The aim of this study was to develop a deep learning-based method for segmentation of bones in CT scans and test its accuracy compared to manual delineation, as a first step in the creation of an automated PET/CT-based method for quantifying skeletal tumour burden. Convolutional neural networks (CNNs) were trained to segment 49 bones using manual segmentations from 100 CT scans. After training, the CNN-based segmentation method was tested on 46 patients with prostate cancer, who had undergone 18F-choline-PET/CT and 18F-NaF PET/CT less than three weeks apart. Bone volumes were calculated from the segmentations. The network's performance was compared with manual segmentations of five bones made by an experienced physician. Accuracy of the spatial overlap between automated CNN-based and manual segmentations of these five bones was assessed using the Sørensen-Dice index (SDI). Reproducibility was evaluated applying the Bland-Altman method. The median (SD) volumes of the five selected bones were by CNN and manual segmentation: Th7 41 (3.8) and 36 (5.1), L3 76 (13) and 75 (9.2), sacrum 284 (40) and 283 (26), 7th rib 33 (3.9) and 31 (4.8), sternum 80 (11) and 72 (9.2), respectively. Median SDIs were 0.86 (Th7), 0.85 (L3), 0.88 (sacrum), 0.84 (7th rib) and 0.83 (sternum). The intraobserver volume difference was less with CNN-based than manual approach: Th7 2% and 14%, L3 7% and 8%, sacrum 1% and 3%, 7th rib 1% and 6%, sternum 3% and 5%, respectively. The average volume difference measured as ratio volume difference/mean volume between the two CNN-based segmentations was 5-6% for the vertebral column and ribs and ≤3% for other bones. The new deep learning-based method for automated segmentation of bones in CT scans provided highly accurate bone volumes in a fast and automated way and, thus, appears to be a valuable first step in the development of a clinical useful processing procedure providing reliable skeletal segmentation as a key part of quantification of skeletal metastases.

In recent years, coaching has received special attention as a method to improve healthy lifestyle behaviours. The fact that coaching has found its way into healthcare and may provide new ways of engaging the patients and making them accountable for their health, justifies the need for an overview of the evidence regarding coaching interventions used in patient care, the effect of the interventions, and the quality of the studies published. However, in order to provide a clear definition of the coaching interventions selected for this review, we have found it necessary to distinguish between health coaching and life coaching. In this review, we will only focus on the latter method and on that basis assess the health related outcomes of life coaching.

Objective: Cardiac events are a major cause of death in patients with idiopathic inflammatory myopathies. The study objective was in a controlled setting to describe cardiac abnormalities by noninvasive methods in a cohort of patients with polymyositis (PM) or dermatomyositis (DM) and to identify predictors for cardiac dysfunction.Methods: In a cross‐sectional study, 76 patients with PM/DM and 48 matched healthy controls (HCs) were assessed by serum levels of cardiac troponin I, electrocardiography, Holter monitoring, echocardiography with tissue Doppler imaging, and quantitative cardiac ^99mTc‐pyrophosphate (^99mTc‐PYP) scintigraphy.Results: Compared to HCs, patients with PM/DM more frequently had left ventricular diastolic dysfunction (LVDD) (12% versus 0%; P = 0.02) and longer QRS and QT intervals (P = 0.007 and P < 0.0001, respectively). In multivariate analysis, factors associated with LVDD were age (P = 0.001), disease duration (P = 0.004), presence of myositis‐specific or ‐associated autoantibodies (P = 0.05), and high cardiac ^99mTc‐PYP uptake (P = 0.006). In multivariate analysis of the pooled data for patients and HCs, a diagnosis of PM/DM (P < 0.0001) was associated with LVDD.Conclusion: Patients with PM or DM had an increased prevalence of cardiac abnormalities compared to HCs. LVDD was a common occurrence in PM/DM patients and correlated to disease duration. In addition, the association of LVDD with myositis‐specific or ‐associated autoantibodies and high cardiac ^99mTc‐PYP uptake supports the notion of underlying autoimmunity and myocardial inflammation in patients with PM/DM.

This study evaluates the relative importance of several mechanisms possibly involved in the natriuresis elicited by slow sodium loading, i.e. the renin‐angiotensin‐aldosterone system (RAAS), mean arterial blood pressure (MAP), glomerular filtration rate (GFR), atrial natriuretic peptide (ANP), oxytocin and nitric oxide (NO). Eight seated subjects on standardised sodium intake (30 mmol NaCl day⁻¹) received isotonic saline intravenously (NaLoading: 20 μmol Na⁺ kg⁻¹ min⁻¹ or ≈11 ml min⁻¹ for 240 min). NaLoading did not change MAP or GFR (by clearance of ⁵¹Cr‐EDTA). Significant natriuresis occurred within 1 h (from 9 ± 3 to 13 ± 2 μmol min⁻¹). A 6‐fold increase was found during the last hour of infusion as plasma renin activity, angiotensin II (ANGII) and aldosterone decreased markedly. Sodium excretion continued to increase after NaLoading. During NaLoading, plasma renin activity and ANGII were linearly related (R= 0.997) as were ANGII and aldosterone (R= 0.999). The slopes were 0.40 pm ANGII (mi.u. renin activity)⁻¹ and 22 pm aldosterone (pm ANGII)⁻¹. Plasma ANP and oxytocin remained unchanged, as did the urinary excretion rates of cGMP and NO metabolites (NO_x). In conclusion, sodium excretion may increase 7‐fold without changes in MAP, GFR, plasma ANP, plasma oxytocin, and cGMP‐ and NO_x excretion, but concomitant with marked decreases in circulating RAAS components. The immediate renal response to sodium excess appears to be fading of ANGII‐mediated tubular sodium reabsorption. Subsequently the decrease in aldosterone may become important.

Sodium ¹⁸F-fluoride (¹⁸F-NaF) PET/CT imaging is a promising imaging technique for assessment of atherosclerosis, but is hampered by a lack of validated quantification protocols. Both personal characteristics and technical factors can affect quantification of arterial ¹⁸F-NaF uptake. This study investigated if blood activity, renal function, injected dose, circulating time, and PET/CT system affect quantification of arterial ¹⁸F-NaF uptake. Methods: Eighty-nine healthy subjects were prospectively examined by ¹⁸F-NaF PET/CT imaging. Arterial ¹⁸F-NaF uptake was quantified at the level of the ascending aorta, aortic arch, descending thoracic aorta, and coronary arteries by calculating the maximum ¹⁸F-NaF activity (NaFmax), the maximum target-to-background ratio (TBRmax/mean), and the maximum blood subtracted ¹⁸F-NaF activity (bsNaFmax). Multivariable linear regression assessed the effect of personal characteristics and technical factors on quantification of arterial ¹⁸F-NaF uptake. Results: NaFmax and TBRmax/mean were dependent on blood activity (β = .34 to .44, P < .001 and β = -.68 to -.58, P < .001, respectively) and PET/CT system (β = -.80 to -.53, P < .001 and β = -.80 to -.23, P < .031, respectively). bsNaFmax depended on PET/CT system (β = -.91 to -.57, P < .001), but not blood activity. This finding was observed at the level of the ascending aorta, aortic arch, descending thoracic aorta, as well as the coronary arteries. In addition to blood activity and PET/CT system, injected dose affected quantification of arterial ¹⁸F-NaF uptake, whereas renal function and circulating time did not. Conclusion: Prospective evaluation of 89 healthy subjects demonstrated that quantification of arterial ¹⁸F-NaF uptake is affected by blood activity, injected dose, and PET/CT system. Therefore, blood activity, injected dose, and PET/CT system should be taken into account to generate accurate estimates of arterial ¹⁸F-NaF uptake.