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To assess epidemiology, clinical presentation, treatment and overall survival of adult patients with renal sarcomas, the 2004-2016 SEER and NCDB databases were queried for adult patients diagnosed with renal sarcoma, calculating average annual age-adjusted incidence rates (AAIR) and average annual percentage change (AAPC) as well as overall survival (OS). In n = 1279 included renal sarcoma patients, AAIR remained constant over the study period (average 0.53 cases/1million; AAPC = 0.7, p = 0.6). Leiomyosarcoma (AAIR 0.14 cases/1 million) and malignant rhabdoid tumors (0.06 cases/1 million) were most common. Sarcoma histiotypes demonstrated considerable heterogeneity regarding demographic and cancer-related variables. Patients presented with advanced local extent (T3 33.3%; T4 14.2%) or distant metastases (29.1%) and commonly underwent surgical resection (81.6%). Longer OS was independently associated with younger age, female sex, lower comorbidity index, low T stage, negative surgical margins, absence of tumor necrosis or distant metastases and leiomyosarcoma histiotype (multivariable p < 0.05 each). Treatment efficacy varied according to sarcoma histiotype (interaction p < 0.001). Accounting for 0.25% of renal malignancies, renal sarcomas include 43 histiotypes with distinct epidemiology, clinical presentation, outcomes and sensitivity to systemic therapy, thereby reflecting soft-tissue sarcoma behavior. Renal sarcoma treatment patterns follow recommendations by renal cancer guidelines with surgical resection as the cornerstone of therapy., (© 2024. The Author(s).)

In recent years, a variety of deep learning networks for cardiac MRI (CMR) segmentation have been developed and analyzed. However, nearly all of them are focused on cine CMR under breathold. In this work, accuracy of deep learning methods is assessed for volumetric analysis (via segmentation) of the left ventricle in real-time free-breathing CMR at rest and under exercise stress. Data from healthy volunteers (n = 15) for cine and real-time free-breathing CMR at rest and under exercise stress were analyzed retrospectively. Exercise stress was performed using an ergometer in the supine position. Segmentations of two deep learning methods, a commercially available technique (comDL) and an openly available network (nnU-Net), were compared to a reference model created via the manual correction of segmentations obtained with comDL. Segmentations of left ventricular endocardium (LV), left ventricular myocardium (MYO), and right ventricle (RV) are compared for both end-systolic and end-diastolic phases and analyzed with Dice's coefficient. The volumetric analysis includes the cardiac function parameters LV end-diastolic volume (EDV), LV end-systolic volume (ESV), and LV ejection fraction (EF), evaluated with respect to both absolute and relative differences. For cine CMR, nnU-Net and comDL achieve Dice's coefficients above 0.95 for LV and 0.9 for MYO, and RV. For real-time CMR, the accuracy of nnU-Net exceeds that of comDL overall. For real-time CMR at rest, nnU-Net achieves Dice's coefficients of 0.94 for LV, 0.89 for MYO, and 0.90 for RV and the mean absolute differences between nnU-Net and the reference are 2.9 mL for EDV, 3.5 mL for ESV, and 2.6% for EF. For real-time CMR under exercise stress, nnU-Net achieves Dice's coefficients of 0.92 for LV, 0.85 for MYO, and 0.83 for RV and the mean absolute differences between nnU-Net and reference are 11.4 mL for EDV, 2.9 mL for ESV, and 3.6% for EF. Deep learning methods designed or trained for cine CMR segmentation can perform well on real-time CMR. For real-time free-breathing CMR at rest, the performance of deep learning methods is comparable to inter-observer variability in cine CMR and is usable for fully automatic segmentation. For real-time CMR under exercise stress, the performance of nnU-Net could promise a higher degree of automation in the future., (© 2024. The Author(s).)

Absorption-based clinical computed tomography (CT) is the current imaging method of choice in the diagnosis of lung diseases. Many pulmonary diseases are affecting microscopic structures of the lung, such as terminal bronchi, alveolar spaces, sublobular blood vessels or the pulmonary interstitial tissue. As spatial resolution in CT is limited by the clinically acceptable applied X-ray dose, a comprehensive diagnosis of conditions such as interstitial lung disease, idiopathic pulmonary fibrosis or the characterization of small pulmonary nodules is limited and may require additional validation by invasive lung biopsies. Propagation-based imaging (PBI) is a phase sensitive X-ray imaging technique capable of reaching high spatial resolutions at relatively low applied radiation dose levels. In this publication, we present technical refinements of PBI for the characterization of different artificial lung pathologies, mimicking clinically relevant patterns in ventilated fresh porcine lungs in a human-scale chest phantom. The combination of a very large propagation distance of 10.7 m and a photon counting detector with [Formula: see text] pixel size enabled high resolution PBI CT with significantly improved dose efficiency, measured by thermoluminescence detectors. Image quality was directly compared with state-of-the-art clinical CT. PBI with increased propagation distance was found to provide improved image quality at the same or even lower X-ray dose levels than clinical CT. By combining PBI with iodine k-edge subtraction imaging we further demonstrate that, the high quality of the calculated iodine concentration maps might be a potential tool for the analysis of lung perfusion in great detail. Our results indicate PBI to be of great value for accurate diagnosis of lung disease in patients as it allows to depict pathological lesions non-invasively at high resolution in 3D. This will especially benefit patients at high risk of complications from invasive lung biopsies such as in the setting of suspected idiopathic pulmonary fibrosis (IPF)., (© 2023. The Author(s).)

Cardiovascular magnetic resonance (CMR) imaging provides reliable assessments of biventricular morphology and function. Since manual post-processing is time-consuming and prone to observer variability, efforts have been directed towards novel artificial intelligence-based fully automated analyses. Hence, we sought to investigate the impact of artificial intelligence-based fully automated assessments on the inter-study variability of biventricular volumes and function. Eighteen participants (11 with normal, 3 with heart failure and preserved and 4 with reduced ejection fraction (EF)) underwent serial CMR imaging at in median 63 days (range 49-87) interval. Short axis cine stacks were acquired for the evaluation of left ventricular (LV) mass, LV and right ventricular (RV) end-diastolic, end-systolic and stroke volumes as well as EF. Assessments were performed manually (QMass, Medis Medical Imaging Systems, Leiden, Netherlands) by an experienced (3 years) and inexperienced reader (no active reporting, 45 min of training with five cases from the SCMR consensus data) as well as fully automated (suiteHEART, Neosoft, Pewaukee, WI, USA) without any manual corrections. Inter-study reproducibility was overall excellent with respect to LV volumetric indices, best for the experienced observer (intraclass correlation coefficient (ICC) > 0.98, coefficient of variation (CoV, < 9.6%) closely followed by automated analyses (ICC > 0.93, CoV < 12.4%) and lowest for the inexperienced observer (ICC > 0.86, CoV < 18.8%). Inter-study reproducibility of RV volumes was excellent for the experienced observer (ICC > 0.88, CoV < 10.7%) but considerably lower for automated and inexperienced manual analyses (ICC > 0.69 and > 0.46, CoV < 22.8% and < 28.7% respectively). In this cohort, fully automated analyses allowed reliable serial investigations of LV volumes with comparable inter-study reproducibility to manual analyses performed by an experienced CMR observer. In contrast, RV automated quantification with current algorithms still relied on manual post-processing for reliability.

Glomerular filtration rate (GFR) declines with age by approx. 1 ml/min/m 2 per year beginning in the third decade of life. At 70 years of age > 40 ml/min/m 2 of GFR will be lost. Thus, factors affecting loss of GFR have significant public health implications. Furthermore, the definition of chronic kidney disease based on GFR may not be appropriate for the elderly. We analyzed factors affecting absolute and relative change of eGFR over a 5 year period in 12,381 participants of the Gutenberg Health Study. We estimated GFR at baseline and after 5 years of follow-up by two different equations. Association with the decline of estimated GFR (eGFR) was assessed by multivariable regression analysis. We confirmed a median loss of eGFR per year of approx. 1 ml/min/m 2 . Aside from albuminuria systolic blood pressure was most strongly associated with faster decline of eGFR followed by echocardiographic evidence of left ventricular diastolic dysfunction and reduced ejection fraction. White blood cell count showed a moderate association with eGFR loss. Diastolic blood pressure, serum uric acid and serum albumin were associated with slower GFR decline in multivariable analysis. Sensitivity analysis with exclusion of individuals taking diuretics, antihypertensive, antidiabetic, or lipid lowering drugs confirmed these associations.

Cerebral hypoperfusion in the first hours after subarachnoid haemorrhage (SAH) is a major determinant of poor neurological outcome. However, the underlying pathophysiology is only partly understood. Here we induced neutropenia in C57BL/6N mice by anti-Ly6G antibody injection, induced SAH by endovascular filament perforation, and analysed cerebral cortical perfusion with laser SPECKLE contrast imaging to investigate the role of neutrophils in mediating cerebral hypoperfusion during the first 24 h post-SAH. SAH induction significantly increased the intracranial pressure (ICP), and significantly reduced the cerebral perfusion pressure (CPP). At 3 h after SAH, ICP had returned to baseline and CPP was similar between SAH and sham mice. However, in SAH mice with normal neutrophil counts cortical hypoperfusion persisted. Conversely, despite similar CPP, cortical perfusion was significantly higher at 3 h after SAH in mice with neutropenia. The levels of 8-iso-prostaglandin-F2α in the subarachnoid haematoma increased significantly at 3 h after SAH in animals with normal neutrophil counts indicating oxidative stress, which was not the case in neutropenic SAH animals. These results suggest that neutrophils are important mediators of cortical hypoperfusion and oxidative stress early after SAH. Targeting neutrophil function and neutrophil-induced oxidative stress could be a promising new approach to mitigate cerebral hypoperfusion early after SAH.

Given the importance of gland morphology in grading prostate cancer (PCa), automatically differentiating between epithelium and other tissues is an important prerequisite for the development of automated methods for detecting PCa. We propose a new deep learning method to segment epithelial tissue in digitised hematoxylin and eosin (H&E) stained prostatectomy slides using immunohistochemistry (IHC) as reference standard. We used IHC to create a precise and objective ground truth compared to manual outlining on H&E slides, especially in areas with high-grade PCa. 102 tissue sections were stained with H&E and subsequently restained with P63 and CK8/18 IHC markers to highlight epithelial structures. Afterwards each pair was co-registered. First, we trained a U-Net to segment epithelial structures in IHC using a subset of the IHC slides that were preprocessed with color deconvolution. Second, this network was applied to the remaining slides to create the reference standard used to train a second U-Net on H&E. Our system accurately segmented both intact glands and individual tumour epithelial cells. The generalisation capacity of our system is shown using an independent external dataset from a different centre. We envision this segmentation as the first part of a fully automated prostate cancer grading pipeline.

Vessel wall stiffening is an important clinical parameter, but it is unknown whether platelets, key elements in the pathogenesis of arterial thrombosis, are associated with arterial stiffness. The present studies sought to determine whether mean platelet volume (MPV), a potential marker of platelet activation, is linked to vascular elasticity as assessed by the augmentation index (AIx), in 15,010 individuals from the population-based Gutenberg Health Study. Multivariable analysis showed that MPV in both males (β 0.776; 95 th CI [0.250;1.16]; p = 0.0024) and females (β 0.881[0.328;1.43]; p = 0.0018) is strongly associated with AIx. Individuals with MPV and AIx above the sex-specific medians had worse survival. Association analysis between MPV-related genetic variants and arterial stiffness identified four genetic variants in males and one in females related with AIx. Cox regression analysis for mortality identified one of these joint genetic variants close to ring finger protein 145 gene (RNF145, rs10076782) linked with increased mortality (hazard ratio 2.02; 95 th CI [1.35;3.02]; p = 0.00061). Thus, these population-based data demonstrate a close relation between platelet volume as a potential marker of platelet activation and arterial stiffness in both sexes. Further research is warranted to further elucidate the mechanisms underlying larger platelets' role in arterial stiffening including the role of shared common genetics.