Development and validation of a rapid and robust method to determine visceral adipose tissue volume using computed tomography images

Defining and Addressing Research Priorities in Cancer Cachexia through Transdisciplinary Collaboration

For many patients, the cancer continuum includes a syndrome known as cancer-associated cachexia (CAC), which encompasses the unintended loss of body weight and muscle mass, and is often associated with fat loss, decreased appetite, lower tolerance and poorer response to treatment, poor quality of life, and reduced survival. Unfortunately, there are no effective therapeutic interventions to completely reverse cancer cachexia and no FDA-approved pharmacologic agents; hence, new approaches are urgently needed. In May of 2022, researchers and clinicians from Moffitt Cancer Center held an inaugural retreat on CAC that aimed to review the state of the science, identify knowledge gaps and research priorities, and foster transdisciplinary collaborative research projects. This review summarizes research priorities that emerged from the retreat, examples of ongoing collaborations, and opportunities to move science forward. The highest priorities identified include the need to (1) evaluate patient-reported outcome (PRO) measures obtained in clinical practice and assess their use in improving CAC-related outcomes; (2) identify biomarkers (imaging, molecular, and/or behavioral) and novel analytic approaches to accurately predict the early onset of CAC and its progression; and (3) develop and test interventions (pharmacologic, nutritional, exercise-based, and through mathematical modeling) to prevent CAC progression and improve associated symptoms and outcomes.

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

PURPOSE

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSION

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.

Automated segmentation of whole-body CT images for body composition analysis in pediatric patients using a deep neural network

OBJECTIVES

To develop an automatic segmentation algorithm using a deep neural network with transfer learning applicable to whole-body PET-CT images in children.

METHODS

For model development, we utilized transfer learning with a pre-trained model based on adult patients. We used CT images of 31 pediatric patients under 19 years of age (mean age, 9.6 years) who underwent PET-CT from institution #1 for transfer learning. Two radiologists manually labeled the skin, bone, muscle, abdominal visceral fat, subcutaneous fat, internal organs, and central nervous system in each CT slice and used these as references. For external validation, we collected 14 pediatric PET/CT scans from institution #2 (mean age, 9.1 years). The Dice similarity coefficients (DSCs), sensitivities, and precision were compared between the algorithms before and after transfer learning. In addition, we evaluated segmentation performance according to sex, age (≤ 8 vs. > 8 years), and body mass index (BMI, ≤ 20 vs. > 20 kg/m²).

RESULTS

The algorithm after transfer learning showed better performance than the algorithm before transfer learning for all body compositions (p < 0.001). The average DSC, sensitivity, and precision of each algorithm before and after transfer learning were 98.23% and 99.28%, 98.16% and 99.28%, and 98.29% and 99.28%, respectively. The segmentation performance of the algorithm was generally not affected by age, sex, or BMI, except for precision in the body muscle compartment.

CONCLUSION

The developed model with transfer learning enabled accurate and fully automated segmentation of multiple tissues on whole-body CT scans in children.

KEY POINTS

• We utilized transfer learning with a pre-trained segmentation algorithm for adult to develop an algorithm for automated segmentation of pediatric whole-body CT. • This algorithm showed excellent performance and was not affected by sex, age, or body mass index, except for precision in body muscle.

Expected and paradoxical effects of obesity on cancer treatment response

Obesity, whose prevalence is pandemic and continuing to increase, is a major preventable and modifiable risk factor for diabetes and cardiovascular diseases, as well as for cancer. Furthermore, epidemiological studies have shown that obesity is a negative independent prognostic factor for several oncological outcomes, including overall and cancer-specific survival, for several site-specific cancers as well as for all cancers combined. Yet, a recently growing body of evidence suggests that sometimes overweight and obesity may associate with better outcomes, and that immunotherapy may show improved response among obese patients compared with patients with a normal weight. The so-called 'obesity paradox' has been reported in several advanced cancer as well as in other diseases, albeit the mechanisms behind this unexpected relationship are still not clear. Aim of this review is to explore the expected as well as the paradoxical relationship between obesity and cancer prognosis, with a particular emphasis on the effects of cancer therapies in obese people.

Optimal cutoff values for visceral fat volume to predict metabolic syndrome in a Korean population

Previous studies have reported the association between visceral fat and metabolic syndrome (MS); however, just few studies have been conducted to evaluate the relationship between actual visceral fat volume (VFV) and MS. This study aimed to obtain 3 dimensional VFV and subcutaneous fat volume (SFV) using abdominal computed tomography (CT) and determine MS-predictive cutoff values.A total of 250 individuals, aged 27 to 80 years, who underwent health screening with abdominal CT between November 2019 and May 2020 were included. The subcutaneous (SFA) and visceral (VFA) fat areas were quantified using axial images obtained at the level of the lowest to the highest part of the umbilicus. The SFV and VFV were quantified from the highest level of the liver dome to the pelvic floor on axial CT images. The Aquarius iNtuition software program (TeraRecon, Foster City, CA) was used to calculate the SFA, VFA, SFV, and VFV. Subcutaneous fat mass and visceral fat mass (VFM) were measured using an adipose tissue density of 0.9 g/mL. We used the modified criteria of MS proposed by the Third National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults and waist circumference of ≥90 cm in men and ≥85 cm in women to define MS. Multivariate analysis of covariance was used to compare the fat areas, volumes, and mass according to the presence of MS and sex. Additionally, a receiver operating characteristic curve analysis was performed to determine the cutoff values for VFV, SFV, VFM, subcutaneous fat mass, VFA, and SFA associated with MS.Of the assessed variables, VFV and VFM had the highest area under the receiver operating characteristic curve value for predicting MS in both men and women: 0.811 (95% confidence interval, 0.743-0.868) for men and 0.826 (95% confidence interval, 0.727-0.900) for women. The MS-predictive cutoff values were 4852 cm3 and 4366.8 g for men and 3101 cm3 and 2790.9 g for women, respectively. Further, large, population-based studies are needed to validate these cutoff values.

The Prognostic Impact of Body Composition for Locally Advanced Breast Cancer Patients Who Received Neoadjuvant Chemotherapy

Simple Summary

We aimed to determine the prognostic role of body composition in patients with breast cancer who received neoadjuvant chemotherapy. Previous studies suggested that body composition is a better indicator of breast cancer treatment outcome than body mass index. A comprehensive body composition analysis found that a low ratio of total visceral adipose tissue to subcutaneous adipose tissue was associated with shorter overall survival. This finding will lead to further investigation of the role of body composition in outcomes for patients with locally advanced breast cancer.

Abstract

Our previous study indicated that a high amount of visceral adipose tissue was associated with poor survival outcomes in patients with early breast cancer who received neoadjuvant chemotherapy. However, inconsistency was observed in the prognostic role of body composition in breast cancer treatment outcomes. In the present study, we aimed to validate our previous research by performing a comprehensive body composition analysis in patients with a standardized clinical background. We included 198 patients with stage III breast cancer who underwent neoadjuvant chemotherapy between January 2007 and June 2015. The impact of body composition on pathologic complete response and survival outcomes was determined. Body composition measurements had no significant effect on pathologic complete response. Survival analysis showed a low ratio of total visceral adipose tissue to subcutaneous adipose tissue (V/S ratio ≤ 34) was associated with shorter overall survival. A changepoint method determined that a V/S ratio cutoff of 34 maximized the difference in overall survival. Our study indicated the prognostic effect of body composition measurements in patients with locally advanced breast cancer compared to those with early breast cancer. Further investigation will be needed to clarify the biological mechanism underlying the association of V/S ratio with prognosis in locally advanced breast cancer.

Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks

OBJECTIVES

Body tissue composition is a long-known biomarker with high diagnostic and prognostic value not only in cardiovascular, oncological, and orthopedic diseases but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible, and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging.

METHODS

Therefore, an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five different semantic body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for segmenting these body regions, followed by subclassifying adipose tissue and muscle using known Hounsfield unit limits.

RESULTS

The Sørensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99.

CONCLUSIONS

Our results show that fully automated body composition analysis on routine CT imaging can provide stable biomarkers across the whole abdomen and not just on L3 slices, which is historically the reference location for analyzing body composition in the clinical routine.

KEY POINTS

• Our study enables fully automated body composition analysis on routine abdomen CT scans. • The best segmentation models for semantic body region segmentation achieved an averaged Sørensen Dice score of 0.9553. • Subclassified tissue volumes achieved intra-class correlation coefficients over 0.99.

Obesity and Breast Cancer: The Role of Crown-Like Structures in Breast Adipose Tissue in Tumor Progression, Prognosis, and Therapy

Obesity is associated with increased risk and aggressiveness of many types of cancer. Women with obesity and breast cancer are more likely to be diagnosed with larger and higher-grade tumors and have higher incidence of metastases than lean individuals. Increasing evidence indicates that obesity includes systemic, chronic low-grade inflammation, and that adipose tissue can act as an important endocrine site, secreting a variety of substances that may regulate inflammation, immune response, and cancer predisposition. Obesity-associated inflammation appears to be initially mediated by macrophage infiltration into adipose tissue. Macrophages can surround damaged or necrotic adipocytes, forming "crown-like" structures (CLS). CLS are increased in breast adipose tissue from breast cancer patients and are more abundant in patients with obesity conditions. Moreover, the CLS index-ratio from individuals with obesity seems to influence breast cancer recurrence rates and survival. In this review, we discuss the most recent cellular and molecular mechanisms involved in CLS establishment in the white adipose tissue of women with obesity and their implications for breast cancer biology. We also explain how CLS influence the tumor microenvironment and affect breast cancer behavior. Targeting breast adipose tissue CLS can be a crucial therapeutic tool in cancer treatment, especially in patients with obesity.

Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment

OBJECTIVE

To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segmentations as the reference standard.

MATERIALS AND METHODS

We manually segmented 200 axial CT images at the supra-acetabular level in 200 subjects, labeling background, subcutaneous adipose tissue (SAT), muscle, inter-muscular adipose tissue (IMAT), bone, and miscellaneous intra-pelvic content. The dataset was randomly divided into training (180/200) and test (20/200) datasets. Data augmentation was utilized to enlarge the training dataset and all images underwent preprocessing with histogram equalization. Our model was trained for 50 epochs using the U-Net architecture with batch size of 8, learning rate of 0.0001, Adadelta optimizer and a dropout of 0.20. The Dice (F1) score was used to assess similarity between the manual segmentations and the CNN predicted segmentations.

RESULTS

The CNN model with data augmentation of N = 3000 achieved accurate segmentation of body composition for all classes. The Dice scores were as follows: background (1.00), miscellaneous intra-pelvic content (0.98), SAT (0.97), muscle (0.95), IMAT (0.91), and bone (0.92). Mean time to automatically segment one CT image was 0.07 s (GPU) and 2.51 s (CPU).

CONCLUSIONS

Our CNN-based model enables accurate automated segmentation of multiple tissues on pelvic CT images, with promising implications for body composition studies.

Dual-Energy X-Ray Absorptiometry Compared to Computed Tomography for Visceral Adiposity Assessment Among Gastrointestinal and Pancreatic Cancer Survivors

Dual-energy x-ray absorptiometry (DXA) for visceral adipose tissue (VAT) assessment is used as an alternative to computed tomography (CT) for research purposes in apparently healthy and clinical populations. It is unknown whether DXA is comparable to CT among cancer survivors, especially in cases where VAT assessment may be affected by treatment history and side effects and become more challenging to assess, such as a history of surgical gastrointestinal resection and/or ascites. The purpose of this study was to determine the level of agreement between DXA and CT when assessing VAT area and volume among cancer survivors. One hundred Gastrointestinal and pancreatic cancer survivors underwent abdominal and pelvis CT and whole-body DXA within 48 hours. Bland-Altman analysis revealed that in women and men, DXA VAT-area estimates were larger and smaller, respectively, and was consistently smaller in estimates for VAT-volume. Correlations from linear regression analysis revealed statistically significant positive correlations between measurement methods. Overall, while DXA VAT estimates are highly correlated with CT VAT estimates, DXA estimates show substantial bias which indicates the two methods are not interchangeable in this population. Further research is warranted with a larger, more homogeneous sample to develop better estimates of the bias.

Evaluation of the Severity of Hyperlipidemia Pancreatitis Using CT-measured Visceral Adipose Tissue

BACKGROUND

Computed tomography-measured visceral adipose tissue (VAT) and the distribution of VAT are highly correlated with the severity and prognosis of acute pancreatitis (AP). To date, all available data are from the overall AP patient population; no subgroup analysis has been conducted to evaluate patients with moderately severe AP or patients with hyperlipidemia acute pancreatitis (HLAP) as independent populations. Currently, studies on the relationship between VAT and HLAP are lacking.

MATERIALS AND METHODS

A total of 235 patients with moderately severe AP or severe acute pancreatitis were divided into 2 groups according to whether hyperlipidemia was present: the HLAP group and the non-HLAP group. The general inpatient information was collected, and computed tomography was used to measure VAT, subcutaneous adipose tissue (SAT), total adipose tissue, and VAT/SAT (V/S). The data were subjected to t test, χ test, matrix scatter plot, logistic regression, and receiver operating characteristic analyses to evaluate the relationship between VAT and HLAP severity.

RESULTS

Significant differences were observed in VAT, SAT, total adipose tissue, and triglycerides (TGs) between the HLAP group and the non-HLAP group (P<0.001). Significant correlations were observed between VAT and body mass index (r=0.425, P=0.017) and between VAT and TG (r=0.367, P=0.042). In the HLAP group, VAT, V/S, TG, and local complications may have significant effects on disease severity. The receiver operating characteristic curves showed that VAT and V/S were more reliable than TGs in evaluating disease severity [area under the curve (AUC) of VAT: 0.819, P<0.001; AUC of V/S: 0.855, P<0.001; AUC of TG: 0.671, P=0.04]. Disease severity was reliably evaluated at 139 cm, the cut-off value of VAT. The cut-off value of V/S was 1.145; high V/S was associated with extended intensive care unit stay. VAT and its distribution had no significant effects on mortality.

CONCLUSIONS

For patients with moderately severe to severe HLAP, VAT was correlated with body mass index and TG. VAT and V/S were valuable factors for evaluating disease severity and prognosis. However, VAT had no effect on mortality, and VAT could not be used to evaluate patients with moderately severe to severe non-HLAP.

Intra-Abdominal Adipose Tissue Quantification by Alternative Versus Reference Methods: A Systematic Review and Meta-Analysis

OBJECTIVE

This meta-analysis aimed to assess the agreement between intra-abdominal adipose tissue (IAAT) quantified by alternative methods and the reference standards, computed tomography (CT) and magnetic resonance imaging (MRI).

METHODS

MEDLINE and EMBASE electronic databases were systematically searched to identify studies that quantified IAAT thickness, area, or volume by a comparator method and CT or MRI. Using an inverse variance weighted approach (random-effects model), the mean differences and 95% limits of agreement (LoA) were pooled between methods.

RESULTS

The meta-analysis included 24 studies using four comparator methods. The pooled mean differences were -0.3 cm (95% LoA: -3.4 to 3.2 cm; P = 0.400) for ultrasound and -11.6 cm² (95% LoA: -43.1 to 19.9 cm² ; P = 0.004) for bioelectrical impedance analysis. Dual-energy x-ray absorptiometry (DXA) quantified both IAAT area and volume with mean differences of 8.1 cm² (95% LoA: -98.9 to 115.1 cm² ; P = 0.061) and 10 cm³ (95% LoA: -280 to 300 cm³ ; P = 0.808), respectively.

CONCLUSIONS

Ultrasound and DXA measure IAAT with minimal bias from CT or MRI, while bioelectrical impedance analysis systematically underestimates IAAT. However, with the exception of DXA for IAAT volume, the wide LoA caution against clinical or research use of the comparator methods and emphasize the need to optimize alternatives to the reference standards.

Difference in Visceral Adipose Tissue in Pregnancy and Postpartum and Related Changes in Maternal Insulin Resistance

OBJECTIVE

To measure the difference between first-trimester and postpartum visceral adipose tissue (VAT), the agreement of this difference with change in body mass index, and whether a difference in VAT is associated with insulin resistance or glucose mishandling.

METHODS

Prospective study of 93 women with singleton pregnancies without a history of diabetes. Visceral adipose tissue depth was sonographically assessed at 11 to 14 weeks and at 6 to 12 weeks postpartum. Metabolic measures, sampled at 24 to 28 weeks and 6 to 12 weeks postpartum, included homeostatic model assessment of insulin resistance, insulin sensitivity index composite, and area under the 75-g oral glucose tolerance test curve.

RESULTS

First-trimester VAT depth explained only 37% (95% confidence interval [CI], 22-52) of the variation in postpartum VAT depth. There was limited agreement between the net change in postpartum minus first-trimester VAT depth and that same net change for body mass index (Cohen's kappa, 0.26; 95% CI, 0.05-0.47). Those with a net gain in VAT depth demonstrated poorer insulin sensitivity index postpartum than women with a net regression in VAT depth-a difference of -2.0 (95% CI, -3.3 to -0.69).

CONCLUSION

Sonographic assessment of postpartum VAT is feasible and may provide insight to metabolic changes between pregnancy and postpartum, beyond body mass index.

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.