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Purpose Circumferential enhancement on MR vessel wall imaging has been proposed as a biomarker of a higher risk of rupture in intracranial aneurysms. Focal enhancement is frequently encountered in unruptured aneurysms, but its implication for risk stratification and patient management remains unclear. This study investigates the association of focal wall enhancement with hemodynamic and morphological risk factors and histologic markers of wall inflammation and degeneration. Methods Patients with an unruptured middle cerebral artery aneurysm who underwent 3D rotational angiography and 3T MR vessel wall imaging showing focal wall enhancement were included. Hemodynamic parameters were calculated based on flow simulations and compared between enhanced regions and the entire aneurysm surface. Morphological parameters were semiautomatically extracted and quantitatively associated with wall enhancement. Histological analysis included detection of vasa vasorum, CD34, and myeloperoxidase staining in a subset of patients. Results Twenty-two aneurysms were analyzed. Enhanced regions were significantly associated with lower AWSS, lower maxOSI, and increased LSA. In multivariate analysis, higher ellipticity index was an independent predictor of wall enhancement. Histologic signs of inflammation and degeneration and higher PHASES score were significantly associated with focal enhancement. Conclusion Focal wall enhancement is colocalized with hemodynamic factors that have been related to a higher rupture risk. It is correlated with morphological factors linked to rupture risk, higher PHASES score, and histologic markers of wall destabilization. The results support the hypothesis that focal enhancement could serve as a surrogate marker for aneurysm instability.

Background The reliable detection of SARS-CoV-2 has become one of the most important contributions to COVID-19 crisis management. With the publication of the first sequences of SARS-CoV-2, several diagnostic PCR assays have been developed and published. In addition to in-house assays the market was flooded with numerous commercially available ready-to-use PCR kits, with both approaches showing alarming shortages in reagent supply. Aim Here we present a resource-efficient in-house protocol for the PCR detection of SARS-CoV-2 RNA in patient specimens (RKI/ZBS1 SARS-CoV-2 protocol). Methods Two duplex one-step real-time RT-PCR assays are run simultaneously and provide information on two different SARS-CoV-2 genomic regions. Each one is duplexed with a control that either indicates potential PCR inhibition or proves the successful extraction of nucleic acid from the clinical specimen. Results Limit of RNA detection for both SARS-CoV-2 assays is below 10 genomes per reaction. The protocol enables testing specimens in duplicate across the two different SARS-CoV-2 PCR assays, saving reagents by increasing testing capacity. The protocol can be run on various PCR cyclers with several PCR master mix kits. Conclusion The presented RKI/ZBS1 SARS-CoV-2 protocol represents a cost-effective alternative in times of shortages when commercially available ready-to-use kits may not be available or affordable.

Bats are reservoirs of emerging viruses that are highly pathogenic to other mammals, including humans. Despite the diversity and abundance of bat viruses, to date they have not been shown to harbor exogenous retroviruses. Here we report the discovery and characterization of a group of koala retrovirus-related (KoRV-related) gammaretroviruses in Australian and Asian bats. These include the Hervey pteropid gammaretrovirus (HPG), identified in the scat of the Australian black flying fox (Pteropus alecto), which is the first reproduction-competent retrovirus found in bats. HPG is a close relative of KoRV and the gibbon ape leukemia virus (GALV), with virion morphology and Mn2+-dependent virion-associated reverse transcriptase activity typical of a gammaretrovirus. In vitro, HPG is capable of infecting bat and human cells, but not mouse cells, and displays a similar pattern of cell tropism as KoRV-A and GALV. Population studies reveal the presence of HPG and KoRV-related sequences in several locations across northeast Australia, as well as serologic evidence for HPG in multiple pteropid bat species, while phylogenetic analysis places these bat viruses as the basal group within the KoRV-related retroviruses. Taken together, these results reveal bats to be important reservoirs of exogenous KoRVrelated gammaretroviruses.

BackgroundParameters of body composition have prognostic potential in patients with oncologic diseases. The aim of the present study was to analyse the prognostic potential of radiomics-based parameters of the skeletal musculature and adipose tissues in patients with advanced hepatocellular carcinoma (HCC).MethodsRadiomics features were extracted from a cohort of 297 HCC patients as post hoc sub-study of the SORAMIC randomized controlled trial. Patients were treated with selective internal radiation therapy (SIRT) in combination with sorafenib or with sorafenib alone yielding two groups: (1) sorafenib monotherapy (n = 147) and (2) sorafenib and SIRT (n = 150). The main outcome was 1-year survival. Segmentation of muscle tissue and adipose tissue was used to retrieve 881 features. Correlation analysis and feature cleansing yielded 292 features for each patient group and each tissue type. We combined 9 feature selection methods with 10 feature set compositions to build 90 feature sets. We used 11 classifiers to build 990 models. We subdivided the patient groups into a train and validation cohort and a test cohort, that is, one third of the patient groups.ResultsWe used the train and validation set to identify the best feature selection and classification model and applied it to the test set for each patient group. Classification yields for patients who underwent sorafenib monotherapy an accuracy of 75.51% and area under the curve (AUC) of 0.7576 (95% confidence interval [CI]: 0.6376–0.8776). For patients who underwent treatment with SIRT and sorafenib, results are accuracy = 78.00% and AUC = 0.8032 (95% CI: 0.6930–0.9134).ConclusionsParameters of radiomics-based analysis of the skeletal musculature and adipose tissue predict 1-year survival in patients with advanced HCC. The prognostic value of radiomics-based parameters was higher in patients who were treated with SIRT and sorafenib.

Purpose Most recently transformer models became the state of the art in various medical image segmentation tasks and challenges, outperforming most of the conventional deep learning approaches. Picking up on that trend, this study aims at applying various transformer models to the highly challenging task of colorectal cancer (CRC) segmentation in CT imaging and assessing how they hold up to the current state-of-the-art convolutional neural network (CNN), the nnUnet. Furthermore, we wanted to investigate the impact of the network size on the resulting accuracies, since transformer models tend to be significantly larger than conventional network architectures. Methods For this purpose, six different transformer models, with specific architectural advancements and network sizes were implemented alongside the aforementioned nnUnet and were applied to the CRC segmentation task of the medical segmentation decathlon. Results The best results were achieved with the Swin-UNETR, D-Former, and VT-Unet, each transformer models, with a Dice similarity coefficient (DSC) of 0.60, 0.59 and 0.59, respectively. Therefore, the current state-of-the-art CNN, the nnUnet could be outperformed by transformer architectures regarding this task. Furthermore, a comparison with the inter-observer variability (IOV) of approx. 0.64 DSC indicates almost expert-level accuracy. The comparatively low IOV emphasizes the complexity and challenge of CRC segmentation, as well as indicating limitations regarding the achievable segmentation accuracy. Conclusion As a result of this study, transformer models underline their current upward trend in producing state-of-the-art results also for the challenging task of CRC segmentation. However, with ever smaller advances in total accuracies, as demonstrated in this study by the on par performances of multiple network variants, other advantages like efficiency, low computation demands, or ease of adaption to new tasks become more and more relevant.

Radiofrequency ablation was introduced recently to treat spinal metastases, which are among the most common metastases. These minimally-invasive interventions are most often image-guided by flat-panel CT scans, withholding soft tissue contrast like MR imaging. Image fusion of diagnostic MR and operative CT images could provide important and useful information during interventions. Diagnostic MR and interventional flat-panel CT scans of 19 patients, who underwent radiofrequency ablations of spinal metastases were obtained. Our presented approach piecewise rigidly registers single vertebrae using normalized gradient fields and embeds them within a fused image. Registration accuracy was determined via Euclidean distances between corresponding landmark pairs of ground truth data. Our method resulted in an average registration error of 2.35mm. An optimal image fusion performed by landmark registrations achieved an average registration error of 1.70mm. Additionally, intra- and inter-reader variability was determined, resulting in mean distances of corresponding landmark pairs of 1.05mm (MRI) and 1.03mm (flat-panel CT) for the intra-reader variability and 1.36mm and 1.28mm for the inter-reader variability, respectively. Our multi-segmental approach with normalized gradient fields as image similarity measure can handle spine deformations due to patient positioning and avoid time-consuming manually performed registration. Thus, our method can provide practical and applicable intervention support without significantly delaying the clinical workflow or additional workload. [Display omitted] •A multi-rigid and image-based registration approach for spinal images is proposed.•The algorithm takes spine deformations and interventional CT imaging into account.•The presented approach is accurate and precise enough for intervention support.

Background/Objectives: Microwave ablation (MWA) is the leading therapy method for treating patients with liver cancer. MWA simulation is used to further improve the therapy and to help develop new devices. Methods: A water-cooled ablation needle was reconstructed. MWA simulations of a polyacrylamide phantom were carried out and compared with a representative clinical example (tumor diameter: 8.75 mm). The Arrhenius damage model and a critical temperature approach of 60 °C were applied to assess the necrosis zones. Finally, the simulation results were compared to the corresponding MR measurements. Results: Most of the heating in the simulation took place at a distance of 5 mm along the transverse axis and 20 mm along the longitudinal axis above the needle tip. The calculated Dice scores for the Arrhenius model were 0.77/0.53 for the phantom/clinical case. For the critical temperature approach, Dice scores of 0.60/0.66 for the phantom/clinical case were achieved. Conclusions: The comparison between simulated and measured temperature increases showed an excellent agreement. However, differences in the predicted necrosis volume might be caused by omitting consideration of the heat sink effect, especially in the clinical case. Nevertheless, this workflow enables short MWA simulation times (approximately 3 min) and demonstrates a step towards possible integration into daily clinical use.

Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical practice due to the increasing incidence of liver cancer each year. Whereas various network variants with overall promising results in the field of medical image segmentation have been successfully developed over the last years, almost all of them struggle with the challenge of accurately segmenting hepatic lesions in magnetic resonance imaging (MRI). This led to the idea of combining elements of convolutional and transformer-based architectures to overcome the existing limitations. This work presents a hybrid network called SWTR-Unet, consisting of a pretrained ResNet, transformer blocks as well as a common Unet-style decoder path. This network was primarily applied to single-modality non-contrast-enhanced liver MRI and additionally to the publicly available computed tomography (CT) data of the liver tumor segmentation (LiTS) challenge to verify the applicability on other modalities. For a broader evaluation, multiple state-of-the-art networks were implemented and applied, ensuring a direct comparability. Furthermore, correlation analysis and an ablation study were carried out, to investigate various influencing factors on the segmentation accuracy of the presented method. With Dice scores of averaged 98+-2% for liver and 81+-28% lesion segmentation on the MRI dataset and 97+-2% and 79+-25%, respectively on the CT dataset, the proposed SWTR-Unet proved to be a precise approach for liver and hepatic lesion segmentation with state-of-the-art results for MRI and competing accuracy in CT imaging. The achieved segmentation accuracy was found to be on par with manually performed expert segmentations as indicated by inter-observer variabilities for liver lesion segmentation. In conclusion, the presented method could save valuable time and resources in clinical practice.

This study's objective was to segment spinal metastases in diagnostic MR images using a deep learning-based approach. Segmentation of such lesions can present a pivotal step towards enhanced therapy planning and validation, as well as intervention support during minimally invasive and image-guided surgeries like radiofrequency ablations. For this purpose, we used a U-Net like architecture trained with 40 clinical cases including both, lytic and sclerotic lesion types and various MR sequences. Our proposed method was evaluated with regards to various factors influencing the segmentation quality, e.g. the used MR sequences and the input dimension. We quantitatively assessed our experiments using Dice coefficients, sensitivity and specificity rates. Compared to expertly annotated lesion segmentations, the experiments yielded promising results with average Dice scores up to 77.6% and mean sensitivity rates up to 78.9%. To our best knowledge, our proposed study is one of the first to tackle this particular issue, which limits direct comparability with related works. In respect to similar deep learning-based lesion segmentations, e.g. in liver MR images or spinal CT images, our experiments showed similar or in some respects superior segmentation quality. Overall, our automatic approach can provide almost expert-like segmentation accuracy in this challenging and ambitious task.