Sarcopenia is a muscle disorder associated with loss of muscle mass, strength and function. Early screening, diagnosis and treatment may improve outcome in different disease conditions. A wide variety of tools for estimation of muscle mass is available and each tool has specific technical requirements. However, different investigational settings and lack of homogeneity of populations influence the definition of gold standards, proving it difficult to systematically adopt these tools. Recently, the European Working Group on Sarcopenia in Older People (EWGSOP) published a revised recommendation (EWGSOP-2) and algorithm for using tools for screening and diagnosing sarcopenia. However, agreement of the EWGSOP2 criteria with other classifications is poor and although an overview of available tools is valuable, for the purpose of clinical decision-making the reverse is useful; a given scenario asks for the most suitable tools. Tools were identified for screening, diagnostics and longitudinal monitoring of muscle mass. For each of these clinical scenarios the most appropriate tools were listed and for each technique their usability is specified based on sensitivity and specificity. Based on this information a specific recommendation is made for each clinical scenario. This narrative review provides an overview of currently available tools and future developments for different clinical scenarios such as screening, diagnosis and longitudinal monitoring of alterations in muscle status. It supports clinical decision-making in choosing the right tools for muscle mass quantification depending on the need within a given clinical scenario as well as the local availability and expertise.

Manual segmentation of muscle and adipose compartments from computed tomography (CT) axial images is a potential bottleneck in early rapid detection and quantification of sarcopenia. A prototype deep learning neural network was trained on a multi-center collection of 3413 abdominal cancer surgery subjects to automatically segment truncal muscle, subcutaneous adipose tissue and visceral adipose tissue at the L3 lumbar vertebral level. Segmentations were externally tested on 233 polytrauma subjects. Although after severe trauma abdominal CT scans are quickly and robustly delivered, with often motion or scatter artefacts, incomplete vertebral bodies or arms that influence image quality, the concordance was generally very good for the body composition indices of Skeletal Muscle Radiation Attenuation (SMRA) (Concordance Correlation Coefficient (CCC) = 0.92), Visceral Adipose Tissue index (VATI) (CCC = 0.99) and Subcutaneous Adipose Tissue Index (SATI) (CCC = 0.99). In conclusion, this article showed an automated and accurate segmentation system to segment the cross-sectional muscle and adipose area L3 lumbar spine level on abdominal CT. Future perspectives will include fine-tuning the algorithm and minimizing the outliers.

Sarcopenia is defined as a loss of muscle mass and function, which can be caused by normal ageing or factors such as physical inactivity. Severe health consequences caused by sarcopenia highlight the need for early identification. Computed Tomography (CT) imaging, often mentioned as the gold standard due to its accuracy, is costly and not routinely performed in daily clinical care. Ultrasound of the rectus femoris, however, is low in costs and easily accessible. The aim is to present the current and most recent literature regarding the diagnostic value of ultrasound measurements of the rectus femoris for the diagnosis of sarcopenia in adults. The databases PubMed and Web of Science were used to search for studies comparing ultrasound of the rectus femoris with a reference test to diagnose sarcopenia in adults. The quality of the final eligible studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies, version 2, tool (QUADAS-2). Six studies were included in this systematic review. The muscle thickness and cross-sectional area of the rectus femoris were assessed and compared with the reference tests CT, Dual-Energy X-ray Absorptiometry (DXA), and Bioelectrical Impedance Analysis (BIA). Half of the studies had a low risk of bias on all QUADAS-2 domains. Three studies reported statistical significant outcomes and diagnostic values ranging from 60 to 81% sensitivity and 51 to 94% specificity. Ultrasound of the rectus femoris muscle to diagnose sarcopenia has been shown to be a promising method in multiple clinical populations. However, there were some limitations such as a high methodological heterogeneity. Future research should develop standardized protocols and determine clear cut-off values to allow for a better implementation of ultrasound in clinical practice.

Sarcopenia is a muscle disease that involves loss of muscle strength and physical function and is associated with adverse health effects. Even though sarcopenia has attracted increasing attention in the literature, many research findings have not yet been translated into clinical practice. In this article, we aim to validate a deep learning neural network for automated segmentation of L3 CT slices and aim to explore the potential for clinical utilization of such a tool for clinical practice. A deep learning neural network was trained on a multi-centre collection of 3413 abdominal cancer surgery subjects to automatically segment muscle, subcutaneous and visceral adipose tissue at the L3 lumbar vertebral level. 536 Polytrauma subjects were used as an independent test set to show generalizability. The Dice Similarity Coefficient was calculated to validate the geometric similarity. Quantitative agreement was quantified using Bland-Altman's Limits of Agreement interval and Lin's Concordance Correlation Coefficient. To determine the potential clinical usability, randomly selected segmentation images were presented to a panel of experienced clinicians to rate on a Likert scale. Deep learning results gave excellent agreement versus a human expert operator for all of the body composition indices, with Concordance Correlation Coefficient for skeletal muscle index of 0.92, Skeletal muscle radiation attenuation 0.94, Visceral Adipose Tissue index 0.99 and Subcutaneous Adipose Tissue Index 0.99. Triple-blinded visual assessment of segmentation by clinicians correlated only to the Dice coefficient, but had no association to quantitative body composition metrics which were accurate irrespective of clinicians' visual rating. A deep learning method for automatic segmentation of truncal muscle, visceral and subcutaneous adipose tissue on individual L3 CT slices has been independently validated against expert human-generated results for an enlarged polytrauma registry dataset. Time efficiency, consistency and high accuracy relative to human experts suggest that quantitative body composition analysis with deep learning should is a promising tool for clinical application in a hospital setting.

Microwave-based sensing for tissue analysis is recently gaining interest due to advantages such as non-ionizing radiation and non-invasiveness. We have developed a set of transmission sensors for microwave-based real-time sensing to quantify muscle mass and quality. In connection, we verified the sensors by 3D simulations, tested them in a laboratory on a homogeneous three-layer tissue model, and collected pilot clinical data in 20 patients and 25 healthy volunteers. This report focuses on initial sensor designs for the Muscle Analyzer System (MAS), their simulation, laboratory trials and clinical trials followed by developing three new sensors and their performance comparison. In the clinical studies, correlation studies were done to compare MAS performance with other clinical standards, specifically the skeletal muscle index, for muscle mass quantification. The results showed limited signal penetration depth for the Split Ring Resonator (SRR) sensor. New sensors were designed incorporating Substrate Integrated Waveguides (SIW) and a bandstop filter to overcome this problem. The sensors were validated through 3D simulations in which they showed increased penetration depth through tissue when compared to the SRR. The second-generation sensors offer higher penetration depth which will improve clinical data collection and validation. The bandstop filter is fabricated and studied in a group of volunteers, showing more reliable data that warrants further continuation of this development.

Background: Early prediction of response to concurrent chemoradiotherapy (cCRT) could aid to further optimize treatment regimens for locally advanced cervical cancer (LACC) in the future. Purpose: To explore whether quantitative parameters from baseline (pre-therapy) magnetic resonance imaging (MRI) and FDG-PET/computed tomography (CT) have potential as predictors of early response to cCRT. Material and Methods: Forty-six patients with LACC undergoing cCRT after staging with FDG-PET/CT and MRI were retrospectively analyzed. Primary tumor volumes were delineated on FDG-PET/CT, T2-weighted (T2W)-MRI and diffusion-weighted MRI (DWI) to extract the following quantitative parameters: T2W volume; T2W signal_mean; DWI volume; ADC_mean; ADC_SD; MTV_42%; and SUV_max. Outcome was the early treatment response, defined as the residual tumor volume on MRI 3–4 weeks after start of external beam radiotherapy with chemotherapy (before the start of brachytherapy): patients with a residual tumor volume <10 cm³ were classified as early responders. Imaging parameters were analyzed together with FIGO stage to assess their performance to predict early response, using multivariable logistic regression analysis with bi-directional variable selection. Leave-one-out cross-validation with bootstrapping was used to simulate performance in a new, independent dataset. Results: T2W volume (OR 0.94, P = 0.003) and SUV_max (OR 1.15, P = 0.18) were identified as independent predictors in multivariable analysis, rendering a model with an AUC of 0.82 in the original dataset, and AUC of 0.68 (95% CI 0.41–0.81) from cross-validation. Conclusion: Although the predictive performance achieved in this small exploratory dataset was limited, these preliminary data suggest that parameters from baseline MRI and FDG-PET/CT (in particular pre-therapy tumor volume) may contribute to prediction of early response to cCRT in cervical cancer.