Understanding cancer-induced cachexia: imaging the flame and its fuel

Extracting value from total-body PET/CT image data - the emerging role of artificial intelligence

The evolution of Positron Emission Tomography (PET), culminating in the Total-Body PET (TB-PET) system, represents a paradigm shift in medical imaging. This paper explores the transformative role of Artificial Intelligence (AI) in enhancing clinical and research applications of TB-PET imaging. Clinically, TB-PET's superior sensitivity facilitates rapid imaging, low-dose imaging protocols, improved diagnostic capabilities and higher patient comfort. In research, TB-PET shows promise in studying systemic interactions and enhancing our understanding of human physiology and pathophysiology. In parallel, AI's integration into PET imaging workflows-spanning from image acquisition to data analysis-marks a significant development in nuclear medicine. This review delves into the current and potential roles of AI in augmenting TB-PET/CT's functionality and utility. We explore how AI can streamline current PET imaging processes and pioneer new applications, thereby maximising the technology's capabilities. The discussion also addresses necessary steps and considerations for effectively integrating AI into TB-PET/CT research and clinical practice. The paper highlights AI's role in enhancing TB-PET's efficiency and addresses the challenges posed by TB-PET's increased complexity. In conclusion, this exploration emphasises the need for a collaborative approach in the field of medical imaging. We advocate for shared resources and open-source initiatives as crucial steps towards harnessing the full potential of the AI/TB-PET synergy. This collaborative effort is essential for revolutionising medical imaging, ultimately leading to significant advancements in patient care and medical research.

Fully Automated, Semantic Segmentation of Whole-Body 18F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence

We introduce multiple-organ objective segmentation (MOOSE) software that generates subject-specific, multiorgan segmentation using data-centric artificial intelligence principles to facilitate high-throughput systemic investigations of the human body via whole-body PET imaging. Methods: Image data from 2 PET/CT systems were used in training MOOSE. For noncerebral structures, 50 whole-body CT images were used, 30 of which were acquired from healthy controls (14 men and 16 women), and 20 datasets were acquired from oncology patients (14 men and 6 women). Noncerebral tissues consisted of 13 abdominal organs, 20 bone segments, subcutaneous fat, visceral fat, psoas muscle, and skeletal muscle. An expert panel manually segmented all noncerebral structures except for subcutaneous fat, visceral fat, and skeletal muscle, which were semiautomatically segmented using thresholding. A majority-voting algorithm was used to generate a reference-standard segmentation. From the 50 CT datasets, 40 were used for training and 10 for testing. For cerebral structures, 34 ¹⁸F-FDG PET/MRI brain image volumes were used from 10 healthy controls (5 men and 5 women imaged twice) and 14 nonlesional epilepsy patients (7 men and 7 women). Only ¹⁸F-FDG PET images were considered for training: 24 and 10 of 34 volumes were used for training and testing, respectively. The Dice score coefficient (DSC) was used as the primary metric, and the average symmetric surface distance as a secondary metric, to evaluate the automated segmentation performance. Results: An excellent overlap between the reference labels and MOOSE-derived organ segmentations was observed: 92% of noncerebral tissues showed DSCs of more than 0.90, whereas a few organs exhibited lower DSCs (e.g., adrenal glands [0.72], pancreas [0.85], and bladder [0.86]). The median DSCs of brain subregions derived from PET images were lower. Only 29% of the brain segments had a median DSC of more than 0.90, whereas segmentation of 60% of regions yielded a median DSC of 0.80-0.89. The results of the average symmetric surface distance analysis demonstrated that the average distance between the reference standard and the automatically segmented tissue surfaces (organs, bones, and brain regions) lies within the size of image voxels (2 mm). Conclusion: The proposed segmentation pipeline allows automatic segmentation of 120 unique tissues from whole-body ¹⁸F-FDG PET/CT images with high accuracy.

Increased skeletal intermuscular fat is associated with reduced exercise capacity in cancer survivors: a cross-sectional study

BACKGROUND

Cancer survivors experience on average a 20% reduction in peak exercise capacity (VO2 peak) post-cancer treatment. Intermuscular fat (IMF) is a strong predictor of reduced exercise capacity in heart failure (HF) patients; however it is unknown whether increased IMF is related to reduced VO2 peak in cancer survivors.

METHODS AND RESULTS

Twenty eight individuals: 14 cancer survivors > 12-months post-cancer treatment and 14 individuals without cancer were matched on age, gender, and body mass index (BMI). Participants underwent magnetic resonance imaging (MRI) assessments of IMF within the paraspinal muscles, VO2 peak and exercise-associated measures of left ventricular ejection fraction (LVEF). Blinded analyses were performed. Associations between the ratio of IMF to skeletal muscle (SM) were estimated using Pearson's partial correlation coefficients. Individuals with cancer and non-cancer comparators were of similar age (54 ± 17 versus 54 ± 15 years; p = 1.0), gender (5 men and 9 women, both groups), and BMI (27 ± 4 versus 26 ± 4; p = 0.57). Peak VO2 was 22% lower in cancer survivors versus non-cancer comparators (26.9 vs 34.3 ml/kg/min; p = 0.005), and was correlated with IMF:SM in both cancer survivors and non-cancer individuals after accounting for exercise-associated LVEF, resting LVEF, BMI, other body fat depots, and cardiovascular disease (CVD) co-morbidities (p < 0.001 to 0.08 for all adjusted correlations).

CONCLUSION

Among cancer survivors that previously received anthracyclines, increased intermuscular fat is associated with reduced VO2 peak even after accounting for exercise-associated cardiac function. This suggests IMF is important in the development of exercise intolerance, an outcome experienced by a large number of cancer survivors.

Cinnamaldehyde regulates H2 O 2 -induced skeletal muscle atrophy by ameliorating the proteolytic and antioxidant defense systems

Skeletal muscle atrophy/wasting is associated with impaired protein metabolism in diverse physiological and pathophysiological conditions. Elevated levels of reactive oxygen species (ROS), disturbed redox status, and weakened antioxidant defense system are the major contributing factors toward atrophy. Regulation of protein metabolism by controlling ROS levels and its associated catabolic pathways may help in treating atrophy and related clinical conditions. Although cinnamaldehyde (CNA) enjoys the established status of antioxidant and its role in ROS management is reported, impact of CNA on skeletal muscle atrophy and related pathways is still unexplored. In the current study, the impact of CNA on C2C12 myotubes and the possible protection of cultured cells from H ₂ O ₂ -induced atrophy is examined. Myotubes were treated with H ₂ O ₂ in the presence and absence of CNA and the changes in the antioxidative, proteolytic systems, and mitochondrial functions were scored. Morphological analysis showed significant protective effects of CNA on length, diameter, and nuclei fusion index of myotubes. The evaluation of biochemical markers of atrophy; creatine kinase, lactate dehydrogenase, succinate dehydrogenase along with the study of muscle-specific structural protein (i.e., myosin heavy chain-fast [MHCf] type) showed significant protection of proteins by CNA. CNA pretreatment not only checked the activation of proteolytic systems (ubiquitin-proteasome E3-ligases [MuRF1/Atrogin1]), autophagy [Beclin1/LC3B], cathepsin L, calpain, caspase), but also prevented any alteration in the activities of antioxidative defense enzymes (catalase, glutathione- S-transferase, glutathione-peroxidase, superoxide dismutase, glutathione reductase). The results suggest that CNA protects myotubes from H ₂ O ₂ -induced atrophy by inhibiting/resisting the amendments in proteolytic systems and maintains cellular redox-balance.

Micro-computed tomography for non-invasive evaluation of muscle atrophy in mouse models of disease

Muscle wasting occurs during various chronic diseases and precedes death in humans as in mice. The evaluation of the degree of muscle atrophy in diseased mouse models is often overlooked since it requires the sacrifice of the animals for muscle examination or expensive instrumentation and highly qualified personnel, such as Magnetic Resonance Imaging (MRI). Very often behavioral tests for muscle strength evaluation are used as an outcome measurement in preclinical therapeutic trials. However, these tests are easy to perform serially, but not enough sensitive to detect early muscle changes during disease progression. Monitoring muscle loss in living animals could allow to perform more informative preclinical trials with a better evaluation of therapeutic benefit with respect to muscle wasting. We developed a non-invasive procedure based on micro-computed tomography (micro-CT) without contrast agents to monitor hind limb muscle wasting in mouse models of amyotrophic lateral sclerosis (ALS) and cancer cachexia: the transgenic SOD1^G93A mouse and the colon adenocarcinoma C26-bearing mouse, respectively. We established the scanning procedure and the parameters to consider in the reconstructed images to calculate the Index of Muscle Mass (IMM). The coefficient of variance for the whole procedure was 2.2%. We performed longitudinally micro-CT scan of hind limbs in SOD1^G93A mice at presymptomatic and symptomatic stages of the disease and calculated the IMM. We found that IMM in SOD1^G93A mice was lower than age-matched controls even before symptom onset. We also detected a further decrease in IMM as disease progresses, most markedly just before disease onset. We performed the same analyses in the C26-based mouse model losing quickly body and muscle mass because of cancer cachexia. Overall, we found that the reduced muscle content detected by micro-CT mirrored the reduced muscle weight in both disease models. We developed a fast, precise and easy-to-conduct imaging procedure to monitor hind limb muscle mass, useful in therapeutic preclinical trials but also in proof-of-principle studies to identify the onset of muscle wasting. This method could be widely applied to other disease models characterized by muscle wasting, to assist drug development and search for early biomarkers of muscle atrophy. Moreover, reducing the number of mice needed for the experiments and being less distressing are in line with the 3R principle embodied in national and international directives for animal research.

Research on cachexia, sarcopenia and skeletal muscle in cardiology

BACKGROUND

The awareness of cardiac cachexia, i.e. involuntary weight loss in patients with underlying cardiovascular disease, has increased over the last two decades.

METHODS AND RESULTS

This mini-review looks at recent research in the cardiovascular literature that is relevant to the areas of interest of the Journal of Cachexia, Sarcopenia and Muscle. It identifies significant research in the last 3 years on the obesity paradox, the causes and effects of skeletal muscle wasting, animal models of cachexia and emerging treatment ideas in cardiac cachexia.

CONCLUSIONS

Assuming a similar literature in the fields of cancer, chronic obstructive pulmonary disease, chronic renal failure and chronic liver failure, the emergence of cachexia as a vibrant area of clinical and experimental research seems assured.