Delayed (18)FDG PET/CT imaging at 180 minutes improves quantitation of atherosclerotic plaque inflammation over imaging at 90 minutes. Therefore, the optimal acquisition time-point to assess atherosclerotic plaque inflammation lies beyond the advocated time-point of 90 minutes after (18)FDG administration.

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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. Methods: 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 18 F-choline-PET/CT and 18 F-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. Results: 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. Conclusion: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.

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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 Results. Compared to HCs, patients with PM/DM more frequently had left ventricular diastolic dysfunction (LVDD) (12% versus 0%; P 5 0.02) and longer QRS and QT intervals (P 5 0.007 and P < 0.0001, respectively). In multivariate analysis, factors associated with LVDD were age (P 5 0.001), disease duration (P 5 0.004), presence of myositis-specific or -associated autoantibodies (P 5 0.05), and high cardiac 99m Tc-PYP uptake (P 5 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 99m Tc-PYP uptake supports the notion of underlying autoimmunity and myocardial inflammation in patients with PM/DM.