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The standardized uptake value (SUV) is widely used for quantitative evaluation in oncological FDG-PET but has well-known shortcomings as a measure of the tumor’s glucose consumption. selleck compound (SUR) of tumor SUV and arterial blood SUV (BSUV) possesses an increased prognostic value but requires image-based BSUV determination, typically in the aortic lumen. However, accurate manual ROI delineation requires care and imposes an additional workload, which makes the SUR approach less attractive for clinical routine. The goal of the present work was the development of a fully automated method for BSUV determination in whole-body PET/CT.

Automatic delineation of the aortic lumen was performed with a convolutional neural network (CNN), using the U-Net architecture. A total of 946 FDG PET/CT scans from several sites were used for network training (N = 366) and testing (N = 580). #link# For all scans, the aortic lumen was manually delineated, avoiding areas affected by motion-induced attenuation artifacts oe SUR computation without increasing the workload in the clinical setting.In recent times, there has been a growing interest in understanding the impact of gender on disease biology and clinical outcomes in Philadelphia-negative chronic myeloproliferative neoplasms. Among those, polycythemia vera (PV) is characterized by increased thrombotic risk, systemic symptoms, and overall reduced survival. Here, we aim to summarize data on whether and to what extent female sex can affect PV biology and outcome. To this end, we will discuss the latest acquisitions in terms of pathogenesis, diagnosis, epidemiology, clinical presentation and symptoms burden, thrombotic risk and related treatment strategies, and prognosis in female patients affected by PV.Purpose Hepatic arterial infusion chemotherapy (HAIC) is one of the options to treat unresectable hepatocellular carcinoma (HCC). The majority of HCC patients suffer great pain in the course of HAIC treatment. To improve the quality of life and the efficacy of HAIC treatment, the causes of pain, the choice of an analgesic regimen, and the relationship between pain and prognosis of HCC were analyzed. Methods A total of 376 HCC patients under HAIC in our hospital were recriuted between March 2017 and September 2019. Multivariate linear regression analysis (stepwise) was used to calculate the potential factors related to the severe pain in HCC patients under HAIC. Analgesics treatments were carried out based on the results of the visual analogue scale (VAS) score which was used to evaluate the pain. Results The mean value of the VAS score is 3.604, which indicates that the pain in most patients is mild and endurable. Intra-arterial lidocaine injection is an effective method in most patients (96%, 361 of 376), and the total score of VAS is reduced from 1355 to 195 following lidocaine injection. Multivariate analysis suggestes that oxaliplatin (OXA) preparation time, hepatic artery diameter and OXA manufacturers (R2 = 0.859) are influential factors for pain scores. Conclusion This study demonstrates an effective way to systematically assess and ease pain in HCC patients with HAIC treatment. OXA preparation time, hepatic artery diameter, and OXA manufacturers are the potential influencing factors for pain. This work presented here will provide a detailed understanding of the clinical application of HAIC in advanced HCC patients.Galectins, a family of highly conserved β-galactoside-binding proteins, control tumor progression by modulating different hallmarks of cancer. Galectin-1 (Gal-1), a proto-type member of this family, plays essential roles in tumor angiogenesis and immunosuppression by cross-linking glycosylated receptors on the surface of endothelial and immune cells. Targeted disruption of Gal-1 suppresses tumor growth by counteracting aberrant angiogenesis and reinforcing antitumor immunity in several experimental settings. Given the multiple therapeutic benefits associated with Gal-1 blockade, several Gal-1 inhibitors, including glycan-based competitors, antagonistic peptides, aptamers and neutralizing monoclonal antibodies, have been designed and evaluated in pre-clinical tumor models. Here we report the biochemical and functional characterization of a newly developed neutralizing anti-human Gal-1 monoclonal antibody (Gal-1-mAb3), which specifically recognizes a unique epitope in Gal-1 protein and exerts both angioregulatory and immunomodulatory activities. Blockade of Gal-1 function using Gal-1-mAb3, might be relevant not only in cancer but also in other pathologic conditions characterized by aberrant angiogenesis and uncontrolled immunosuppression.In recent years, there has been a dramatic increase in research papers about machine learning (ML) and artificial intelligence in radiology. With so many papers around, it is of paramount importance to make a proper scientific quality assessment as to their validity, reliability, effectiveness, and clinical applicability. Due to methodological complexity, the papers on ML in radiology are often hard to evaluate, requiring a good understanding of key methodological issues. In this review, we aimed to guide the radiology community about key methodological aspects of ML to improve their academic reading and peer-review experience. Key aspects of ML pipeline were presented within four broad categories study design, data handling, modelling, and reporting. Sixteen key methodological items and related common pitfalls were reviewed with a fresh perspective database size, robustness of reference standard, information leakage, feature scaling, reliability of features, high dimensionality, perturbations in feature selection, class balance, bias-variance trade-off, hyperparameter tuning, performance metrics, generalisability, clinical utility, comparison with traditional tools, data sharing, and transparent reporting.Key Points• Machine learning is new and rather complex for the radiology community.• Validity, reliability, effectiveness, and clinical applicability of studies on machine learning can be evaluated with a proper understanding of key methodological concepts about study design, data handling, modelling, and reporting.• Understanding key methodological concepts will provide a better academic reading and peer-review experience for the radiology community.