Automated CT-derived skeletal muscle mass determination in lower hind limbs of mice using a 3D U-Net deep learning network

Targeted nutritional intervention attenuates experimental lung cancer cachexia

BACKGROUND

Cachexia, a syndrome with high prevalence in non-small cell lung cancer patients, impairs quality of life and reduces tolerance and responsiveness to cancer therapy resulting in decreased survival. Optimal nutritional care is pivotal in the treatment of cachexia and a recommended cornerstone of multimodal therapy. Here, we investigated the therapeutic effect of an intervention diet consisting of a specific combination of high protein, leucine, fish oil, vitamin D, galacto-oligosaccharides, and fructo-oligosaccharides on the development and progression of cachexia in an orthotopic lung cancer mouse model.

METHODS

Eleven-week-old male 129S2/Sv mice were orthotopically implanted with 344P lung epithelial tumour cells or vehicle (control). Seven days post-implantation tumour-bearing (TB) mice were allocated to either intervention- or isocaloric control diet. Cachexia was defined as 5 days of consecutive body weight loss, after which mice were euthanized for tissue analyses.

RESULTS

TB mice developed cachexia accompanied by significant loss of skeletal muscle mass and epididymal fat mass compared with sham operated mice. The cachectic endpoint was significantly delayed (46.0 ± 15.2 vs. 34.7 ± 11.4 days), and the amount (-1.57 ± 0.62 vs. -2.13 ± 0.57 g) and progression (-0.26 ± 0.14 vs. -0.39 ± 0.11 g/day) of body weight loss were significantly reduced by the intervention compared with control diet. Moreover, systemic inflammation (pentraxin-2 plasma levels) and alterations in molecular markers for proteolysis and protein synthesis, indicative of muscle atrophy signalling in TB-mice, were suppressed in skeletal muscle by the intervention diet.

CONCLUSIONS

Together, these data demonstrate the potential of this multinutrient intervention, targeting multiple components of cachexia, as integral part of lung cancer management.

Self-adaptive deep learning-based segmentation for universal and functional clinical and preclinical CT image analysis

BACKGROUND

Methods to monitor cardiac functioning non-invasively can accelerate preclinical and clinical research into novel treatment options for heart failure. However, manual image analysis of cardiac substructures is resource-intensive and error-prone. While automated methods exist for clinical CT images, translating these to preclinical μCT data is challenging. We employed deep learning to automate the extraction of quantitative data from both CT and μCT images.

METHODS

We collected a public dataset of cardiac CT images of human patients, as well as acquired μCT images of wild-type and accelerated aging mice. The left ventricle, myocardium, and right ventricle were manually segmented in the μCT training set. After template-based heart detection, two separate segmentation neural networks were trained using the nnU-Net framework.

RESULTS

The mean Dice score of the CT segmentation results (0.925 ± 0.019, n = 40) was superior to those achieved by state-of-the-art algorithms. Automated and manual segmentations of the μCT training set were nearly identical. The estimated median Dice score (0.940) of the test set results was comparable to existing methods. The automated volume metrics were similar to manual expert observations. In aging mice, ejection fractions had significantly decreased, and myocardial volume increased by age 24 weeks.

CONCLUSIONS

With further optimization, automated data extraction expands the application of (μ)CT imaging, while reducing subjectivity and workload. The proposed method efficiently measures the left and right ventricular ejection fraction and myocardial mass. With uniform translation between image types, cardiac functioning in diastolic and systolic phases can be monitored in both animals and humans.

Applying deep learning to segmentation of murine lung tumors in pre-clinical micro-computed tomography

Lung cancer remains a leading cause of cancer-related death, but scientists have made great strides in developing new treatments recently, partly owing to the use of genetically engineered mouse models (GEMMs). GEMM tumors represent a translational model that recapitulates human disease better than implanted models because tumors develop spontaneously in the lungs. However, detection of these tumors relies on in vivo imaging tools, specifically micro-Computed Tomography (micro-CT or µCT), and image analysis can be laborious with high inter-user variability. Here we present a deep learning model trained to perform fully automated segmentation of lung tumors without the interference of other soft tissues. Trained and tested on 100 3D µCT images (18,662 slices) that were manually segmented, the model demonstrated a high correlation to manual segmentations on the testing data (r2=0.99, DSC=0.78) and on an independent dataset (n = 12 3D scans or 2328 2D slices, r2=0.97, DSC=0.73). In a comparison against manual segmentation performed by multiple analysts, the model (r2=0.98, DSC=0.78) performed within inter-reader variability (r2=0.79, DSC=0.69) and close to intra-reader variability (r2=0.99, DSC=0.82), all while completing 5+ hours of manual segmentations in 1 minute. Finally, when applied to a real-world longitudinal study (n = 55 mice), the model successfully detected tumor progression over time and the differences in tumor burden between groups induced with different virus titers, aligning well with more traditional analysis methods. In conclusion, we have developed a deep learning model which can perform fast, accurate, and fully automated segmentation of µCT scans of murine lung tumors.

A novel orthotopic mouse model replicates human lung cancer cachexia

INTRODUCTION

Cancer cachexia, highly prevalent in lung cancer, is a debilitating syndrome characterized by involuntary loss of skeletal muscle mass and is associated with poor clinical outcome, decreased survival and negative impact on tumour therapy. Various lung tumour-bearing animal models have been used to explore underlying mechanisms of cancer cachexia. However, these models do not simulate anatomical and immunological features key to lung cancer and associated muscle wasting. Overcoming these shortcomings is essential to translate experimental findings into the clinic. We therefore evaluated whether a syngeneic, orthotopic lung cancer mouse model replicates systemic and muscle-specific alterations associated with human lung cancer cachexia.

METHODS

Immune competent, 11 weeks old male 129S2/Sv mice, were randomly allocated to either (1) sham control group or (2) tumour-bearing group. Syngeneic lung epithelium-derived adenocarcinoma cells (K-rasG12D ; p53R172HΔG ) were inoculated intrapulmonary into the left lung lobe of the mice. Body weight and food intake were measured daily. At baseline and weekly after surgery, grip strength was measured and tumour growth and muscle volume were assessed using micro cone beam CT imaging. After reaching predefined surrogate survival endpoint, animals were euthanized, and skeletal muscles of the lower hind limbs were collected for biochemical analysis.

RESULTS

Two-third of the tumour-bearing mice developed cachexia based on predefined criteria. Final body weight (-13.7 ± 5.7%; P < 0.01), muscle mass (-13.8 ± 8.1%; P < 0.01) and muscle strength (-25.5 ± 10.5%; P < 0.001) were reduced in cachectic mice compared with sham controls and median survival time post-surgery was 33.5 days until humane endpoint. Markers for proteolysis, both ubiquitin proteasome system (Fbxo32 and Trim63) and autophagy-lysosomal pathway (Gabarapl1 and Bnip3), were significantly upregulated, whereas markers for protein synthesis (relative phosphorylation of Akt, S6 and 4E-BP1) were significantly decreased in the skeletal muscle of cachectic mice compared with control. The cachectic mice exhibited increased pentraxin-2 (P < 0.001) and CXCL1/KC (P < 0.01) expression levels in blood plasma and increased mRNA expression of IκBα (P < 0.05) in skeletal muscle, indicative for the presence of systemic inflammation. Strikingly, RNA sequencing, pathway enrichment and miRNA expression analyses of mouse skeletal muscle strongly mirrored alterations observed in muscle biopsies of patients with lung cancer cachexia.

CONCLUSIONS

We developed an orthotopic model of lung cancer cachexia in immune competent mice. Because this model simulates key aspects specific to cachexia in lung cancer patients, it is highly suitable to further investigate the underlying mechanisms of lung cancer cachexia and to test the efficacy of novel intervention strategies.

A fully automated micro‑CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy

Micro-computed tomography (µCT)-based imaging plays a key role in monitoring disease progression and response to candidate drugs in various animal models of human disease, but manual image processing is still highly time-consuming and prone to operator bias. Focusing on an established mouse model of bleomycin (BLM)-induced lung fibrosis we document, here, the ability of a fully automated deep-learning (DL)-based model to improve and speed-up lung segmentation and the precise measurement of morphological and functional biomarkers in both the whole lung and in individual lobes. µCT-DL whose results were overall highly consistent with those of more conventional, especially histological, analyses, allowed to cut down by approximately 45-fold the time required to analyze the entire dataset and to longitudinally follow fibrosis evolution and response to the human-use-approved drug Nintedanib, using both inspiratory and expiratory μCT. Particularly significant advantages of this µCT-DL approach, are: (i) its reduced experimental variability, due to the fact that each animal acts as its own control and the measured, operator bias-free biomarkers can be quantitatively compared across experiments; (ii) its ability to monitor longitudinally the spatial distribution of fibrotic lesions, thus eliminating potential confounding effects associated with the more severe fibrosis observed in the apical region of the left lung and the compensatory effects taking place in the right lung; (iii) the animal sparing afforded by its non-invasive nature and high reliability; and (iv) the fact that it can be integrated into different drug discovery pipelines with a substantial increase in both the speed and robustness of the evaluation of new candidate drugs. The µCT-DL approach thus lends itself as a powerful new tool for the precision preclinical monitoring of BLM-induced lung fibrosis and other disease models as well. Its ease of operation and use of standard imaging instrumentation make it easily transferable to other laboratories and to other experimental settings, including clinical diagnostic applications.

11β-HSD1 determines the extent of muscle atrophy in a model of acute exacerbation of COPD

Muscle atrophy is an extrapulmonary complication of acute exacerbations (AE) in chronic obstructive pulmonary disease (COPD). The endogenous production and therapeutic application of glucocorticoids (GCs) have been implicated as drivers of muscle loss in AE-COPD. The enzyme 11 β-hydroxysteroid dehydrogenase 1 (11β-HSD1) activates GCs and contributes toward GC-induced muscle wasting. To explore the potential of 11βHSD1 inhibition to prevent muscle wasting here, the objective of this study was to ascertain the contribution of endogenous GC activation and amplification by 11βHSD1 in skeletal muscle wasting during AE-COPD. Emphysema was induced by intratracheal (IT) instillation of elastase to model COPD in WT and 11βHSD1/KO mice, followed by vehicle or IT-LPS administration to mimic AE. µCT scans were obtained prior and at study endpoint 48 h following IT-LPS, to assess emphysema development and muscle mass changes, respectively. Plasma cytokine and GC profiles were determined by ELISA. In vitro, myonuclear accretion and cellular response to plasma and GCs were determined in C2C12 and human primary myotubes. Muscle wasting was exacerbated in LPS-11βHSD1/KO animals compared with WT controls. RT-qPCR and western blot analysis showed elevated catabolic and suppressed anabolic pathways in muscle of LPS-11βHSD1/KO animals relative to WTs. Plasma corticosterone levels were higher in LPS-11βHSD1/KO animals, whereas C2C12 myotubes treated with LPS-11βHSD1/KO plasma or exogenous GCs displayed reduced myonuclear accretion relative to WT counterparts. This study reveals that 11β-HSD1 inhibition aggravates muscle wasting in a model of AE-COPD, suggesting that therapeutic inhibition of 11β-HSD1 may not be appropriate to prevent muscle wasting in this setting.

Roadmap for precision preclinical x-ray radiation studies

This Roadmap paper covers the field of precision preclinical x-ray radiation studies in animal models. It is mostly focused on models for cancer and normal tissue response to radiation, but also discusses other disease models. The recent technological evolution in imaging, irradiation, dosimetry and monitoring that have empowered these kinds of studies is discussed, and many developments in the near future are outlined. Finally, clinical translation and reverse translation are discussed.

Virtual monoenergetic micro-CT imaging in mice with artificial intelligence

Micro cone-beam computed tomography (µCBCT) imaging is of utmost importance for carrying out extensive preclinical research in rodents. The imaging of animals is an essential step prior to preclinical precision irradiation, but also in the longitudinal assessment of treatment outcomes. However, imaging artifacts such as beam hardening will occur due to the low energetic nature of the X-ray imaging beam (i.e., 60 kVp). Beam hardening artifacts are especially difficult to resolve in a ‘pancake’ imaging geometry with stationary source and detector, where the animal is rotated around its sagittal axis, and the X-ray imaging beam crosses a wide range of thicknesses. In this study, a seven-layer U-Net based network architecture (vMonoCT) is adopted to predict virtual monoenergetic X-ray projections from polyenergetic X-ray projections. A Monte Carlo simulation model is developed to compose a training dataset of 1890 projection pairs. Here, a series of digital anthropomorphic mouse phantoms was derived from the reference DigiMouse phantom as simulation geometry. vMonoCT was trained on 1512 projection pairs (= 80%) and tested on 378 projection pairs (= 20%). The percentage error calculated for the test dataset was 1.7 ± 0.4%. Additionally, the vMonoCT model was evaluated on a retrospective projection dataset of five mice and one frozen cadaver. It was found that beam hardening artifacts were minimized after image reconstruction of the vMonoCT-corrected projections, and that anatomically incorrect gradient errors were corrected in the cranium up to 15%. Our results disclose the potential of Artificial Intelligence to enhance the µCBCT image quality in biomedical applications. vMonoCT is expected to contribute to the reproducibility of quantitative preclinical applications such as precision irradiations in X-ray cabinets, and to the evaluation of longitudinal imaging data in extensive preclinical studies.

Automatic contouring of normal tissues with deep learning for preclinical radiation studies

Objective.Delineation of relevant normal tissues is a bottleneck in image-guided precision radiotherapy workflows for small animals. A deep learning (DL) model for automatic contouring using standardized 3D micro cone-beam CT (μCBCT) volumes as input is proposed, to provide a fully automatic, generalizable method for normal tissue contouring in preclinical studies.Approach.A 3D U-net was trained to contour organs in the head (whole brain, left/right brain hemisphere, left/right eye) and thorax (complete lungs, left/right lung, heart, spinal cord, thorax bone) regions. As an important preprocessing step, Hounsfield units (HUs) were converted to mass density (MD) values, to remove the energy dependency of theμCBCT scanner and improve generalizability of the DL model. Model performance was evaluated quantitatively by Dice similarity coefficient (DSC), mean surface distance (MSD), 95th percentile Hausdorff distance (HD_95p), and center of mass displacement (ΔCoM). For qualitative assessment, DL-generated contours (for 40 and 80 kV images) were scored (0: unacceptable, manual re-contouring needed - 5: no adjustments needed). An uncertainty analysis using Monte Carlo dropout uncertainty was performed for delineation of the heart.Main results.The proposed DL model and accompanying preprocessing method provide high quality contours, with in general median DSC > 0.85, MSD < 0.25 mm, HD_95p < 1 mm and ΔCoM < 0.5 mm. The qualitative assessment showed very few contours needed manual adaptations (40 kV: 20/155 contours, 80 kV: 3/155 contours). The uncertainty of the DL model is small (within 2%).Significance.A DL-based model dedicated to preclinical studies has been developed for multi-organ segmentation in two body sites. For the first time, a method independent of image acquisition parameters has been quantitatively evaluated, resulting in sub-millimeter performance, while qualitative assessment demonstrated the high quality of the DL-generated contours. The uncertainty analysis additionally showed that inherent model variability is low.

Deep learning-based segmentation of the thorax in mouse micro-CT scans

For image-guided small animal irradiations, the whole workflow of imaging, organ contouring, irradiation planning, and delivery is typically performed in a single session requiring continuous administration of anaesthetic agents. Automating contouring leads to a faster workflow, which limits exposure to anaesthesia and thereby, reducing its impact on experimental results and on animal wellbeing. Here, we trained the 2D and 3D U-Net architectures of no-new-Net (nnU-Net) for autocontouring of the thorax in mouse micro-CT images. We trained the models only on native CTs and evaluated their performance using an independent testing dataset (i.e., native CTs not included in the training and validation). Unlike previous studies, we also tested the model performance on an external dataset (i.e., contrast-enhanced CTs) to see how well they predict on CTs completely different from what they were trained on. We also assessed the interobserver variability using the generalized conformity index ([Formula: see text]) among three observers, providing a stronger human baseline for evaluating automated contours than previous studies. Lastly, we showed the benefit on the contouring time compared to manual contouring. The results show that 3D models of nnU-Net achieve superior segmentation accuracy and are more robust to unseen data than 2D models. For all target organs, the mean surface distance (MSD) and the Hausdorff distance (95p HD) of the best performing model for this task (nnU-Net 3d_fullres) are within 0.16 mm and 0.60 mm, respectively. These values are below the minimum required contouring accuracy of 1 mm for small animal irradiations, and improve significantly upon state-of-the-art 2D U-Net-based AIMOS method. Moreover, the conformity indices of the 3d_fullres model also compare favourably to the interobserver variability for all target organs, whereas the 2D models perform poorly in this regard. Importantly, the 3d_fullres model offers 98% reduction in contouring time.

Inter-observer variability of organ contouring for preclinical studies with cone beam Computed Tomography imaging

Background and purpose

In preclinical radiation studies, there is great interest in quantifying the radiation response of healthy tissues. Manual contouring has significant impact on the treatment-planning because of variation introduced by human interpretation. This results in inconsistencies when assessing normal tissue volumes. Evaluation of these discrepancies can provide a better understanding on the limitations of the current preclinical radiation workflow. In the present work, interobserver variability (IOV) in manual contouring of rodent normal tissues on cone-beam Computed Tomography, in head and thorax regions was evaluated.

Materials and methods

Two animal technicians performed manually (assisted) contouring of normal tissues located within the thorax and head regions of rodents, 20 cases per body site. Mean surface distance (MSD), displacement of center of mass (ΔCoM), DICE similarity coefficient (DSC) and the 95th percentile Hausdorff distance (HD₉₅) were calculated between the contours of the two observers to evaluate the IOV.

Results

For the thorax organs, right lung had the lowest IOV (ΔCoM: 0.08 ± 0.04 mm, DSC: 0.96 ± 0.01, MSD:0.07 ± 0.01 mm, HD₉₅:0.20 ± 0.03 mm) while spinal cord, the highest IOV (ΔCoM:0.5 ± 0.3 mm, DSC:0.81 ± 0.05, MSD:0.14 ± 0.03 mm, HD₉₅:0.8 ± 0.2 mm). Regarding head organs, right eye demonstrated the lowest IOV (ΔCoM:0.12 ± 0.08 mm, DSC: 0.93 ± 0.02, MSD: 0.15 ± 0.04 mm, HD₉₅: 0.29 ± 0.07 mm) while complete brain, the highest IOV (ΔCoM: 0.2 ± 0.1 mm, DSC: 0.94 ± 0.02, MSD: 0.3 ± 0.1 mm, HD₉₅: 0.5 ± 0.1 mm).

Conclusions

Our findings reveal small IOV, within the sub-mm range, for thorax and head normal tissues in rodents. The set of contours can serve as a basis for developing an automated delineation method for e.g., treatment planning.

Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans

Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models.

Mouse models of sarcopenia: classification and evaluation

Sarcopenia is a progressive and widespread skeletal muscle disease that is related to an increased possibility of adverse consequences such as falls, fractures, physical disabilities and death, and its risk increases with age. With the deepening of the understanding of sarcopenia, the disease has become a major clinical disease of the elderly and a key challenge of healthy ageing. However, the exact molecular mechanism of this disease is still unclear, and the selection of treatment strategies and the evaluation of its effect are not the same. Most importantly, the early symptoms of this disease are not obvious and are easy to ignore. In addition, the clinical manifestations of each patient are not exactly the same, which makes it difficult to effectively study the progression of sarcopenia. Therefore, it is necessary to develop and use animal models to understand the pathophysiology of sarcopenia and develop therapeutic strategies. This paper reviews the mouse models that can be used in the study of sarcopenia, including ageing models, genetically engineered models, hindlimb suspension models, chemical induction models, denervation models, and immobilization models; analyses their advantages and disadvantages and application scope; and finally summarizes the evaluation of sarcopenia in mouse models.

Automated quantification of COVID-19 severity and progression using chest CT images

OBJECTIVE

To develop and test computer software to detect, quantify, and monitor progression of pneumonia associated with COVID-19 using chest CT scans.

METHODS

One hundred twenty chest CT scans from subjects with lung infiltrates were used for training deep learning algorithms to segment lung regions and vessels. Seventy-two serial scans from 24 COVID-19 subjects were used to develop and test algorithms to detect and quantify the presence and progression of infiltrates associated with COVID-19. The algorithm included (1) automated lung boundary and vessel segmentation, (2) registration of the lung boundary between serial scans, (3) computerized identification of the pneumonitis regions, and (4) assessment of disease progression. Agreement between radiologist manually delineated regions and computer-detected regions was assessed using the Dice coefficient. Serial scans were registered and used to generate a heatmap visualizing the change between scans. Two radiologists, using a five-point Likert scale, subjectively rated heatmap accuracy in representing progression.

RESULTS

There was strong agreement between computer detection and the manual delineation of pneumonic regions with a Dice coefficient of 81% (CI 76-86%). In detecting large pneumonia regions (> 200 mm3), the algorithm had a sensitivity of 95% (CI 94-97%) and specificity of 84% (CI 81-86%). Radiologists rated 95% (CI 72 to 99) of heatmaps at least "acceptable" for representing disease progression.

CONCLUSION

The preliminary results suggested the feasibility of using computer software to detect and quantify pneumonic regions associated with COVID-19 and to generate heatmaps that can be used to visualize and assess progression.

KEY POINTS

• Both computer vision and deep learning technology were used to develop computer software to quantify the presence and progression of pneumonia associated with COVID-19 depicted on CT images. • The computer software was tested using both quantitative experiments and subjective assessment. • The computer software has the potential to assist in the detection of the pneumonic regions, monitor disease progression, and assess treatment efficacy related to COVID-19.

Nutritional Interventions in Cancer Cachexia: Evidence and Perspectives From Experimental Models

Cancer cachexia is a complex metabolic syndrome characterized by involuntary skeletal muscle loss and is associated with poor clinical outcome, decreased survival and negatively influences cancer therapy. No curative treatments are available for cancer cachexia, but nutritional intervention is recommended as a cornerstone of multimodal therapy. Optimal nutritional care is pivotal in the treatment of cancer cachexia, and the effects of nutrients may extend beyond provision of adequate energy uptake, targeting different mechanisms or metabolic pathways that are affected or deregulated by cachexia. The evidence to support this notion derived from nutritional intervention studies in experimental models of cancer cachexia is systematically discussed in this review. Moreover, experimental variables and readout parameters to determine skeletal muscle wasting and cachexia are methodologically evaluated to allow critical comparison of similar studies. Single- and multinutrient intervention studies including qualitative modulation of dietary protein, dietary fat, and supplementation with specific nutrients, such as carnitine and creatine, were reviewed for their efficacy to counteract muscle mass loss and its underlying mechanisms in experimental cancer cachexia. Numerous studies showed favorable effects on impaired protein turnover and related metabolic abnormalities of nutritional supplementation in parallel with a beneficial impact on cancer-induced muscle wasting. The combination of high quality nutrients in a multitargeted, multinutrient approach appears specifically promising, preferentially as a multimodal intervention, although more studies investigating the optimal quantity and combination of nutrients are needed. During the review process, a wide variation in timing, duration, dosing, and route of supplementation, as well as a wide variation in animal models were observed. Better standardization in dietary design, and the development of experimental models that better recapitulate the etiology of human cachexia, will further facilitate successful translation of experimentally-based multinutrient, multimodal interventions into clinical practice.