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Simple Summary: Routine clinical approaches for cancer diagnosis demand fast, cost-efficient, and reliable methods, and the implementation of these methods within clinical settings. Currently, histopathology is the gold standard for tissue-based clinical diagnosis. Recently, spatially resolved molecular profiling techniques such as mass spectrometry imaging (MSI) or infrared spectroscopy imaging (IRI) have increasingly contributed to clinical research, e.g., by differentiating cancer subtypes using molecular fingerprints. However, the adoption of the corresponding workflows in clinical routines remains challenging, especially for fresh-frozen tissue specimens. Here, we present a novel device based on 3D-printing technology, which facilitates the sample preparation of needle biopsies for correlated clinical tissue analysis. It enables the use of a combination of MSI and IRI on fresh-frozen clinical samples, with a histopathological examination of the same needle core after formalin fixation and paraffin embedding (FFPE). This device and workflow can pave the way for a more profound understanding of biomolecular processes in cancer and, thus, facilitate more accurate diagnosis. The complex molecular alterations that underlie cancer pathophysiology are studied in depth with omics methods using bulk tissue extracts. For spatially resolved tissue diagnostics using needle biopsy cores, however, histopathological analysis using stained FFPE tissue and the immunohistochemistry (IHC) of a few marker proteins is currently the main clinical focus. Today, spatial omics imaging using MSI or IRI is an emerging diagnostic technology for the identification and classification of various cancer types. However, to conserve tissue-specific metabolomic states, fast, reliable, and precise methods for the preparation of fresh-frozen (FF) tissue sections are crucial. Such methods are often incompatible with clinical practice, since spatial metabolomics and the routine histopathology of needle biopsies currently require two biopsies for FF and FFPE sampling, respectively. Therefore, we developed a device and corresponding laboratory and computational workflows for the multimodal spatial omics analysis of fresh-frozen, longitudinally sectioned needle biopsies to accompany standard FFPE histopathology of the same biopsy core. As a proof-of-concept, we analyzed surgical human liver cancer specimens using IRI and MSI with precise co-registration and, following FFPE processing, by sequential clinical pathology analysis of the same biopsy core. This workflow allowed for a spatial comparison between different spectral profiles and alterations in tissue histology, as well as a direct comparison for histological diagnosis without the need for an extra biopsy. [ABSTRACT FROM AUTHOR]

Abdominal aortic aneurysms (AAA) may remain clinically silent until they enlarge and patients present with a potentially lethal rupture. This necessitates early detection and elective treatment. The goal of this study was to develop an easy-to-train algorithm which is capable of automated AAA screening in CT scans and can be applied to an intra-hospital environment. Three deep convolutional neural networks (ResNet, VGG-16 and AlexNet) were adapted for 3D classification and applied to a dataset consisting of 187 heterogenous CT scans. The 3D ResNet outperformed both other networks. Across the five folds of the first training dataset it achieved an accuracy of 0.856 and an area under the curve (AUC) of 0.926. Subsequently, the algorithms performance was verified on a second data set containing 106 scans, where it ran fully automated and resulted in an accuracy of 0.953 and an AUC of 0.971. A layer-wise relevance propagation (LRP) made the decision process interpretable and showed that the network correctly focused on the aortic lumen. In conclusion, the deep learning-based screening proved to be robust and showed high performance even on a heterogeneous multi-center data set. Integration into hospital workflow and its effect on aneurysm management would be an exciting topic of future research. [ABSTRACT FROM AUTHOR]

Radioembolization with 90Y-microspheres has been reported to induce contralateral liver hypertrophy with simultaneous ipsilateral control of tumor growth. The aim of the present systematic review was to summarize the evidence of contralateral liver hypertrophy and oncological outcome following unilateral treatment with radioembolization. A systematic literature search using the MEDLINE, EMBASE, and Cochrane libraries for studies published between 2008 and 2020 was performed. A total of 16 studies, comprising 602 patients, were included. The median kinetic growth rate per week of the contralateral liver lobe was 0.7% and declined slightly over time. The local tumor control was 84%. Surgical resection after radioembolization was carried out in 109 out of 362 patients (30%). Although the available data suggest that radioembolization prior to major hepatectomy is safe with a promising oncological outcome, the definitive role of radioembolization requires assessment within controlled clinical trials. [ABSTRACT FROM AUTHOR]

Radioembolization with 90 Y-microspheres has been reported to induce contralateral liver hypertrophy with simultaneous ipsilateral control of tumor growth. The aim of the present systematic review was to summarize the evidence of contralateral liver hypertrophy and oncological outcome following unilateral treatment with radioembolization. A systematic literature search using the MEDLINE, EMBASE, and Cochrane libraries for studies published between 2008 and 2020 was performed. A total of 16 studies, comprising 602 patients, were included. The median kinetic growth rate per week of the contralateral liver lobe was 0.7% and declined slightly over time. The local tumor control was 84%. Surgical resection after radioembolization was carried out in 109 out of 362 patients (30%). Although the available data suggest that radioembolization prior to major hepatectomy is safe with a promising oncological outcome, the definitive role of radioembolization requires assessment within controlled clinical trials.