Cachexia, a multifactorial wasting syndrome, is highly prevalent among advanced-stage cancer patients. Unlike weight loss in healthy humans, the progressive loss of body weight in cancer cachexia primarily implicates lean body mass, caused by an aberrant metabolism and systemic inflammation. This may lead to disease aggravation, poorer quality of life, and increased mortality. Timely detection is, therefore, crucial, as is the careful monitoring of cancer progression, in an effort to improve management, facilitate individual treatment and minimize disease complications. A detailed analysis of body composition and tissue changes using imaging modalities—that is, computed tomography, magnetic resonance imaging, (18F) fluoro-2-deoxy-d-glucose (18FDG) PET and dual-energy X-ray absorptiometry—shows great premise for charting the course of cachexia. Quantitative and qualitative changes to adipose tissue, organs, and muscle compartments, particularly of the trunk and extremities, could present important biomarkers for phenotyping cachexia and determining its onset in patients. In this review, we present and compare the imaging techniques that have been used in the setting of cancer cachexia. Their individual limitations, drawbacks in the face of clinical routine care, and relevance in oncology are also discussed.
Objectives To evaluate a compressed sensing artificial intelligence framework (CSAI) to accelerate MRI acquisition of the ankle. Methods Thirty patients were scanned at 3T. Axial T2-w, coronal T1-w, and coronal/sagittal intermediate-w scans with fat saturation were acquired using compressed sensing only (12:44 min, CS), CSAI with an acceleration factor of 4.6–5.3 (6:45 min, CSAI2x), and CSAI with an acceleration factor of 6.9–7.7 (4:46 min, CSAI3x). Moreover, a high-resolution axial T2-w scan was obtained using CSAI with a similar scan duration compared to CS. Depiction and presence of abnormalities were graded. Signal-to-noise and contrast-to-noise were calculated. Wilcoxon signed-rank test and Cohen’s kappa were used to compare CSAI with CS sequences. Results The correlation was perfect between CS and CSAI2x (κ = 1.0) and excellent for CS and CSAI3x (κ = 0.86–1.0). No significant differences were found for the depiction of structures between CS and CSAI2x and the same abnormalities were detected in both protocols. For CSAI3x the depiction was graded lower (p ≤ 0.001), though most abnormalities were also detected. For CSAI2x contrast-to-noise fluid/muscle was higher compared to CS (p ≤ 0.05), while no differences were found for other tissues. Signal-to-noise and contrast-to-noise were higher for CSAI3x compared to CS (p ≤ 0.05). The high - resolution axial T2-w sequence specifically improved the depiction of tendons and the tibial nerve (p ≤ 0.005). Conclusions Acquisition times can be reduced by 47% using CSAI compared to CS without decreasing diagnostic image quality. Reducing acquisition times by 63% is feasible but should be reserved for specific patients. The depiction of specific structures is improved using a high-resolution axial T2-w CSAI scan. Key Points • Prospective study showed that CSAI enables reduction in acquisition times by 47% without decreasing diagnostic image quality. • Reducing acquisition times by 63% still produces images with an acceptable diagnostic accuracy but should be reserved for specific patients. • CSAI may be implemented to scan at a higher resolution compared to standard CS images without increasing acquisition times.
Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): This work was supported by the German Center for Cardiovascular Research (DZHK grant number Deutsches Zentrum für Herz- Kreislaufforschung 81 × 3600606 to D.B.). Abstract: Background/Introduction Reticulated platelets (RPs) are prothrombotic RNA-rich platelets suggested to be detrimental in patients with chronic coronary syndrome (CCS) and high on treatment platelet reactivity. In addition, circulating RPs levels are independent predictor for adverse cardiovascular events in CCS patients and other pathological settings. However, RPs biology still need to be investigated. Purpose We thought to investigate the RPs proteome on single-cell level at rest and after activation using time-of-flight mass cytometry (CyTOF). Method Thrombocytes from peripheral blood of 11 CCS patients were isolated, prepared for CyTOF and stained with a custom-made CyTOF-antibody panel of 20 antibodies targeting important transmembrane proteins (anti-CD9, anti-CD29, anti-CD31, anti-CD36-, anti-CD40, anti-CD41, anti-CD42a, anti-CD42b-, anti-CD47, anti-CD61, anti-CD62P-, anti-CD63, anti-CD69, anti-CD107a, anti-CD154, anti-GPVI, anti-GPIIbIIa complex, anti-Par1, anti-PEAR-1 and the negative control anti-CD3 coupled with different metal isotopes). Two samples were prepared from each patient: one baseline sample (non-stimulated platelets) and one sample stimulated with 10 μM thrombin receptor-activating peptide (TRAP). According to previous experiences and common practice, we detected RPs and mature platelets (MPs) based on their RNA content. We analyzed the results with a custom bioinformatic pipeline comparing RPs to MPs expression. Earth mover’s distance (EMD) was assed as a measure of differential expression. Results While our bioinformatic analysis revealed that all transmembrane markers are significantly higher expressed in the larger RPs compared to MPs, not all markers differ to the same extend. Interestingly, the four markers with the highest calculated EMD (values in brackets) are all key regulators of platelet activation and aggregation: the collagen receptor GPVI (34.18), the collagen integrin receptor unit CD29 (ITGB1: 33.17), the adhesion protein CD9 (32.94) and the von Willebrand receptor unit CD42b (GPIbalpha) (30.08) (Figure 1A). Regarding the activation marker expression upon TRAP stimulation, RPs show higher median signal intensities of all four activation markers compared to MPs (Figure 1B and C). Especially, the markers CD107a (LAMP-1) and CD154 (CD40L) are expressed in MPs only to a very low extend, whereas there is a clear overexpression in RPs. Conclusion This dataset provides the first high resolution analysis of RPs proteome at rest and upon activation. The pro-thrombotic profile of RPs explains their hyperactivity and could offer the first biomolecular explanation of the detrimental role of RPs in CCS patients. In addition, this dataset provide high resolution biomolecular information which could be useful to personalize antiplatelet therapy in patients with high RPs levels.
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