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Objective The exchange of health-related data is subject to regional laws and regulations, such as the General Data Protection Regulation (GDPR) in the EU or the Health Insurance Portability and Accountability Act (HIPAA) in the United States, resulting in non-trivial challenges for researchers and educators when working with these data. In pathology, the digitization of diagnostic tissue samples inevitably generates identifying data that can consist of sensitive but also acquisition-related information stored in vendor-specific file formats. Distribution and off-clinical use of these Whole Slide Images (WSIs) are usually done in these formats, as an industry-wide standardization such as DICOM is yet only tentatively adopted and slide scanner vendors currently do not provide anonymization functionality. Methods We developed a guideline for the proper handling of histopathological image data particularly for research and education with regard to the GDPR. In this context, we evaluated existing anonymization methods and examined proprietary format specifications to identify all sensitive information for the most common WSI formats. This work results in a software library that enables GDPR-compliant anonymization of WSIs while preserving the native formats. Results Based on the analysis of proprietary formats, all occurrences of sensitive information were identified for file formats frequently used in clinical routine, and finally, an open-source programming library with an executable CLI tool and wrappers for different programming languages was developed. Conclusions Our analysis showed that there is no straightforward software solution to anonymize WSIs in a GDPR-compliant way while maintaining the data format. We closed this gap with our extensible open-source library that works instantaneously and offline.

Background The identity management is a central component in medical research. Patients are recruited from various sites, which requires an error tolerant record linkage method, to ensure that patients are registered only once. In large research projects or institutions, the identity management has to deal with several thousands or millions of patients. In environments with large numbers of patients the register process could lead to high runtimes caused by record linkage. The Central Biomaterial Bank of the Charité (ZeBanC) searched for an identity management solution, which can handle millions of patients in large research projects with an acceptable performance. The goal of this paper was to simulate the registration of several million patients using the E-PIX service at Charité – Universitätsmedizin Berlin. The E-PIX service was evaluated in terms of needed runtimes, memory requirements, and processor utilization. A total of at least 20 million patients had to be registered. The runtimes to register patients into databases with various sizes should be examined, and the maximum number of patients, which the E-PIX service could handle, should be determined. Methods Tools were set up or developed to measure the needed runtimes, the memory used and the processor usage to register patients into various sizes of databases. To generate runtimes close to reality, modified patient data based on transposed real patient data were used for the simulation. The transposed patient data were sent to E-PIX to measure the runtimes of the registration process. This measurement was repeated for various database sizes. Results E-PIX is suitable to manage multi-million patients within a dataset. With the given hardware, it was possible to register a total of more than 30 million patients. It was possible to register more than 16 thousand patients per day into this database. Conclusions The E-PIX tool fulfills the requirements of the Charité to be used for large research projects. The use of E-PIX is intended for the research context in the Charité.

International audience; The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) files for diagnostic purposes has increased in the last few years. The increasing performance of technical components and the Food and Drug Administration (FDA) approval of systems for primary diagnosis led to increased interest in applying DP workflows. However, despite this revolutionary transition, real world data suggest that a fully digital approach to the histological workflow has been implemented in only a minority of pathology laboratories. The objective of this study is to facilitate the implementation of DP workflows in pathology laboratories, helping those involved in this process of transformation to identify: (a) the scope and the boundaries of the DP transformation; (b) how to introduce automation to reduce errors; (c) how to introduce appropriate quality control to guarantee the safety of the process and (d) the hardware and software needed to implement DP systems inside the pathology laboratory. The European Society of Digital and Integrative Pathology (ESDIP) provided consensus-based recommendations developed through discussion among members of the Scientific Committee. The recommendations are thus based on the expertise of the panel members and on the agreement obtained after virtual meetings. Prior to publication, the recommendations were reviewed by members of the ESDIP Board. The recommendations comprehensively cover every step of the implementation of the digital workflow in the anatomic pathology department, emphasizing the importance of interoperability, automation and tracking of the entire process before the introduction of a scanning facility. Compared to the available national and international guidelines, the present document represents a practical, handy reference for the correct implementation of the digital workflow in Europe.

Background and objective: Artificial intelligence (AI) apps hold great potential to make pathological diagnoses more accurate and time efficient. Widespread use of AI in pathology is hampered by interface incompatibilities between pathology software. We studied the existing interfaces in order to develop the EMPAIA App Interface, an open standard for the integration of pathology AI apps. Methods: The EMPAIA App Interface relies on widely-used web communication protocols and containerization. It consists of three parts: A standardized format to describe the semantics of an app, a mechanism to deploy and execute apps in computing environments, and a web API through which apps can exchange data with a host application. Results: Five commercial AI app manufacturers successfully adapted their products to the EMPAIA App Interface and helped improve it with their feedback. Open source tools facilitate the adoption of the interface by providing reusable data access and scheduling functionality and enabling automatic validation of app compliance. Conclusions: Existing AI apps and pathology software can be adapted to the EMPAIA App Interface with little effort. It is a viable alternative to the proprietary interfaces of current software. If enough vendors join in, the EMPAIA App Interface can help to advance the use of AI in pathology.

Background: The evaluation and improvement of MRI-based grading schemes such as PI-RADS requires the correlation of histological findings with MRI images. Several approaches have been described to join the two modalities. However, these approaches have in common that they all require modification of the pathology workflow. Here, we describe a novel method to utilize vertex models for the correlation of histological findings with MRI images without affecting the histological sectioning protocol. Methods: Three patients with prostate cancer who underwent radical prostatectomy as well as presurgical MRI were selected. Prostates are segmented on the MRI images to extract a 3D vertex model using 3D Slicer. Histological sectioning is then applied on the vertex model to yield reference planes for the tissue slides. Afterwards, slides and annotated tumor regions are aligned with the reference planes using the Coherent Point Drift algorithm. Carcinomas can then be visualized in 3D using the three different techniques of block representation, convex hull and Metaballs. Results: 3D Slicer allows importing vertex models for segmentation purposes and thus the reconstructed tumors can be utilized as annotations in the original MRI images. Registration results are evaluated using the Jaccard index, which is computed by dividing the intersection area between tissue slide and reference plane by their union area. To evaluate the 3D reconstructions, the tumor-to-tissue ratio of the histological slides is computed. Block representation results in 76% of the original ratio while the convex hull causes a strong increase of 456%. The Metaball algorithm comprises a threshold parameter which affects the size of the 3D volume. Tumor-to-tissue ratios can be matched perfectly by selecting appropriate threshold values. Conclusions: Our introduced method allows the correlation of histological findings with MRI images without the need to adapt the pathologists' workflow. In fact, the accuracy of 3D reconstruction techniques needs to be further optimized, but in return the method can be applied to the entire set of archive cases.

The aim of this work is to develop a colourimetrically justified method for determination of colour fidelity of WSI scanners. We measure the absolute accuracy of colour reproduction and assess the scanner’s ability to resolve similar colours, which seems to be even more important for diagnostic and research tasks. Along this, some theoretical background is given which helps in understanding of common sources of failures in colour reproduction. The work was done in the framework of 2-nd and 3-rd International Scanner Contest. We hope this publication will be useful for scanner manufactures as well as for academical and clinical users.

Biobanks have become central structures for the provision of data. Raw data such as whole slide images (WSI) are stored in separate systems (PACS) or in biobank information systems. If segmentation or comparable information is to be exchanged in the context of the compilation of cases for the application of AI systems, the limits of proprietary systems are quickly reached. In order to provide these valuable (meta-) data for the description of biological tissue samples, WSI and analysis results, common standards for data exchange are required. Such standards are not currently available. Ongoing standardization developments, in particular the establishment of DICOM for digital pathology in the field of virtual microscopy, will help to provide some of the missing standards. DICOM alone, however, cannot close the gap due to some of the peculiarities of WSIs and corresponding analysis results. We propose a flexible, modular and expandable storage system - OBDEX (Open Block Data Exchange System), which allows the exchange of meta- and analysis data about tissue blocks, glass slides, WSI and analysis results. OBDEX is based on the FAIR data principles and uses only freely available protocols, standards and software libraries. This facilitates data exchange between institutions and working groups. It supports them in identifying suitable cases and samples for virtual studies and in exchanging relevant metadata. At the same time, OBDEX will offer the possibility to store deep learning models for exchange. A publication of source code, interface descriptions and documentation under a free software license is planned.

ObjectiveTo characterize muscle fiber necrosis in immune-mediated necrotizing myopathies (IMNM) with anti–signal recognition particle (SRP) or anti–3-hydroxy-3-methylglutarylcoenzyme A reductase (HMGCR) antibodies and to explore its underlying molecular immune mechanisms.MethodsMuscle biopsies from patients with IMNM were analyzed and compared to biopsies from control patients with myositis. In addition to immunostaining and reverse transcription PCR on muscle samples, in vitro immunostaining on primary muscle cells was performed.ResultsCreatine kinase levels and muscle regeneration correlated with the proportion of necrotic fibers (r = 0.6, p < 0.001). CD68+iNOS+ macrophages and a Th-1 immune environment were chiefly involved in ongoing myophagocytosis of necrotic fibers. T-cell densities correlated with necrosis but no signs of cytotoxicity were detected. Activation of the classical pathway of the complement cascade, accompanied by deposition of sarcolemmal immunoglobulins, featured involvement of humoral immunity. Presence of SRP and HMGCR proteins on altered myofibers was reproduced on myotubes exposed to purified patient-derived autoantibodies. Finally, a correlation between sarcolemmal complement deposits and fiber necrosis was observed (r = 0.4 and p = 0.004). Based on these observations, we propose to update the pathologic criteria of IMNM.ConclusionThese data further corroborate the pathogenic role of anti-SRP and anti-HMGCR autoantibodies in IMNM, highlighting humoral mechanisms as key players in immunity and myofiber necrosis.

The European Society for Digital and Integrative Pathology (ESDIP) was formally founded in 2016 in Berlin. After a well-participated annual general meeting, ESDIP members elected a new active structure for the next term of office. The priority goals of this new and highly motivated team will be to support the digital transformation in the pathology laboratories, to build inter-institutional bridges for cooperation, to establish a solid educational program, and to increase the collaboration with industry partners.

Modern image analysis techniques based on artificial intelligence (AI) have great potential to improve the quality and efficiency of diagnostic procedures in pathology and to detect novel biomarkers. Despite thousands of published research papers on applications of AI in pathology, hardly any research implementations have matured into commercial products for routine use. Bringing an AI solution for pathology to market poses significant technological, business, and regulatory challenges. In this paper, we provide a comprehensive overview and advice on how to meet these challenges. We outline how research prototypes can be turned into a product-ready state and integrated into the IT infrastructure of clinical laboratories. We also discuss business models for profitable AI solutions and reimbursement options for computer assistance in pathology. Moreover, we explain how to obtain regulatory approval so that AI solutions can be launched as in vitro diagnostic medical devices. Thus, this paper offers computer scientists, software companies, and pathologists a road map for transforming prototypes of AI solutions into commercial products.

The anti-melanoma differentiation-associated gene 5 (MDA5) autoantibody is specifically associated with dermatomyositis (DM). Nevertheless, anti–MDA5 + -patients experience characteristic symptoms distinct from classic DM, including severe signs of extramuscular involvement; however, the clinical signs of myopathy are mild or even absent. The morphological and immunological features are not yet described in adulthood. Data concerning the pathophysiology of anti-MDA5 DM are sparse; however, the importance of the interferon (IFN) type I pathway involved in DM has been shown. Our aim was to define morphological alterations of the skeletal muscle and the intrinsic immune response of anti–MDA5-positive DM patients. Immunohistological and RT-PCR analysis of muscle biopsy specimens from anti-MDA5 and classic DM were compared. Those with anti-MDA5 DM did not present the classic features of perifascicular fiber atrophy and major histocompatibility complex class I expression. They did not show significant signs of capillary loss; tubuloreticular formations were observed less frequently. Inflammation was focal, clustering around single vessels but significantly less intense. Expression of IFN-stimulated genes was up-regulated in anti-MDA5 DM; however, the IFN score was significantly lower. Characteristic features were observed in anti-MDA5 DM and not in classic DM patients. Only anti-MDA5 DM showed numerous nitric oxide synthase 2–positive muscle fibers with sarcoplasmic colocalization of markers of regeneration and cell stress. Anti–MDA5-positive patients demonstrate a morphological pattern distinct from classic DM.

In this paper, cell nuclei attributed relational graphs are extensively studied and comparatively analyzed for effective knowledge description and classification in H&E stained whole slide images of gastric cancer. This includes design and implementation of multiple graph variations with diverse tissue component characteristics and architectural properties to obtain enhanced image representations, followed by hierarchical ensemble learning and classification. A detailed comparative analysis of the proposed graph-based methods, also with the established low-level, object-level and high-level image descriptions is performed, that further leads to a hybrid approach combining salient visual information. Quantitative evaluation of investigated methods suggests the suitability of particular graph variants for automatic classification using H&E stained histopathological gastric cancer whole slide images based on HER2 immunohistochemistry.

To characterize muscle fiber necrosis in immune-mediated necrotizing myopathies (IMNM) with anti-signal recognition particle (SRP) or anti-3-hydroxy-3-methylglutarylcoenzyme A reductase (HMGCR) antibodies and to explore its underlying molecular immune mechanisms.Muscle biopsies from patients with IMNM were analyzed and compared to biopsies from control patients with myositis. In addition to immunostaining and reverse transcription PCR on muscle samples, in vitro immunostaining on primary muscle cells was performed.Creatine kinase levels and muscle regeneration correlated with the proportion of necrotic fibers (These data further corroborate the pathogenic role of anti-SRP and anti-HMGCR autoantibodies in IMNM, highlighting humoral mechanisms as key players in immunity and myofiber necrosis.

Deep learning using convolutional neural networks is an actively emerging field in histological image analysis. This study explores deep learning methods for computer-aided classification in H&E stained histopathological whole slide images of gastric carcinoma. An introductory convolutional neural network architecture is proposed for two computerized applications, namely, cancer classification based on immunohistochemical response and necrosis detection based on the existence of tumor necrosis in the tissue. Classification performance of the developed deep learning approach is quantitatively compared with traditional image analysis methods in digital histopathology requiring prior computation of handcrafted features, such as statistical measures using gray level co-occurrence matrix, Gabor filter-bank responses, LBP histograms, gray histograms, HSV histograms and RGB histograms, followed by random forest machine learning. Additionally, the widely known AlexNet deep convolutional framework is comparatively analyzed for the corresponding classification problems. The proposed convolutional neural network architecture reports favorable results, with an overall classification accuracy of 0.6990 for cancer classification and 0.8144 for necrosis detection.

Introduction/ Background Biobanks and their operators are gaining popularity. However, they face a range of growing challenges. Many of these biobanks used to be smaller and midsized collections of data and samples, but are now being developed into centralized networks within clinics and consortia. These large biobanks require the integration of as many sources of information as possible, including imaging data in addition to any relevant structured data. This is especially the case in the field of virtual microscopy. Data collection is increasingly being orchestrated by workflow based systems and must eventually be incorporated entirely into rule based IT-processes. Aims In order to achieve this goal, the Charité University Medicine Berlin, VMscope GmbH, and Kairos GmbH formed a consortium in the framework of the joint project, “Biobanking 3.0”, which is funded by the German Federal Ministry for Education and Research (BMBF). Furthermore, after this successful project, a second KMU funded project called POST (Patient-centric Open Multi Center Study Tool) will begin in the autumn of 2015 to expand the data integration to pathology information systems in a standardized manner. Methods One of the project goals is to use an IT-platform with an integrated workflow-engine to orchestrate all necessary processes and document all findings. Defined SOPs during sample extraction and information acquisition can be executed, and all result data can be made available to local research groups as well as project partners. Results Thus, the platform pursues the research approach of personalized medicine, which requires exact controlling of therapy cycles in a logical and chronological order., Diagnostic Pathology, Vol 1 No 8 (2016): 13. European Congress on Digital Pathology

This paper describes a novel graph-based method for efficient representation and subsequent classification in histological whole slide images of gastric cancer. Her2/neu immunohistochemically stained and haematoxylin and eosin stained histological sections of gastric carcinoma are digitized. Immunohistochemical staining is used in practice by pathologists to determine extent of malignancy, however, it is laborious to visually discriminate the corresponding malignancy levels in the more commonly used haematoxylin and eosin stain, and this study attempts to solve this problem using a computer-based method. Cell nuclei are first isolated at high magnification using an automatic cell nuclei segmentation strategy, followed by construction of cell nuclei attributed relational graphs of the tissue regions. These graphs represent tissue architecture comprehensively, as they contain information about cell nuclei morphology as vertex attributes, along with knowledge of neighborhood in the form of edge linking and edge attributes. Global graph characteristics are derived and ensemble learning is used to discriminate between three types of malignancy levels, namely, non-tumor, Her2/neu positive tumor and Her2/neu negative tumor. Performance is compared with state of the art methods including four texture feature groups (Haralick, Gabor, Local Binary Patterns and Varma Zisserman features), color and intensity features, and Voronoi diagram and Delaunay triangulation. Texture, color and intensity information is also combined with graph-based knowledge, followed by correlation analysis. Quantitative assessment is performed using two cross validation strategies. On investigating the experimental results, it can be concluded that the proposed method provides a promising way for computer-based analysis of histopathological images of gastric cancer.

Background and Purpose— Pre- and intrahospital time delays are major concerns in acute stroke care. Telemedicine-equipped ambulances may improve time management and identify patients with stroke eligible for thrombolysis by an early prehospital stroke diagnosis. The aims of this study were (1) to develop a telestroke ambulance prototype; (2) to test the reliability of stroke severity assessment; and (3) to evaluate its feasibility in the prehospital emergency setting. Methods— Mobil, real-time audio–video streaming telemedicine devices were implemented into advanced life support ambulances. Feasibility of telestroke ambulances and reliability of the National Institutes of Health Stroke Scale assessment were tested using current wireless cellular communication technology (third generation) in a prehospital stroke scenario. Two stroke actors were trained in simulation of differing right and left middle cerebral artery stroke syndromes. National Institutes of Health Stroke Scale assessment was performed by a hospital-based stroke physician by telemedicine, by an emergency physician guided by telemedicine, and “a posteriori” on the basis of video documentation. Results— In 18 of 30 scenarios, National Institutes of Health Stroke Scale assessment could not be performed due to absence or loss of audio–video signal. In the remaining 12 completed scenarios, interrater agreement of National Institutes of Health Stroke Scale examination between ambulance and hospital and ambulance and “a posteriori” video evaluation was moderate to good with weighted κ values of 0.69 (95% CI, 0.51–0.87) and 0.79 (95% CI, 0.59–0.98), respectively. Conclusion— Prehospital telestroke examination was not at an acceptable level for clinical use, at least on the basis of the used technology. Further technical development is needed before telestroke is applicable for prehospital stroke management during patient transport.

Ahnlich wie die digitalen Verfahren die radiologische Bildgebung revolutioniert haben, wird die virtuelle Mikroskopie die Pathologie verandern. Dies gilt im besonderen Mase auch fur die onkologische Diagnostik. Die Digitalisierung wird nicht nur die arbeitsteilige Kooperation der Pathologen vereinfachen und beschleunigen, sondern auch durch die verstarkte Nutzung quantitativer Methoden bei der Einschatzung morphologischer Veranderungen zu geringerer Variabilitat und groserer Reproduzierbarkeit verhelfen. Die virtuelle Mikroskopie ermoglicht den Pathologen eine noch aktivere und unmittelbarere Teilnahme an interdisziplinaren Tumorkonferenzen.

Digital image analysis of histological datasets is a currently expanding field of research. With different stains, magnifications and types of tissues, histological images are inherently complex in nature and contain a wide variety of visual information. Several image analysis techniques are being explored in this direction. However, graph-based methods are gaining most popularity, as these methods can describe tissue architecture and provide adequate numeric information for subsequent computer-based analysis. Graphs have the ability to represent spatial arrangements and neighborhood relationships of different tissue components, which are essential characteristics observed visually by pathologists during investigation of specimens. In this paper, we present a comprehensive review of the graph-based methods explored so far in digital histopathology. We also discuss the current limitations and suggest future directions in graph-based tissue image analysis., Diagnostic Pathology, 2015

The anti-melanoma differentiation-associated gene 5 (MDA5) autoantibody is specifically associated with dermatomyositis (DM). Nevertheless, anti-MDA5(+)-patients experience characteristic symptoms distinct from classic DM, including severe signs of extramuscular involvement; however, the clinical signs of myopathy are mild or even absent. The morphological and immunological features are not yet described in adulthood. Data concerning the pathophysiology of anti-MDA5 DM are sparse; however, the importance of the interferon (IFN) type I pathway involved in DM has been shown. Our aim was to define morphological alterations of the skeletal muscle and the intrinsic immune response of anti-MDA5-positive DM patients. Immunohistological and RT-PCR analysis of muscle biopsy specimens from anti-MDA5 and classic DM were compared. Those with anti-MDA5 DM did not present the classic features of perifascicular fiber atrophy and major histocompatibility complex class I expression. They did not show significant signs of capillary loss; tubuloreticular formations were observed less frequently. Inflammation was focal, clustering around single vessels but significantly less intense. Expression of IFN-stimulated genes was up-regulated in anti-MDA5 DM; however, the IFN score was significantly lower. Characteristic features were observed in anti-MDA5 DM and not in classic DM patients. Only anti-MDA5 DM showed numerous nitric oxide synthase 2-positive muscle fibers with sarcoplasmic colocalization of markers of regeneration and cell stress. Anti-MDA5-positive patients demonstrate a morphological pattern distinct from classic DM.

The transcription factor ZEB1 has gained attention in tumor biology of epithelial cancers because of its function in epithelial-mesenchymal transition, DNA repair, stem cell biology and tumor-induced immunosuppression, but its role in gliomas with respect to invasion and prognostic value is controversial. We characterized ZEB1 expression at single cell level in 266 primary brain tumors and present a comprehensive dataset of high grade gliomas with Ki67, p53, IDH1, and EGFR immunohistochemistry, as well as EGFR FISH. ZEB1 protein expression in glioma stem cell lines was compared to their parental tumors with respect to gene expression subtypes based on RNA-seq transcriptomic profiles. ZEB1 is widely expressed in glial tumors, but in a highly variable fraction of cells. In glioblastoma, ZEB1 labeling index is higher in tumors with EGFR amplification or IDH1 mutation. Co-labeling studies showed that tumor cells and reactive astroglia, but not immune cells contribute to the ZEB1 positive population. In contrast, glioma cell lines constitutively express ZEB1 irrespective of gene expression subtype. In conclusion, our data indicate that immune infiltration likely contributes to differential labelling of ZEB1 and confounds interpretation of bulk ZEB1 expression data. publishedVersion

Background Computer-based analysis of digitalized histological images has been gaining increasing attention, due to their extensive use in research and routine practice. The article aims to contribute towards the description and retrieval of histological images by employing a structural method using graphs. Due to their expressive ability, graphs are considered as a powerful and versatile representation formalism and have obtained a growing consideration especially by the image processing and computer vision community. Methods The article describes a novel method for determining similarity between histological images through graph-theoretic description and matching, for the purpose of content-based retrieval. A higher order (region-based) graph-based representation of breast biopsy images has been attained and a tree-search based inexact graph matching technique has been employed that facilitates the automatic retrieval of images structurally similar to a given image from large databases. Results The results obtained and evaluation performed demonstrate the effectiveness and superiority of graph-based image retrieval over a common histogram-based technique. The employed graph matching complexity has been reduced compared to the state-of-the-art optimal inexact matching methods by applying a pre-requisite criterion for matching of nodes and a sophisticated design of the estimation function, especially the prognosis function. Conclusion The proposed method is suitable for the retrieval of similar histological images, as suggested by the experimental and evaluation results obtained in the study. It is intended for the use in Content Based Image Retrieval (CBIR)-requiring applications in the areas of medical diagnostics and research, and can also be generalized for retrieval of different types of complex images. Virtual Slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1224798882787923.