the ability of pericoronary FAI to predict ISR in patients undergoing percutaneous coronary intervention (PCI).

MATERIALS AND METHODS

Patients who underwent coronary CT angiography (CCTA) before PCI within 1 week between January 2017 and December 2019 at our hospital and had follow-up invasive coronary angiography (ICA) or CCTA were enrolled. Pericoronary FAI was measured at the site where stents would be placed. ISR was defined as ≥ 50% diameter stenosis at follow-up ICA or CCTA in the in-stent area. Multivariable analysis using mixed effects logistic regression models was performed to test the association between pericoronary FAI and ISR at lesion level.

RESULTS

A total of 126 patients with 180 target lesions were included in the study. During 22.5 months of mean interval time from index PCI to follow-up ICA or CCTA, ISR occurred in 40 (22.2%, 40/180) stents. Pericoronary FAI was associated with a higher risk of ISR (adjusted OR = 1.12, p = 0.028). The optimum cutoff was - 69.6 HU. Integrating the dichotomous pericoronary FAI into current state of the art prediction model for ISR improved the prediction ability of the model significantly (△area under the curve =  + 0.064; p = 0.001).

CONCLUSION

Pericoronary FAI around lesions with subsequent stent placement is independently associated with ISR and could improve the ability of current prediction model for ISR.

CLINICAL RELEVANCE STATEMENT

Pericoronary fat attenuation index can be used to identify the lesions with high risk for in-stent restenosis. These lesions may benefit from extra anti-inflammation treatment to avoid in-stent restenosis.

KEY POINTS

• Pericoronary fat attenuation index reflects the local coronary inflammation. • Pericoronary fat attenuation index around lesions with subsequent stents placement can predict in-stent restenosis. • Pericoronary fat attenuation index can be used as a marker for future in-stent restenosis.

Predicting major adverse cardiac events using radiomics nomogram of pericoronary adipose tissue based on CCTA: A multi-center study

BACKGROUND

The evolution of coronary atherosclerotic heart disease (CAD) is intricately linked to alterations in the pericoronary adipose tissue (PCAT). In recent epochs, characteristics of the PCAT have progressively ascended as focal points of research in CAD risk stratification and individualized clinical decision-making. Harnessing radiomic methodologies allows for the meticulous extraction of imaging features from these adipose deposits. Coupled with machine learning paradigms, we endeavor to establish predictive models for the onset of major adverse cardiovascular events (MACE).

PURPOSE

To appraise the predictive utility of radiomic features of PCAT derived from coronary computed tomography angiography (CCTA) in forecasting MACE.

METHODS

We retrospectively incorporated data from 314 suspected or confirmed CAD patients admitted to our institution from June 2019 to December 2022. An additional cohort of 242 patients from two external institutions was encompassed for external validation. The endpoint under consideration was the occurrence of MACE after a 1-year follow-up. MACE was delineated as cardiovascular mortality, newly diagnosed myocardial infarction, hospitalization (or re-hospitalization) for heart failure, and coronary target vessel revascularization occurring more than 30 days post-CCTA examination. All enrolled patients underwent CCTA scanning. Radiomic features were meticulously extracted from the optimal diastolic phase axial slices of CCTA images. Feature reduction was achieved through a composite feature selection algorithm, laying the groundwork for the radiomic signature model. Both univariate and multivariate analyses were employed to assess clinical variables. A multifaceted logistic regression analysis facilitated the crafting of a clinical-radiological-radiomic combined model (or nomogram). Receiver operating characteristic (ROC) curves, calibration, and decision curve analyses (DCA) were delineated, with the area under the ROC curve (AUCs) computed to gauge the predictive prowess of the clinical model, radiomic model, and the synthesized ensemble.

RESULTS

A total of 12 radiomic features closely associated with MACE were identified to establish the radiomic model. Multivariate logistic regression results demonstrated that smoking, age, hypertension, and dyslipidemia were significantly correlated with MACE. In the integrated nomogram, which amalgamated clinical, imaging, and radiomic parameters, the diagnostic performance was as follows: 0.970 AUC, 0.949 accuracy (ACC), 0.833 sensitivity (SEN), 0.981 specificity (SPE), 0.926 positive predictive value (PPV), and 0.955 negative predictive value (NPV). The calibration curve indicated a commendable concordance of the nomogram, and the decision curve analysis underscored its superior clinical utility.

CONCLUSIONS

The integration of radiomic signatures from PCAT based on CCTA, clinical indices, and imaging parameters into a nomogram stands as a promising instrument for prognosticating MACE events.

Use of Artificial Intelligence Including Multimodal Systems to Improve the Management of Cardiovascular Disease

The rising prevalence of cardiovascular disease presents an escalating challenge for current health services, which are grappling with increasing demands. Innovative changes are imperative to sustain the delivery of high-quality patient care. Recent technologic advances have resulted in the emergence of artificial intelligence as a viable solution. Advanced algorithms are now capable of performing complex analysis of large volumes of data rapidly and with exceptional accuracy. Multimodality artificial intelligence systems handle a diverse range of data including images, text, video, and audio. Compared with single-modality systems, multimodal artificial intelligence systems appear to hold promise for enhancing overall performance and enabling smoother integration into existing workflows. Such systems can empower physicians with clinical decision support and enhanced efficiency. Owing to the complexity of the field, however, truly multimodal artificial intelligence is still scarce in the management of cardiovascular disease. This article aims to cover current research, emerging trends, and the future utilisation of artificial intelligence in the management of cardiovascular disease, with a focus on multimodality systems.

Association study of NAFLD with pericoronary adipose tissue and pericardial adipose tissue: Diagnosis of stable CAD patients with NAFLD based on radiomic features

BACKGROUND AND AIM

Nonalcoholic fatty liver disease (NAFLD) is prone to complicated cardiovascular disease, and we aimed to identify patients with NAFLD who are prone to developing stable coronary artery disease (CAD).

METHODS AND RESULTS

We retrospectively recruited adults who underwent coronary computed tomography angiography (CTA). A total of 127 NAFLD patients and 127 non-NAFLD patients were included in this study. Clinical features and imaging parameters were analysed, mainly including pericardial adipose tissue (PAT), pericoronary adipose tissue (PCAT), and radiomic features of 6792 PCATs. The inflammatory associations of NAFLD patients with PAT and PCAT were analysed. Clinical features (model 1), CTA parameters (model 2), the radscore (model 3), and a composite model (model 4) were constructed to identify patients with NAFLD with stable CAD. The presence of NAFLD resulted in a greater inflammatory involvement in all three coronary arteries (all P < 0.01) and was associated with increased PAT volume (r = 0.178**, P < 0.05). In the presence of NAFLD, the mean CT value of the PAT was significantly correlated with the fat attenuation index (FAI) in all three vessels and had the strongest correlation with the RCA FAI (r = 0.55, p < 0.001). A total of 9 radiomic features were screened by LASSO regression to calculate radiomic scores. In the model comparison, model 4 had the best performance of all models (AUC 0.914 [0.863-0.965]) and the highest overall diagnostic value of the model (sensitivity: 0.814, specificity: 0.941).

CONCLUSIONS

NAFLD correlates with PAT volume and PCAT inflammation. Furthermore, combining clinical features, CTA parameters, and radiomic scores can improve the efficiency of early diagnosis of stable CAD in patients with NAFLD.

Enhancing cardiovascular risk stratification: Radiomics of coronary plaque and perivascular adipose tissue - Current insights and future perspectives

Radiomics, the quantitative extraction and mining of features from radiological images, has recently emerged as a promising source of non-invasive image-based cardiovascular biomarkers, potentially revolutionizing diagnostics and risk assessment. This review explores its application within coronary plaques and pericoronary adipose tissue, particularly focusing on plaque characterization and cardiac events prediction. By shedding light on the current state-of-the-art, achievements, and prospective avenues, this review contributes to a deeper understanding of the evolving landscape of radiomics in the context of coronary arteries. Finally, open challenges and existing gaps are emphasized to underscore the need for future efforts aimed at ensuring the robustness and reliability of radiomics studies, facilitating their clinical translation.

Noninvasive Atherosclerotic Phenotyping: The Next Frontier into Understanding the Pathobiology of Coronary Artery Disease

PURPOSE OF REVIEW

Despite recent advances, coronary artery disease remains one of the leading causes of mortality worldwide. Noninvasive imaging allows atherosclerotic phenotyping by measurement of plaque burden, morphology, activity and inflammation, which has the potential to refine patient risk stratification and guide personalized therapy. This review describes the current and emerging roles of advanced noninvasive cardiovascular imaging methods for the assessment of coronary artery disease.

RECENT FINDINGS

Cardiac computed tomography enables comprehensive, noninvasive imaging of the coronary vasculature, and is used to assess luminal stenoses, coronary calcifications, and distinct adverse plaque characteristics, helping to identify patients prone to future events. Novel software tools, implementing artificial intelligence solutions, can automatically quantify and characterize atherosclerotic plaque from standard computed tomography datasets. These quantitative imaging biomarkers have been shown to improve patient risk stratification beyond clinical risk scores and current clinical interpretation of cardiac computed tomography. In addition, noninvasive molecular imaging in higher risk patients can be used to assess plaque activity and plaque thrombosis. Noninvasive imaging allows unique insight into the burden, morphology and activity of atherosclerotic coronary plaques. Such phenotyping of atherosclerosis can potentially improve individual patient risk prediction, and in the near future has the potential for clinical implementation.

A cloud-based medical device for predicting cardiac risk in suspected coronary artery disease: a rapid review and conceptual economic model

Background

The CaRi-Heart® device estimates risk of 8-year cardiac death, using a prognostic model, which includes perivascular fat attenuation index, atherosclerotic plaque burden and clinical risk factors.

Objectives

To provide an Early Value Assessment of the potential of CaRi-Heart Risk to be an effective and cost-effective adjunctive investigation for assessment of cardiac risk, in people with stable chest pain/suspected coronary artery disease, undergoing computed tomography coronary angiography. This assessment includes conceptual modelling which explores the structure and evidence about parameters required for model development, but not development of a full executable cost-effectiveness model.

Data sources

Twenty-four databases, including MEDLINE, MEDLINE In-Process and EMBASE, were searched from inception to October 2022.

Methods

Review methods followed published guidelines. Study quality was assessed using Prediction model Risk Of Bias ASsessment Tool. Results were summarised by research question: prognostic performance; prevalence of risk categories; clinical effects; costs of CaRi-Heart. Exploratory searches were conducted to inform conceptual cost-effectiveness modelling.

Results

The only included study indicated that CaRi-Heart Risk may be predictive of 8 years cardiac death. The hazard ratio, per unit increase in CaRi-Heart Risk, adjusted for smoking, hypercholesterolaemia, hypertension, diabetes mellitus, Duke index, presence of high-risk plaque features and epicardial adipose tissue volume, was 1.04 (95% confidence interval 1.03 to 1.06) in the model validation cohort. Based on Prediction model Risk Of Bias ASsessment Tool, this study was rated as having high risk of bias and high concerns regarding its applicability to the decision problem specified for this Early Value Assessment. We did not identify any studies that reported information about the clinical effects or costs of using CaRi-Heart to assess cardiac risk. Exploratory searches, conducted to inform the conceptual cost-effectiveness modelling, indicated that there is a deficiency with respect to evidence about the effects of changing existing treatments or introducing new treatments, based on assessment of cardiac risk (by any method), or on measures of vascular inflammation (e.g. fat attenuation index). A de novo conceptual decision-analytic model that could be used to inform an early assessment of the cost effectiveness of CaRi-Heart is described. A combination of a short-term diagnostic model component and a long-term model component that evaluates the downstream consequences is anticipated to capture the diagnosis and the progression of coronary artery disease.

Limitations

The rapid review methods and pragmatic additional searches used to inform this Early Value Assessment mean that, although areas of potential uncertainty have been described, we cannot definitively state where there are evidence gaps.

Conclusions

The evidence about the clinical utility of CaRi-Heart Risk is underdeveloped and has considerable limitations, both in terms of risk of bias and applicability to United Kingdom clinical practice. There is some evidence that CaRi-Heart Risk may be predictive of 8-year risk of cardiac death, for patients undergoing computed tomography coronary angiography for suspected coronary artery disease. However, whether and to what extent CaRi-Heart represents an improvement relative to current standard of care remains uncertain. The evaluation of the CaRi-Heart device is ongoing and currently available data are insufficient to fully inform the cost-effectiveness modelling.

Future work

A large (n = 15,000) ongoing study, NCT05169333, the Oxford risk factors and non-invasive imaging study, with an estimated completion date of February 2030, may address some of the uncertainties identified in this Early Value Assessment.

Study registration

This study is registered as PROSPERO CRD42022366496.

Funding

This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135672) and is published in full in Health Technology Assessment; Vol. 28, No. 31. See the NIHR Funding and Awards website for further award information.

Protocol and rationale of the Australian multicentre registry for serial cardiac computed tomography angiography (ARISTOCRAT): a prospective observational study of the natural history of pericoronary adipose tissue attenuation and radiomics

Background

Vascular inflammation plays a crucial role in the development of atherosclerosis and atherosclerotic plaque rupture resulting in acute coronary syndrome (ACS). Pericoronary adipose tissue (PCAT) attenuation quantified from routine coronary computed tomography angiography (CCTA) has emerged as a promising non-invasive imaging biomarker of coronary inflammation. However, a detailed understanding of the natural history of PCAT attenuation is required before it can be used as a surrogate endpoint in trials of novel therapies targeting coronary inflammation. This article aims to explore the natural history of PCAT attenuation and its association with changes in plaque characteristics.

Methods

The Australian natuRal hISTOry of periCoronary adipose tissue attenuation, RAdiomics and plaque by computed Tomographic angiography (ARISTOCRAT) registry is a multi-centre observational registry enrolling patients undergoing clinically indicated serial CCTA in 9 centres across Australia. CCTA scan parameters will be matched across serial scans. Quantitative analysis of plaque and PCAT will be performed using semiautomated software.

Discussion

The primary endpoint is to explore temporal changes in patient-level and lesion-level PCAT attenuation by CCTA and their associations with changes in plaque characteristics. Secondary endpoints include evaluating: (I) impact of statin therapy on PCAT attenuation and plaque characteristics; and (II) changes in PCAT attenuation and plaque characteristics in specific subgroups according to sex and risk factors. ARISTOCRAT will further our understanding of the natural history of PCAT attenuation and its association with changes in plaque characteristics.

Trial Registration

This study has been prospectively registered with the Australia and New Zealand Clinical Trials Registry (ACTRN12621001018808).

Coronary CTA-based radiomic signature of pericoronary adipose tissue predict rapid plaque progression

OBJECTIVES

To explore the value of radiomic features derived from pericoronary adipose tissue (PCAT) obtained by coronary computed tomography angiography for prediction of coronary rapid plaque progression (RPP).

METHODS

A total of 1233 patients from two centers were included in this multicenter retrospective study. The participants were divided into training, internal validation, and external validation cohorts. Conventional plaque characteristics and radiomic features of PCAT were extracted and analyzed. Random Forest was used to construct five models. Model 1: clinical model. Model 2: plaque characteristics model. Model 3: PCAT radiomics model. Model 4: clinical + radiomics model. Model 5: plaque characteristics + radiomics model. The evaluation of the models encompassed identification accuracy, calibration precision, and clinical applicability. Delong' test was employed to compare the area under the curve (AUC) of different models.

RESULTS

Seven radiomic features, including two shape features, three first-order features, and two textural features, were selected to build the PCAT radiomics model. In contrast to the clinical model and plaque characteristics model, the PCAT radiomics model (AUC 0.85 for training, 0.84 for internal validation, and 0.81 for external validation; p < 0.05) achieved significantly higher diagnostic performance in predicting RPP. The separate combination of radiomics with clinical and plaque characteristics model did not further improve diagnostic efficacy statistically (p > 0.05).

CONCLUSION

Radiomic feature analysis derived from PCAT significantly improves the prediction of RPP as compared to clinical and plaque characteristics. Radiomic analysis of PCAT may improve monitoring RPP over time.

CRITICAL RELEVANCE STATEMENT

Our findings demonstrate PCAT radiomics model exhibited good performance in the prediction of RPP, with potential clinical value.

KEY POINTS

Rapid plaque progression may be predictable with radiomics from pericoronary adipose tissue. Fibrous plaque volume, diameter stenosis, and fat attenuation index were identified as risk factors for predicting rapid plaque progression. Radiomics features of pericoronary adipose tissue can improve the predictive ability of rapid plaque progression.

Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges

This article reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidly and accurately process data for the prediction and diagnosis of acute cardiac conditions. The review examines AI's impact on patient health across various diagnostic tools such as echocardiography, electrocardiography, coronary angiography, cardiac computed tomography, and magnetic resonance imaging, discusses the regulatory landscape for AI in health care, and categorises AI algorithms by their risk levels. Furthermore, it addresses the challenges of data quality, generalisability, bias, transparency, and regulatory considerations, underscoring the necessity for inclusive data and robust validation processes. The review concludes with future perspectives on integrating AI into clinical workflows and the ongoing need for research, regulation, and innovation to harness AI's full potential in improving acute cardiac care.

Radiomics analysis of lesion-specific pericoronary adipose tissue to predict major adverse cardiovascular events in coronary artery disease

OBJECTIVE

To investigate the prognostic performance of radiomics analysis of lesion-specific pericoronary adipose tissue (PCAT) for major adverse cardiovascular events (MACE) with the guidance of CT derived fractional flow reserve (CT-FFR) in coronary artery disease (CAD).

MATERIALS AND METHODS

The study retrospectively analyzed 608 CAD patients who underwent coronary CT angiography. Lesion-specific PCAT was determined by the lowest CT-FFR value and 1691 radiomic features were extracted. MACE included cardiovascular death, nonfatal myocardial infarction, unplanned revascularization and hospitalization for unstable angina. Four models were generated, incorporating traditional risk factors (clinical model), radiomics score (Rad-score, radiomics model), traditional risk factors and Rad-score (clinical radiomics model) and all together (combined model). The model performances were evaluated and compared with Harrell concordance index (C-index), area under curve (AUC) of the receiver operator characteristic.

RESULTS

Lesion-specific Rad-score was associated with MACE (adjusted HR = 1.330, p = 0.009). The combined model yielded the highest C-index of 0.718, which was higher than clinical model (C-index = 0.639), radiomics model (C-index = 0.653) and clinical radiomics model (C-index = 0.698) (all p < 0.05). The clinical radiomics model had significant higher C-index than clinical model (p = 0.030). There were no significant differences in C-index between clinical or clinical radiomics model and radiomics model (p values were 0.796 and 0.147 respectively). The AUC increased from 0.674 for clinical model to 0.721 for radiomics model, 0.759 for clinical radiomics model and 0.773 for combined model.

CONCLUSION

Radiomics analysis of lesion-specific PCAT is useful in predicting MACE. Combination of lesion-specific Rad-score and CT-FFR shows incremental value over traditional risk factors.

Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study

BACKGROUND

Coronary computed tomography angiography (CCTA) is the first line investigation for chest pain, and it is used to guide revascularisation. However, the widespread adoption of CCTA has revealed a large group of individuals without obstructive coronary artery disease (CAD), with unclear prognosis and management. Measurement of coronary inflammation from CCTA using the perivascular fat attenuation index (FAI) Score could enable cardiovascular risk prediction and guide the management of individuals without obstructive CAD. The Oxford Risk Factors And Non-invasive imaging (ORFAN) study aimed to evaluate the risk profile and event rates among patients undergoing CCTA as part of routine clinical care in the UK National Health Service (NHS); to test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE) in patients with or without CAD; and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system in a UK population.

METHODS

This multicentre, longitudinal cohort study included 40 091 consecutive patients undergoing clinically indicated CCTA in eight UK hospitals, who were followed up for MACE (ie, myocardial infarction, new onset heart failure, or cardiac death) for a median of 2·7 years (IQR 1·4-5·3). The prognostic value of FAI Score in the presence and absence of obstructive CAD was evaluated in 3393 consecutive patients from the two hospitals with the longest follow-up (7·7 years [6·4-9·1]). An AI-enhanced cardiac risk prediction algorithm, which integrates FAI Score, coronary plaque metrics, and clinical risk factors, was then evaluated in this population.

FINDINGS

In the 2·7 year median follow-up period, patients without obstructive CAD (32 533 [81·1%] of 40 091) accounted for 2857 (66·3%) of the 4307 total MACE and 1118 (63·7%) of the 1754 total cardiac deaths in the whole of Cohort A. Increased FAI Score in all the three coronary arteries had an additive impact on the risk for cardiac mortality (hazard ratio [HR] 29·8 [95% CI 13·9-63·9], p<0·001) or MACE (12·6 [8·5-18·6], p<0·001) comparing three vessels with an FAI Score in the top versus bottom quartile for each artery. FAI Score in any coronary artery predicted cardiac mortality and MACE independently from cardiovascular risk factors and the presence or extent of CAD. The AI-Risk classification was positively associated with cardiac mortality (6·75 [5·17-8·82], p<0·001, for very high risk vs low or medium risk) and MACE (4·68 [3·93-5·57], p<0·001 for very high risk vs low or medium risk). Finally, the AI-Risk model was well calibrated against true events.

INTERPRETATION

The FAI Score captures inflammatory risk beyond the current clinical risk stratification and CCTA interpretation, particularly among patients without obstructive CAD. The AI-Risk integrates this information in a prognostic algorithm, which could be used as an alternative to traditional risk factor-based risk calculators.

FUNDING

British Heart Foundation, NHS-AI award, Innovate UK, National Institute for Health and Care Research, and the Oxford Biomedical Research Centre.

Radiomics of pericoronary adipose tissue on computed tomography angiography predicts coronary heart disease in patients with type 2 diabetes mellitus

BACKGROUND

Diabetes is a common chronic metabolic disease. The progression of the disease promotes vascular inflammation and the formation of atherosclerosis, leading to cardiovascular disease. The coronary artery perivascular adipose tissue attenuation index based on CCTA is a new noninvasive imaging biomarker that reflects the spatial changes in perivascular adipose tissue attenuation in CCTA images and the inflammation around the coronary arteries. In this study, a radiomics approach is proposed to extract a large number of image features from CCTA in a high-throughput manner and combined with clinical diagnostic data to explore the predictive ability of vascular perivascular adipose imaging data based on CCTA for coronary heart disease in diabetic patients.

METHODS

R language was used for statistical analysis to screen the variables with significant differences. A presegmentation model was used for CCTA vessel segmentation, and the pericoronary adipose region was screened out. PyRadiomics was used to calculate the radiomics features of pericoronary adipose tissue, and SVM, DT and RF were used to model and analyze the clinical data and radiomics data. Model performance was evaluated using indicators such as PPV, FPR, AAC, and ROC.

RESULTS

The results indicate that there are significant differences in age, blood pressure, and some biochemical indicators between diabetes patients with and without coronary heart disease. Among 1037 calculated radiomic parameters, 18.3% showed significant differences in imaging omics features. Three modeling methods were used to analyze different combinations of clinical information, internal vascular radiomics information and pericoronary vascular fat radiomics information. The results showed that the dataset of full data had the highest ACC values under different machine learning models. The support vector machine method showed the best specificity, sensitivity, and accuracy for this dataset.

CONCLUSIONS

In this study, the clinical data and pericoronary radiomics data of CCTA were fused to predict the occurrence of coronary heart disease in diabetic patients. This provides information for the early detection of coronary heart disease in patients with diabetes and allows for timely intervention and treatment.

The Plaque Analysis Classifies the Coronary Artery Disease-Reporting and Data System (CAD-RADS) Stenosis and Plaque Burden Categories: Association of the Plaque Features, Fat Attenuation Index, Coronary Computed Tomography Fractional Flow Reserve, and the Combination of Stenosis and Calcification

BACKGROUND

The coronary artery disease-reporting and data system (CAD-RADS) 2.0 is used to standardize the reporting of coronary computed tomography angiography (CCTA) results. Artificial intelligence software can quantify the plaque composition, fat attenuation index, and fractional flow reserve.

OBJECTIVE

To analyze plaque features of varying severity in patients with a combination of CAD-RADS stenosis and plaque burden categorization and establish a random forest classification model.

METHODS

The data of 100 patients treated between April 2021 and February 2022 were retrospectively collected. The most severe plaque observed in each patient was the target lesion. Patients were categorized into three groups according to CAD-RADS: CAD-RADS 1-2 + P0-2, CAD-RADS 3-4B + P0-2, and CAD-RADS 3-4B + P3-4. Differences and correlations between variables were assessed between groups. AUC, accuracy, precision, recall, and F1 score were used to evaluate the diagnostic performance.

RESULTS

A total of 100 patients and 178 arteries were included. The differences of computed tomography fractional flow reserve (CT-FFR) (H = 23.921, p < 0.001), the volume of lipid component (H = 12.996, p = 0.002), the volume of fibro-lipid component (H = 8.692, p = 0.013), the proportion of lipid component volume (H = 22.038, p < 0.001), the proportion of fibro-lipid component volume (H = 11.731, p = 0.003), the proportion of calcification component volume (H = 11.049, p = 0.004), and plaque type (χ2 = 18.110, p = 0.001) was statistically significant.

CONCLUSION

CT-FFR, volume and proportion of lipid and fibro-lipid components of plaques, the proportion of calcified components, and plaque type were valuable for CAD-RADS stenosis + plaque burden classification, especially CT-FFR, volume, and proportion of lipid and fibro-lipid components. The model built using the random forest was better than the clinical model (AUC: 0.874 vs. 0.647).

Interpretable Radiomic Signature for Breast Microcalcification Detection and Classification

Breast microcalcifications are observed in 80% of mammograms, and a notable proportion can lead to invasive tumors. However, diagnosing microcalcifications is a highly complicated and error-prone process due to their diverse sizes, shapes, and subtle variations. In this study, we propose a radiomic signature that effectively differentiates between healthy tissue, benign microcalcifications, and malignant microcalcifications. Radiomic features were extracted from a proprietary dataset, composed of 380 healthy tissue, 136 benign, and 242 malignant microcalcifications ROIs. Subsequently, two distinct signatures were selected to differentiate between healthy tissue and microcalcifications (detection task) and between benign and malignant microcalcifications (classification task). Machine learning models, namely Support Vector Machine, Random Forest, and XGBoost, were employed as classifiers. The shared signature selected for both tasks was then used to train a multi-class model capable of simultaneously classifying healthy, benign, and malignant ROIs. A significant overlap was discovered between the detection and classification signatures. The performance of the models was highly promising, with XGBoost exhibiting an AUC-ROC of 0.830, 0.856, and 0.876 for healthy, benign, and malignant microcalcifications classification, respectively. The intrinsic interpretability of radiomic features, and the use of the Mean Score Decrease method for model introspection, enabled models' clinical validation. In fact, the most important features, namely GLCM Contrast, FO Minimum and FO Entropy, were compared and found important in other studies on breast cancer.

Cardiac CT: Competition, complimentary or confounder

Coronary CT angiography (CCTA) has been gradually adopted into clinical practice over the last two decades. CCTA has high diagnostic accuracy, prognostic value, and unique features such as assessment of plaque composition. CCTA-derived functional assessment techniques such as fractional flow reserve and CT perfusion are also available and can increase the diagnostic specificity of the modality. These properties propound CCTA as a competitor of functional testing in diagnosis of obstructive CAD, however, utilizing CCTA in a concomitant fashion to potentiate the performance of the latter can lead to better patient care and may provide more accurate prognostic information. Although multiple diagnostic challenges such as evaluation of calcified segments, stents, and small distal vessels still exist, the technologic developments in hardware as well as growing incorporation of artificial intelligence to daily practice are all set to augment the diagnostic and prognostic role of CCTA in cardiovascular disorders.

Radiomics of pericardial fat: a new frontier in heart failure discrimination and prediction

OBJECTIVES

To use pericardial adipose tissue (PAT) radiomics phenotyping to differentiate existing and predict future heart failure (HF) cases in the UK Biobank.

METHODS

PAT segmentations were derived from cardiovascular magnetic resonance (CMR) studies using an automated quality-controlled model to define the region-of-interest for radiomics analysis. Prevalent (present at time of imaging) and incident (first occurrence after imaging) HF were ascertained using health record linkage. We created balanced cohorts of non-HF individuals for comparison. PyRadiomics was utilised to extract 104 radiomics features, of which 28 were chosen after excluding highly correlated ones (0.8). These features, plus sex and age, served as predictors in binary classification models trained separately to detect (1) prevalent and (2) incident HF. We tested seven modeling methods using tenfold nested cross-validation and examined feature importance with explainability methods.

RESULTS

We studied 1204 participants in total, 297 participants with prevalent (60 ± 7 years, 21% female) and 305 with incident (61 ± 6 years, 32% female) HF, and an equal number of non-HF comparators. We achieved good discriminative performance for both prevalent (voting classifier; AUC: 0.76; F1 score: 0.70) and incident (light gradient boosting machine: AUC: 0.74; F1 score: 0.68) HF. Our radiomics models showed marginally better performance compared to PAT area alone. Increased PAT size (maximum 2D diameter in a given column or slice) and texture heterogeneity (sum entropy) were important features for prevalent and incident HF classification models.

CONCLUSIONS

The amount and character of PAT discriminate individuals with prevalent HF and predict incidence of future HF.

CLINICAL RELEVANCE STATEMENT

This study presents an innovative application of pericardial adipose tissue (PAT) radiomics phenotyping as a predictive tool for heart failure (HF), a major public health concern. By leveraging advanced machine learning methods, the research uncovers that the quantity and characteristics of PAT can be used to identify existing cases of HF and predict future occurrences. The enhanced performance of these radiomics models over PAT area alone supports the potential for better personalised care through earlier detection and prevention of HF.

KEY POINTS

•PAT radiomics applied to CMR was used for the first time to derive binary machine learning classifiers to develop models for discrimination of prevalence and prediction of incident heart failure. •Models using PAT area provided acceptable discrimination between cases of prevalent or incident heart failure and comparator groups. •An increased PAT volume (increased diameter using shape features) and greater texture heterogeneity captured by radiomics texture features (increased sum entropy) can be used as an additional classifier marker for heart failure.

Predicting major adverse cardiovascular events within 3 years by optimization of radiomics model derived from pericoronary adipose tissue on coronary computed tomography angiography: a case-control study

BACKGROUND

Coronary inflammation induces changes in pericoronary adipose tissue (PCAT) can be detected by coronary computed tomography angiography (CCTA). Our aim was to investigate whether different PCAT radiomics model based on CCTA could improve the prediction of major adverse cardiovascular events (MACE) within 3 years.

METHODS

This retrospective study included 141 consecutive patients with MACE and matched to patients with non-MACE (n = 141). Patients were randomly assigned into training and test datasets at a ratio of 8:2. After the robust radiomics features were selected by using the Spearman correlation analysis and the least absolute shrinkage and selection operator, radiomics models were built based on different machine learning algorithms. The clinical model was then calculated according to independent clinical risk factors. Finally, an overall model was established using the radiomics features and the clinical factors. Performance of the models was evaluated for discrimination degree, calibration degree, and clinical usefulness.

RESULTS

The diagnostic performance of the PCAT model was superior to that of the RCA-model, LAD-model, and LCX-model alone, with AUCs of 0.723, 0.675, 0.664, and 0.623, respectively. The overall model showed superior diagnostic performance than that of the PCAT-model and Cli-model, with AUCs of 0.797, 0.723, and 0.706, respectively. Calibration curve showed good fitness of the overall model, and decision curve analyze demonstrated that the model provides greater clinical benefit.

CONCLUSION

The CCTA-based PCAT radiomics features of three major coronary arteries have the potential to be used as a predictor for MACE. The overall model incorporating the radiomics features and clinical factors offered significantly higher discrimination ability for MACE than using radiomics or clinical factors alone.

The Sociodemographic Biases in Machine Learning Algorithms: A Biomedical Informatics Perspective

Artificial intelligence models represented in machine learning algorithms are promising tools for risk assessment used to guide clinical and other health care decisions. Machine learning algorithms, however, may house biases that propagate stereotypes, inequities, and discrimination that contribute to socioeconomic health care disparities. The biases include those related to some sociodemographic characteristics such as race, ethnicity, gender, age, insurance, and socioeconomic status from the use of erroneous electronic health record data. Additionally, there is concern that training data and algorithmic biases in large language models pose potential drawbacks. These biases affect the lives and livelihoods of a significant percentage of the population in the United States and globally. The social and economic consequences of the associated backlash cannot be underestimated. Here, we outline some of the sociodemographic, training data, and algorithmic biases that undermine sound health care risk assessment and medical decision-making that should be addressed in the health care system. We present a perspective and overview of these biases by gender, race, ethnicity, age, historically marginalized communities, algorithmic bias, biased evaluations, implicit bias, selection/sampling bias, socioeconomic status biases, biased data distributions, cultural biases and insurance status bias, conformation bias, information bias and anchoring biases and make recommendations to improve large language model training data, including de-biasing techniques such as counterfactual role-reversed sentences during knowledge distillation, fine-tuning, prefix attachment at training time, the use of toxicity classifiers, retrieval augmented generation and algorithmic modification to mitigate the biases moving forward.

Using artificial intelligence to study atherosclerosis from computed tomography imaging: A state-of-the-art review of the current literature

With the enormous progress in the field of cardiovascular imaging in recent years, computed tomography (CT) has become readily available to phenotype atherosclerotic coronary artery disease. New analytical methods using artificial intelligence (AI) enable the analysis of complex phenotypic information of atherosclerotic plaques. In particular, deep learning-based approaches using convolutional neural networks (CNNs) facilitate tasks such as lesion detection, segmentation, and classification. New radiotranscriptomic techniques even capture underlying bio-histochemical processes through higher-order structural analysis of voxels on CT images. In the near future, the international large-scale Oxford Risk Factors And Non-invasive Imaging (ORFAN) study will provide a powerful platform for testing and validating prognostic AI-based models. The goal is the transition of these new approaches from research settings into a clinical workflow. In this review, we present an overview of existing AI-based techniques with focus on imaging biomarkers to determine the degree of coronary inflammation, coronary plaques, and the associated risk. Further, current limitations using AI-based approaches as well as the priorities to address these challenges will be discussed. This will pave the way for an AI-enabled risk assessment tool to detect vulnerable atherosclerotic plaques and to guide treatment strategies for patients.

A Novel Data Augmentation Method for Radiomics Analysis Using Image Perturbations

Radiomics extracts hundreds of features from medical images to quantitively characterize a region of interest (ROI). When applying radiomics, imbalanced or small dataset issues are commonly addressed using under or over-sampling, the latter being applied directly to the extracted features. Aim of this study is to propose a novel balancing and data augmentation technique by applying perturbations (erosion, dilation, contour randomization) to the ROI in cardiac computed tomography images. From the perturbed ROIs, radiomic features are extracted, thus creating additional samples. This approach was tested addressing the clinical problem of distinguishing cardiac amyloidosis (CA) from aortic stenosis (AS) and hypertrophic cardiomyopathy (HCM). Twenty-one CA, thirty-two AS and twenty-one HCM patients were included in the study. From each original and perturbed ROI, 107 radiomic features were extracted. The CA-AS dataset was balanced using the perturbation-based method along with random over-sampling, adaptive synthetic (ADASYN) and the synthetic minority oversampling technique (SMOTE). The same methods were tested to perform data augmentation dealing with CA and HCM. Features were submitted to robustness, redundancy, and relevance analysis testing five feature selection methods (p-value, least absolute shrinkage and selection operator (LASSO), semi-supervised LASSO, principal component analysis (PCA), semi-supervised PCA). Support vector machine performed the classification tasks, and its performance were evaluated by means of a 10-fold cross-validation. The perturbation-based approach provided the best performances in terms of f1 score and balanced accuracy in both CA-AS (f1 score: 80%, AUC: 0.91) and CA-HCM (f1 score: 86%, AUC: 0.92) classifications. These results suggest that ROI perturbations represent a powerful approach to address both data balancing and augmentation issues.

Radiomics Analysis of Pericoronary Adipose Tissue From Baseline Coronary Computed Tomography Angiography Enables Prediction of Coronary Plaque Progression

PURPOSE

The relationship between plaque progression and pericoronary adipose tissue (PCAT) radiomics has not been comprehensively evaluated. We aim to predict plaque progression with PCAT radiomics features and evaluate their incremental value over quantitative plaque characteristics.

PATIENTS AND METHODS

Between January 2009 and December 2020, 500 patients with suspected or known coronary artery disease who underwent serial coronary computed tomography angiography (CCTA) ≥2 years apart were retrospectively analyzed and randomly stratified into a training and testing data set with a ratio of 7:3. Plaque progression was defined with annual change in plaque burden exceeding the median value in the entire cohort. Quantitative plaque characteristics and PCAT radiomics features were extracted from baseline CCTA. Then we built 3 models including quantitative plaque characteristics (model 1), PCAT radiomics features (model 2), and the combined model (model 3) to compare the prediction performance evaluated by area under the curve.

RESULTS

The quantitative plaque characteristics of the training set showed the values of noncalcified plaque volume (NCPV), fibrous plaque volume, lesion length, and PCAT attenuation were larger in the plaque progression group than in the nonprogression group ( P < 0.05 for all). In multivariable logistic analysis, NCPV and PCAT attenuation were independent predictors of coronary plaque progression. PCAT radiomics exhibited significantly superior prediction over quantitative plaque characteristics both in the training (area under the curve: 0.814 vs 0.615, P < 0.001) and testing (0.736 vs 0.594, P = 0.007) data sets.

CONCLUSIONS

NCPV and PCAT attenuation were independent predictors of coronary plaque progression. PCAT radiomics derived from baseline CCTA achieved significantly better prediction than quantitative plaque characteristics.

Advances in Clinical Imaging of Vascular Inflammation: A State-of-the-Art Review

Vascular inflammation is a major contributor to cardiovascular disease, particularly atherosclerotic disease, and early detection of vascular inflammation may be key to the ultimate reduction of residual cardiovascular morbidity and mortality. This review paper discusses the progress toward the clinical utility of noninvasive imaging techniques for assessing vascular inflammation, with a focus on coronary atherosclerosis. A discussion of multiple modalities is included: computed tomography (CT) imaging (the major focus of the review), cardiac magnetic resonance, ultrasound, and positron emission tomography imaging. The review covers recent progress in new technologies such as the novel CT biomarkers of coronary inflammation (eg, the perivascular fat attenuation index), new inflammation-specific tracers for positron emission tomography-CT imaging, and others. The strengths and limitations of each modality are explored, highlighting the potential for multi-modality imaging and the use of artificial intelligence image interpretation to improve both diagnostic and prognostic potential for common conditions such as coronary artery disease.

Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature

Artificial intelligence (AI) has the ability to completely transform the healthcare industry by enhancing diagnosis, treatment, and resource allocation. To ensure patient safety and equitable access to healthcare, it also presents ethical and practical issues that need to be carefully addressed. Its integration into healthcare is a crucial topic. To realize its full potential, however, the ethical issues around data privacy, prejudice, and transparency, as well as the practical difficulties posed by workforce adaptability and statutory frameworks, must be addressed. While there is growing knowledge about the advantages of AI in healthcare, there is a significant lack of knowledge about the moral and practical issues that come with its application, particularly in the setting of emergency and critical care. The majority of current research tends to concentrate on the benefits of AI, but thorough studies that investigate the potential disadvantages and ethical issues are scarce. The purpose of our article is to identify and examine the ethical and practical difficulties that arise when implementing AI in emergency medicine and critical care, to provide solutions to these issues, and to give suggestions to healthcare professionals and policymakers. In order to responsibly and successfully integrate AI in these important healthcare domains, policymakers and healthcare professionals must collaborate to create strong regulatory frameworks, safeguard data privacy, remove prejudice, and give healthcare workers the necessary training.

Device innovation in cardiovascular medicine: a report from the European Society of Cardiology Cardiovascular Round Table

Research performed in Europe has driven cardiovascular device innovation. This includes, but is not limited to, percutaneous coronary intervention, cardiac imaging, transcatheter heart valve implantation, and device therapy of cardiac arrhythmias and heart failure. An important part of future medical progress involves the evolution of medical technology and the ongoing development of artificial intelligence and machine learning. There is a need to foster an environment conducive to medical technology development and validation so that Europe can continue to play a major role in device innovation while providing high standards of safety. This paper summarizes viewpoints on the topic of device innovation in cardiovascular medicine at the European Society of Cardiology Cardiovascular Round Table, a strategic forum for high-level dialogue to discuss issues related to the future of cardiovascular health in Europe. Devices are developed and improved through an iterative process throughout their lifecycle. Early feasibility studies demonstrate proof of concept and help to optimize the design of a device. If successful, this should ideally be followed by randomized clinical trials comparing novel devices vs. accepted standards of care when available and the collection of post-market real-world evidence through registries. Unfortunately, standardized procedures for feasibility studies across various device categories have not yet been implemented in Europe. Cardiovascular imaging can be used to diagnose and characterize patients for interventions to improve procedural results and to monitor devices long term after implantation. Randomized clinical trials often use cardiac imaging-based inclusion criteria, while less frequently trials randomize patients to compare the diagnostic or prognostic value of different modalities. Applications using machine learning are increasingly important, but specific regulatory standards and pathways remain in development in both Europe and the USA. Standards are also needed for smart devices and digital technologies that support device-driven biomonitoring. Changes in device regulation introduced by the European Union aim to improve clinical evidence, transparency, and safety, but they may impact the speed of innovation, access, and availability. Device development programmes including dialogue on unmet needs and advice on study designs must be driven by a community of physicians, trialists, patients, regulators, payers, and industry to ensure that patients have access to innovative care.