Computerized Automated Quantification of Subcutaneous and Visceral Adipose Tissue From Computed Tomography Scans: Development and Validation Study

Associations between visceral adipose and renal artery calcification: Results from the multi-ethnic study of atherosclerosis

Background

Visceral adipose tissue (VAT) has been associated with higher levels of atherosclerosis. Renal artery calcification (RAC) secondary to atherosclerosis has been found to be associated with an increase in all-cause mortality.

Methods

1 978 participants underwent CT imaging to measure VAT and RAC. Rate ratio regression was used to estimate prevalence ratios (PRs) for the presence of RAC, while linear regression was used to estimate linear coefficients for the severity of RAC.

Results

1 196 participants had complete VAT and RAC measurements. In adjusted models, VAT area was not associated with RAC presence (PR 1.02, 95 % CI 0.89, 1.16, p = 0.80), while greater VAT density was inversely, but not significantly, associated with RAC presence (PR 0.89, 95 % CI 0.78, 1.02, p = 0.10). Among 354 participants with RAC > 0, VAT area was significantly associated with RAC severity (slope 63.32, 95 % CI 11.84, 114.81, p = 0.02), while VAT density was not associated (slope 9.78, 95 % CI -40.87, 60.44, p = 0.71).

Conclusions

VAT area and density are not significantly associated with RAC presence, while greater VAT area is significantly associated with RAC severity among those with RAC > 0. Our results are the first describing the relationship between VAT and RAC, and are in contrast to previous literature demonstrating a significant association between VAT and coronary artery calcification.

Mechanisms of combined deer antler polysaccharides and postbiotics supplementation for regulating obesity in mice

Objective

This study investigated the mechanisms related to lipid metabolism regulation after combined supplementation with deer antler polysaccharides and postbiotics.

Methods

Thirty-two male mice were divided into high-fat diet, HD + deer antler polysaccharides, HD + Bacillus coagulans postbiotics, and HD + deer antler polysaccharides + B. coagulans postbiotics groups. The diets contained 60% fat. After 9 weeks, the effects of deer antler polysaccharides and postbiotics on lipid metabolism were assessed through blood biochemical, histological tissue staining, and polymerase chain reaction analyses.

Results

Supplementation with deer antler polysaccharides and postbiotics significantly inhibited weight gain in obese mice, reduced serum total cholesterol, triglyceride, and low-density lipoprotein levels and markedly increased the serum high-density lipoprotein level. Additionally, hepatic lipid droplet accumulation and adipocyte hypertrophy improved. The expressions of the lipid synthesis genes, sterol regulatory element-binding protein 1 (i.e. SREBP-1c), and fatty acid synthase (i.e. FAS), significantly decreased, while peroxisome proliferator-activated receptor alpha (i.e. PPAR-α) and acyl-CoA oxidase 1 (i.e. ACOX1) expression significantly increased. The expressions of the inflammation-related genes, tumor necrosis factor-alpha (i.e. TNF-α), interleukin (IL)-6, and IL-1 also significantly decreased.

Conclusion

Thus, combined deer antler polysaccharides and postbiotic supplementation regulated obesity in mice, potentially by modulating lipid synthesis and inflammation-related gene expression.

The association between regional adiposity, cognitive function, and dementia-related brain changes: a systematic review

Background

Adiposity has been previously associated with cognitive impairment and Alzheimer's disease and related disorders (ADRD). Body mass index (BMI) is the most common measure of global adiposity, but inconsistent results were found since it is a global measurement. BMI does not represent regional fat distribution which differs between sexes, race, and age. Regional fat distribution may contribute differently to cognitive decline and Alzheimer's disease (AD)-related brain changes. Fat-specific targeted therapies could lead to personalized improvement of cognition. The goal of this systematic review is to explore whether regional fat depots, rather than central obesity, should be used to understand the mechanism underlying the association between adiposity and brain.

Methods

This systematic review included 33 studies in the English language, conducted in humans aged 18 years and over with assessment of regional adiposity, cognitive function, dementia, and brain measures. We included only studies that have assessed regional adiposity using imaging technics and excluded studies that were review articles, abstract only or letters to editor. Studies on children and adolescents, animal studies, and studies of patients with gastrointestinal diseases were excluded. PubMed, PsychInfo and web of science were used as electronic databases for literature search until November 2022.

Results

Based on the currently available literature, the findings suggest that different regional fat depots are likely associated with increased risk of cognitive impairment, brain changes and dementia, especially AD. However, different regional fat depots can have different cognitive outcomes and affect the brain differently. Visceral adipose tissue (VAT) was the most studied regional fat, along with liver fat through non-alcoholic fatty liver disease (NAFLD). Pancreatic fat was the least studied regional fat.

Conclusion

Regional adiposity, which is modifiable, may explain discrepancies in associations of global adiposity, brain, and cognition. Specific regional fat depots lead to abnormal secretion of adipose factors which in turn may penetrate the blood brain barrier leading to brain damage and to cognitive decline.

Simple anthropometric measures to predict visceral adipose tissue area in middle-aged Indonesian men

The diagnosing of central obesity requires ethnic-specific cut-offs of waist circumference (WC) and body mass index (BMI). This study aims to develop formulas to predict visceral adipose tissue (VAT) area based on WC and BMI to determine the cut-off points of central obesity in Indonesia. We conducted a cross-sectional study among 32 middle-aged Indonesian men. VAT area was measured using an abdominal CT scan, whereas WC and BMI were assessed through anthropometric measurements. Linear regression analysis was performed to define the formulas to predict VAT area using WC and BMI. Next, the optimal cut-off values of WC and BMI were determined using ROC curve analysis. Strong positive correlations were found between WC and VAT as well as BMI and VAT (r = 0.78; r = 0.67, p <0.001). The formula to predict VAT area from WC was -182.65 + (3.35 × WC), whereas the formula to predict VAT area from BMI was -57.22 + (6.95 × BMI). These formulas predicted WC of 88.5 cm and BMI of 23.9 kg/m2 as the optimal cut-off values for central obesity in middle-aged Indonesian men.

Mechanical Behavior of Subcutaneous and Visceral Abdominal Adipose Tissue in Patients with Obesity

The mechanical characterization of adipose tissues is important for various medical purposes, including plastic surgery and biomechanical applications, such as computational human body models for the simulation of surgical procedures or injury prediction, for example, in the evaluation of vehicle crashworthiness. In this context, the measurement of human subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) mechanical properties in relation to subject characteristics may be really relevant. The aim of this work was to properly characterize the mechanical response of adipose tissues in patients with obesity. Then, the data were exploited to develop a reliable finite element model of the adipose tissues characterized by a constitutive material model that accounted for nonlinear elasticity and time dependence. Mechanical tests have been performed on both SAT and VAT specimens, which have been harvested from patients with severe obesity during standard laparoscopic sleeve gastrectomy intervention. The experimental campaign included indentation tests, which permitted us to obtain the initial/final indentation stiffnesses for each specimen. Statistical results revealed a higher statistical stiffness in SAT than in VAT, with an initial/final indentation stiffness of 1.65 (SD ± 0.29) N/30.30 (SD ± 20) N compared to 1.29 (SD ± 0.30) N/21.00 (SD ± 16) N. Moreover, the results showed that gender, BMI, and age did not significantly affect the stiffness. The experimental results were used in the identification of the constitutive parameters to be inserted in the constitutive material model. Such constitutive characterization of VAT and SAT mechanics can be the starting point for the future development of more accurate computational models of the human adipose tissue and, in general, of the human body for the optimization of numerous medical and biomechanical procedures and applications.

Subcutaneous, but not visceral, adipose tissue as a marker for prognosis in gastric cancer patients with cachexia

BACKGROUND & AIMS

Adipose tissue loss is one of the features in patients with cancer cachexia. However, whether subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) contribute differently to the progress of cancer cachexia in gastric cancer patients with cachexia remains unclear. This study aim to investigate the effect of SAT and VAT in gastric cancer patients with cachexia.

METHODS

Gastric cancer patients who underwent surgery were divided into cancer cachexia group and non-cachexia group. A new deep learning system was developed to segment SAT and VAT from the computed tomography images at the third lumbar vertebra. Indexes of SAT (SATI) and VAT (VATI) were compared between cachexia and non-cachexia groups. The prognostic values of SATI and VATI for patients with gastric cancer cachexia were analyzed by Kaplan-Meier method and Cox regression.

RESULTS

A total of 1627 gastric cancer patients (411 cachexia and 1216 non-cachexia) were included in this study. A new V-Net-Based segmentation deep learning system was developed to quickly (0.02 s/image) and accurately segment SAT (dice scores = 0.96) and VAT (dice scores = 0.98). The SATI of gastric cancer patients with cachexia were significantly lower than non-cachexia patients (44.91 ± 0.90 vs. 50.92 ± 0.71 cm²/m², P < 0.001), whereas no significant difference was detected in VATI (35.98 ± 0.84 VS. 37.90 ± 0.45 cm²/m², P = 0.076). Cachexia patients with low SATI showed poor survival than those with high SATI (HR = 1.35; 95% CI = 1.06-1.74). In contrast, VATI did not show close correlation with survival in patients with cachexia (HR = 1.18; 95% CI = 0.92-1.51).

CONCLUSION

SAT and VAT showed different effects on gastric cancer patients with cachexia. More attention should be paid to the loss of SAT during the progress of cancer cachexia.

Toward a Routine Assessment of Visceral Adipose Tissue Volume from Computed Tomographic Data

OBJECTIVE

The study's aim was to determine to what extent total visceral adipose tissue (VAT) volume (V_VAT-T ) measured from segmented VAT areas (A_VAT ) on all axial computed tomography (CT) sections (thickness of 5 mm) between the diaphragm and pelvic floor can be predicted by the A_VAT of commonly selected landmark sections in patients with overweight or obesity.

METHODS

A total of 113 patients (31 females, 82 males) with images of full abdominopelvic coverage and proper image quality were included (BMI = 25.0-64.1 kg/m² , 29.5 ± 4.9 kg/m² ). Linear regression between A_VAT and V_VAT-T (reference) was used to determine approximate equations for VAT volume for all parameters (single sex, different anatomical landmarks or lumbar intervertebral disc spaces, one or five axial sections). Agreement was evaluated by the multivariate coefficient of determination and by the SD of the percentage difference (s_d% ) between the estimated VAT volume on one or five sections and V_VAT-T .

RESULTS

The V_VAT-T was 0.9 to 8.4 (3.8 ± 2.2) L for females and 2.7 to 11.7 (5.6 ± 2.1) L for males. Best agreement was found at L2-3 (s_d%  = 14.3%-15.5%) for females and at L1-2 or L2-3 (11.7%-12.4%) for males. Agreement at the umbilicus or the femoral heads was poor (20.2%-57.9%). Segmentation of one or five sections was substantially faster (11/70 seconds) than whole-abdomen processing (15 minutes).

CONCLUSIONS

V_VAT-T can be rapidly estimated by VAT segmentation of axial CT sections at sex-specific lumbar intervertebral disc spaces.

Cardiovascular Benefits and Lipid Profile Changes 5 Years After Bariatric Surgery: A Comparative Study Between Sleeve Gastrectomy and Roux-en-Y Gastric Bypass

INTRODUCTION

Visceral adipose tissue has been linked with cardiovascular events. Visceral adiposity index (VAI) is a routinely applicable tool for evaluation of visceral adipose dysfunction and linked to 10 year-cardiovascular risk. No previous studies have evaluated the changes over time of the VAI in patients who underwent different types of bariatric surgery.

MATERIALS AND METHODS

We reviewed data of 42 patients who underwent laparoscopic sleeve gastrectomy (LSG) and 61 patients who underwent laparoscopic Roux-en-Y gastric bypass (LRYGB). VAI, lipid profile, and several anthropometric variables were measured before and after 5 years following surgery.

RESULTS

During the studied time period, the BMI was similar between LSG and LRYGB patients (34.1 vs 31.6; p = 0.191), but the percentage of total weight loss (%TWL) for LRYGB was significantly higher than LSG (31.3% vs 23.0%; p < 0.001). LRYGB patients had a significant improvement of all lipid parameters evaluated over time, while LSG patients experienced only a reduction in triglycerides (TG) levels and an increase in HDL cholesterol (HDL-C). VAI values were similar in the two groups at baseline as well at the last follow-up point (5-year VAI, LSG: 0.93, RYGB: 0.93; p = 0.951). At multivariate regression analysis, 5-year-%TWL was the only independent predictor of a greater amount of VAI reduction over time.

CONCLUSION

Bariatric surgery, independent of the type of surgical procedure, decreases the cardiovascular disease (CVD) risks due to weight loss and improvement of lipid parameters. VAI could be a useful tool to better identify eligible patients for bariatric surgery and to determine the success of surgery.

Muscle Characteristics Obtained Using Computed Tomography as Prognosticators in Patients with Castration-Resistant Prostate Cancer

Limited studies have investigated the correlation between body composition and prostate cancer outcomes. We analyzed the effect of muscle mass and quality on castration-resistant prostate cancer (CRPC) outcomes. Skeletal muscle index (SMI) and skeletal muscle attenuation (SMA) were measured for 411 patients at the L3 vertebral level using computed tomography at CRPC diagnosis and were dived to low and high groups at the value of median. Analysis of the skeletal phenotypes and age (<70 and >70 years) was performed to evaluate the effect of SMI and SMA. The median survival rates for patients with low and high SMI were 19 and 24 months (p = 0.015), and those with low and high SMAs were 15 and 26 months (p < 0.001), respectively. In the subgroup analysis by age, SMA was a significant prognosticator in both groups, while SMI was a significant prognosticator only in patients aged >70 years. Patients with low SMA + low SMI had the worst prognosis. Muscle characteristics seems to be a prognosticator in survival of CRPC patients and may be considered in treatment planning.

Trunk muscle quality and quantity predict the development of metabolic syndrome and the increase in the number of its components in individuals without metabolic syndrome

BACKGROUND AND AIMS

The metabolic syndrome has been reported by cross-sectional studies to have an association with skeletal muscle quality and quantity. Using a longitudinal study design, this study aimed to explicate the association between muscle characteristics assessed with computed tomography (CT) and the incidence and progression of metabolic syndrome.

METHODS AND RESULTS

In this retrospective study on a cohort of employees undergoing annual physical examinations, we evaluated data from 554 participants without metabolic syndrome. The cross-sectional skeletal muscle area was determined based on CT data at the level of the third lumbar vertebra, and the skeletal muscle density (SMD) and skeletal muscle index (SMI) were measured. The participants were divided into four study groups according to the sex-specific median values for SMI and SMD. We followed the participants for a mean period of 3.1 years. In the sex- and age-adjusted model, SMI and SMD had an interaction effect on the longitudinal change in number of metabolic syndrome components (β = -0.074, p = 0.0727). Multiple regression analyses revealed that both low SMI and SMD was significantly associated with the change (β = 0.131, p = 0.0281), whereas the low SMI and high SMD, and high SMI and low SMD were not. Both low SMI and SMD (hazard ratio (HR), 2.42; 95% confidence interval, 1.28-4.78) showed an increased adjusted HR for incident metabolic syndrome.

CONCLUSION

The participants with both low quality and quantity of skeletal muscles were associated with the incidence and progression of metabolic syndrome, whereas those with only low quantity or quality of skeletal muscles were not.

ABCNet: A new efficient 3D dense-structure network for segmentation and analysis of body tissue composition on body-torso-wide CT images

PURPOSE

Quantification of body tissue composition is important for research and clinical purposes, given the association between the presence and severity of several disease conditions, such as the incidence of cardiovascular and metabolic disorders, survival after chemotherapy, etc., with the quantity and quality of body tissue composition. In this work, we aim to automatically segment four key body tissues of interest, namely subcutaneous adipose tissue, visceral adipose tissue, skeletal muscle, and skeletal structures from body-torso-wide low-dose computed tomography (CT) images.

METHOD

Based on the idea of residual Encoder-Decoder architecture, a novel neural network design named ABCNet is proposed. The proposed system makes full use of multiscale features from four resolution levels to improve the segmentation accuracy. This network is built on a uniform convolutional unit and its derived units, which makes the ABCNet easy to implement. Several parameter compression methods, including Bottleneck, linear increasing feature maps in Dense Blocks, and memory-efficient techniques, are employed to lighten the network while making it deeper. The strategy of dynamic soft Dice loss is introduced to optimize the network in coarse-to-fine tuning. The proposed segmentation algorithm is accurate, robust, and very efficient in terms of both time and memory.

RESULTS

A dataset composed of 38 low-dose unenhanced CT images, with 25 male and 13 female subjects in the age range 31-83 yr and ranging from normal to overweight to obese, is utilized to evaluate ABCNet. We compare four state-of-the-art methods including DeepMedic, 3D U-Net, V-Net, Dense V-Net, against ABCNet on this dataset. We employ a shuffle-split fivefold cross-validation strategy: In each experimental group, 18, 5, and 15 CT images are randomly selected out of 38 CT image sets for training, validation, and testing, respectively. The commonly used evaluation metrics - precision, recall, and F1-score (or Dice) - are employed to measure the segmentation quality. The results show that ABCNet achieves superior performance in accuracy of segmenting body tissues from body-torso-wide low-dose CT images compared to other state-of-the-art methods, reaching 92-98% in common accuracy metrics such as F1-score. ABCNet is also time-efficient and memory-efficient. It costs about 18 h to train and an average of 12 sec to segment four tissue components from a body-torso-wide CT image, on an ordinary desktop with a single ordinary GPU.

CONCLUSIONS

Motivated by applications in body tissue composition quantification on large population groups, our goal in this paper was to create an efficient and accurate body tissue segmentation method for use on body-torso-wide CT images. The proposed ABCNet achieves peak performance in both accuracy and efficiency that seems hard to improve any more. The experiments performed demonstrate that ABCNet can be run on an ordinary desktop with a single ordinary GPU, with practical times for both training and testing, and achieves superior accuracy compared to other state-of-the-art segmentation methods for the task of body tissue composition analysis from low-dose CT images.

Low-attenuation muscle is a predictor of diabetes mellitus: A population-based cohort study

OBJECTIVES

Diabetes mellitus is a major global public health issue. Cross-sectional studies have demonstrated that skeletal muscle quality and quantity by computed tomography (CT) is related to glucose metabolism. However, to our knowledge, no longitudinal study has yet to elucidate the association between muscle quality determined by CT and glucose metabolism. Thus, the aim of this study was to evaluate the association between muscle quality and glucose metabolism.

METHODS

In this retrospective study, we evaluated data from 621 middle-aged Japanese individuals without diabetes mellitus from a cohort of employees undergoing annual physical examinations. The cross-sectional skeletal muscle area was determined based on CT data at the level of the third lumbar vertebrae, and the skeletal muscle index (SMI) and density (SMD) were calculated. Low-attenuation muscle (LAM) and normal-attenuation muscle (NAM) were identified and quantified using thresholds of -29 to +29 HU and +30 to +150 HU, respectively.

RESULTS

We followed the individuals for a mean period of 3 y, and 27 of them developed diabetes mellitus during this period. Multiple Cox regression analyses revealed that, even after adjustment for visceral fat area index, the LAM index (hazard ratio [HR], 3.07; 95% confidence interval [CI], 1.00-7.52) showed an increased adjusted HR for incident diabetes mellitus. When total SMI and SMD were used in the same models, only total SMD (HR, 0.90; 95% CI, 0.81-0.99) showed a decreased adjusted HR for incident diabetes mellitus.

CONCLUSIONS

Both LAM index and total SMD were associated with a higher risk for incident diabetes mellitus, whereas NAM index and total SMI were not.

Relationship between nonalcoholic fatty liver disease and muscle quality as well as quantity evaluated by computed tomography

BACKGROUND & AIMS

Sarcopenia is reported to be associated with nonalcoholic fatty liver disease (NAFLD). Evaluation of skeletal muscle attenuation and area by computed tomography (CT) may represent a promising approach for evaluation of the risk of NAFLD. We examined the association between skeletal muscle characteristics and NAFLD and investigated the combined effect of these parameters on the prevalence of NAFLD.

METHODS

In this cross-sectional study, we analysed data from 632 middle-aged Japanese subjects without daily alcohol intake (353 men and 279 women) from a cohort of employees undergoing annual health examinations. The cross-sectional skeletal muscle area was evaluated on the basis of CT data at the level of the third lumbar vertebrae, and the skeletal muscle index (SMI) and density (SMD) were calculated. The subjects were divided into four study groups according to their SMI and SMD relative to median values.

RESULTS

One hundred forty men and forty-three women had NAFLD. Total SMI (odds ratio [OR] per 1.0 cm² /kg/m² increase 0.43, 95% confidence interval [CI] 0.29-0.64 in men and OR 0.21, 95% CI 0.10-0.42 in women) and total SMD (OR, per 1.0 Hounsfield Unit increase 0.88, 95% CI 0.83-0.93 in men and 0.88, 0.82-0.95 in women) were significantly associated with the prevalence of NAFLD after adjusting for covariates. The subgroup with simultaneous presence of low SMI and low SMD was associated with a significantly higher prevalence of NAFLD compared with other groups.

CONCLUSIONS

Both SMI and SMD are independently associated with the prevalence of NAFLD.

Adipose Tissue Distribution and Cardiovascular Disease Risk Among Breast Cancer Survivors

PURPOSE

Cardiovascular disease (CVD) is a major source of morbidity and mortality among breast cancer survivors. Although body mass index (BMI) is associated with CVD risk, adipose tissue distribution may better identify patients with a high risk of CVD after breast cancer.

METHODS

Among 2,943 patients with nonmetastatic breast cancer without prior CVD, we used International Classification of Diseases (9th and 10th revisions) codes to identify incidence of nonfatal stroke, myocardial infarction, heart failure, or CVD death. From clinically acquired computed tomography scans obtained near diagnosis, we measured visceral adiposity (centimeters squared), subcutaneous adiposity (centimeters squared), and intramuscular adiposity (fatty infiltration into muscle [Hounsfield Units, scored inversely]). We estimated hazard ratios (HRs) and 95% CIs per SD increase in adiposity accounting for competing risks and adjusting for demographics, smoking, cancer treatment, and pre-existing CVD risk factors.

RESULTS

Mean (SD) age was 56 (12) years. Over a median follow-up of 6 years, 328 CVD events occurred. Each SD increase in visceral or intramuscular adiposity was associated with an increase in CVD risk (HR, 1.15 [95% CI, 1.03 to 1.29] and HR, 1.21 [95% CI, 1.06 to 1.37]), respectively). Excess visceral and intramuscular adiposity occurred across all BMI categories. Among normal-weight patients, each SD greater visceral adiposity increased CVD risk by 70% (HR, 1.70 [95% CI, 1.10 to 2.62]).

CONCLUSION

Visceral and intramuscular adiposity were associated with increased CVD incidence after breast cancer diagnosis, independent of pre-existing CVD risk factors and cancer treatments. The increased CVD incidence among normal-weight patients with greater visceral adiposity would go undetected with BMI alone. Measures of adipose tissue distribution may help identify high-risk patients and tailor CVD prevention strategies.

Relationship between metabolic syndrome and trunk muscle quality as well as quantity evaluated by computed tomography

BACKGROUND & AIMS

Metabolic syndrome is a cluster of metabolic abnormalities. Skeletal muscle attenuation and area evaluated by computer tomography (CT) may provide valuable information about patients with metabolic disorder. Therefore, we examined the association between skeletal muscle characteristics and metabolic syndrome, and investigated the combined effect of quantitative and qualitative muscle parameters.

METHODS

In this cross-sectional study, we analyzed 808 middle-aged Japanese subjects. The cross-sectional area of skeletal muscle was evaluated based on CT at the third lumbar vertebrae. The subjects were divided into four groups according to the median levels of skeletal muscle index (SMI) and density (SMD).

RESULTS

Eighty-five men and twenty-two women had metabolic syndrome. In the adjusted model, SMI and SMD had an interaction effect on the number of metabolic syndrome components (p = 0.0398 in men and p = 0.0306 in women). When SMI and SMD were considered as independent variables for multiple regression analysis, SMI (β = -0.155, p = 0.0019 in men and β = -0.295, p < 0.0001 in women) and SMD (β = -0.355, p < 0.0001 in men and β = -0.324, p < 0.0001 in women) were both independently associated with the number of metabolic syndrome components. The subgroup with presence of low SMI and low SMD levels had a significantly higher prevalence of metabolic syndrome than that observed in other groups.

CONCLUSIONS

Therefore, we suggest that not only muscle quantity but also quality is independently associated with metabolic syndrome.

Semiautomated Measure of Abdominal Adiposity Using Computed Tomography Scan Analysis

BACKGROUND

The obesity epidemic has prompted the need to better understand the impact of adipose tissue on human pathophysiology. However, accurate, efficient, and replicable models of quantifying adiposity have yet to be developed and clinically implemented. We propose a novel semiautomated radiologic method of measuring the visceral fat area (VFA) using computed tomography scan analysis.

MATERIALS AND METHODS

We obtained a cohort of 100 patients with rectal adenocarcinoma, with a median age of 60.9 y (age range: 35-87 y) and an average body mass index of 28.8 kg/m2 ± 6.56 kg/m2. The semiautomated quantification method of adiposity was developed using a commercial imaging suite. The method was compared to two manual delineations performed using two different picture archiving communication systems. We quantified VFA, subcutaneous fat area (SFA), total fat area (TFA), and visceral-to-subcutaneous fat ratio (V/S ratio) on computed tomography axial slices that were at the L4-L5 intervertebral level.

RESULTS

The semiautomated method was comparable to manual measurements for TFA, VFA, and SFA with intraclass correlation (ICC) of 0.99, 0.97, and 0.96, respectively. However, the ICC for the V/S ratio was only 0.44, which led to the identification of technical outliers that were identified using robust regression. After removal of these outliers, the ICC improved to 0.99 for TFA, VFA, and SFA and 0.97 for the V/S ratio. Measurements from the manual methodology highly correlated between the two picture archiving communication system platforms, with ICC of 0.98 for TFA, 0.98 for VFA, 0.96 for SFA, and 0.95 for the V/S ratio.

CONCLUSIONS

This semiautomated method is able to generate precise and reproducible results. In the future, this method may be applied on a larger scale to facilitate risk stratification of patients using measures of abdominal adiposity.

Automated and accurate quantification of subcutaneous and visceral adipose tissue from magnetic resonance imaging based on machine learning

Accurate measuring of subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) is vital for the research of many diseases. The localization and quantification of SAT and VAT by computed tomography (CT) expose patients to harmful ionizing radiation. Magnetic resonance imaging (MRI) is a safe and painless test. The aim of this paper is to explore a practical method for the segmentation of SAT and VAT based on the iterative decomposition of water and fat with echo asymmetry and least square estimation‑iron quantification (IDEAL-IQ) technology and machine learning. The approach involves two main steps. First, a deep network is designed to segment the inner and outer boundaries of SAT in fat images and the peritoneal cavity contour in water images. Second, after mapping the peritoneal cavity contour onto the fat images, the assumption-free K-means++ with a Markov chain Monte Carlo (AFK-MC²) clustering method is used to obtain the VAT content. An MRI data set from 75 subjects is utilized to construct and evaluate the new strategy. The Dice coefficients for the SAT and VAT content obtained from the proposed method and the manual measurements performed by experts are 0.96 and 0.97, respectively. The experimental results indicate that the proposed method and the manual measurements exhibit high reliability.

Quantification of body-torso-wide tissue composition on low-dose CT images via automatic anatomy recognition

PURPOSE

Quantification of body composition plays an important role in many clinical and research applications. Radiologic imaging techniques such as Dual-energy X-ray absorptiometry (DXA), magnetic resonance imaging (MRI), and computed tomography (CT) imaging make accurate quantification of the body composition possible. However, most current imaging-based methods need human interaction to quantify multiple tissues. When dealing with whole-body images of many subjects, interactive methods become impractical. This paper presents an automated, efficient, accurate, and practical body composition quantification method for low-dose CT images.

METHOD

Our method, named automatic anatomy recognition body composition analysis (AAR-BCA), aims to quantify four tissue components in body torso (BT) - subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), bone tissue, and muscle tissue - from CT images of given whole-body positron emission tomography/computed tomography (PET/CT) acquisitions. AAR-BCA consists of three key steps - modeling BT with its ensemble of key objects from a population of patient images, recognition or localization of these objects in a given patient image I, and delineation and quantification of the four tissue components in I guided by the recognized objects. In the first step, from a given set of patient images and the associated delineated objects, a fuzzy anatomy model of the key object ensemble, including anatomic organs, tissue regions, and tissue interfaces, is built where the objects are organized in a hierarchical order. The second step involves recognizing, or finding roughly the location of, each object in any given whole-body image I of a patient following the object hierarchy and guided by the built model. The third step makes use of this fuzzy localization information of the objects and the intensity distributions of the four tissue components, already learned and encoded in the model, to optimally delineate in a fuzzy manner and quantify these components. All parameters in our method are determined from training datasets.

RESULTS

Thirty-eight low-dose CT images from different subjects are tested in a fivefold cross-validation strategy for evaluating AAR-BCA with a 23-15 train-test dataset division. For BT, over all objects, AAR-BCA achieves a false-positive volume fraction (FPVF) of 3.7% and false-negative volume fraction (FNVF) of 3.8%. Notably, SAT achieves both a FPVF and FNVF under 3%. For bone tissue, it achieves a FPVF and a FNVF both under 3.5%. For VAT tissue, the FNVF of 4.8% is higher than for other objects and so also for muscle (4.7%). The level of accuracy for the four tissue components in individual body subregions mostly remains at the same level as for BT. The processing time required per patient image is under a minute.

CONCLUSIONS

Motivated by applications in cancer and systemic diseases, our goal in this paper was to seek a practical method for body composition quantification which is automated, accurate, and efficient, and works on BT in low-dose CT. The proposed AAR-BCA method toward this goal can quantify four tissue components including SAT, VAT, bone tissue, and muscle tissue in the body torso with under 5% overall error. All needed parameters can be automatically estimated from the training datasets.

Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis

BACKGROUND

Advances in magnetic resonance imaging and the introduction of clinical decision support systems has underlined the need for an analysis tool to extract and analyze relevant information from magnetic resonance imaging data to aid decision making, prevent errors, and enhance health care.

OBJECTIVE

The aim of this study was to design and develop a modular medical image region of interest analysis tool and repository (MIROR) for automatic processing, classification, evaluation, and representation of advanced magnetic resonance imaging data.

METHODS

The clinical decision support system was developed and evaluated for diffusion-weighted imaging of body tumors in children (cohort of 48 children, with 37 malignant and 11 benign tumors). Mevislab software and Python have been used for the development of MIROR. Regions of interests were drawn around benign and malignant body tumors on different diffusion parametric maps, and extracted information was used to discriminate the malignant tumors from benign tumors.

RESULTS

Using MIROR, the various histogram parameters derived for each tumor case when compared with the information in the repository provided additional information for tumor characterization and facilitated the discrimination between benign and malignant tumors. Clinical decision support system cross-validation showed high sensitivity and specificity in discriminating between these tumor groups using histogram parameters.

CONCLUSIONS

MIROR, as a diagnostic tool and repository, allowed the interpretation and analysis of magnetic resonance imaging images to be more accessible and comprehensive for clinicians. It aims to increase clinicians' skillset by introducing newer techniques and up-to-date findings to their repertoire and make information from previous cases available to aid decision making. The modular-based format of the tool allows integration of analyses that are not readily available clinically and streamlines the future developments.

Fully automated segmentation and quantification of visceral and subcutaneous fat at abdominal CT: application to a longitudinal adult screening cohort

OBJECTIVE

To investigate a fully automated CT-based adiposity tool, applying it to a longitudinal adult screening cohort.

METHODS

A validated automated adipose tissue segmentation algorithm was applied to non-contrast abdominal CT scans in 8852 consecutive asymptomatic adults (mean age, 57.1 years; 3926 M/4926 F) undergoing colonography screening. The tool was also applied to follow-up CT scans in a subset of 1584 individuals undergoing longitudinal surveillance (mean interval, 5.6 years). Visceral and subcutaneous adipose tissue (VAT and SAT) volumes were segmented at levels T12-L5. Primary adipose results are reported herein for the L1 level as mean cross-sectional area. CT-based adipose measurements at initial CT and change over time were analyzed.

RESULTS

Mean VAT values were significantly higher in males (205.8 ± 107.5 vs 108.1 ± 82.4 cm²; p < 0.001), whereas mean SAT values were significantly higher in females (171.3 ± 111.3 vs 124.3 ± 79.7 cm²; p < 0.001). The VAT/SAT ratio at L1 was three times higher in males (1.8 ± 0.7 vs 0.6 ± 0.4; p < 0.001). At longitudinal follow-up CT, mean VAT/SAT ratio change was positive in males, but negative in females. Among the 502 individuals where the VAT/SAT ratio increased at follow-up CT, 333 (66.3%) were males. Half of patients (49.6%; 786/1585) showed an interval increase in both VAT and SAT at follow-up CT.

CONCLUSION

This robust, fully automated CT adiposity tool allows for both individualized and population-based assessment of visceral and subcutaneous abdominal fat. Such data could be automatically derived at abdominal CT regardless of the study indication, potentially allowing for opportunistic cardiovascular risk stratification. Advances in knowledge: The CT-based adiposity tool described herein allows for fully automated measurement of visceral and subcutaneous abdominal fat, which can be used for assessing cardiovascular risk, metabolic syndrome, and for change over time.

Low vitamin D status and obesity: Role of nutritionist

Low vitamin D status and obesity have concomitantly reached epidemic levels worldwide. Up to now the direction of the association between low vitamin D status and obesity, the exact mechanisms responsible for this association and the clinical usefulness to increase vitamin D status for reducing adiposity still warrant further evaluation. The aim of the present review was to examine the current evidence linking low vitamin D status and obesity in relation to the role of the nutritionist. On the one side, considering obesity as a causal factor, low sun exposure in obese individuals due to their sedentary lifestyle and less outdoor activity, vitamin D sequestration in adipose tissue, and volumetric dilution of ingested or cutaneously synthesized vitamin D₃ in the large fat mass of obese patients, might represent some of the factors playing a major role in the pathogenesis of the low vitamin D status. On the other side, the expression of both vitamin D₃ receptors and enzymes responsible for vitamin D₃ metabolism in adipocytes depicted a role for the low vitamin D status per se in the development of obesity by modulating adipocyte differentiation and lipid metabolism. Nutritionists need to accurately address the aspects influencing the low vitamin D status in obesity and the vitamin D supplementation in obese individuals.

Automatic Segmentation and Quantification of White and Brown Adipose Tissues from PET/CT Scans

In this paper, we investigate the automatic detection of white and brown adipose tissues using Positron Emission Tomography/Computed Tomography (PET/CT) scans, and develop methods for the quantification of these tissues at the whole-body and body-region levels. We propose a patient-specific automatic adiposity analysis system with two modules. In the first module, we detect white adipose tissue (WAT) and its two sub-types from CT scans: Visceral Adipose Tissue (VAT) and Subcutaneous Adipose Tissue (SAT). This process relies conventionally on manual or semi-automated segmentation, leading to inefficient solutions. Our novel framework addresses this challenge by proposing an unsupervised learning method to separate VAT from SAT in the abdominal region for the clinical quantification of central obesity. This step is followed by a context driven label fusion algorithm through sparse 3D Conditional Random Fields (CRF) for volumetric adiposity analysis. In the second module, we automatically detect, segment, and quantify brown adipose tissue (BAT) using PET scans because unlike WAT, BAT is metabolically active. After identifying BAT regions using PET, we perform a co-segmentation procedure utilizing asymmetric complementary information from PET and CT. Finally, we present a new probabilistic distance metric for differentiating BAT from non-BAT regions. Both modules are integrated via an automatic body-region detection unit based on one-shot learning. Experimental evaluations conducted on 151 PET/CT scans achieve state-of-the-art performances in both central obesity as well as brown adiposity quantification.