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.
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.
Introduction/ Background Comfortable navigation through diagnostic images is a prospective challenge for the acceptance of virtual microscopy applications in routine pathology 1,2. Tracing different ...regions of interest through multiple sections on one or several slides is a typical task in diagnostic slide examination. This laborious and time-consuming co-localization is currently executed by pathologists. Retaining the relative positions of tissue structures while alternating between multiple slides is still not feasible in a satisfactory manner in conventional nor virtual microscopy. Aims To address this issue we present a more comfortable and intuitive method to read slides using computer-assisted navigation. Furthermore, we demonstrate the strengths of our method by applying it to large series of serial colorectal tissue sections, creating new kinds of visualizations of different adenomatous mucosal architectures in human tissue, while looking for human correlates of lesions recently described in mice 3. Methods Histological images contain multiple distortions from different sources in the laboratory and digitalization process. An interconnection model was created to describe distortions by several layers, providing a normalized tissue representation. Layers were associated with specific distortions with each layer serving as a new level of abstraction. The first layers enabled a coarse alignment of tissue sections. Further alignment is achieved by piecewise, multi-resolution, SIFT-based 4 correspondence extraction and refinement. Inside the convex hull of all fiducial points local affine transformations were applied whereas a global affine transformation was used on the outside. Animated stacks were generated for regions of interest using local rigid transformations to preserve exact morphological coherences. For subsequent creation of 3D models, the relevant histological objects within these images were annotated by pathologists, partly using computer assisted segmentation based on active contours 5. These annotations were used subsequently to create simplified 3D models by applying VTK 6. Results The presented methods provide an efficient means to retrieve correspondences and additional spatial information from serial sections of histological slides. They also show good applicability for specimen from different origin. Alignment methods can be applied to generate block-centric visualizations such as parallel and transparent viewing of multiple stains. Moreover, the generated stack videos and 3D models demonstrate the very good accuracy of section alignment even in large series. The visualizations enable pathologists and researchers to grasp the 3D structural relationships in the tissue at a glance, providing an excellent tool to communicate more complex histomorphological findings. Interestingly, we see two kinds of tubular adenomas, which could imply multiple ways to tubular adenoma formation in FAP-patients, possibly akin to the recent observations in mice 3.
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.
Pathology laboratories are increasingly using digital workflows. This has the potential of increasing lab efficiency, but the digitization process also involves major challenges. Several reports have ...been published describing the individual experiences of specific laboratories with the digitization process. However, a comprehensive overview of the lessons learned is still lacking. We provide an overview of the lessons learned for different aspects of the digitization process, including digital case management, digital slide reading, and computer-aided slide reading. We also cover metrics used for monitoring performance and pitfalls and corresponding values observed in practice. The overview is intended to help pathologists, IT decision-makers, and administrators to benefit from the experiences of others and to implement the digitization process in an optimal way to make their own laboratory future-proof.
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 (WSI) is 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.
Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, ...including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and vendor-neutral EMPAIA initiative addresses these challenges. Here, we provide an overview of EMPAIA's achievements and lessons learned. EMPAIA integrates various stakeholders of the pathology AI ecosystem, i.e., pathologists, computer scientists, and industry. In close collaboration, we developed technical interoperability standards, recommendations for AI testing and product development, and explainability methods. We implemented the modular and open-source EMPAIA platform and successfully integrated 14 AI-based image analysis apps from 8 different vendors, demonstrating how different apps can use a single standardized interface. We prioritized requirements and evaluated the use of AI in real clinical settings with 14 different pathology laboratories in Europe and Asia. In addition to technical developments, we created a forum for all stakeholders to share information and experiences on digital pathology and AI. Commercial, clinical, and academic stakeholders can now adopt EMPAIA's common open-source interfaces, providing a unique opportunity for large-scale standardization and streamlining of processes. Further efforts are needed to effectively and broadly establish AI assistance in routine laboratory use. To this end, a sustainable infrastructure, the non-profit association EMPAIA International, has been established to continue standardization and support broad implementation and advocacy for an AI-assisted digital pathology future.
Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is ...important to evaluate their predictive performance and obtain regulatory approval. This assessment requires appropriate test datasets. However, compiling such datasets is challenging and specific recommendations are missing. A committee of various stakeholders, including commercial AI developers, pathologists, and researchers, discussed key aspects and conducted extensive literature reviews on test datasets in pathology. Here, we summarize the results and derive general recommendations for the collection of test datasets. We address several questions: Which and how many images are needed? How to deal with low-prevalence subsets? How can potential bias be detected? How should datasets be reported? What are the regulatory requirements in different countries? The recommendations are intended to help AI developers demonstrate the utility of their products and to help regulatory agencies and end users verify reported performance measures. Further research is needed to formulate criteria for sufficiently representative test datasets so that AI solutions can operate with less user intervention and better support diagnostic workflows in the future.
Tenofovir-diphosphate (TFV-DP) in dried blood spots (DBS) is an objective long-term adherence measure, but data are limited on its ability to predict virologic suppression (VS) in people on ...antiretroviral (ARV) treatment. There are also no data comparing DBS TFV-DP with plasma ARV concentrations as predictors of VS.
Women who were on a first-line regimen of tenofovir, emtricitabine, and efavirenz (EFV) were enrolled in a cross-sectional study. Plasma EFV and tenofovir (TFV), DBS TFV-DP assays, and 30-day self-reported adherence were evaluated as predictors of VS (<50 copies/mL) with the area under the curve of receiver operating characteristics and logistic regression.
We enrolled 137 women; mean age of 33 years; median 4 years on antiretroviral therapy; 88 (64%) had VS. In receiver operating characteristics analyses: DBS TFV-DP 0.926 (95% CI: 0.876 to 0.976) had a higher area under the curve than plasma TFV 0.864 (0.797 to 0.932); P = 0.006, whereas plasma EFV 0.903 (0.839-0.967) was not significantly different from DBS TFV-DP (P = 0.138) or plasma TFV (P = 0.140); all ARV assays performed better than self-report. The association of TFV-DP in DBS with VS strengthened with increasing concentrations reference <350 fmol/punch: 350-699 fmol/punch aOR 37 (8-178); 700-1249 fmol/punch aOR 47 (13-175); ≥1250 fmol/punch aOR 175 (20-1539). "White coat adherence" (defined as DBS TFV-DP <350 fmol/punch with detectable plasma TFV) was only detected in 4 women.
Plasma EFV, TFV, and DBS TFV-DP were all strong predictors of VS. EFV or TFV assays have potential for development as point-of-care assays for use as objective adherence measures in resource-limited settings.
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