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Recent advances in cancer research and diagnostics largely rely on new developments in microscopic or molecular profiling techniques, offering high levels of detail with respect to either spatial or molecular features, but usually not both. Here, we present an explainable machine-learning approach for the integrated profiling of morphological, molecular and clinical features from breast cancer histology. First, our approach allows for the robust detection of cancer cells and tumour-infiltrating lymphocytes in histological images, providing precise heatmap visualizations explaining the classifier decisions. Second, molecular features, including DNA methylation, gene expression, copy number variations, somatic mutations and proteins are predicted from histology. Molecular predictions reach balanced accuracies up to 78%, whereas accuracies of over 95% can be achieved for subgroups of patients. Finally, our explainable AI approach allows assessment of the link between morphological and molecular cancer properties. The resulting computational multiplex-histology analysis can help promote basic cancer research and precision medicine through an integrated diagnostic scoring of histological, clinical and molecular features. Cancers are complex diseases that are increasingly studied using a diverse set of omics data. At the same time, histological images show the interaction of cells, which is not visible with bulk omics methods. Binder and colleagues present a method to learn from both kinds of data, such that molecular markers can be associated with visible patterns in the tissue samples and be used for more accurate breast cancer diagnosis.

Immune cell locomotion is associated with amoeboid migration, a flexible mode of movement, which depends on rapid cycles of actin polymerization and actomyosin contraction 1 . Many immune cells do not necessarily require integrins, the major family of adhesion receptors in mammals, to move productively through three-dimensional tissue spaces 2 , 3 . Instead, they can use alternative strategies to transmit their actin-driven forces to the substrate, explaining their migratory adaptation to changing external environments 4 – 6 . However, whether these generalized concepts apply to all immune cells is unclear. Here, we show that the movement of mast cells (immune cells with important roles during allergy and anaphylaxis) differs fundamentally from the widely applied paradigm of interstitial immune cell migration. We identify a crucial role for integrin-dependent adhesion in controlling mast cell movement and localization to anatomical niches rich in KIT ligand, the major mast cell growth and survival factor. Our findings show that substrate-dependent haptokinesis is an important mechanism for the tissue organization of resident immune cells. Immune cells are generally considered to be able to move through tissues using nonadhesive amoeboid migration mechanics. Here, the authors show that, unlike other immune cells, mast cells do not use this method and instead are completely reliant on integrin–ECM interactions.

Automated image analysis of cells and tissues has been an active research field in medical informatics for decades but has recently attracted increased attention due to developments in computer and microscopy hardware and the awareness that scientific and diagnostic pathology require novel approaches to perform objective quantitative analyses of cellular and tissue specimens. Model-based approaches use a priori information on cell shape features to obtain the segmentation, which may introduce a bias favouring the detection of cell nuclei only with certain properties. In this study we present a novel contour-based “minimum-model” cell detection and segmentation approach that uses minimal a priori information and detects contours independent of their shape. This approach avoids a segmentation bias with respect to shape features and allows for an accurate segmentation (precision = 0.908; recall = 0.859; validation based on ∼8000 manually-labeled cells) of a broad spectrum of normal and disease-related morphological features without the requirement of prior training.

Background The virtual microscopy is widely accepted in Pathology for educational purposes and teleconsultation but is far from the routine use in surgical pathology due to the technical requirements and some limitations. A technical problem is the limited bandwidth of a usual network and the delayed transmission rate and presentation time on the screen. Methods In this study the process of secondary diagnostic was evaluated using the \"T.Konsult Pathologie\" service of the Professional Association of German Pathologists within the German breast cancer screening program. The characteristics of the access to the WSI (Whole Slide Images) have been analyzed to explore the possibilities of prefetching and caching to reduce the presentation and transfer time with the goal to increase user acceptance. The log files of the web server were analyzed to reconstruct the movements of the pathologist on the WSI and to create the observation path. Using a specialized tool the observation paths were extracted automatically from the log files. The attributes linearity, 3-point-linearity, changes per request, and number of consecutive requests were calculated to design, develop and evaluate different caching and prefetching strategies. Results The analysis of the observation paths showed that a complete accordance of two image requests is a very rare event. But more frequently a partial covering of two requested image areas can be found. In total 257 diagnostic paths from 131 WSI have been extracted and analysed. On average a diagnostic path consists of 16 image requests and takes 189 seconds between first and last image request. The mean linearity was 0,41 and the mean 3-point-linearity 0,85. Three different caching algorithms have been compared with respect to hit rate and additional image requests on the WSI server. Tests demonstrated that 95% of the diagnostic paths could be loaded without any deletion of entries in the cache (cache size 12,2 Megapixel). If the image parts are stored after JPEG compression this complies with less than 2 MB. Discussion WSI telepathology is a technology which offers the possibility to break the limitations of conventional static telepathology. The complete histological slide may be investigated instead of sets of images of lesions sampled by the presenting pathologist. The benefit is demonstrated by the high diagnostic security of 95% accordance between first and second diagnosis.

Multiple independent studies have shown that tumor-infiltrating lymphocytes (TIL) are prognostic in breast cancer with potential relevance for response to immune-checkpoint inhibitor therapy. Although many groups are currently evaluating TIL, there is no standardized system for diagnostic applications. This study reports the results of two ring studies investigating TIL conducted by the International Working Group on Immuno-oncology Biomarkers. The study aim was to determine the intraclass correlation coefficient (ICC) for evaluation of TIL by different pathologists. A total of 120 slides were evaluated by a large group of pathologists with a web-based system in ring study 1 and a more advanced software-system in ring study 2 that included an integrated feedback with standardized reference images. The predefined aim for successful ring studies 1 and 2 was an ICC above 0.7 (lower limit of 95% confidence interval (CI)). In ring study 1 the prespecified endpoint was not reached (ICC: 0.70; 95% CI: 0.62–0.78). On the basis of an analysis of sources of variation, we developed a more advanced digital image evaluation system for ring study 2, which improved the ICC to 0.89 (95% CI: 0.85–0.92). The Fleiss' kappa value for <60 vs ≥60% TIL improved from 0.45 (ring study 1) to 0.63 in RS2 and the mean concordance improved from 88 to 92%. This large international standardization project shows that reproducible evaluation of TIL is feasible in breast cancer. This opens the way for standardized reporting of tumor immunological parameters in clinical studies and diagnostic practice. The software-guided image evaluation approach used in ring study 2 may be of value as a tool for evaluation of TIL in clinical trials and diagnostic practice. The experience gained from this approach might be applicable to the standardization of other diagnostic parameters in histopathology.

Background Automated image analysis methods are becoming more and more important to extract and quantify image features in microscopy-based biomedical studies and several commercial or open-source tools are available. However, most of the approaches rely on pixel-wise operations, a concept that has limitations when high-level object features and relationships between objects are studied and if user-interactivity on the object-level is desired. Results In this paper we present an open-source software that facilitates the analysis of content features and object relationships by using objects as basic processing unit instead of individual pixels. Our approach enables also users without programming knowledge to compose “analysis pipelines“ that exploit the object-level approach. We demonstrate the design and use of example pipelines for the immunohistochemistry-based cell proliferation quantification in breast cancer and two-photon fluorescence microscopy data about bone-osteoclast interaction, which underline the advantages of the object-based concept. Conclusions We introduce an open source software system that offers object-based image analysis. The object-based concept allows for a straight-forward development of object-related interactive or fully automated image analysis solutions. The presented software may therefore serve as a basis for various applications in the field of digital image analysis.

HintergrundBei nichtinfektiösem Versagen von Kniegelenkendoprothesen mit Metall-Polyethylen-Gleitpaarung liegen Fallbeschreibungen mit ausgeprägten periimplantären entzündlichen Reaktionen und Nekrosenbildungen vor.Ziel der ArbeitAufgrund der histopathologischen Ähnlichkeiten zu den dysfunktionellen Metall-auf-Metall(MoM)-Hüftgelenkendoprothesen wird der Typus einer MoM-ähnlichen-Reaktion bei Kniegelenkendoprothesen (ARMD-KEP) vorgeschlagen und ein histopathologischer Vergleich durchgeführt.Material und MethodenDie vorliegende Analyse bewertet 5 ARMD-KEP-Fälle anhand der „Synovial-like interface membrane“(SLIM)-Konsensusklassifikation, des Partikelalgorithmus, des CD3-Focus-Scores und des ALVAL-Scores. Als Vergleichsgruppen dienten 11 Fälle von MoM-Hüftendoprothese mit adverser und 20 Kniegelenkendoprothesenfälle ohne adverse Reaktion.ErgebnisseBei den ARMD-KEP-Fällen lag durchweg ein SLIM Typ VI vor. Der ALVAL-Score betrug im Median 10. Mittels CD3-Focus-Score konnte bei allen Fällen der Gruppe eine adverse Reaktion bestätigt werden. Partikelkorrosionen ließen sich bei 2 von 5 Fällen finden.SchlussfolgerungIn seltenen Fällen kann auch im Knie eine MoM-ähnliche adverse Reaktion vorliegen, wobei die entzündlichen und immunologischen Ausprägungen denen der adversen-MoM-Reaktion in der Hüfte ähneln. Als Pathogenesemechanismen können diskutiert werden: sekundärer Metall-Metall-Kontakt, Belastung des Kopplungsmechanismus und Korrosion der Metallkomponenten. Analog zur „trunnionosis“ in der Hüfte wird der Begriff „Hingiose“ für Korrosionserscheinungen in dysfunktionellen Situationen bei gekoppelten KEP-Systemen vorgeschlagen.

Many leukocytes use fast and flexible amoeboid migration strategies to move autonomously throughout tissues. Here, we show that the movement of mast cells (MCs), leukocytes with important roles during allergies and anaphylaxis, fundamentally differs from this rapid adhesion-free leukocyte migration. We identify a crucial role for integrin-dependent adhesion in controlling slow MC movement, which shapes the positioning and network-like tissue distribution of this long-lived immune cell type. In contrast to other immune and non-immune cells, MCs cannot compensate for the lack of integrin function by switching to another migration mode. Single-cell RNA-sequencing revealed a special role for integrins in defining a mature MC phenotype in the periarteriolar tissue space where several stromal cell types provide an anatomical niche rich in Kit ligand, the major MC growth and survival factor. Collectively, this study highlights substrate-dependent haptokinesis as an important mechanism for MC network formation and the tissue organization of resident immune cells.

Recent advances in cancer research largely rely on new developments in microscopic or molecular profiling techniques offering high level of detail with respect to either spatial or molecular features, but usually not both. Here, we present a novel machine learning-based computational approach that allows for the identification of morphological tissue features and the prediction of molecular properties from breast cancer imaging data. This integration of microanatomic information of tumors with complex molecular profiling data, including protein or gene expression, copy number variation, gene methylation and somatic mutations, provides a novel means to computationally score molecular markers with respect to their relevance to cancer and their spatial associations within the tumor microenvironment.