The more concentrated, the better represented? The geographical concentration of immigrants and their descriptive representation in the German mixed-member system

<p>Supplemental material, IPS796263_supplemental_data for The more concentrated, the better represented? The geographical concentration of immigrants and their descriptive representation in the German mixed-member system by Lucas Geese and Diana Schacht in International Political Science Review</p

De geschade heerlijkheid : politiek gedrag van vrouwen en mannen in Nederland, 1918-1988

Item does not contain fulltextRijksuniversiteit Leiden, 1 januari 1989Promotor : Onbekend, N.N

Contrast Media Administration in Coronary Computed Tomography Angiography - A Systematic Review

Background Various different injection parameters influence enhancement of the coronary arteries. There is no consensus in the literature regarding the optimal contrast media (CM) injection protocol. The aim of this study is to provide an update on the effect of different CM injection parameters on the coronary attenuation in coronary computed tomographic angiography (CCTA).Method Studies published between January 2001 and May 2014 identified by Pubmed, Embase and MEDLINE were evaluated. Using predefined inclusion criteria and a data extraction form, the content of each eligible study was assessed. Initially, 2551 potential studies were identified. After applying our criteria, 36 studies were found to be eligible. Studies were systematically assessed for quality based on the validated Quality Assessment of Diagnostic Accuracy Studies (QUADAS)-II checklist.Results Extracted data proved to be heterogeneous and often incomplete. The injection protocol and outcome of the included publications were very diverse and results are difficult to compare. Based on the extracted data, it remains unclear which of the injection parameters is the most important determinant for adequate attenuation. It is likely that one parameter which combines multiple parameters (e.g. IDR) will be the most suitable determinant of coronary attenuation in CCTA protocols.Conclusion Research should be directed towards determining the influence of different injection parameters and defining individualized optimal IDRs tailored to patient-related factors (ideally in large randomized trials).</p

Noninvasive Glioblastoma Testing: Multimodal Approach to Monitoring and Predicting Treatment Response

Glioblastoma is the most aggressive adult primary brain tumor which is incurable despite intensive multimodal treatment. Inter- and intratumoral heterogeneity poses one of the biggest barriers in the diagnosis and treatment of glioblastoma, causing differences in treatment response and outcome. Noninvasive prognostic and predictive tests are highly needed to complement the current armamentarium. Noninvasive testing of glioblastoma uses multiple techniques that can capture the heterogeneity of glioblastoma. This set of diagnostic approaches comprises advanced MRI techniques, nuclear imaging, liquid biopsy, and new integrated approaches including radiogenomics and radiomics. New treatment options such as agents targeted at driver oncogenes and immunotherapy are currently being developed, but benefit for glioblastoma patients still has to be demonstrated. Understanding and unraveling tumor heterogeneity and microenvironment can help to create a treatment regime that is patient-tailored to these specific tumor characteristics. Improved noninvasive tests are crucial to this success. This review discusses multiple diagnostic approaches and their effect on predicting and monitoring treatment response in glioblastoma

Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19.

peer reviewedThe coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention

Deep learning for the fully automated segmentation of the inner ear on MRI

Abstract Segmentation of anatomical structures is valuable in a variety of tasks, including 3D visualization, surgical planning, and quantitative image analysis. Manual segmentation is time-consuming and deals with intra and inter-observer variability. To develop a deep-learning approach for the fully automated segmentation of the inner ear in MRI, a 3D U-net was trained on 944 MRI scans with manually segmented inner ears as reference standard. The model was validated on an independent, multicentric dataset consisting of 177 MRI scans from three different centers. The model was also evaluated on a clinical validation set containing eight MRI scans with severe changes in the morphology of the labyrinth. The 3D U-net model showed precise Dice Similarity Coefficient scores (mean DSC-0.8790) with a high True Positive Rate (91.5%) and low False Discovery Rate and False Negative Rates (14.8% and 8.49% respectively) across images from three different centers. The model proved to perform well with a DSC of 0.8768 on the clinical validation dataset. The proposed auto-segmentation model is equivalent to human readers and is a reliable, consistent, and efficient method for inner ear segmentation, which can be used in a variety of clinical applications such as surgical planning and quantitative image analysis