Integrating the Health-care Enterprise Pathology and Laboratory Medicine Guideline for Digital Pathology Interoperability

Dash, Rajesh; Jones, Nicholas; Merrick, Riki; Haroske, G.; Harrison, J. Hartwell; Sayers, Craig; Haarselhorst, Nick; Wintell, Mikael; Herrmann, Markus D.; Macary, François

doi:10.4103/jpi.jpi_98_20

Cited by 16 publications

(12 citation statements)

References 10 publications

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“…have recently discussed the delicate role of the WSI as accurate reproductions of the original glass slides, especially in the implementation of digital pathology for primary diagnosis purposes. [ 9 ] As explained by the authors, physical glass slides are generated by sectioning the paraffin block and “classic” QC checks generally consist in the case-by-case comparison of the obtained tissue section with the cut surface of the block. Although this is a consolidated practice in every pathology laboratory, it represents a time-consuming task for both pathologists and technicians, with extremely low compliance and as a consequence, a still elevated risk of tissue inconsistencies in the analogical/traditional workflow.…”

Section: Discussionmentioning

confidence: 99%

“…In an automated workflow, this practice should be carried out without physically retrieving the paraffin blocks from the archive every time it is needed, with the possibility to systematically capture and catalog the cut surface of the tissue block for later comparison to the microscopic (or digital) image. Moreover, according to the authors,[ 9 ] this important (and today, entirely manual) QC check remains a gap in the market today. However, the recent introduction of BlocDoc drastically changed the routine practice of an already digital-oriented laboratory in Sicily, Italy,[ 4 ] with more than 10.000 block surfaces captured and different potential material discrepancies detected.…”

Section: Discussionmentioning

confidence: 99%

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What is Essential is (No More) Invisible to the Eyes: The Introduction of BlocDoc in the Digital Pathology Workflow

L’Imperio

Gibilisco

Fraggetta³

2021

Journal of Pathology Informatics

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Background: The implementation of a fully digital workflow in any anatomic pathology department requires a complete conversion to a tracked system. Ensuring the strict correspondence of the material submitted for the analysis, from the accessioning to the reporting phase, is mandatory in the anatomic pathology laboratory, especially when implementing the digital pathology for primary histological diagnosis. The proposed solutions, up to now, rely on the verification that all the materials present in the glass slide are also present in the whole slide images (WSIs). Although different methods have already been implemented for this purpose (e.g., the “macroimage” of the digital slide, representing the overview of the glass slide), the recent introduction of a device to capture the cut surface of paraffin blocks put the quality control of the digital workflow a step forward, allowing to match the digitized slide with the corresponding block. This system may represent a reliable, easy-to-use alternative to further reduce tissue inconsistencies between material sent to the lab and the final glass slides or WSIs. Methods: The Anatomic Pathology of the Gravina Hospital in Caltagirone, Sicily, Italy, has implemented the application of the BlocDoc devices (SPOT Imaging, Sterling Heights, USA) in its digital workflow. The instruments were positioned next to every microtome/sectioning station, with the possibility to capture the “normal” and the polarized image of the cut surface of the blocks directly by the technician. The presence of a monitor in the BlocDoc device allowed the technician to check the concordance between the cut surface of the block and the material on the corresponding slide. The link between BlocDoc and the laboratory information system, through the presence of the 2D barcode, allowed the pathologists to access the captured image of the cut surface of the block at the pathologist workstation, thus enabling the direct comparison between this image and the WSI (thumbnail and “macroimage”). Results: During the implementation period, more than 10.000 (11.248) blocks were routinely captured using the BlocDoc. The employment of this approach allowed a drastic reduction of the discordances and tissue inconsistencies. The implementation of the BlocDoc in the routine allowed the detection of two different types of “errors,” the so-called “systematic” and “occasional” ones. The first type was intrinsic of some specific specimens (e.g., transurethral resection of the prostate, nasal polypectomies, and piecemeal uterine myomectomies) characterized by the three-dimensional nature of the fragments and affected almost 100% of these samples. On the other hand, the “occasional” errors, mainly due to inexperience or extreme caution of the technicians in handling tiny specimens, affected 98 blocks (0.9%) of these samples and progressively reduced with the rising confidence with the BlocDoc. One of these cases was clinically relevant. No problems ...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

What is Essential is (No More) Invisible to the Eyes: The Introduction of BlocDoc in the Digital Pathology Workflow

L’Imperio

Gibilisco

Fraggetta³

2021

Journal of Pathology Informatics

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“…The LIS may evolve into a multimodality "pathologist cockpit" that not only provides LIS functions but also displays pathology imaging and other medical imaging, supplies analytical tools, provides access to clinical data (e.g., Electronic Health Record [EHR]) [19] as well as other data sources [20]. A more recent guideline paper [21] underlined the importance of digital pathology interoperability, with a LIS being able to connect all the instruments present in the laboratory to support critical DP use cases. Moreover, increasing requests for molecular and genetic tests on pathology specimens (e.g., next-generation sequencing) impose further innovation in LISs to integrate and optimize these data with the traditional pathology report for optimal patient management [22] in an integrative model.…”

Section: The Role and Potentialities Of Laboratory Information (Management) System (Lis/lims) And Informatics Resourcesmentioning

confidence: 99%

Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP)

Fraggetta¹,

L’Imperio

Ameisen³

et al. 2021

Diagnostics

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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.

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“…This motivated us to develop the highdicom library, which provides high-level abstractions for reading and creating image-derived DICOM objects using the Python programming language to facilitate the development of ML models that can interface with established medical imaging systems upon deployment. Hospitals around the world have established an extensive enterprise imaging infrastructure, workflows, and software applications based on DICOM [14] and pathology and radiology are converging towards using DICOM for communication of digital images [15][16][17][18]. However, existing pathology as well as radiology systems primarily rely on non-standard formats and interfaces for the storage and exchange of image annotations or computational image analysis results.…”

Section: Introductionmentioning

confidence: 99%

Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology

Bridge¹,

Gorman²,

Pieper³

et al. 2021

Preprint

Self Cite

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Machine learning (ML) is revolutionizing image-based diagnostics in pathology and radiology. ML models have shown promising results in research settings, but the lack of interoperability between ML systems and enterprise medical imaging systems has been a major barrier for clinical integration and evaluation. The DICOM ® standard specifies Information Object Definitions (IODs) and Services for the representation and communication of digital images and related information, including image-derived annotations and analysis results. However, the complexity of the standard represents an obstacle for its adoption in the ML community and creates a need for software libraries and tools that simplify working with data sets in DICOM format.Here we present the highdicom library, which provides a high-level application programming interface (API) for the Python programming language that abstracts low-level details of the standard and enables encoding and decoding of image-derived information in DICOM format in a few lines of Python code. The highdicom library leverages NumPy arrays for efficient data representation and ties into the extensive Python ecosystem for image processing and machine learning. Simultaneously, by simplifying creation and parsing of DICOM-compliant files, highdicom achieves interoperability with the medical imaging systems that hold the data used to train and run ML models, and ultimately communicate and store model outputs for clinical use. We demonstrate through experiments with slide microscopy and computed tomography imaging, that, by bridging these two ecosystems, highdicom enables developers and researchers to train and evaluate state-of-the-art ML models in pathology and radiology while remaining compliant with the DICOM standard and interoperable with clinical systems at all stages.To promote standardization of ML research and streamline the ML model development and deployment process, we made the library available free and open-source at https://github.c om/mghcomputationalpathology/highdicom.

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Integrating the Health-care Enterprise Pathology and Laboratory Medicine Guideline for Digital Pathology Interoperability

Cited by 16 publications

References 10 publications

What is Essential is (No More) Invisible to the Eyes: The Introduction of BlocDoc in the Digital Pathology Workflow

What is Essential is (No More) Invisible to the Eyes: The Introduction of BlocDoc in the Digital Pathology Workflow

Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP)

Highdicom: A Python library for standardized encoding of image annotations and machine learning model outputs in pathology and radiology

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