Recent studies have highlighted the significance of Epicardial Adipose Tissue (EAT) on the development of Heart Failure (HF). Rather than simple EAT volume, we predicted HF from pathophysiologically-inspired EAT features opportunistically extracted from low-cost (no-cost at our institution) CT Calcium Score (CTCS) images. We segmented EAT using our deep learning algorithm, DeepFat, and collected 42 hand-crafted features (fat-omics), such as volume, spatial, thickness, and HU value distribution, where HU is thought to be an indicator of inflammation. We included readily available clinical features (e.g., Age, sex, and BMI). We used a large database of HF-enriched patients (N=1,988, HF: 5.13%) and a Cox proportional hazards model with elastic-net feature reduction and evaluated with training and testing of 80%/20% respectively. High-risk features (e.g., mean EAT thickness, EAT mean HU, and smoking) were identified using univariate analyses. Fat-omics + clinical features predicted HF with c-index (training/testing) of (78.1/72.7), respectively, exceeding results for BMI alone, EAT volume, sac volume, and clinical features. Importantly, the combined model (fatomics + clinical features) gave better stratification of patients into low- and high-risk groups using Kaplan-Meier plots with an NRI=0.11 compared to the model using clinical features alone. A univariate model based on the Agatston score gave training/testing (62.7/62.9), indicating that the fat and clinical features from CTCS images are more effective at predicting HF than traditional calcium scoring. Our combined model (fat-omics + clinical features) also showcases that the location and intensity of the EAT buildup is also a significant factor in predicting risk of HF onset and can change the relative importance of clinical features such as smoking status and sex.
Coronary calcium Agatston score and Epicardial Adipose Tissue (EAT) volume, as measured from CT Calcium Score (CTCS) images, are known risk factors for Major Adverse Cardiovascular Events (MACE). Here, we present greatly-expanded analysis using Coronary Artery Calcification (CAC) features, which more thoroughly capture pathophysiology of atherosclerosis, and EAT features, including HU thought to reflect inflammation, a harbinger of atherosclerosis. MACE-enriched dataset (2316 patients, 13.6% MACE) was divided into balanced training/testing (70/30). We employed manually segmented CACs and automatically segmented EAT using DeepFat. Calcium-omics and fat-omics features were crafted to capture pathophysiology. Elastic-net was employed for feature reduction, and Cox proportional hazards model was used to design novel calcium-fat-omics model. Baseline Agatston and EAT volume models yielded two-year-AUC training/testing results of (72.7%/68.2%) and (60.7%/55.6%), respectively. Our novel comprehensive analyses with some readily available clinical features gave improved results: calcium-omics (82.6%/72.2%), fat-omics (76.7%/71.7%), and calcium-fat-omics (83.7%/73.6%). In Kaplan-Meier survival analysis, the calcium-fat-omics model greatly improved risk stratification as compared to the standard Agatston model with five-risk intervals, suggesting improvement for personalized medicine.
Patients with Diabetes Mellitus (DM) have an increased risk of Major Adverse Cardiac Events (MACE), commonly stratified via the Agatston score. In this study, we investigated Coronary Artery Calcification (CAC) patterns in patients with DM, to understand its impact on cardiovascular health. By individually segmenting CACs from over 25,000 Computed Tomography Calcium Score (CTCS) images and integrating clinical data, we created a propensity-matched cohort to isolate the effect of DM on calcification patterns by controlling for confounding variables such as age, gender, BMI, and medication use. We hand-crafted a novel set of 67 calcium-omics imaging features capturing the distribution, shape, density, and more of individual CACs and aggregated CAC features per artery. Diabetic patients, compared to nondiabetic, exhibited significantly higher Agatston (p-value: ⪅0.0005) and larger volume scores (p-value: 0.0018). Interestingly, diabetic patients had fewer calcified arteries (p-value: ⪅0005) and lower density calcifications than nondiabetic patients. Although previous studies have reported that DM leads to higher Agatston scores, to our knowledge, no studies have reported that this is driven by high-volume, low-density calcifications present in only one to two arteries. These findings suggest a distinct phenotype indicative of the continuing development of new lesions in affected arteries, possibly contributing to the increased incidence of MACE. This exploration of diabetes-related CAC patterns enhanced our understanding of the mechanisms of atherosclerotic cardiovascular disease in DM, emphasizing the need for targeted interventions and redefined cardiovascular risk models for this vulnerable population.
Predicting Major Adverse Cardiovascular Events (MACE) accurately is crucial for implementing personalized medicine interventions effectively. Recent research has highlighted the significance of thoracic fat deposits, specifically Epicardial Adipose Tissue (EAT) and Paracardial Adipose Tissue (PAT), in predicting MACE. Their proximity to the coronary arteries and potential role in stimulating inflammation and atherosclerosis development contribute to their predictive utility. In this study, we developed a MACE prediction model based on Cox proportional hazards model with elastic net regularization, incorporating hand-crafted image features derived from EAT and PAT in non-contrast, CT Calcium Score (CTCS) exams. We constructed and collected morphological, intensity, and spatial features from manually corrected, deep learning-based adipose segmentation. To highlight the influence of imaging features, our preliminary study utilized a MACE-enriched cohort of 400 individuals (56% MACE) from a CLARIFY study of the University Hospitals of Cleveland. We divided the cohort into training (80%) and held-out testing (20%). We obtained c-index (training/testing) results for EAT-omics (0.66/0.69), and PAT-omics (0.64/0.67) models, respectively, both much better than the traditional EAT volume model gave (0.53/0.53). Notably, we identified high-risk features, including negative HU skewness in EAT, likely an indicator of fatty inflammation. Similar measurements in PAT did not. The improved discrimination with EAT and its connection to inflammation markers is consistent with its direct vascular communication with the myocardium and coronary vasculature. As PAT is outside the pericardial sac, it does not have direct vascular communication. These promising preliminary findings suggest that an AI adipose analysis can be a useful add-on to improve MACE prediction from CTCS exams.
Non-contrast, cardiac CT Calcium Score (CTCS) images provide a low-cost cardiovascular disease screening exam to guide therapeutics. We are extending standard Agatston score to include cardiovascular risk assessments from features of epicardial adipose tissue, pericoronary adipose tissue, heart size, and more, which are currently extracted from Coronary CT Angiography (CCTA) images. To aid such determinations, we developed a deep-learning method to synthesize Virtual CT Angiography (VCTA) images from CTCS images. We retrospectively collected 256 patients who underwent CCTA and CTCS from our hospitals (MacKay and UH). Training on 205 patients from UH, we used the contrastive, unpaired translation method to create VCTA images. Testing on 51 patients from Mackay, we generated VCTA images that compared favorably to the matched CCTA images with enhanced coronaries and ventricular cavity that were well delineated from surrounding tissues (epicardial adipose tissue and myocardium). The automated segmentation of myocardium and left-ventricle cavity in VCTA showed strong agreement with the measurements obtained from CCTA. The measured percent volume differences between VCTA and CCTA segmentation were 2±8% for the myocardium and 5±10% for the left-ventricle cavity, respectively. Manually segmented coronary arteries from VCTA and CTCS (with guidance from registered CCTA) aligned well. Centerline displacements were within 50% of coronary artery diameter (4mm). Pericoronary adipose tissue measurements using the axial disk method showed excellent agreements between measurements from VCTA ROIs and manual segmentations (e.g., average HU differences were typically <3HU). Promising results suggest that VCTA can be used to add assessments indicative of cardiovascular risk from CTCS images.
We extracted features of fat depots from computed tomography calcium score (CTCS) images to predict a future major adverse cardiovascular event (MACE). Our work builds on two observations: 1) Agatston score for coronary calcifications is known to be predictive and 2) studies have shown association of epicardial adipose tissue (EAT) volume with MACE. We extracted many features of fat depots (fat-omics) and used feature assessments in modeling to predict MACE. We used time-to-event Cox model with an elastic net regularization to identify the most compelling fat-omics features, including morphological (e.g., volume and thickness) and intensity statistics (e.g., mean and max HU). We collected and engineered EAT features from a 6-year cohort study of 339 individuals (58.7%MACE) from the University Hospitals Cleveland. The cohort was MACE-enriched to balance data and to enable precise determination of best features. We found that body mass index (BMI) was not a good surrogate for EAT volume, as the correlation was minimal. The 2-year ROC result of fat-omics model was superior to other univariate models (i.e., BMI, EAT volume, and Agatston), with AUC=0.72 compared to (0.56, 0.54, and 0.57), respectively. In addition, high- and low-risk stratification was improved. In a further experiment using 166 zero-Agatston cases with 59%MACE, fat-omics model outperformed BMI or EAT. Fat-omics had AUC=0.66 compared to (0.56,0.49), respectively. Promising results indicate the importance of EAT fat-omics over traditional BMI, EAT volume, and Agatston score. Fat-omics with calcifications analyses may significantly improve MACE prediction from CTCS images.
Epicardial (EAT) and paracardial (PAT) adipose tissues (inside and outside the pericardial sac, respectively) are thought to be associated with major adverse cardiovascular events (MACE). Our long-term goal is to include PAT and EAT in a comprehensive survival analysis of MACE. Here we developed an automated method for segmenting PAT in computed tomography calcium score (CTCS) scans. Analysts identified the top and bottom heart slices by anatomical evidence, and segmented PAT in a slice-by-slice basis. Our proposed PAT segmentation approach (DeepPAT) used preprocessing steps and a multi-class automated semantic segmentation (DeepLab-v3plus) network. Preprocessing steps incorporated filtering to reduce noise, window-leveling to draw attention to sac, and morphological operations to close gaps within mask volumes. DeepPAT was trained/tested on (30/22) CTCS scans from the University Hospitals of Cleveland. The output mask voxels were classified as either enclosed sac, PAT, or background. PAT region is further thresholded with standard fat HU range [-190, -30]. The DeepPAT showed excellent segmentation compared to ground truth (manual) with an average Dice score (82.5%±3.93) and correlation of (R=99.23%, P<<0.001). PAT volume difference was (4.08%±7.78) while the PAT mean HU value changed (2.65%±4.72). The EAT and PAT volumes had a noticeable correlation R=82.9% (P<<0.001). Volumes for MACE/no-MACE (5/17 patients) subgroups showed significance for PAT (P= 0.023), while EAT had better significance (P=0.004). Mean HU values showed less significance in both PAT (p=0.81) and EAT (p=0.18). Our research results offer valuable insights that can be utilized for cardiovascular risk assessment studies.
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