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Objectives To reduce the dose of intravenous iodine-based contrast media (ICM) in CT through virtual contrast-enhanced images using generative adversarial networks. Methods Dual-energy CTs in the arterial phase of 85 patients were randomly split into an 80/20 train/test collective. Four different generative adversarial networks (GANs) based on image pairs, which comprised one image with virtually reduced ICM and the original full ICM CT slice, were trained, testing two input formats (2D and 2.5D) and two reduced ICM dose levels (−50% and −80%). The amount of intravenous ICM was reduced by creating virtual non-contrast series using dual-energy and adding the corresponding percentage of the iodine map. The evaluation was based on different scores (L1 loss, SSIM, PSNR, FID), which evaluate the image quality and similarity. Additionally, a visual Turing test (VTT) with three radiologists was used to assess the similarity and pathological consistency. Results The −80% models reach an SSIM of > 98%, PSNR of > 48, L1 of between 7.5 and 8, and an FID of between 1.6 and 1.7. In comparison, the −50% models reach a SSIM of > 99%, PSNR of > 51, L1 of between 6.0 and 6.1, and an FID between 0.8 and 0.95. For the crucial question of pathological consistency, only the 50% ICM reduction networks achieved 100% consistency, which is required for clinical use. Conclusions The required amount of ICM for CT can be reduced by 50% while maintaining image quality and diagnostic accuracy using GANs. Further phantom studies and animal experiments are required to confirm these initial results. Key Points • The amount of contrast media required for CT can be reduced by 50% using generative adversarial networks . • Not only the image quality but especially the pathological consistency must be evaluated to assess safety . • A too pronounced contrast media reduction could influence the pathological consistency in our collective at 80% .

\"The sixth book of the Iliad includes some of the most memorable and best-loved episodes in the whole poem: it holds meaning and interest for many different people, not just students of ancient Greek. Book 6 describes how Glaukos and Diomedes, though fighting on opposite sides, recognise an ancient bond of hospitality and exchange gifts on the battlefield. It then follows Hector as he enters the city of Troy and meets the most important people in his life: his mother, Helen and Paris, and finally his wife and baby son. It is above all through the loving and fraught encounter between Hector and Andromache that Homer exposes the horror of war. This edition is suitable for undergraduates at all levels, and students in the upper forms of schools. The Introduction requires no knowledge of Greek and is intended for all readers interested in Homer\"-- Provided by publisher.

This book proposes a new approach to the study of ancient Greek and Mesopotamian literature. Ranging from Homer and Gilgamesh to Herodotus and the Babylonian-Greek author Berossos, it paints a picture of two literary cultures that, over the course of time, became profoundly entwined. Along the way, the book addresses many questions of crucial importance to the student of the ancient world: how did the literature of Greece relate to that of its eastern neighbours? What did ancient readers from different cultures think it meant to be human? Who invented the writing of universal history as we know it? How did the Greeks come to divide the world into Greeks and 'barbarians', and what happened when they came to live alongside those 'barbarians' after the conquests of Alexander the Great? In addressing these questions, the book draws on cutting-edge research in comparative literature, postcolonial studies and archive theory.

Productive angiogenesis, a prerequisite for tumour growth, depends on the balanced release of angiogenic and angiostatic factors by different cell types within hypoxic tumours. Natural killer (NK) cells kill cancer cells and infiltrate hypoxic tumour areas. Cellular adaptation to low oxygen is mediated by Hypoxia-inducible factors (HIFs). We found that deletion of HIF-1α in NK cells inhibited tumour growth despite impaired tumour cell killing. Tumours developing in these conditions were characterised by a high-density network of immature vessels, severe haemorrhage, increased hypoxia, and facilitated metastasis due to non-productive angiogenesis. Loss of HIF-1α in NK cells increased the bioavailability of the major angiogenic cytokine vascular endothelial growth factor (VEGF) by decreasing the infiltration of NK cells that express angiostatic soluble VEGFR-1. In summary, this identifies the hypoxic response in NK cells as an inhibitor of VEGF-driven angiogenesis, yet, this promotes tumour growth by allowing the formation of functionally improved vessels. Tumour hypoxia influences both the immune responses and angiogenesis. Here, the authors show that HIF-1α deletion in NK cells impairs NK cytotoxic activity but inhibit tumour growth by decreasing the infiltration of NK cells that express angiostatic soluble VEGFR-1, thus resulting in non-functional angiogenesis.

Recently, radiomics has emerged as a non-invasive, imaging-based tissue characterization method in multiple cancer types. One limitation for robust and reproducible analysis lies in the inter-reader variability of the tumor annotations, which can potentially cause differences in the extracted feature sets and results. In this study, the diagnostic potential of a rapid and clinically feasible VOI (Volume of Interest)-based approach to radiomics is investigated to assess MR-derived parameters for predicting molecular subtype, hormonal receptor status, Ki67- and HER2-Expression, metastasis of lymph nodes and lymph vessel involvement as well as grading in patients with breast cancer. A total of 98 treatment-naïve patients (mean 59.7 years, range 28.0-89.4) with BI-RADS 5 and 6 lesions who underwent a dedicated breast MRI prior to therapy were retrospectively included in this study. The imaging protocol comprised dynamic contrast-enhanced T1-weighted imaging and T2-weighted imaging. Tumor annotations were obtained by drawing VOIs around the primary tumor lesions followed by thresholding. From each segmentation, 13.118 quantitative imaging features were extracted and analyzed with machine learning methods. Validation was performed by 5-fold cross-validation with 25 repeats. Predictions for molecular subtypes obtained AUCs of 0.75 (HER2-enriched), 0.73 (triple-negative), 0.65 (luminal A) and 0.69 (luminal B). Differentiating subtypes from one another was highest for HER2-enriched vs triple-negative (AUC 0.97), followed by luminal B vs triple-negative (0.86). Receptor status predictions for Estrogen Receptor (ER), Progesterone Receptor (PR) and Hormone receptor positivity yielded AUCs of 0.67, 0.69 and 0.69, while Ki67 and HER2 Expressions achieved 0.81 and 0.62. Involvement of the lymph vessels could be predicted with an AUC of 0.8, while lymph node metastasis yielded an AUC of 0.71. Models for grading performed similar with an AUC of 0.71 for Elston-Ellis grading and 0.74 for the histological grading. Our preliminary results of a rapid approach to VOI-based tumor-annotations for radiomics provides comparable results to current publications with the perks of clinical suitability, enabling a comprehensive non-invasive platform for breast tumor decoding and phenotyping.

A poor nutritional status is associated with worse pulmonary function and survival in people with cystic fibrosis (pwCF). CF transmembrane conductance regulator modulators can improve pulmonary function and body weight, but more data is needed to evaluate its effects on body composition. In this retrospective study, a pre-trained deep-learning network was used to perform a fully automated body composition analysis on chest CTs from 66 adult pwCF before and after receiving elexacaftor/tezacaftor/ivacaftor (ETI) therapy. Muscle and adipose tissues were quantified and divided by bone volume to obtain body size-adjusted ratios. After receiving ETI therapy, marked increases were observed in all adipose tissue ratios among pwCF, including the total adipose tissue ratio (+ 46.21%, p  < 0.001). In contrast, only small, but statistically significant increases of the muscle ratio were measured in the overall study population (+ 1.63%, p = 0.008). Study participants who were initially categorized as underweight experienced more pronounced effects on total adipose tissue ratio (p = 0.002), while gains in muscle ratio were equally distributed across BMI categories (p = 0.832). Our findings suggest that ETI therapy primarily affects adipose tissues, not muscle tissue, in adults with CF. These effects are primarily observed among pwCF who were initially underweight. Our findings may have implications for the future nutritional management of pwCF.

Patients with neuroendocrine tumors of gastro-entero-pancreatic origin (GEP-NET) experience changes in fat and muscle composition. Dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) are currently used to analyze body composition. Changes thereof could indicate cancer progression or response to treatment. This study examines the correlation between CT-based (computed tomography) body composition analysis (BCA) and DXA or BIA measurement. 74 GEP-NET-patients received whole-body [68Ga]-DOTATOC-PET/CT, BIA, and DXA-scans. BCA was performed based on the non-contrast-enhanced, 5 mm, whole-body-CT images. BCA from CT shows a strong correlation between body fat ratio with DXA (r = 0.95, ρC = 0.83) and BIA (r = 0.92, ρC = 0.76) and between skeletal muscle ratio with BIA: r = 0.81, ρC = 0.49. The deep learning-network achieves highly accurate results (mean Sørensen-Dice-score 0.93). Using BCA on routine Positron emission tomography/CT-scans to monitor patients’ body composition in the diagnostic workflow can reduce additional exams whilst substantially amplifying measurement in slower progressing cancers such as GEP-NET.

The complex process of manual biomarker extraction from body composition analysis (BCA) has far restricted the analysis of SARS-CoV-2 outcomes to small patient cohorts and a limited number of tissue types. We investigate the association of two BCA-based biomarkers with the development of severe SARS-CoV-2 infections for 918 patients (354 female, 564 male) regarding disease severity and mortality (186 deceased). Multiple tissues, such as muscle, bone, or adipose tissue are used and acquired with a deep-learning-based, fully-automated BCA from computed tomography images of the chest. The BCA features and markers were univariately analyzed with a Shapiro–Wilk and two-sided Mann–Whitney-U test. In a multivariate approach, obtained markers were adjusted by a defined set of laboratory parameters promoted by other studies. Subsequently, the relationship between the markers and two endpoints, namely severity and mortality, was investigated with regard to statistical significance. The univariate approach showed that the muscle volume was significant for female ( p severity  ≤ 0.001, p mortality  ≤ 0.0001) and male patients ( p severity  = 0.018, p mortality  ≤ 0.0001) regarding the severity and mortality endpoints. For male patients, the intra- and intermuscular adipose tissue (IMAT) ( p  ≤ 0.0001), epicardial adipose tissue (EAT) ( p  ≤ 0.001) and pericardial adipose tissue (PAT) ( p  ≤ 0.0001) were significant regarding the severity outcome. With the mortality outcome, muscle ( p  ≤ 0.0001), IMAT ( p  ≤ 0.001), EAT ( p  = 0.011) and PAT ( p  = 0.003) remained significant. For female patients, bone ( p  ≤ 0.001), IMAT ( p  = 0.032) and PAT ( p  = 0.047) were significant in univariate analyses regarding the severity and bone ( p  = 0.005) regarding the mortality. Furthermore, the defined sarcopenia marker ( p  ≤ 0.0001, for female and male) was significant for both endpoints. The cardiac marker was significant for severity (p female  = 0.014, p male  ≤ 0.0001) and for mortality (p female  ≤ 0.0001, p male  ≤ 0.0001) endpoint for both genders. The multivariate logistic regression showed that the sarcopenia marker was significant ( p severity  = 0.006, p mortality  = 0.002) for both endpoints (OR severity  = 0.42, 95% CI severity : 0.23–0.78, OR mortality  = 0.34, 95% CI mortality : 0.17–0.67). The cardiac marker showed significance (p = 0.018) only for the severity endpoint (OR = 1.42, 95% CI 1.06–1.90). The association between BCA-based sarcopenia and cardiac biomarkers and disease severity and mortality suggests that these biomarkers can contribute to the risk stratification of SARS-CoV-2 patients. Patients with a higher cardiac marker and a lower sarcopenia marker are at risk for a severe course or death. Whether those biomarkers hold similar importance for other pneumonia-related diseases requires further investigation.

The study aimed to develop and evaluate an algorithm based on the YOLOv8x framework to automatically detect and identify cerebrospinal fluid (CSF) shunt valves. This approach seeks to streamline the diagnostic process identifying shunt valve types and pressure levels. A retrospective cohort of 2701 anonymized radiographs comprising six types of CSF shunt valves was used. Data augmentation techniques such as flipping, scaling, and mosaic augmentation were applied during training to enhance robustness. The dataset was split into 80% training and 20% testing subsets as part of a 5-fold cross-validation. Validation was conducted on a separate test set of 295 images using metrics such as mean Average Precision (mAP) at intersection over union thresholds of 50% (mAP50) as well as precision, recall, and F1-scores as metrics. Additionally, a class-based reference image assignment system was used to link the detected valves with the corresponding manufacturer images. These paired images were then independently reviewed by two radiologists to assess the accuracy of the algorithm’s classifications. The algorithm achieved a weighted mAP50 of 0.884 and a weighted average F1-score of 94.8%. High F1-scores were observed for Codman Certas (99.6%) and Codman Hakim (99.6%), with lower scores for less common valves like proGAV (30.8%). Radiologists were able to identify both correct and incorrect classifications made by the algorithm with 100% accuracy, due to the integrated safety mechanism. This safety mechanism relies on the fully automated linking of detected valves with the corresponding manufacturer images. In Conclusion the automated system demonstrated high efficiency in detecting and classifying CSF shunt valves, significantly simplifying the diagnostic workflow. Moreover, the integration of a robust safety mechanism ensures that potential misclassifications are identified and corrected.