A systematic review of automated segmentation of 3D computed-tomography scans for volumetric body composition analysis

Ethnic differences in CT derived abdominal body composition measures: a comparative retrospect pilot study between European and Inuit study population

Understanding ethnic variations in body composition is crucial for assessing health risks. Universal models may not suit all ethnicities, and there is limited data on the Inuit population. This study aimed to compare body composition between Inuit and European adults using computed tomography (CT) scans and to investigate the influence of demographics on these measurements. A retrospective analysis was conducted on 50 adults (29 Inuit and 21 European) who underwent standard trauma CT scans. Measurements focused on skeletal muscle index (SMI), various fat indices, and densities at the third lumbar vertebra level, analyzed using the Wilcoxon-Mann-Whitney test and multiple linear regression. Inuit women showed larger fat tissue indices and lower muscle and fat densities than European women. Differences in men were less pronouncehd, with only Intramuscular fat density being lower among Inuit men. Regression indicated that SMI was higher among men, and skeletal muscle density decreased with Inuit ethnicity and age, while visceral fat index was positively associated with age. This study suggests ethnic differences in body composition measures particularly among women, and indicates the need for Inuit-specific body composition models. It higlights the importance of further research into Inuit-specific body composition measurements for better health risk assessment.

Prevalence and survival implications of malnutrition and sarcopenia in metastatic breast cancer: A longitudinal analysis

BACKGROUND

Malnutrition and sarcopenia are challenges for patients with metastatic breast cancer and have been proposed as independent prognostic factors. Very few studies have addressed the temporal evolution of these parameters and, notably, the separate and combined analysis of sarcopenia and malnutrition. This study aimed to i) determine the prevalence of malnutrition and sarcopenia, individually and combined, and their evolution over time, ii) identify risk factors for each condition, and iii) explore their impact on overall survival (OS).

METHODS

This retrospective study was conducted on 111 patients treated for at least a third-line metastatic breast cancer at the Institut Curie between January 1st and March 31st, 2018. Solitary malnutrition was defined from weight loss and body mass index values while solitary sarcopenia was defined solely based on low muscle mass. We analyzed solitary malnutrition, solitary sarcopenia, and then malnutrition with or without sarcopenia, at three key stages (T1: diagnosis of metastasis, T2: initiation of third-line treatment, and T3: 3-month re-evaluation). Univariate and multivariate logistic regression analyses were conducted to investigate the risk factors. We performed Cox proportional hazards analyses for each variable.

RESULTS

At T1, the prevalence of solitary malnutrition, solitary sarcopenia and malnutrition with or without sarcopenia was 18.6%, 36.1% and 48.9% respectively, increasing to 27.7%, 45.5% and 56.6% at T2. At T2, in multivariate logistic regression analyses, patients aged over 60 years were at an elevated risk of experiencing solitary malnutrition as well as malnutrition with or without sarcopenia, but not solitary sarcopenia. In multivariate analyses, solitary malnutrition was significantly associated with poorer OS (HR 2.2 [95% CI 1.1-4.1], p = 0.02), while solitary sarcopenia and malnutrition with or without sarcopenia showed no association.

CONCLUSION

Solitary malnutrition and sarcopenia were highly prevalent in patients with metastatic breast cancer, affecting around a quarter and half of patients respectively at third-line treatment initiation. Notably, solitary malnutrition emerged as a prognostic factor for overall survival, whereas no significant association was observed for solitary sarcopenia or malnutrition with or without sarcopenia. This highlights the critical need for early identification of patients at risk of malnutrition and the importance of timely intervention.

Using a new artificial intelligence-aided method to assess body composition CT segmentation in colorectal cancer patients

INTRODUCTION

This study aimed to evaluate the accuracy of our own artificial intelligence (AI)-generated model to assess automated segmentation and quantification of body composition-derived computed tomography (CT) slices from the lumber (L3) region in colorectal cancer (CRC) patients.

METHODS

A total of 541 axial CT slices at the L3 vertebra were retrospectively collected from 319 patients with CRC diagnosed during 2012-2019 at a single Australian tertiary institution, Western Health in Melbourne. A two-dimensional U-Net convolutional network was trained on 338 slices to segment muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Manual reading of these same slices of muscle, VAT and SAT was created to serve as ground truth data. The Dice similarity coefficient was used to assess the U-Net-based segmentation performance on both a validation dataset (68 slices) and a test dataset (203 slices). The measurement of cross-sectional area and Hounsfield unit (HU) density of muscle, VAT and SAT were compared between two methods.

RESULTS

The segmentation for muscle, VAT and SAT demonstrated excellent performance for both the validation (Dice similarity coefficients >0.98, respectively) and test (Dice similarity coefficients >0.97, respectively) datasets. There was a strong positive correlation between manual and AI segmentation measurements of body composition for both datasets (Spearman's correlation coefficients: 0.944-0.999, P < 0.001).

CONCLUSIONS

Compared to the gold standard, this fully automated segmentation system exhibited a high accuracy for assessing segmentation and quantification of abdominal muscle and adipose tissues of CT slices at the L3 in CRC patients.

Abdominal adipose tissue and type 2 diabetic kidney disease: adipose radiology assessment, impact, and mechanisms

Diabetic kidney disease (DKD) is a significant healthcare burden worldwide that substantially increases the risk of kidney failure and cardiovascular events. To reduce the prevalence of DKD, extensive research is being conducted to determine the risk factors and consequently implement early interventions. Patients with type 2 diabetes mellitus (T2DM) are more likely to be obese. Abdominal adiposity is associated with a greater risk of kidney damage than general obesity. Abdominal adipose tissue can be divided into different fat depots according to the location and function, including visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), perirenal adipose tissue (PAT), and renal sinus adipose tissue (RSAT), which can be accurately measured by radiology techniques, such as computed tomography (CT) and magnetic resonance imaging (MRI). Abdominal fat depots may affect the development of DKD through different mechanisms, and radiologic abdominal adipose characteristics may serve as imaging indicators of DKD risk. This review will first describe the CT/MRI-based assessment of abdominal adipose depots and subsequently describe the current studies on abdominal adipose tissue and DKD development, as well as the underlying mechanisms in patients of T2DM with DKD.