Quantification Of Adipose Tissue Research Articles

P-784 Visceral and subcutaneous adipose tissue in children born after assisted reproductive technology with frozen and fresh embryo transfer

Abstract Study question Is the distribution of abdominal visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) comparable between children following assisted reproduction and natural conception (NC)? Summary answer Children following frozen embryo transfer (FET) have significantly lower VAT/SAT-ratio than children following NC. However, this is not believed to be clinically important. What is known already It is well-established that an adverse distribution of the abdominal adipose tissue with high VAT/SAT-ratio increases the risk of cardiometabolic diseases, hence the VAT/SAT-ratio is closely related to the metabolic profile. Children conceived after FET and fresh embryo transfer (Fresh-ET) are at risk of being born large-for-gestational-age (LGA) and small-for-gestational-age (SGA), respectively. Both, being born LGA and SGA, increases the risk of obesity in later life. To the best of our knowledge, no studies have reported the VAT/SAT-ratio in children following assisted reproductive technology (ART) (both FET and Fresh-ET). Study design, size, duration Prospective observational cohort study including 150 singletons aged 7- to 10-years old conceived after FET (n = 50), Fresh-ET (n = 50) and NC (n = 50). All children were born in Eastern Denmark. The study is conducted between November 2018 and August 2020. Participants/materials, setting, methods This is a substudy af the “Health in Childhood following Assisted Reproductive Technology” (HiCART) study. The children underwent abdominal magnetic resonance imaging (MRI) for the quantification of abdominal adipose tissue along with measurements of anthropometric measurements, blood pressure, fasting blood samples and Dual-energy X-ray Absorptiometry (DXA) scan. The volumes of VAT and SAT were semi-automatically quantified blinded for the mode of conception. Multivariate linear regression analysis of the VAT/SAT-ratio was performed to adjust for confounders. Main results and the role of chance The VAT/SAT-ratio differed significantly between the three groups (mean SD; FET 0.26 (0.08), Fresh-ET 0.29 (0.07), NC 0.30 (0.08), ANOVA, p = 0.014). Pairwise comparison revealed that children conceived after FET had significantly lower VAT/SAT-ratio as compared to children conceived after NC (p = 0.007). In addition, children following FET tended to have lower VAT/SAT-ratio as compared to the Fresh-ET group (p = 0.059). Multivariate linear regression analysis of the VAT/SAT-ratio confirmed a lower VAT/SAT-ratio in FET after adjustment for potential confounding factors (child age and sex, maternal age at delivery and maternal BMI at pregnancy and child birth weight and BMI). Repeat-analysis of 20 MRI showed good intra-rater reliability of VAT and SAT quantifications. As previously reported in the HiCART studies, there were no differences between the groups in anthropometric measurements including body mass index (BMI), lean body mass, blood pressure or high-sensitivity C-reactive protein (hs-CRP). However, the level of triglycerides was found to be significantly higher in Fresh-ET compared to FET and NC (p = 0.028). Limitations, reasons for caution Selection bias cannot be excluded as only children from Eastern Denmark were invited. The observational nature of a cohort study prevents the establishment of causality. Wider implications of the findings Reassuringly, this study found no clinical important difference in abdominal adipose tissue between children following ART (both FET and Fresh-ET) and NC. However, future studies in larger cohorts are warranted. Trial registration number ClinicalTrials.gov identifier: NCT03719703

Abstract PO1-11-09: Body composition correlates with BMI and menopausal status in patients with breast cancer

Abstract Background: Obesity is associated with higher risk of breast cancer (BC)-related death, all-cause mortality, and recurrence. While used to estimate body fat, body mass index (BMI) is insensitive to body fat distribution and lean muscle mass. We hypothesized that BMI categories (BMI 18.5-24.9 [healthy], 25-29.9 [overweight], and ≥30 kg/m2 [obesity]) would not correlate with CT-derived body composition. Methods: We retrospectively identified 180 patients diagnosed with new or recurrent Stage I-IV BC who presented to Johns Hopkins from 2015-2018 and were part of an institutional database. We extracted demographics, diagnosis date, cancer characteristics, BMI, and CT abdomen pelvis scans from the medical record. Baseline BMI was defined as the closest measurement to diagnosis between 1 year prior and 1 month after diagnosis. Baseline CT was within 6 months of and closest to the baseline BMI date. Fully automated deep-learning algorithms identified L1 and performed muscle and adipose tissue segmentation and quantification. Body composition data included: cross-sectional areas (CSA) of subcutaneous, visceral, and total adipose tissue; the mean, median, and standard deviation of the average of the muscle attenuation (density); and the body wall musculature CSA. ANOVA tests assessed associations between body composition, BMI, and menopausal status. Results: Among 180 patients, 136 (76%) had early-stage BC, and 44 (24%) had metastatic BC. Most patients were women (98%) and non-Hispanic (94%). Patients identified as White (57%), Black (29%), Asian (6%), and Other Race (8%). Sixty percent were post-menopausal. Hormone receptor-positive, HER2-positive, and triple negative subtypes were 56%, 19% and 22%, respectively. At diagnosis, 24% had a healthy BMI while 36% and 41% had overweight and obesity, respectively. All body composition measures were significantly associated with BMI (Table 1). As BMI increased, the average body wall musculature and subcutaneous, visceral, and total adipose tissue CSAs increased while the average muscle attenuation decreased. Specifically, the average visceral adipose tissue CSAs were 2.7 and 4.0-fold larger for overweight and obesity compared to that of the healthy BMI group, respectively, while their average muscle attenuations were 62.4% and 40.6% of the healthy BMI group. Postmenopausal women had 1.5-fold higher visceral adipose tissue CSA and 44.7% less average muscle attenuation compared to premenopausal women. Conclusion: Body composition correlates with all BMI categories in primarily white, non-Hispanic women with breast cancer. As BMI increases, body composition changes reliably, with increased subcutaneous and visceral adipose tissue, increased muscle area, and decreased muscle density. Muscle density, not size/area, is more associated with muscle performance, which can relate to functional status and outcomes. Despite validity concerns, BMI is an accessible and economical surrogate for body adiposity in patients with breast cancer. Postmenopausal status was also associated with greater visceral and total adiposity and lower muscle density. Further investigation is needed to confirm these findings. Table 1: Correlation between body composition, BMI, and menopausal status in patients with breast cancer All measures of body composition were significantly associated with BMI. Visceral adipose tissue increased and muscle density decreased as BMI increased and with post-menopausal status. Citation Format: Terrence Tsou, Tingchang Wang, Amanda Blackford, Ronald Summers, Vered Stearns, Jennifer Sheng. Body composition correlates with BMI and menopausal status in patients with breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-11-09.

Abdominal ultrasonography as a tool to quantify adipose tissue in critically ill patients

Adipose tissue has important roles in both healthy and disease states. Quantifying adipose tissue composition and distribution may help predict outcomes and guide management in intensive care unit patients. Computed tomography is the preferred method for measuring adipose tissue but has drawbacks, such as radiation exposure and the need to transport patients. Ultrasonography is a safer, more convenient alternative for bedside assessment of body fat composition. This review examines the relevance of adipose tissue in critical illness and describes ultrasound techniques to quantify subcutaneous, visceral, and epicardial adipose tissue depots. Standardized protocols to measure subcutaneous fat thickness at multiple sites and approaches to estimate visceral fat using the supraumbilical or xipho-umbilical lines are discussed. A technique to quantify epicardial fat thickness using transthoracic echocardiography is also presented. While more research is needed, bedside ultrasonography shows promise for monitoring adipose tissue changes and their potential associations with outcomes in critically ill patients. Key words: Ultrasonography, critical illness, body composition, subcutaneous adipose tissue, visceral adipose tissue, epicardial adipose tissue

DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR

Background and aimsThis study investigated the additional prognostic value of epicardial adipose tissue (EAT) volume for major adverse cardiovascular events (MACE) in patients undergoing stress cardiac magnetic resonance (CMR) imaging. Methods730 consecutive patients [mean age: 63 ± 10 years; 616 men] who underwent stress CMR for known or suspected coronary artery disease were randomly divided into derivation (n = 365) and validation (n = 365) cohorts. MACE was defined as non-fatal myocardial infarction and cardiac deaths. A deep learning algorithm was developed and trained to quantify EAT volume from CMR. EAT volume was adjusted for height (EAT volume index). A composite CMR-based risk score by Cox analysis of the risk of MACE was created. ResultsIn the derivation cohort, 32 patients (8.7 %) developed MACE during a follow-up of 2103 days. Left ventricular ejection fraction (LVEF) < 35 % (HR 4.407 [95 % CI 1.903–10.202]; p<0.001), stress perfusion defect (HR 3.550 [95 % CI 1.765–7.138]; p<0.001), late gadolinium enhancement (LGE) (HR 4.428 [95%CI 1.822–10.759]; p = 0.001) and EAT volume index (HR 1.082 [95 % CI 1.045–1.120]; p<0.001) were independent predictors of MACE. In a multivariate Cox regression analysis, adding EAT volume index to a composite risk score including LVEF, stress perfusion defect and LGE provided additional value in MACE prediction, with a net reclassification improvement of 0.683 (95%CI, 0.336–1.03; p<0.001). The combined evaluation of risk score and EAT volume index showed a higher Harrel C statistic as compared to risk score (0.85 vs. 0.76; p<0.001) and EAT volume index alone (0.85 vs.0.74; p<0.001). These findings were confirmed in the validation cohort. ConclusionsIn patients with clinically indicated stress CMR, fully automated EAT volume measured by deep learning can provide additional prognostic information on top of standard clinical and imaging parameters.

Post mortem analysis of hepatic volume and lipid content by magnetic resonance imaging and spectroscopy in fixed murine neonates.

Maternal obesity and hyperglycemia are linked to an elevated risk for obesity, diabetes, and steatotic liver disease in the adult offspring. To establish and validate a noninvasive workflow for perinatal metabolic phenotyping, fixed neonates of common mouse strains were analyzed postmortem via magnetic resonance imaging (MRI)/magnetic resonance spectroscopy (MRS) to assess liver volume and hepatic lipid (HL) content. The key advantage of nondestructive MRI/MRS analysis is the possibility of further tissue analyses, such as immunohistochemistry, RNA extraction, and even proteomics, maximizing the data that can be gained per individual and therefore facilitating comprehensive correlation analyses. This study employed an MRI and 1H-MRS workflow to measure liver volume and HL content in 65 paraformaldehyde-fixed murine neonates at 11.7 T. Liver volume was obtained using semiautomatic segmentation of MRI acquired by a RARE sequence with 0.5-mm slice thickness. HL content was measured by a STEAM sequence, applied with and without water suppression. T1 and T2 relaxation times of lipids and water were measured for respective correction of signal intensity. The HL content, given as CH2/(CH2+ H2O), was calculated, and the intrasession repeatability of the method was tested. The established workflow yielded robust results with a variation of ~3% in repeated measurements for HL content determination. HL content measurements were further validated by correlation analysis with biochemically assessed triglyceride contents (R2= 0.795) that were measured in littermates. In addition, image quality also allowed quantification of subcutaneous adipose tissue and stomach diameter. The highest HL content was measured in C57Bl/6N (4.2%) and the largest liver volume and stomach diameter in CBA (53.1 mm3 and 6.73 mm) and NMRI (51.4 mm3 and 5.96 mm) neonates, which also had the most subcutaneous adipose tissue. The observed effects were independent of sex and litter size. In conclusion, we have successfully tested and validated a robust MRI/MRS workflow that allows assessment of morphology and HL content and further enables paraformaldehyde-fixed tissue-compatible subsequent analyses in murine neonates.

Radiological biomarkers reflecting visceral fat distribution help distinguish inflammatory bowel disease subtypes: a multicenter cross-sectional study

ObjectivesTo achieve automated quantification of visceral adipose tissue (VAT) distribution in CT images and screen out parameters with discriminative value for inflammatory bowel disease (IBD) subtypes.MethodsThis retrospective multicenter study included Crohn’s disease (CD) and ulcerative colitis (UC) patients from three institutions between 2012 and 2021, with patients with acute appendicitis as controls. An automatic VAT segmentation algorithm was developed using abdominal CT scans. The VAT volume, as well as the coefficient of variation (CV) of areas within the lumbar region, was calculated. Binary logistic regression and receiver operating characteristic analysis was performed to evaluate the potential of indicators to distinguish between IBD subtypes.ResultsThe study included 772 patients (365 CDs, median age [inter-quartile range] = 31.0. (25.0, 42.0) years, 255 males; 241 UCs, 46.0 (34.0, 55.5) years, 138 males; 166 controls, 40.0 (29.0, 53.0) years, 80 males). CD patients had lower VAT volume (CD = 1584.95 ± 1128.31 cm3, UC = 1855.30 ± 1326.12 cm3, controls = 2470.91 ± 1646.42 cm3) but a higher CV (CD = 29.42 ± 15.54 %, p = 0.006 and p ˂ 0.001) compared to UC and controls (25.69 ± 12.61 % vs. 23.42 ± 15.62 %, p = 0.11). Multivariate analysis showed CV was a significant predictor for CD (odds ratio = 6.05 (1.17, 31.12), p = 0.03). The inclusion of CV improved diagnostic efficiency (AUC = 0.811 (0.774, 0.844) vs. 0.803 (0.766, 0.836), p = 0.08).ConclusionCT-based VAT distribution can serve as a potential biomarker for distinguishing IBD subtypes.Critical relevance statementVisceral fat distribution features extracted from CT images using an automated segmentation algorithm (1.14 min) show differences between Crohn’s disease and ulcerative colitis and are promising for practical radiological screening.Key points• Radiological parameters reflecting visceral fat distribution were extracted for the discrimination of Crohn’s disease (CD) and ulcerative colitis (UC).• In CD, visceral fat was concentrated in the lower lumbar vertebrae, and the coefficient of variation was a significant predictor (OR = 6.05 (1.17, 31.12), p = 0.03).• The differences between CD, UC, and controls are promising for practical radiological screening.Graphical

Deep learning analysis of epicardial adipose tissue to predict cardiovascular risk in heavy smokers

BackgroundHeavy smokers are at increased risk for cardiovascular disease and may benefit from individualized risk quantification using routine lung cancer screening chest computed tomography. We investigated the prognostic value of deep learning-based automated epicardial adipose tissue quantification and compared it to established cardiovascular risk factors and coronary artery calcium.MethodsWe investigated the prognostic value of automated epicardial adipose tissue quantification in heavy smokers enrolled in the National Lung Screening Trial and followed for 12.3 (11.9–12.8) years. The epicardial adipose tissue was segmented and quantified on non-ECG-synchronized, non-contrast low-dose chest computed tomography scans using a validated deep-learning algorithm. Multivariable survival regression analyses were then utilized to determine the associations of epicardial adipose tissue volume and density with all-cause and cardiovascular mortality (myocardial infarction and stroke).ResultsHere we show in 24,090 adult heavy smokers (59% men; 61 ± 5 years) that epicardial adipose tissue volume and density are independently associated with all-cause (adjusted hazard ratios: 1.10 and 1.38; P < 0.001) and cardiovascular mortality (adjusted hazard ratios: 1.14 and 1.78; P < 0.001) beyond demographics, clinical risk factors, body habitus, level of education, and coronary artery calcium score.ConclusionsOur findings suggest that automated assessment of epicardial adipose tissue from low-dose lung cancer screening images offers prognostic value in heavy smokers, with potential implications for cardiovascular risk stratification in this high-risk population.

Adipose Tissue Quantification Improves the Prognostic Value of GLIM Criteria in Advanced Gastric Cancer Patients.

The present study investigated whether the risk of recurrence after curative surgery could be further stratified by combining the Global Leadership Initiative on Malnutrition (GLIM) criteria and changes in subcutaneous (SAT) and visceral (VAT) adipose tissue mass after surgery in patients with advanced gastric cancer (AGC). This study retrospectively analyzed 302 patients with AGC who underwent curative surgery. Based on the GLIM criteria, patients were classified into malnourished and non-malnourished groups. The cross-sectional areas of SAT and VAT were measured from preoperative and 6-month post-operative computed tomography (CT) images. Multivariate survival analyses demonstrated that GLIM-defined malnutrition (p = 0.008) and loss of VAT after surgery (p = 0.008) were independent risk factors for recurrence-free survival (RFS). Evaluation of the prognostic value of combining the two independent predictors showed that malnourished patients with a marked loss of VAT had the worst 5-year RFS rate of 35.2% (p < 0.001). Preoperative GLIM-defined malnutrition and a loss of VAT during the first 6 months after surgery were independent predictors for RFS in patients with AGC. Changes in the VAT area after surgery could further enhance the prognostic value of the GLIM criteria for predicting the risk of gastric cancer recurrence.

MRI Methods to Visualize and Quantify Adipose Tissue in Health and Disease.

MRI is the modality of choice for a vast range of pathologies but also a sensitive probe into human physiology and tissue function. For this reason, several methodologies have been developed and continuously evolve in order to non-invasively monitor underlying phenomena in human adipose tissue that were difficult to assess in the past through visual inspection of standard imaging modalities. To this end, this work describes the imaging methodologies used in medical practice and lists the most important quantitative markers related to adipose tissue physiology and pathology that are currently supporting diagnosis, longitudinal evaluation and patient management decisions. The underlying physical principles and the resulting markers are presented and associated with frequently encountered pathologies in radiology in order to set the frame of the ability of MRI to reveal the complex role of adipose tissue, not as an inert tissue but as an active endocrine organ.

CMR-based analysis of atrial epicardial adipose tissue aids identification of a low-risk group for the presence of LA fibrosis

Abstract Introduction There is growing evidence that occurrence of atrial fibrillation (AF) is influenced by epicardial adipose tissue, which triggers left atrial (LA) remodeling through an inflammatory reaction, leading to dilatation and fibrosis. Identification of patients with advanced stage LA remodeling and fibrosis resulting in low voltage zones (LVZ) can contribute to individualized decision making in pulmonary vein isolation (PVI) treatment. Objective To establish a prediction model utilizing cardiac magnetic resonance (CMR) derived atrial epicardial adipose tissue quantification for the presence of LVZ during electroanatomic mapping that corresponds to LA fibrosis. Methods 66 patients admitted for primary AF ablation routinely underwent a preprocedural CMR scan. Dedicated adipose tissue enhancing sequences (2D fat/water separation) using short axis slices covering the LA were employed to create a 3D epicardial adipose tissue (EAT) model reconstruction. In addition, an LA angiography using gadolinium contrast was performed to create a 3D LA model. Model segmentation and volume quantification were performed with dedicated software (Figure 1). In the subsequent PVI an electroanatomic map of the LA was created to assess the presence of LVZ, defined by point measurements ≤0,5mV on the LA wall. Results General patient characteristics were 55% male gender, mean age 68 ± 9.8 years, median BMI 28 (IQR 26-31), median LVEF 60% (IQR 56-64), paroxysmal AF present in 38% of patients. LVZ was present in 37.9% patients during mapping (n=25). Patients were divided into LVZ positive (LVZ+) and LVZ negative (LVZ-) groups. LA volume (LAV) and LAV/BSA (LAVi) were higher in LVZ+ [145.3±32.7 ml and 75.6±17.9 ml/m² (n=25)] compared to LVZ- patients [120.8±31.7 ml and 58.1±14.8 ml/m² (n=41)], each p&amp;lt;0.05. LA-EAT volume (LA-EATV) and LA-EATV/BSA (LA-ETAVi) were higher in LVZ+ [64.2±23.5 ml and 33.0±11.3 ml/m² (n=20)] compared to LVZ- patients [52.7±16.7 ml and 25.2±7.8 ml/m² (n=38)], each p&amp;lt;0.05 (Figure 2a/b). A moderate correlation with the presence of LVZ is present for LAVi (r=0.47, p&amp;lt;0.001, n=66) and LA-EATVi (r=0.31, p=0.003, n=58). Both parameters are independent (r=0.194, p=0.145). A correlation with LVZ size can be described for LAVi (r=0.552, p&amp;lt;0.01) and LA-EATVi (r=0.303, p=0.05). A binomial logistic regression model using a combination of the two independent CMR-derived parameters LAVi and LA-EATVi (log odds (LVZ) = -6.691 + 0.054*LAVi + 0.086*LA-EATVi) was able to predict the presence of LVZ with an area under the ROC curve of 0.82 (p&amp;lt;0.001) and Nagelkerke's R² of 0.36 (Figure 2c). Overall accuracy in classification was 75.9% with a sensitivity of 55% and a specificity of 87%. Odds ratio for LAVi were 1.06 [1.02-1.10] and for LA-EATVi 1.09 [1.02-1.18]. Summary: This CMR-based combined prediction model utilizing LA volume and LA adipose tissue volume allows identification of low-risk patients for the presence of LA fibrosis.Figure 1:3D visualisation of structures

Performance of ensemble of UNets for epicardial adipose tissue quantification: a multi-center APOLLO study

Abstract Background Epicardial adipose tissue (EAT) surrounding the heart and coronary arteries, has been proposed to be a potential therapeutic target for the prevention of cardiac remodeling and arrhythmias in cardiovascular disease. Specifically, EAT volume is positively correlated with these cardiovascular events and therapeutic options are being developed with the aim to reduce EAT volume. EAT volume can be measured from non-contrast CT scans, but requires manual segmentation of the pericardium to isolate the EAT, which is prone to inter-observer variability. Purpose We aimed to develop a new AI EAT toolkit for automated quantification of EAT volume from multi-center non-contrast CT data and assess its performance against clinical annotations from four trained human experts. Methods This is a multicenter study which performs CT scans in 5000 Asian Admixture patients (APOLLO study). In the current stage of this study, NCCT data analysis was conducted in 1003 patients (mean age of 58±11 years; 33% females, with ethnicity breakdown of 72% Chinese, 20% Indian/Malay, 8% Others). Of these patients, 52% had hypertension, 19% had diabetes, 69% had hyperlipidemia, and 31% had obesity. AI EAT tool was developed via a novel deep learning framework using an ensemble of UNets (See Figure 1 and 2). The performance of AI EAT tool was evaluated with respect to expert clinical segmentations of four independent raters for a test dataset of 50 patient scans using dice similarity coefficient (DSC), Pearson correlation, and Bland-Altman analysis. Results The AI EAT quantification process took less than 20 seconds per subject, compared with 20-30 minutes for expert readers. Compared to clinical ground truth, our AI EAT achieved a DSC of 96.0 ± 1.0% and 89.1 ± 3.0% for pericardium and EAT segmentations, respectively. There was strong agreement between the AI EAT and clinical ground truth in deriving the EAT volume (r=0.99, p&amp;lt;0.001) and minimal error of 5.3±2.6%. The Bland-Altman analysis revealed the mean bias between the predicted EAT volume and the ground truth volumes to be 0.95 cm³ with the limits of agreement as [-12.86 cm³, 14.77 cm³]. Conclusion Ensemble of UNets based AI EAT tool is a novel method with high performance for the quantification of epicardial adipose tissue volume, as a potential diagnostic and prognostic tool for cardiac remodeling and arrhythmias.Figure 1.Ensemble of UNets framework.Figure 2.UNet Architecture used.

Quantification of epicardial adipose tissue by computed tomographic scanning as a prognostic criterion of atrial fibrillation recurrence after catheter ablation.

Aim This study focused on a systematic review and meta-analysis on the predictive role of quantifying the epicardial adipose tissue (EAT) volume using data of computed tomography (CT) in patients after catheter ablation for atrial fibrillation (AF).Material and methods We performed a search in PubMed and Google Scholar for studies that examined the predictive value of EAT volume measured by CT for AF recurrence in patients after undergoing pulmonary venous isolation. Risk ratio (RR) values from studies, where similar scoring criteria were available, were pooled for the meta-analysis.Results Eighteen studies were selected from 901 publications for these systematic review and meta-analysis. In total, 4087 patients were included in this analysis (mean age, 59.0 years; mean follow-up duration, 14.9 mos). Patients with recurrent AF after ablation had higher left atrial EAT volume compared to patients without relapse (weighted mean difference, 5.99 ml; 95% CI: -10.04 to -1.94; p = 0.004). An increase in left atrial EAT volume per ml was significantly associated with the development of AF recurrence after ablation (RR 1.08; 95% CI: 1.01 to 1.16; p=0.03). Patients with recurrent AF after ablation also had higher total EAT values than patients without relapse (difference in weighted values, 11.67 ml; 95% CI: -19.81 to -3.54; p = 0.005). However, no significant association was found between the total EAT volume and the risk of AF relapse (RR 1.00; 95% CI: 1.00 to 1.01; p=0.06).Conclusions The volume of left atrial EAT measured by CT has a significant predictive value in AF patients after catheter ablation and can be used for stratification of the risk for recurrent AF.

The U-Net Family for Epicardial Adipose Tissue Segmentation and Quantification in Low-Dose CT

Epicardial adipose tissue (EAT) is located between the visceral pericardium and myocardium, and EAT volume is correlated with cardiovascular risk. Nowadays, many deep learning-based automated EAT segmentation and quantification methods in the U-net family have been developed to reduce the workload for radiologists. The automatic assessment of EAT on non-contrast low-dose CT calcium score images poses a greater challenge compared to the automatic assessment on coronary CT angiography, which requires a higher radiation dose to capture the intricate details of the coronary arteries. This study comprehensively examined and evaluated state-of-the-art segmentation methods while outlining future research directions. Our dataset consisted of 154 non-contrast low-dose CT scans from the ROBINSCA study, with two types of labels: (a) region inside the pericardium and (b) pixel-wise EAT labels. We selected four advanced methods from the U-net family: 3D U-net, 3D attention U-net, an extended 3D attention U-net, and U-net++. For evaluation, we performed both four-fold cross-validation and hold-out tests. Agreement between the automatic segmentation/quantification and the manual quantification was evaluated with the Pearson correlation and the Bland–Altman analysis. Generally, the models trained with label type (a) showed better performance compared to models trained with label type (b). The U-net++ model trained with label type (a) showed the best performance for segmentation and quantification. The U-net++ model trained with label type (a) efficiently provided better EAT segmentation results (hold-out test: DCS = 80.18±0.20%, mIoU = 67.13±0.39%, sensitivity = 81.47±0.43%, specificity = 99.64±0.00%, Pearson correlation = 0.9405) and EAT volume compared to the other U-net-based networks and the recent EAT segmentation method. Interestingly, our findings indicate that 3D convolutional neural networks do not consistently outperform 2D networks in EAT segmentation and quantification. Moreover, utilizing labels representing the region inside the pericardium proved advantageous in training more accurate EAT segmentation models. These insights highlight the potential of deep learning-based methods for achieving robust EAT segmentation and quantification outcomes.

Abdominal fat quantification using convolutional networks

ObjectivesTo present software for automated adipose tissue quantification of abdominal magnetic resonance imaging (MRI) data using fully convolutional networks (FCN) and to evaluate its overall performance—accuracy, reliability, processing effort, and time—in comparison with an interactive reference method.Materials and methodsSingle-center data of patients with obesity were analyzed retrospectively with institutional review board approval. Ground truth for subcutaneous (SAT) and visceral adipose tissue (VAT) segmentation was provided by semiautomated region-of-interest (ROI) histogram thresholding of 331 full abdominal image series. Automated analyses were implemented using UNet-based FCN architectures and data augmentation techniques. Cross-validation was performed on hold-out data using standard similarity and error measures.ResultsThe FCN models reached Dice coefficients of up to 0.954 for SAT and 0.889 for VAT segmentation during cross-validation. Volumetric SAT (VAT) assessment resulted in a Pearson correlation coefficient of 0.999 (0.997), relative bias of 0.7% (0.8%), and standard deviation of 1.2% (3.1%). Intraclass correlation (coefficient of variation) within the same cohort was 0.999 (1.4%) for SAT and 0.996 (3.1%) for VAT.ConclusionThe presented methods for automated adipose-tissue quantification showed substantial improvements over common semiautomated approaches (no reader dependence, less effort) and thus provide a promising option for adipose tissue quantification.Clinical relevance statementDeep learning techniques will likely enable image-based body composition analyses on a routine basis. The presented fully convolutional network models are well suited for full abdominopelvic adipose tissue quantification in patients with obesity.Key Points• This work compared the performance of different deep-learning approaches for adipose tissue quantification in patients with obesity.• Supervised deep learning–based methods using fully convolutional networks were suited best.• Measures of accuracy were equal to or better than the operator-driven approach.

Towards visceral fat estimation at population scale: correlation of visceral adipose tissue assessment using three-dimensional cross-sectional imaging with BIA, DXA, and single-slice CT.

In terms of assessing obesity-associated risk, quantification of visceral adipose tissue (VAT) has become increasingly important in risk assessment for cardiovascular and metabolic diseases. However, differences exist in the accuracy of various modalities, with a lack of up-to-date comparison with three-dimensional whole volume assessment. Using CT or MRI three-dimensional whole volume VAT as a reference, we evaluated the correlation of various commonly used modalities and techniques namely body impedance analysis (BIA), dual-energy x-ray absorptiometry (DXA) as well as single slice CT to establish how these methods compare. We designed the study in two parts. First, we performed an intra-individual comparison of the 4558 participants from the UK Biobank cohorts with matching data of MRI abdominal body composition, DXA with VAT estimation, and BIA. Second, we evaluated 174 CT scans from the publicly available dataset to assess the correlation of the commonly used single-slice technique compared to three-dimensional VAT volume. Across the UK Biobank cohort, the DXA-derived VAT measurement correlated better (R2 0.94, p<0.0001) than BIA (R2 0.49, p<0.0001) with reference three-dimensional volume on MRI. However, DXA-derived VAT correlation was worse for participants with a BMI of < 20 (R2 = 0.62, p=0.0013). A commonly used single slice method on CT demonstrated a modest correlation (R2 between 0.51 - 0.64), with best values at L3- and L4 (R2 L3 = 0.63, p<0.0001; L4 = 0.64, p<0.0001) compared to reference three-dimensional volume. Combining multiple slices yielded a better correlation, with a strong correlation when L2-L3 levels were combined (R2 = 0.92, p<0.0001). When deployed at scale, DXA-derived VAT volume measurement shows excellent correlation with three-dimensional volume on MRI based on the UK Biobank cohort. Whereas a single slice CT technique demonstrated moderate correlation with three-dimensional volume on CT, with a stronger correlation achieved when multiple levels were combined.

Automated Quantification of Epicardial Adipose Tissue on Cardiac CT Unlocks Best Prediction of Post-Cardiac Surgery AF Risk

Automated Quantification of Epicardial Adipose Tissue on Cardiac CT Unlocks Best Prediction of Post-Cardiac Surgery AF Risk

Comparison of CT and Dixon MR Abdominal Adipose Tissue Quantification Using a Unified Computer-Assisted Software Framework.

Reliable and objective measures of abdominal fat distribution across imaging modalities are essential for various clinical and research scenarios, such as assessing cardiometabolic disease risk due to obesity. We aimed to compare quantitative measures of subcutaneous (SAT) and visceral (VAT) adipose tissues in the abdomen between computed tomography (CT) and Dixon-based magnetic resonance (MR) images using a unified computer-assisted software framework. This study included 21 subjects who underwent abdominal CT and Dixon MR imaging on the same day. For each subject, two matched axial CT and fat-only MR images at the L2-L3 and the L4-L5 intervertebral levels were selected for fat quantification. For each image, an outer and an inner abdominal wall regions as well as SAT and VAT pixel masks were automatically generated by our software. The computer-generated results were then inspected and corrected by an expert reader. There were excellent agreements for both abdominal wall segmentation and adipose tissue quantification between matched CT and MR images. Pearson coefficients were 0.97 for both outer and inner region segmentation, 0.99 for SAT, and 0.97 for VAT quantification. Bland-Altman analyses indicated minimum biases in all comparisons. We showed that abdominal adipose tissue can be reliably quantified from both CT and Dixon MR images using a unified computer-assisted software framework. This flexible framework has a simple-to-use workflow to measure SAT and VAT from both modalities to support various clinical research applications.

Analysis of volume and topography of adipose tissue in the trunk: Results of MRI of 11,141 participants in the German National Cohort

This research addresses the assessment of adipose tissue (AT) and spatial distribution of visceral (VAT) and subcutaneous fat (SAT) in the trunk from standardized magnetic resonance imaging at 3 T, thereby demonstrating the feasibility of deep learning (DL)-based image segmentation in a large population-based cohort in Germany (five sites). Volume and distribution of AT play an essential role in the pathogenesis of insulin resistance, a risk factor of developing metabolic/cardiovascular diseases. Cross-validated training of the DL-segmentation model led to a mean Dice similarity coefficient of >0.94, corresponding to a mean absolute volume deviation of about 22 ml. SAT is significantly increased in women compared to men, whereas VAT is increased in males. Spatial distribution shows age- and body mass index-related displacements. DL-based image segmentation provides robust and fast quantification of AT (≈15 s per dataset versus 3 to 4 hours for manual processing) and assessment of its spatial distribution from magnetic resonance images in large cohort studies.

Assessing the histopathological features of rectal adenocarcinoma with chemical shift-encoded sequence (CSE)-MRI and diffusion-weighted imaging (DWI).

It is of clinical importance to assess the histopathological features of rectal cancer. The adipose tissue microenvironment is closely associated with tumor formation and progression. The chemical shift-encoded magnetic resonance imaging (CSE-MRI) sequence can noninvasively quantify adipose tissue. In this study, we aimed to investigate the feasibility of using CSE-MRI and diffusion-weighted imaging (DWI) to predict the histopathological features of rectal adenocarcinoma. In this retrospective study, 84 patients with rectal adenocarcinoma and 30 healthy controls were consecutively enrolled at the Tongji Hospital of Tongji Medical College of Huazhong University of Science and Technology. CSE-MRI and DWI sequences were performed. The intratumoral proton density fat fraction (PDFF) and R2* of rectal tumors and normal rectal walls were measured. The histopathological features, including pathological T/N stage, tumor grade, mesorectum fascia (MRF) involvement, and extramural venous invasion (EMVI) status were analyzed. The Mann-Whitney test, Spearman correlation, and receiver operating characteristic (ROC) curves were used for statistical analyses. Patients with rectal adenocarcinoma demonstrated significantly lower PDFF and R2* values than did the control participants (5.35%±1.70% vs. 11.55%±3.41%, P<0.001; 35.60 s-1±7.30 s-1 vs. 40.15 s-1±5.72 s-1, P=0.003). PDFF and R2* were significantly different in the discrimination of T/N stage, tumor grade, and MRF/EMVI status (P=0.000-0.005). A significant difference was only noted in the differentiation of the T stage for the apparent diffusion coefficient (ADC) (1.09±0.26×10-3 mm2/s vs. 1.00±0.11×10-3 mm2/s; P=0.001). PDFF and R2* exhibited positive correlations with all the histopathological features (r=0.306-0.734; P=0.000-0.005), while ADC was negatively correlated with the T stage (r=-0.380; P<0.001). PDFF demonstrated diagnostic ability, with a sensitivity of 95.00% and a specificity of 87.50%, while R2* had a sensitivity of 95.00% and a specificity of 79.20% in differentiating T stage; both demonstrated a better diagnostic performance than did ADC. Quantitative CSE-MRI imaging might serve as a noninvasive biomarker for assessing the histopathological features of rectal adenocarcinoma.

Epicardial adipose tissue and obstructive coronary artery disease in acute chest pain: the EPIC-ACS study.

We tested the hypothesis that epicardial adipose tissue (EAT) quantification improves the prediction of the presence of obstructive coronary artery disease (CAD) in patients presenting with acute chest pain to the emergency department. Within this prospective observational cohort study, we included 657 consecutive patients (mean age 58.06 ± 18.04 years, 53% male) presenting to the emergency department with acute chest pain suggestive of acute coronary syndrome between December 2018 and August 2020. Patients with ST-elevation myocardial infarction, haemodynamic instability, or known CAD were excluded. As part of the initial workup, we performed bedside echocardiography for quantification of EAT thickness by a dedicated study physician, blinded to all patient characteristics. Treating physicians remained unaware of the results of the EAT assessment. The primary endpoint was defined as the presence of obstructive CAD, as detected in subsequent invasive coronary angiography. Patients reaching the primary endpoint had significantly more EAT than patients without obstructive CAD (7.90 ± 2.56 mm vs. 3.96 ± 1.91 mm, P < 0.0001). In a multivariable regression analysis, a 1 mm increase in EAT thickness was associated with a nearby two-fold increased odds of the presence of obstructive CAD [1.87 (1.64-2.12), P < 0.0001]. Adding EAT to a multivariable model of the GRACE score, cardiac biomarkers and traditional risk factors significantly improved the area under the receiver operating characteristic curve (0.759-0.901, P < 0.0001). Epicardial adipose tissue strongly and independently predicts the presence of obstructive CAD in patients presenting with acute chest pain to the emergency department. Our results suggest that the assessment of EAT may improve diagnostic algorithms of patients with acute chest pain.