MRI adipose tissue and muscle composition analysis-a review of automation techniques

Body composition analysis by radiological imaging - methods, applications, and prospects

BACKGROUND

 This review discusses the quantitative assessment of tissue composition in the human body (body composition, BC) using radiological methods. Such analyses are gaining importance, in particular, for oncological and metabolic problems. The aim is to present the different methods and definitions in this field to a radiological readership in order to facilitate application and dissemination of BC methods. The main focus is on radiological cross-sectional imaging.

METHODS

 The review is based on a recent literature search in the US National Library of Medicine catalog (pubmed.gov) using appropriate search terms (body composition, obesity, sarcopenia, osteopenia in conjunction with imaging and radiology, respectively), as well as our own work and experience, particularly with MRI- and CT-based analyses of abdominal fat compartments and muscle groups.

RESULTS AND CONCLUSION

 Key post-processing methods such as segmentation of tomographic datasets are now well established and used in numerous clinical disciplines, including bariatric surgery. Validated reference values are required for a reliable assessment of radiological measures, such as fatty liver or muscle. Artificial intelligence approaches (deep learning) already enable the automated segmentation of different tissues and compartments so that the extensive datasets can be processed in a time-efficient manner - in the case of so-called opportunistic screening, even retrospectively from diagnostic examinations. The availability of analysis tools and suitable datasets for AI training is considered a limitation.

KEY POINTS

  · Radiological imaging methods are increasingly used to determine body composition (BC).. · BC parameters are usually quantitative and well reproducible.. · CT image data from routine clinical examinations can be used retrospectively for BC analysis.. · Prospectively, MRI examinations can be used to determine organ-specific BC parameters.. · Automated and in-depth analysis methods (deep learning or radiomics) appear to become important in the future..

CITATION FORMAT

· Linder N, Denecke T, Busse H. Body composition analysis by radiological imaging - methods, applications, and prospects. Fortschr Röntgenstr 2024; DOI: 10.1055/a-2263-1501.

Abdominal fat quantification using convolutional networks

OBJECTIVES

To present software for automated adipose tissue quantification of abdominal magnetic resonance imaging (MRI) data using fully convolutional networks (FCN) and to evaluate its overall performance-accuracy, reliability, processing effort, and time-in comparison with an interactive reference method.

MATERIALS AND METHODS

Single-center data of patients with obesity were analyzed retrospectively with institutional review board approval. Ground truth for subcutaneous (SAT) and visceral adipose tissue (VAT) segmentation was provided by semiautomated region-of-interest (ROI) histogram thresholding of 331 full abdominal image series. Automated analyses were implemented using UNet-based FCN architectures and data augmentation techniques. Cross-validation was performed on hold-out data using standard similarity and error measures.

RESULTS

The FCN models reached Dice coefficients of up to 0.954 for SAT and 0.889 for VAT segmentation during cross-validation. Volumetric SAT (VAT) assessment resulted in a Pearson correlation coefficient of 0.999 (0.997), relative bias of 0.7% (0.8%), and standard deviation of 1.2% (3.1%). Intraclass correlation (coefficient of variation) within the same cohort was 0.999 (1.4%) for SAT and 0.996 (3.1%) for VAT.

CONCLUSION

The presented methods for automated adipose-tissue quantification showed substantial improvements over common semiautomated approaches (no reader dependence, less effort) and thus provide a promising option for adipose tissue quantification.

CLINICAL RELEVANCE STATEMENT

Deep learning techniques will likely enable image-based body composition analyses on a routine basis. The presented fully convolutional network models are well suited for full abdominopelvic adipose tissue quantification in patients with obesity.

KEY POINTS

• This work compared the performance of different deep-learning approaches for adipose tissue quantification in patients with obesity. • Supervised deep learning-based methods using fully convolutional networks  were suited best. • Measures of accuracy were equal to or better than the operator-driven approach.

Resistance training decreased abdominal adiposity in postmenopausal women

OBJECTIVE

To investigate if abdominal adipose tissue volumes and ratios change after a 15-week structured resistance training intervention in postmenopausal women with vasomotor symptoms (VMS).

STUDY DESIGN

Sixty-five postmenopausal women with VMS and low physical activity were randomized to either three days/week supervised resistance training or unchanged physical activity for 15 weeks. Women underwent clinical anthropometric measurements and magnetic resonance imaging (MRI) at baseline and after 15 weeks. MRI was done using a Philips Ingenia 3.0 T MR scanner (Philips, Best, The Netherlands). The per protocol principle was used in the analysis of data.

MAIN OUTCOME MEASUREMENTS

The absolute change from baseline to week 15 in visceral adipose tissue (VAT) volume and the relative ratio (VAT ratio) between VAT and total abdominal adipose tissue (TAAT), i.e. the sum of abdominal subcutaneous adipose tissue (ASAT) and VAT.

RESULTS

There were no significant differences between the groups in characteristics, anthropometry or MRI measures at baseline. Women who were compliant with the intervention (i.e. participated in at least two of the three scheduled training sessions per week) had significantly different reduction over time in ASAT (p = 0.006), VAT (p = 0.002), TAAT (p = 0.003) and fat ratio (p < 0.001) compared with women in the control group.

CONCLUSIONS

Implementation of a 15-week resistance training regimen in midlife may help women to counteract the abdominal fat redistribution associated with the menopausal transition.

CLINICAL TRIALS

gov registered ID: NCT01987778.

Deep learning-based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration

PURPOSE

Fast and accurate thigh muscle segmentation from MRI is important for quantitative assessment of thigh muscle morphology and composition. A novel deep learning (DL) based thigh muscle and surrounding tissues segmentation model was developed for fully automatic and reproducible cross-sectional area (CSA) and fat fraction (FF) quantification and tested in patients at 10 years after anterior cruciate ligament reconstructions.

METHODS

A DL model combining UNet and DenseNet was trained and tested using manually segmented thighs from 16 patients (32 legs). Segmentation accuracy was evaluated using Dice similarity coefficients (DSC) and average symmetric surface distance (ASSD). A UNet model was trained for comparison. These segmentations were used to obtain CSA and FF quantification. Reproducibility of CSA and FF quantification was tested with scan and rescan of six healthy subjects.

RESULTS

The proposed UNet and DenseNet had high agreement with manual segmentation (DSC >0.97, ASSD < 0.24) and improved performance compared with UNet. For hamstrings of the operated knee, the automated pipeline had largest absolute difference of 6.01% for CSA and 0.47% for FF as compared to manual segmentation. In reproducibility analysis, the average difference (absolute) in CSA quantification between scan and rescan was better for the automatic method as compared with manual segmentation (2.27% vs. 3.34%), whereas the average difference (absolute) in FF quantification were similar.

CONCLUSIONS

The proposed method exhibits excellent accuracy and reproducibility in CSA and FF quantification compared with manual segmentation and can be used in large-scale patient studies.

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.

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

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

Semiautomated segmentation of lower extremity MRI reveals distinctive subcutaneous adipose tissue in lipedema: a pilot study

Purpose

Lipedema is a painful subcutaneous adipose tissue (SAT) disease involving disproportionate SAT accumulation in the lower extremities that is frequently misdiagnosed as obesity. We developed a semiautomatic segmentation pipeline to quantify the unique lower-extremity SAT quantity in lipedema from multislice chemical-shift-encoded (CSE) magnetic resonance imaging (MRI).

Approach

Patients with lipedema (n = 15 ) and controls (n = 13 ) matched for age and body mass index (BMI) underwent CSE-MRI acquired from the thighs to ankles. Images were segmented to partition SAT and skeletal muscle with a semiautomated algorithm incorporating classical image processing techniques (thresholding, active contours, Boolean operations, and morphological operations). The Dice similarity coefficient (DSC) was computed for SAT and muscle automated versus ground truth segmentations in the calf and thigh. SAT and muscle volumes and the SAT-to-muscle volume ratio were calculated across slices for decades containing 10% of total slices per participant. The effect size was calculated, and Mann-Whitney U test applied to compare metrics in each decade between groups (significance: two-sided P < 0.05 ).

Results

Mean DSC for SAT segmentations was 0.96 in the calf and 0.98 in the thigh, and for muscle was 0.97 in the calf and 0.97 in the thigh. In all decades, mean SAT volume was significantly elevated in participants with versus without lipedema (P < 0.01 ), whereas muscle volume did not differ. Mean SAT-to-muscle volume ratio was significantly elevated (P < 0.001 ) in all decades, where the greatest effect size for distinguishing lipedema was in the seventh decade approximately midthigh (r = 0.76 ).

Conclusions

The semiautomated segmentation of lower-extremity SAT and muscle from CSE-MRI could enable fast multislice analysis of SAT deposition throughout the legs relevant to distinguishing patients with lipedema from females with similar BMI but without SAT disease.

A Combined Region- and Pixel-Based Deep Learning Approach for Quantifying Abdominal Adipose Tissue in Adolescents Using Dixon Magnetic Resonance Imaging

BACKGROUND

The development of adipose tissue during adolescence may provide valuable insights into obesity-associated diseases. We propose an automated convolutional neural network (CNN) approach using Dixon-based magnetic resonance imaging (MRI) to quantity abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) in children and adolescents.

METHODS

474 abdominal Dixon MRI scans of 136 young healthy volunteers (aged 8-18) were included in this study. For each scan, an axial fat-only Dixon image located at the L2-L3 disc space and another image at the L4-L5 disc space were selected for quantification. For each image, an outer and an inner region around the abdomen wall, as well as SAT and VAT pixel masks, were generated by expert readers as reference standards. A standard U-Net CNN architecture was then used to train two models: one for region segmentation and one for fat pixel classification. The performance was evaluated using the dice similarity coefficient (DSC) with fivefold cross-validation, and by Pearson correlation and the Student's t-test against the reference standards.

RESULTS

For the DSC results, means and standard deviations of the outer region, inner region, SAT, and VAT comparisons were 0.974 ± 0.026, 0.997 ± 0.003, 0.981 ± 0.025, and 0.932 ± 0.047, respectively. Pearson coefficients were 1.000 for both outer and inner regions, and 1.000 and 0.982 for SAT and VAT comparisons, respectively (all p = NS).

CONCLUSION

These results show that our method not only provides excellent agreement with the reference SAT and VAT measurements, but also accurate abdominal wall region segmentation. The proposed combined region- and pixel-based CNN approach provides automated abdominal wall segmentation as well as SAT and VAT quantification with Dixon MRI and enables objective longitudinal assessment of adipose tissues in children during adolescence.

Obesity and Cancer: A Current Overview of Epidemiology, Pathogenesis, Outcomes, and Management

BACKGROUND

Obesity or excess body fat is a major global health challenge that has not only been associated with diabetes mellitus and cardiovascular disease but is also a major risk factor for the development of and mortality related to a subgroup of cancer. This review focuses on epidemiology, the relationship between obesity and the risk associated with the development and recurrence of cancer and the management of obesity.

METHODS

A literature search using PubMed and Google Scholar was performed and the keywords 'obesity' and cancer' were used. The search was limited to research papers published in English prior to September 2022 and focused on studies that investigated epidemiology, the pathogenesis of cancer, cancer incidence and the risk of recurrence, and the management of obesity.

RESULTS

About 4-8% of all cancers are attributed to obesity. Obesity is a risk factor for several major cancers, including post-menopausal breast, colorectal, endometrial, kidney, esophageal, pancreatic, liver, and gallbladder cancer. Excess body fat results in an approximately 17% increased risk of cancer-specific mortality. The relationship between obesity and the risk associated with the development of cancer and its recurrence is not fully understood and involves altered fatty acid metabolism, extracellular matrix remodeling, the secretion of adipokines and anabolic and sex hormones, immune dysregulation, and chronic inflammation. Obesity may also increase treatment-related adverse effects and influence treatment decisions regarding specific types of cancer therapy. Structured exercise in combination with dietary support and behavior therapy are effective interventions. Treatment with glucagon-like peptide-1 analogues and bariatric surgery result in more rapid weight loss and can be considered in selected cancer survivors.

CONCLUSIONS

Obesity increases cancer risk and mortality. Weight-reducing strategies in obesity-associated cancers are important interventions as a key component of cancer care. Future studies are warranted to further elucidate the complex relationship between obesity and cancer with the identification of targets for effective interventions.

Automated Segmentation of Visceral, Deep Subcutaneous, and Superficial Subcutaneous Adipose Tissue Volumes in MRI of Neonates and Young Children

Purpose

To develop and evaluate an automated segmentation method for accurate quantification of abdominal adipose tissue (AAT) depots (superficial subcutaneous adipose tissue [SSAT], deep subcutaneous adipose tissue [DSAT], and visceral adipose tissue [VAT]) in neonates and young children.

Materials and Methods

This was a secondary analysis of prospectively collected data, which used abdominal MRI data from Growing Up in Singapore Towards healthy Outcomes, or GUSTO, a longitudinal mother-offspring cohort, to train and evaluate a convolutional neural network for volumetric AAT segmentation. The data comprised imaging volumes of 333 neonates obtained at early infancy (age ≤2 weeks, 180 male neonates) and 755 children aged either 4.5 years (n = 316, 150 male children) or 6 years (n = 439, 219 male children). The network was trained on images of 761 randomly selected volumes (neonates and children combined) and evaluated on 100 neonatal volumes and 227 child volumes by using 10-fold validation. Automated segmentations were compared with expert-generated manual segmentation. Segmentation performance was assessed using Dice scores.

Results

When the model was tested on the test datasets across the 10 folds, the model had strong agreement with the ground truth for all testing sets, with mean Dice similarity scores for SSAT, DSAT, and VAT, respectively, of 0.960, 0.909, and 0.872 in neonates and 0.944, 0.851, and 0.960 in children. The model generalized well to different body sizes and ages and to all abdominal levels.

Conclusion

The proposed segmentation approach provided accurate automated volumetric assessment of AAT compartments on MR images of neonates and children.Keywords Pediatrics, Deep Learning, Convolutional Neural Networks, Water-Fat MRI, Image Segmentation, Deep and Superficial Subcutaneous Adipose Tissue, Visceral Adipose TissueClinical trial registration no. NCT01174875 Supplemental material is available for this article. © RSNA, 2021.

Assessing visceral and subcutaneous adiposity using segmented T2-MRI and multi-frequency segmental bioelectrical impedance: A sex-based comparative study

BACKGROUND AND AIM

This study aims to quantify abdominal visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) using T2-weighted magnetic resonance imaging (MRI), and assess the extent of its concordance with VAT surface-area measured by a state-of-the-art segmental multi-frequency bioelectrical impedance analysis (BIA) device. A comparison between manual and semi-automated segmentation was conducted. Further, abdominal VAT and SAT sex-based comparison in healthy Arab adults was piloted.

METHODS

A cross-sectional design was followed to recruit subjects. Abdominal VAT and SAT were determined on T2-weighted MRI manually and semi-automatically. Body composition was assessed using a BIA machine. Statistical differences between the abdominal VAT areas defined by BIA, manual, and semi-automated MRI were compared. Correlation between all methods was assessed, and statistical differences between sex abdominal VAT/SAT defined areas were compared.

RESULTS

A total of 165 abdominal T2-weighted MR images taken for 55 overweight/obese adult subjects were analyzed Differences between manual and semi-automated MRI-obtained abdominal VAT and SAT were found statistically significant (P<0.001) for all subjects. Mean abdominal VAT using the BIA technique was found to correlate significantly with manually and semi-automated T2-weighted MRI defined VAT (r=0.7436; P<0.001 and r=0.8275; P<0.001, respectively). Abdominal VAT was significantly (P<0.001) different between male and female subjects accumulating at different abdominal levels.

CONCLUSION

Semi-automatic segmentation showed a stronger significant correlation with BIA compared to manual segmentation, implying a more reliable quantification of abdominal VAT/SAT. Segmental BIA technique may serve as a feasible and convenient assessment tool for the visceral adiposity in obese subjects.

A Deep-Learning-Based, Fully Automated Program to Segment and Quantify Major Spinal Components on Axial Lumbar Spine Magnetic Resonance Images

OBJECTIVE

The paraspinal muscles have been extensively studied on axial lumbar magnetic resonance imaging (MRI) for better understanding of back pain; however, the acquisition of measurements mainly relies on manual segmentation, which is time consuming. The study objective was to develop and validate a deep-learning-based program for automated acquisition of quantitative measurements for major lumbar spine components on axial lumbar MRIs, the paraspinal muscles in particular.

METHODS

This study used a cross-sectional observational design. From the Hangzhou Lumbar Spine Study, T2-weighted axial MRIs at the L4-5 disk level of 120 participants (aged 54.8 years [SD = 15.0]) were selected to develop the deep-learning-based program Spine Explorer (Tulong). Another 30 axial lumbar MRIs were automatically measured by Spine Explorer and then manually measured using ImageJ to acquire quantitative size and compositional measurements for bilateral multifidus, erector spinae, and psoas muscles; the disk; and the spinal canal. Intersection-over-union and Dice score were used to evaluate the performance of automated segmentation. Intraclass coefficients and Bland-Altman plots were used to examine intersoftware agreements for various measurements.

RESULTS

After training, Spine Explorer (Tulong) measures an axial lumbar MRI in 1 second. The intersections-over-union were 83.3% to 88.4% for the paraspinal muscles and 92.2% and 82.1% for the disk and spinal canal, respectively. For various size and compositional measurements of paraspinal muscles, Spine Explorer (Tulong) was in good agreement with ImageJ (intraclass coefficient = 0.85 to approximately 0.99).

CONCLUSION

Spine Explorer (Tulong) is automated, efficient, and reliable in acquiring quantitative measurements for the paraspinal muscles, the disk, and the canal, and various size and compositional measurements were simultaneously obtained for the lumbar paraspinal muscles.

IMPACT

Such an automated program might encourage further epidemiological studies of the lumbar paraspinal muscle degeneration and enhance paraspinal muscle assessment in clinical practice.

Antimicrobial Dosing in Specific Populations and Novel Clinical Methodologies: Obesity

Obesity and its related comorbidities can negatively influence the outcomes of certain infectious diseases. Specific dosing recommendations are often lacking in the product label for patients with obesity that leads to unclear guidance in practice. Higher rates of therapeutic failure have been reported with some fixed dose antibiotics and pragmatic approaches to dose modification are limited for orally administered agents. For i.v. antimicrobials dosed on weight, alternate body size descriptors (ABSDs) have been used to reduce the risk of overdosing. These ABSDs are mathematical transformations of height and weight that represent fat-free weight and follow the same principles as body surface area (BSA)-based dosing of cancer chemotherapy. However, ABSDs are rarely studied in pivotal phase III studies and so can risk the underdosing of antimicrobials in patients with obesity when incorrectly applied in the real-world setting. Specific case examples are presented to highlight these risks. Although general principles may be considered by clinicians, a universal approach to dose modification in obesity is unlikely. Studies that can better distinguish human body phenotypes may help reduce our reliance on height and weight to define dosing. Simple and complex technologies exist to quantify individual body composition that could improve upon our current approach. Early evidence suggests that body composition parameters repurposed from medical imaging data may improve upon height and weight as covariates of drug clearance and distribution. Clinical trials that can integrate human body phenotyping may help us identify new approaches to optimal dose selection of antimicrobials in patients with obesity.

Fully Automated and Standardized Segmentation of Adipose Tissue Compartments via Deep Learning in 3D Whole-Body MRI of Epidemiologic Cohort Studies

PURPOSE

To enable fast and reliable assessment of subcutaneous and visceral adipose tissue compartments derived from whole-body MRI.

MATERIALS AND METHODS

Quantification and localization of different adipose tissue compartments derived from whole-body MR images is of high interest in research concerning metabolic conditions. For correct identification and phenotyping of individuals at increased risk for metabolic diseases, a reliable automated segmentation of adipose tissue into subcutaneous and visceral adipose tissue is required. In this work, a three-dimensional (3D) densely connected convolutional neural network (DCNet) is proposed to provide robust and objective segmentation. In this retrospective study, 1000 cases (average age, 66 years ± 13 [standard deviation]; 523 women) from the Tuebingen Family Study database and the German Center for Diabetes research database and 300 cases (average age, 53 years ± 11; 152 women) from the German National Cohort (NAKO) database were collected for model training, validation, and testing, with transfer learning between the cohorts. These datasets included variable imaging sequences, imaging contrasts, receiver coil arrangements, scanners, and imaging field strengths. The proposed DCNet was compared to a similar 3D U-Net segmentation in terms of sensitivity, specificity, precision, accuracy, and Dice overlap.

RESULTS

Fast (range, 5-7 seconds) and reliable adipose tissue segmentation can be performed with high Dice overlap (0.94), sensitivity (96.6%), specificity (95.1%), precision (92.1%), and accuracy (98.4%) from 3D whole-body MRI datasets (field of view coverage, 450 × 450 × 2000 mm). Segmentation masks and adipose tissue profiles are automatically reported back to the referring physician.

CONCLUSION

Automated adipose tissue segmentation is feasible in 3D whole-body MRI datasets and is generalizable to different epidemiologic cohort studies with the proposed DCNet.Supplemental material is available for this article.© RSNA, 2020.

MRI in the assessment of adipose tissues and muscle composition: how to use it

Body composition analysis based on the characterization of different tissue compartments is currently experiencing increasing attention by a broad range of medical disciplines for both clinical and research questions. However, body composition profiling (BCP) can be performed utilizing different modalities, which all come along with several technical and diagnostic strengths and limitations, respectively. Magnetic resonance imaging (MRI) demonstrates good soft tissue resolution, high contrast between fat and water, and is free from ionizing radiation. This review article represents an overview of imaging techniques for body composition assessment, focussing on qualitative and quantitative methods of assessing adipose tissue and muscles in MRI.

Advancements in magnetic resonance imaging-based biomarkers for muscular dystrophy

Recent years have seen steady progress in the identification of genetic muscle diseases as well as efforts to develop treatment for these diseases. Consequently, sensitive and objective new methods are required to identify and monitor muscle pathology. Magnetic resonance imaging offers multiple potential biomarkers of disease severity in the muscular dystrophies. This Review uses a pathology-based approach to examine the ways in which MRI and spectroscopy have been used to study muscular dystrophies. Methods that have been used to quantitate intramuscular fat, edema, fiber orientation, metabolism, fibrosis, and vascular perfusion are examined, and this Review describes how MRI can help diagnose these conditions and improve upon existing muscle biomarkers by detecting small increments of disease-related change. Important challenges in the implementation of imaging biomarkers, such as standardization of protocols and validating imaging measurements with respect to clinical outcomes, are also described.

Aging and Imaging Assessment of Body Composition: From Fat to Facts

The aging process is characterized by the chronic inflammatory status called "inflammaging", which shares major molecular and cellular features with the metabolism-induced inflammation called "metaflammation." Metaflammation is mainly driven by overnutrition and nutrient excess, but other contributing factors are metabolic modifications related to the specific body composition (BC) changes occurring with age. The aging process is indeed characterized by an increase in body total fat mass and a concomitant decrease in lean mass and bone density, that are independent from general and physiological fluctuations in weight and body mass index (BMI). Body adiposity is also re-distributed with age, resulting in a general increase in trunk fat (mainly abdominal fat) and a reduction in appendicular fat (mainly subcutaneous fat). Moreover, the accumulation of fat infiltration in organs such as liver and muscles also increases in elderly, while subcutaneous fat mass tends to decrease. These specific variations in BC are considered risk factors for the major age-related diseases, such as cardiovascular diseases, type 2 diabetes, sarcopenia and osteoporosis, and can predispose to disabilities. Thus, the maintenance of a balance rate of fat, muscle and bone is crucial to preserve metabolic homeostasis and a health status, positively contributing to a successful aging. For this reason, a detailed assessment of BC in elderly is critical and could be an additional preventive personalized strategy for age-related diseases. Despite BMI and other clinical measures, such as waist circumference measurement, waist-hip ratio, underwater weighing and bioelectrical impedance, are widely used as a surrogate measure for body adiposity, they barely reflect the distribution of body fat. Because of the great advantages offered by imaging tools in research and clinics, the attention of clinicians is now moving to powerful imaging techniques such as computed tomography, magnetic resonance imaging, dual-energy X-ray absorptiometry and ultrasound to obtain a more accurate estimation of BC. The aim of this review is to present the state of the art of the imaging techniques that are currently available to measure BC and that can be applied to the study of BC changes in the elderly, outlining advantages and disadvantages of each technique.

Body composition and sarcopenia: The next-generation of personalized oncology and pharmacology?

Body composition has gained increasing attention in oncology in recent years due to fact that sarcopenia has been revealed to be a strong prognostic indicator for survival across multiple stages and cancer types and a predictive factor for toxicity and surgery complications. Accumulating evidence over the last decade has unraveled the "pharmacology" of sarcopenia. Lean body mass may be more relevant to define drug dosing than the "classical" body surface area or flat-fixed dosing in patients with cancer. Since sarcopenia has a major impact on patient survival and quality of life, therapeutic interventions aiming at reducing muscle loss have been developed and are being prospectively evaluated in randomized controlled trials. It is now acknowledged that this supportive care dimension of oncological management is essential to ensure the success of any anticancer treatment. The field of sarcopenia and body composition in cancer is developing quickly, with (i) the newly identified concept of sarcopenic obesity defined as a specific pathophysiological entity, (ii) unsolved issues regarding the best evaluation modalities and cut-off for definition of sarcopenia on imaging, (iii) first results from clinical trials evaluating physical activity, and (iv) emerging body-composition-tailored drug administration schemes. In this context, we propose a comprehensive review providing a panoramic approach of the clinical, pharmacological and therapeutic implications of sarcopenia and body composition in oncology.