Relationship Between Prepatellar Fat Thickness and Bone Mineral Density.

OBJECTIVE: To evaluate the relationship between bone mineral density (BMD) by measuring the prepatellar fat thickness with knee radiography and to gain a measurement method that has not been done before in the literature. STUDY DESIGN: Cross-sectional descriptive study. Place and Duration of the Study: Department of Physical Medicine and Rehabilitation, Training and Research Hospital, Sanliurfa, Turkiye, between January and June 2020. METHODOLOGY: Patients' age, body mass index (BMI) data, prepatellar fat thickness (mm), L1-L4 total, bone mineral density femoral neck, femur trochanter major, and femur total T scores were recorded. The relationships between these three groups (normal, osteopenia, osteoporosis) and between prepatellar fat tissue measurement were evaluated. One-way analysis of variance (ANOVA) and Post Hoc Tukey tests were used in the analysis. RESULTS:  A statistically significant difference was found in terms of trochanter major T score measurements (X2 = 20.435; p <0.001) and BMI (X2 = 66.535; p <0.001) measurements of prepatellar fat thickness measurement. A statistically significant difference was found between the three groups in terms of prepatellar fat thickness measurement, L1-4 T-score, femoral neck, and femur total values (p <0.001). CONCLUSION:  Prepatellar fat thickness in postmenopausal Turkish women was positively correlated with BMD; BMD increases as the prepatellar fat thickness increases. This explains that perapatellar fat thickness creates a mechanical load on the bones and causes an increase in BMD. KEY WORDS: Osteoporosis, Fat thickness, Bone mineral density.

Deep-Learning Based Automated Segmentation and Quantitative Volumetric Analysis of Orbital Muscle and Fat for Diagnosis of Thyroid Eye Disease.

Purpose: Thyroid eye disease (TED) is characterized by proliferation of orbital tissues and complicated by compressive optic neuropathy (CON). This study aims to utilize a deep-learning (DL)-based automated segmentation model to segment orbital muscle and fat volumes on computed tomography (CT) images and provide quantitative volumetric data and a machine learning (ML)-based classifier to distinguish between TED and TED with CON. Methods: Subjects with TED who underwent clinical evaluation and orbital CT imaging were included. Patients with clinical features of CON were classified as having severe TED, and those without were classified as having mild TED. Normal subjects were used for controls. A U-Net DL-model was used for automatic segmentation of orbital muscle and fat volumes from orbital CTs, and ensemble of Random Forest Classifiers were used for volumetric analysis of muscle and fat. Results: Two hundred eighty-one subjects were included in this study. Automatic segmentation of orbital tissues was performed. Dice coefficient was recorded to be 0.902 and 0.921 for muscle and fat volumes, respectively. Muscle volumes among normal, mild, and severe TED were found to be statistically different. A classification model utilizing volume data and limited patient data had an accuracy of 0.838 and an area under the curve (AUC) of 0.929 in predicting normal, mild TED, and severe TED. Conclusions: DL-based automated segmentation of orbital images for patients with TED was found to be accurate and efficient. An ML-based classification model using volumetrics and metadata led to high diagnostic accuracy in distinguishing TED and TED with CON. By enabling rapid and precise volumetric assessment, this may be a useful tool in future clinical studies.

Peri-coronary fat attenuation index combined with high-risk plaque characteristics quantified from coronary computed tomography angiography for risk stratification in new-onset chest pain individuals without acute myocardial infarction.

This study aims to evaluate the role of the peri-coronary Fat Attenuation Index (FAI) and High-Risk Plaque Characteristics (HRPC) in the assessment of coronary heart disease risk. By conducting coronary CT angiography and coronary angiography on 217 patients with newly developed chest pain (excluding acute myocardial infarction), their degree of vascular stenosis, FAI, and the presence and quantity of HRPC were assessed. The study results demonstrate a correlation between FAI and HRPC, and the combined use of FAI and HRPC can more accurately predict the risk of major adverse cardiovascular events (MACE). Additionally, the study found that patients with high FAI were more prone to exhibit high-risk plaque characteristics, severe stenosis, and multiple vessel disease. After adjustment, the combination of FAI and HRPC improved the ability to identify and reclassify MACE. Furthermore, the study identified high FAI as an independent predictor of MACE in patients undergoing revascularization, while HRPC served as an independent predictor of MACE in patients not undergoing revascularization. These findings suggest the potential clinical value of FAI and HRPC in the assessment of coronary heart disease risk, particularly in patients with newly developed chest pain excluding acute myocardial infarction.

Associations of marrow fat fraction with MR imaging based trabecular bone microarchitecture in first-time diagnosed type 1 diabetes mellitus.

Purpose: To determine whether there are alterations in marrow fat content in individuals first-time diagnosed with type 1 diabetes mellitus (T1DM) and to explore the associations between marrow fat fraction and MRI-based findings in trabecular bone microarchitecture. Method: A case-control study was conducted, involving adults with first-time diagnosed T1DM (n=35) and age- and sex-matched healthy adults (n=46). Dual-energy X-ray absorptiometry and 3 Tesla-MRI of the proximal tibia were performed to assess trabecular microarchitecture and vertebral marrow fat fraction. Multiple linear regression analysis was used to test the associations of marrow fat fraction with trabecular microarchitecture and bone density while adjusting for potential confounding factors. Results: In individuals first-time diagnosed with T1DM, the marrow fat fraction was significantly higher (p < 0.001) compared to healthy controls. T1DM patients also exhibited higher trabecular separation [median (IQR): 2.19 (1.70, 2.68) vs 1.81 (1.62, 2.10), p < 0.001], lower trabecular volume [0.45 (0.30, 0.56) vs 0.53 (0.38, 0.60), p = 0.013], and lower trabecular number [0.37 (0.26, 0.44) vs 0.41 (0.32, 0.47), p = 0.020] compared to controls. However, bone density was similar between the two groups (p = 0.815). In individuals with T1DM, there was an inverse association between marrow fat fraction and trabecular volume (r = -0.69, p < 0.001) as well as trabecular number (r = -0.55, p < 0.001), and a positive association with trabecular separation (r = 0.75, p < 0.001). Marrow fat fraction was independently associated with total trabecular volume (standardized ß = -0.21), trabecular number (ß = -0.12), and trabecular separation (ß = 0.57) of the proximal tibia after adjusting for various factors including age, gender, body mass index, physical activity, smoking status, alcohol consumption, blood glucose, plasma glycated hemoglobin, lipid profile, and bone turnover biomarkers. Conclusions: Individuals first-time diagnosed with T1DM experience expansion of marrow adiposity, and elevated marrow fat content is associated with MRI-based trabecular microstructure.

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.

Fat quantification in dual-layer detector spectral CT: How to handle iron overload, varying tube voltage and radiation dose Indices.

OBJECTIVES: Opposed to other spectral CT techniques, fat quantification in dual-layer detector CT (dlCT) has only recently been developed. The impact of concomitant iron overload and dlCT-specific protocol settings such as the dose right index (DRI), a measure of image noise and tube current, on dlCT fat quantification was unclear. Further, spectral information became newly available <120 kV. Therefore, this study's objective was to evaluate the impact of iron, changing tube voltage, and DRI on dlCT fat quantification. MATERIAL AND METHODS: Phantoms with 0 and 8mg/cm3 iron; 0 and 5mg/cm3 iodine; 0, 10, 20, 35, 50, and 100% fat and liver equivalent, respectively, were scanned with a dlCT (CT7500, Philips, the Netherlands) at 100kV/20DRI, 120kV/20DRI, 140kV/20DRI, and at 120kV/16DRI, 120kV/24DRI. Material decomposition was done for fat, liver, and iodine (A1); for fat, liver, and iron (A2); and for fat, liver, and combined reference values of iodine and iron (A3). All scans were analyzed with reference values from 120kV/20DRI. For statistics, the intraclass correlation coefficient (ICC) and Bland-Altman analyses were used. RESULTS: In phantoms with iron and iodine, results were best for A3 with a mean deviation to phantom fat of 1.3±2.6% (ICC 0.999 [95%-confidence interval 0.996-1]). The standard approach A1 yielded a deviation of -2.5±3.0% (0.998[0.994-0.999]), A2 of 6.1±4.8% (0.991[0.974-0.997]). With A3 and changing tube voltage, the maximal difference between quantified fat and the phantom ground truth occurred at 100kV with 4.6±2.1%. Differences between scans were largest between 100kV and 140kV (2.0%[-7.1-11.2]). The maximal difference of changing DRI occurred between 16 and 24 DRI with 0.4%[-2.2-3.0]. CONCLUSION: For dlCT fat quantification in the presence of iron, material decomposition with combined reference values for iodine and iron delivers the most accurate results. Tube voltage-specific calibration of reference values is advisable while the impact of the DRI on dlCT fat quantification is neglectable.

Epicardial fat volume is associated with primary coronary slow-flow phenomenon in patients with severe aortic stenosis undergoing transcatheter valve implantation.

BACKGROUND: Primary coronary slow flow (CSF) is defined as delayed opacification of the distal epicardial vasculature during coronary angiography in the absence of relevant coronary artery stenoses. Microvascular disease is thought to be the underlying cause of this pathology. Epicardial fat tissue (EFT) is an active endocrine organ directly surrounding the coronary arteries that provides pro-inflammatory factors to the adjacent tissue by paracrine and vasocrine mechanisms. The aim of the present study was to investigate a potential association between EFT and primary CSF and whether EFT can predict the presence of primary CSF. METHODS: Between 2016 and 2017, n = 88 patients with high-grade aortic stenosis who were planned for transcatheter aortic valve implantation (TAVI) were included in this retrospective study. EFT volume was measured by pre-TAVI computed tomography (CT) using dedicated software. The presence of primary CSF was defined based on the TIMI frame count from the pre-TAVI coronary angiograms. RESULTS: Thirty-nine of 88 TAVI patients had CSF (44.3%). EFT volume was markedly higher in patients with CSF (142 ml [IQR 107-180] vs. 113 ml [IQR 89-147]; p = 0.009) and was strongly associated with the presence of CSF (OR 1.012 [95%CI 1.002-1.021]; p = 0.014). After adjustment, EFT volume was still an independent predictor of CSF (OR 1.016 [95%CI 1.004-1.026]; p = 0.009). CONCLUSION: Primary CSF was independently associated with increased EFT volume. Further studies are needed to validate this finding and elucidate whether a causal relationship exists.

Epicardial adipose tissue volume assessed by cardiac CT as a predictor of atrial fibrillation recurrence following catheter ablation.

INTRODUCTION: In patients with atrial fibrillation (AF), up to one third have recurrence after a first catheter ablation (CA). Epicardial adipose tissue (EAT) has been considered to be closely related to AF, with a potential role in its recurrence. We aimed to evaluate the association between the volume of EAT measured by cardiac computed tomography (CT) and AF recurrence after CA. METHODS: Consecutive AF patients underwent a standardized cardiac CT protocol for quantification of EAT, thoracic adipose volume (TAV) and left atrium (LA) volume before CA. An appropriate cut-off of EAT was determined and risk recurrence was estimated. RESULTS: 305 patients (63.6 % male, mean age 57.5 years, 28.2 % persistent AF) were followed for 24 months; 23 % had AF recurrence at 2-year mark, which was associated with higher EAT (p = 0.037) and LAV (p < 0.001). Persistent AF was associated with higher EAT volumes (p = 0.010), TAV (p = 0.003) and LA volumes (p < 0.001). EAT was predictive of AF recurrence (p = 0.044). After determining a cut-off of 92 cm3, survival analysis revealed that EAT volumes > 92 cm3 showed higher recurrence rates at earlier time points after the index ablation procedure (p = 0.006), with a HR of 1.95 (p = 0.008) of AF recurrence at 2-year. After multivariate adjustment, EAT > 92 cm3 remained predictive of AF recurrence (p = 0.028). CONCLUSION: The volume of EAT measured by cardiac CT can predict recurrence of AF after ablation, with a volume above 92 cm3 yielding almost twice the risk of arrhythmia recurrence in the first two years following CA. Higher EAT and TAV are also associated with persistent AF.

Coronary Computed Tomography Angiography-Derived Modified Duke Index Is Associated with Peri-Coronary Fat Attenuation Index and Predicts Severity of Coronary Inflammation.

Background and Objectives: The modified Duke index derived from coronary computed tomography angiography (CCTA) was designed to predict cardiovascular outcomes based on the severity of coronary stenosis. However, it does not take into consideration the presence or severity of peri-coronary inflammation. The peri-coronary fat attenuation index (FAI) is a novel imaging marker determined by CCTA which reflects the degree of inflammation in the coronary tree in patients with coronary artery disease. To assess the association between the modified Duke index assessed by CCTA, cardiovascular risk factors, and peri-coronary inflammation in the coronary arteries of patients with coronary artery disease. Materials and Methods: One hundred seventy-two patients who underwent CCTA for typical angina were assigned into two groups based on the modified Duke index: group 1-patients with low index, ≤3 (n = 107), and group 2-patients with high index, >3 (n = 65). Demographic, clinical, and CCTA data were collected for all patients, and FAI analysis of coronary inflammation was performed. Results: Patients with increased values of the modified Duke index were significantly older compared to those with a low index (61.83 ± 9.89 vs. 64.78 ± 8.9; p = 0.002). No differences were found between the two groups in terms of gender distribution, hypertension, hypercholesterolemia, or smoking history (all p > 0.5). The FAI score was significantly higher in patients from group 2, who presented a significantly higher score of inflammation compared to the patients in group 1, especially at the level of the right coronary artery (FAI score, 20.85 ± 15.80 vs. 14.61 ± 16.66; p = 0.01 for the right coronary artery, 13.85 ± 8.04 vs. 10.91 ± 6.5; p = 0.01 for the circumflex artery, 13.26 ± 10.18 vs. 11.37 ± 8.84; p = 0.2 for the left anterior descending artery). CaRi-Heart® analysis identified a significantly higher risk of future events among patients with a high modified Duke index (34.84% ± 25.86% vs. 16.87% ± 15.80%; p < 0.0001). ROC analysis identified a cut-off value of 12.1% of the CaRi-Heart® risk score for predicting a high severity of coronary lesions, with an AUC of 0.69. Conclusions: The CT-derived modified Duke index correlates well with local perilesional inflammation as assessed using the FAI score at different levels of the coronary circulation.

Determinants of echocardiographic epicardial adipose tissue in a general middle-aged population - The Cardiovascular Risk in Young Finns Study.

Epicardial adipose tissue (EAT) is the cardiac visceral fat depot proposed to play a role in the etiology of various cardiovascular disease outcomes. Little is known about EAT determinants in a general population. We examined cardiometabolic, dietary, lifestyle and socioeconomic determinants of echocardiograpghically measured EAT in early adulthood. Data on cardiometabolic, dietary, lifestyle and socioeconomic factors were collected from participants of the Cardiovascular Risk in Young Finns Study (YFS; N = 1667; age 34-49 years). EAT thickness was measured from parasternal long axis echocardiograms. Multivariable regression analysis was used to study potential EAT determinants. Possible effect modification of sex was addressed. Mean EAT thickness was 4.07 mm (95% CI 4.00-4.17). Multivariable analysis [ß indicating percentage of change in EAT(mm) per one unit increase in determinant variable] indicated female sex (ß = 11.0, P < 0.0001), type 2 diabetes (ß = 14.0, P = 0.02), waist circumference (cm) (ß = 0.38, P < 0.0001), systolic blood pressure (mmHg) (ß = 0.18, P = 0.02) and red meat intake (g/day) (ß = 0.02, P = 0.05) as EAT determinants. Sex-specific analysis revealed age (year) (ß = 0.59, P = 0.01), alcohol intake (drinks/day) (ß = 4.69, P = 0.006), heavy drinking (yes/no) (ß = 30.4, P < 0.0001) as EAT determinants in women and fruit intake (g/day) (ß = -1.0, P = 0.04) in men. In the YFS cohort, waist circumference, systolic blood pressure and red meat intake were directly associated with EAT among all participants. In women, age, alcohol intake, heavy drinking and type 2 diabetes associated directly with EAT, while an inverse association was observed between fruit intake and EAT in men.

More accurate trabecular bone imaging using UTE MRI at the resonance frequency of fat.

High-resolution magnetic resonance imaging (HR-MRI) has been increasingly used to assess the trabecular bone structure. High susceptibility at the marrow/bone interface may significantly reduce the marrow's apparent transverse relaxation time (T2*), overestimating trabecular bone thickness. Ultrashort echo time MRI (UTE-MRI) can minimize the signal loss caused by susceptibility-induced T2* shortening. However, UTE-MRI is sensitive to chemical shift artifacts, which manifest as spatial blurring and ringing artifacts partially due to non-Cartesian sampling. In this study, we proposed UTE-MRI at the resonance frequency of fat to minimize marrow-related chemical shift artifacts and the overestimation of trabecular thickness. Cubes of trabecular bone from six donors (75 ± 4 years old) were scanned using a 3 T clinical scanner at the resonance frequencies of fat and water, respectively, using 3D UTE sequences with five TEs (0.032, 1.1, 2.2, 3.3, and 4.4 ms) and a clinical 3D gradient echo (GRE) sequence at 0.2 × 0.2 × 0.4 mm3 voxel size. Trabecular bone thickness was measured in 30 regions of interest (ROIs) per sample. MRI results were compared with thicknesses obtained from micro-computed tomography (µCT) at 50 µm3 voxel size. Linear regression models were used to calculate the coefficient of determination between MRI- and µCT-based trabecular thickness. All MRI-based trabecular thicknesses showed significant correlations with µCT measurements. The correlations were higher (examined with paired Student's t-test, P < 0.01) for 3D UTE images performed at the fat frequency (R2 = 0.59-0.74, P < 0.01) than those at the water frequency (R2 = 0.18-0.52, P < 0.01) and clinical GRE images (R2 = 0.39-0.47, P < 0.01). Significantly reduced correlations were observed with longer TEs. This study highlighted the feasibility of UTE-MRI at the fat frequency for a more accurate assessment of trabecular bone thickness.

Hepatic steatosis modeling and MRI signal simulations for comparison of single- and dual-R2* models and estimation of fat fraction at 1.5T and 3T.

BACKGROUND AND OBJECTIVE: Magnetic resonance imaging (MRI) has emerged as a noninvasive clinical tool for assessment of hepatic steatosis. Multi-spectral fat-water MRI models, incorporating single or dual transverse relaxation decay rate(s) (R2*) have been proposed for accurate fat fraction (FF) estimation. However, it is still unclear whether single- or dual-R2* model accurately mimics in vivo signal decay for precise FF estimation and the impact of signal-to-noise ratio (SNR) on each model performance. Hence, this study aims to construct virtual steatosis models and synthesize MRI signals with different SNRs to systematically evaluate the accuracy of single- and dual-R2* models for FF and R2* estimations at 1.5T and 3.0T. METHODS: Realistic hepatic steatosis models encompassing clinical FF range (0-60 %) were created using morphological features of fat droplets (FDs) extracted from human liver biopsy samples. MRI signals were synthesized using Monte Carlo simulations for noise-free (SNRideal) and varying SNR conditions (5-100). Fat-water phantoms were scanned with different SNRs to validate simulation results. Fat water toolbox was used to calculate R2* and FF for both single- and dual-R2* models. The model accuracies in R2* and FF estimates were analyzed using linear regression, bias plot and heatmap analysis. RESULTS: The virtual steatosis model closely mimicked in vivo fat morphology and Monte Carlo simulation produced realistic MRI signals. For SNRideal and moderate-high SNRs, water R2* (R2*W) by dual-R2* and common R2* (R2*com) by single-R2* model showed an excellent agreement with slope close to unity (0.95-1.01) and R2 > 0.98 at both 1.5T and 3.0T. In simulations, the R2*com-FF and R2*W-FF relationships exhibited slopes similar to in vivo calibrations, confirming the accuracy of our virtual models. For SNRideal, fat R2* (R2*F) was similar to R2*W and dual-R2* model showed slightly higher accuracy in FF estimation. However, in the presence of noise, dual-R2* produced higher FF bias with decreasing SNR, while leading to only marginal improvement for high SNRs and in regions dominated by fat and water. In contrast, single-R2* model was robust and produced accurate FF estimations in simulations and phantom scans with clinical SNRs. CONCLUSION: Our study demonstrates the feasibility of creating virtual steatosis models and generating MRI signals that mimic in vivo morphology and signal behavior. The single-R2* model consistently produced lower FF bias for clinical SNRs across entire FF range compared to dual-R2* model, hence signifying that single-R2* model is optimal for assessing hepatic steatosis.

CT radiomics based on the peritumoral adipose region of gastric adenocarcinoma for preoperative prediction of lymph node metastasis.

PURPOSE: To construct and validate CT radiomics model based on the peritumoral adipose region of gastric adenocarcinoma to preoperatively predict lymph node metastasis (LNM). METHODS AND METHODS: 293 consecutive gastric adenocarcinoma patients receiving radical gastrectomy with lymph node dissection in two medical institutions were stratified into a development set (from Institution A, n = 237), and an external validation set (from Institution B, n = 56). Volume of interest of peritumoral adipose region was segmented on preoperative portal-phase CT images. The least absolute shrinkage and selection operator method and stepwise logistic regression were used to select features and build radiomics models. Manual classification was performed according to routine CT characteristics. A classifier incorporating the radiomics score and CT characteristics was developed for predicting LNM. Area under the receiver operating characteristic curve (AUC) was used to show discrimination between tumors with and without LNM, and the calibration curves and Brier score were used to evaluate the predictive accuracy. Violin plots were used to show the distribution of radiomics score. RESULTS: AUC values of radiomics model to predict LNM were 0.938, 0.905, and 0.872 in the training, internal test, and external validation sets, respectively, higher than that of manual classification (0.674, all P values < 0.01). The radiomics score of the positive LNM group were higher than that of the negative group in all sets (both P-values < 0.001). The classifier showed no improved predictive power compared with the radiomics signature alone with AUC values of 0.916 and 0.872 in the development and external validation sets, respectively. Multivariate analysis showed that radiomics score was an independent predictor. CONCLUSIONS: Radiomics model based on peritumoral adipose region could be a useful approach for preoperative LNM prediction in gastric adenocarcinoma.

Elexacaftor/tezacaftor/ivacaftor influences body composition in adults with cystic fibrosis: a fully automated CT-based analysis.

A poor nutritional status is associated with worse pulmonary function and survival in people with cystic fibrosis (pwCF). CF transmembrane conductance regulator modulators can improve pulmonary function and body weight, but more data is needed to evaluate its effects on body composition. In this retrospective study, a pre-trained deep-learning network was used to perform a fully automated body composition analysis on chest CTs from 66 adult pwCF before and after receiving elexacaftor/tezacaftor/ivacaftor (ETI) therapy. Muscle and adipose tissues were quantified and divided by bone volume to obtain body size-adjusted ratios. After receiving ETI therapy, marked increases were observed in all adipose tissue ratios among pwCF, including the total adipose tissue ratio (+ 46.21%, p < 0.001). In contrast, only small, but statistically significant increases of the muscle ratio were measured in the overall study population (+ 1.63%, p = 0.008). Study participants who were initially categorized as underweight experienced more pronounced effects on total adipose tissue ratio (p = 0.002), while gains in muscle ratio were equally distributed across BMI categories (p = 0.832). Our findings suggest that ETI therapy primarily affects adipose tissues, not muscle tissue, in adults with CF. These effects are primarily observed among pwCF who were initially underweight. Our findings may have implications for the future nutritional management of pwCF.

Correlation of epicardial adipose tissue and inflammatory indices in patients with STEMI and implications for atrial arrhythmias.

BACKGROUND: Epicardial adipose tissue(EAT) is associated with inflammation in previous studies but is unknown in patients with ST-segment elevation myocardial infarction(STEMI).This study investigated the correlation between epicardial fat and inflammatory cells obtained by cardiac magnetic resonance (CMR) and the effect on atrial arrhythmias in patients with STEMI. METHODS: This was a single-center, retrospective study. We consecutively selected patients who all completed CMR after Percutaneous Coronary Intervention (PCI) from January 2019 to December 2022 and then had regular follow-ups at 1, 3, 6, 9, and 12 months. The enrolled patients were grouped according to the presence or absence of atrial arrhythmia and divided into atrial and non-atrial arrhythmia groups. RESULTS: White blood cell, neutrophil, lymphocyte, C-reactive protein, EATV, LVES, LVED were higher in the atrial arrhythmia group than in the non-atrial arrhythmia group, and LVEF was lower than that in the non-atrial arrhythmia group (p < 0.05); EATV was significantly positively correlated with each inflammatory indices (white blood cell: r = 0.415 p < 0.001, neutrophil:r = 0.386 p < 0.001, lymphocyte:r = 0.354 p < 0.001, C-reactive protein:r = 0.414 p < 0.001); one-way logistic regression analysis showed that risk factors for atrial arrhythmias were age, heart rate, white blood cell, neutrophil, lymphocyte, C-reactive protein, EATV, LVES, LVED; multifactorial logistic regression analysis showed that neutrophil, lymphocyte, C-reactive protein, EATV, and LVES were independent risk factors for atrial arrhythmias; ROC analysis showed that the area under the curve (AUC) for neutrophil was 0.862; the AUC for lymphocyte was 1.95; and the AUC for C-reactive protein was 0.862. reactive protein was 0.852; AUC for LVES was 0.683; and AUC for EATV was 0.869. CONCLUSION: In patients with STEMI, EAT was significantly and positively correlated with inflammatory indices; neutrophil, lymphocyte, C-reactive protein, EATV, and LVES were independent risk factors for atrial arrhythmias and had good predictive value.

Epicardial adipose tissue (EAT) thickness on non-gated chest CT as an alternative to EAT volume on cardiac CT.

BACKGROUND: Epicardial adipose tissue (EAT) volume is usually measured with ECG-gated computed tomography (CT). Measurement of EAT thickness is a more convenient method; however, it is not clear whether EAT thickness measured with non-gated CT is reliable and at which localization it agrees best with the EAT volume. PURPOSE: To examine the agreement between ECG-gated EAT volume and non-gated EAT thickness measured from various localizations and to assess the predictive role of EAT thickness for high EAT volume. MATERIAL AND METHODS: EAT thickness was measured at six locations using non-contrast thorax CT and EAT volume was measured using ECG-gated cardiac CT (n = 68). The correlation and agreement (Bland-Altman plots) between the thicknesses and EAT volume were assessed. RESULTS: EAT thicknesses were significantly correlated with EAT volume (P < 0.001). The highest correlation (r = 0.860) and agreement were observed for the thickness adjacent to the right ventricular free wall. Also, EAT thickness at this location has a strong potential for discriminating high (>125 cm3) EAT volume (area under the ROC curve=0.889, 95% CI=0.801-0.977; P < 0.001). The sensitivity, specificity, and positive and negative predictive values of EAT thickness for high EAT volume were 76.5%, 88.2%, 68.4%, and 91.8%, respectively, for the cutoff value of 5.75 cm; and 47.1%, 100%, 100%, and 85%, respectively, for the cutoff value of 8.10 cm. CONCLUSION: EAT thickness measured on non-gated chest CT adjacent to the right ventricular free wall is a reliable and easy-to-use alternative to the volumetric quantification and has a strong potential to predict high EAT volume.

Predicting the presence of adherent perinephric fat using MRI radiomics combined with machine learning.

OBJECTIVES: Adherent perinephric fat (APF) poses significant challenges to surgical procedures. This study aimed to evaluate the usefulness of machine learning algorithms combined with MRI-based radiomics features for predicting the presence of APF. MATERIALS AND METHODS: Patients with renal cell carcinoma who underwent surgery between April 2019 and February 2022 at Chonnam National University Hwasun Hospital were retrospectively screened, and 119 patients included. Twenty-one and seventeen patients were set aside for the internal and external test sets, respectively. Pre-operative T1-weighted MRI acquired at 60 s following a contrast injection (T1w-60) were collected. For each T1w-60 data, two regions of interest (ROIs) were manually drawn: the perinephric fat tissue and an aorta segment on the same level as the targeted kidney. Preprocessing steps included resizing voxels, N4 Bias Correction filtering, and aorta-based normalization. For each patient, 851 radiomics features were extracted from the ROI of perinephric fat tissue. Gender and BMI were added as clinical factors. Least Absolute Shrinkage and Selection Operator was adopted for feature selection. We trained and evaluated five models using a 4-fold cross validation. The final model was chosen based on the highest mean AUC across four folds. The performance of the final model was evaluated on the internal and external test sets. RESULTS: A total of 15 features were selected in the final set. The final model achieved the accuracy, sensitivity, specificity, and AUC of 81% (95% confidence interval, 61.9-95.2%), 72.7% (42.9-100%), 90% (66.7-100%), and 0.855 (0.615-1.0), respectively on the internal test set, and 88.2% (70.6-100%), 100% (100-100%), 80% (50%-100%), 0.971 (0.871-1.0), respectively on the external test set. CONCLUSIONS: Our study demonstrated the feasibility of machine learning algorithms trained with MRI-based radiomics features for APF prediction. Further studies with a multi-center approach are necessary to validate our findings.

Imaging and prognostic characterization of fat-containing hepatocellular carcinoma subtypes.

PURPOSE: Steatohepatitic hepatocellular carcinoma (SH-HCC) is characterized by intratumoral fat with > 50% inflammatory changes. However, intratumoral fat (with or without inflammation) can also be found in not-otherwise specified HCC (NOS-HCC). We compared the imaging features and outcome of resected HCC containing fat on pathology including SH-HCC (> 50% steatohepatitic component), NOS-HCC with < 50% steatohepatitic component (SH-NOS-HCC), and fatty NOS-HCC (no steatohepatitic component). MATERIAL AND METHODS: From September 2012 to June 2021, 94 patients underwent hepatic resection for fat-containing HCC on pathology. Imaging features and categories were assessed using LIRADS v2018. Fat quantification was performed on chemical-shift MRI. Recurrence-free and overall survival were estimated. RESULTS: Twenty-one patients (26%) had nonalcoholic steatohepatitis (NASH). The median intra-tumoral fat fraction was 8%, with differences between SH-HCC and SH-NOS-HCC (9.5% vs. 5% p = 0.03). There was no difference in major LI-RADS features between all groups; most tumors were classified as LR-4/5. A mosaic architecture on MRI was rare (7%) in SH-HCC, a fat in mass on CT was more frequently depicted (48%) in SH-HCC. A combination of NASH with no mosaic architecture on MRI or NASH with fat in mass on CT yielded excellent specificity for diagnosing SH-HCC (97.6% and 97.7%, respectively). The median recurrence-free and overall survival were 58 and 87 months, with no difference between groups (p = 0.18 and p = 0.69). CONCLUSION: In patients with NASH, an SH-HCC may be suspected in L4/LR-5 observations with no mosaic architecture at MRI or with fat in mass on CT. Oncological outcomes appear similar between fat-containing HCC subtypes.

BODY FAT COMPOSITION IN PATIENTS WITH INFLAMMATORY BOWEL DISEASES: A COMPARATIVE STUDY BETWEEN SKINFOLDS AND ULTRASONOGRAPHY.

BACKGROUND: Inflammatory bowel diseases (IBD) are associated with important changes in nutritional status. OBJECTIVE: The aim of the study was to compare body fat composition between two anthropometric methods: skinfolds and ultrasonography, in patients with IBD. METHODS: Single-center cross-sectional study with IBD patients in remission or active disease. For the agreement analysis between the body fat assessment methods, the Bland Altman method was used. RESULTS: A total of 101 patients with IBD were included, 75 with Crohn's disease and 26 with ulcerative colitis. Approximately 56% of the patients with Crohn's disease and 65.4% of those with ulcerative colitis had a body fat composition above normal levels, with no significant difference between the diseases (P=0.63). The Bland-Altman concordance analysis showed that the methods for assessing the percentage of fat by the adipometer and ultrasound were not in full agreement (P=0.001), despite both presented good correlation (CC 0.961; P=0.000). CONCLUSION: The analysis of body fat percentage in patients with IBD was different between the skinfolds and ultrasound. Both methods can be used to assess the of body fat percentage of patients with IBD. However, monitoring of body fat sequentially and longitudinally should always be performed using the same method throughout the disease course. Prospective longitudinal studies are warranted to precisely define the role of these two methods of measuring body composition in patients with IBD. BACKGROUND: • Inflammatory bowel diseases are associated with changes in nutritional status. BACKGROUND: • Skinfolds measurements and ultrasound are valid methods for assessing body composition and body fat. BACKGROUND: • These methods despite comparable are not identical and are useful in clinical nutritional practices in IBD.

Association of epicardial adipose tissue with early structural and functional cardiac changes in Type 2 diabetes.

BACKGROUND: Dysregulated epicardial adipose tissue (EAT) may contribute to the development of heart failure in Type 2 diabetes (T2D). This study aimed to evaluate the associations between EAT volume and composition with imaging markers of subclinical cardiac dysfunction in people with T2D and no prevalent cardiovascular disease. METHODS: Prospective case-control study enrolling participants with and without T2D and no known cardiovascular disease. Two hundred and fifteen people with T2D (median age 63 years, 60 % male) and thirty-nine non-diabetics (median age 59 years, 62 % male) were included. Using computed tomography (CT), total EAT volume and mean CT attenuation, as well as, low attenuation (Hounsfield unit range -190 to -90) EAT volume were quantified by a deep learning method and volumes indexed to body surface area. Associations with cardiac magnetic resonance-derived left ventricular (LV) volumes and strain indices were assessed using linear regression. RESULTS: T2D participants had higher LV mass/volume ratio (median 0.89 g/mL [0.82-0.99] vs 0.79 g/mL [0.75-0.89]) and lower global longitudinal strain (GLS; 16.1 ± 2.3 % vs 17.2 ± 2.2 %). Total indexed EAT volume correlated inversely with mean CT attenuation. Low attenuation indexed EAT volume was 2-fold higher (18.8 cm3/m2 vs. 9.4 cm3/m2, p < 0.001) in T2D and independently associated with LV mass/volume ratio (ß = 0.002, p = 0.01) and GLS (ß = -0.03, p = 0.03). CONCLUSIONS: Higher EAT volumes seen in T2D are associated with a lower mean CT attenuation. Low attenuation indexed EAT volume is independently, but only weakly, associated with markers of subclinical cardiac dysfunction in T2D.