Background:Sharing digital pathology images for enterprise- wide use into a picture archiving and communication system (PACS) is not yet widely adopted. We share our solution and 3-year experience of transmitting such images to an enterprise image server (EIS).Methods:Gross pathology images acquired by prosectors were integrated with clinical cases into the laboratory information system's image management module, and stored in JPEG2000 format on a networked image server. Automated daily searches for cases with gross images were used to compile an ASCII text file that was forwarded to a separate institutional Enterprise Digital Imaging and Communications in Medicine (DICOM) Wrapper (EDW) server. Concurrently, an HL7-based image order for these cases was generated, containing the locations of images and patient data, and forwarded to the EDW, which combined data in these locations to generate images with patient data, as required by DICOM standards. The image and data were then “wrapped” according to DICOM standards, transferred to the PACS servers, and made accessible on an institution-wide basis.Results:In total, 26,966 gross images from 9,733 cases were transmitted over the 3-year period from the laboratory information system to the EIS. The average process time for cases with successful automatic uploads (n=9,688) to the EIS was 98 seconds. Only 45 cases (0.5%) failed requiring manual intervention. Uploaded images were immediately available to institution- wide PACS users. Since inception, user feedback has been positive.Conclusions:Enterprise- wide PACS- based sharing of pathology images is feasible, provides useful services to clinical staff, and utilizes existing information system and telecommunications infrastructure. PACS-shared pathology images, however, require a “DICOM wrapper” for multisystem compatibility.

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Background:Conventional tissue microarrays (TMAs) consist of cores of tissue inserted into a recipient paraffin block such that a tissue section on a single glass slide can contain numerous patient samples in a spatially structured pattern. Scanning TMAs into digital slides for subsequent analysis by computer-aided diagnostic (CAD) algorithms all offers the possibility of evaluating candidate algorithms against a near-complete repertoire of variable disease morphologies. This parallel interrogation approach simplifies the evaluation, validation, and comparison of such candidate algorithms. A recently developed digital tool, digital core (dCORE), and image microarray maker (iMAM) enables the capture of uniformly sized and resolution-matched images, with these representing key morphologic features and fields of view, aggregated into a single monolithic digital image file in an array format, which we define as an image microarray (IMA). We further define the TMA-IMA construct as IMA-based images derived from whole slide images of TMAs themselves.Methods:Here we describe the first combined use of the previously described dCORE and iMAM tools, toward the goal of generating a higher-order image construct, with multiple TMA cores from multiple distinct conventional TMAs assembled as a single digital image montage. This image construct served as the basis of the carrying out of a massively parallel image analysis exercise, based on the use of the previously described spatially invariant vector quantization (SIVQ) algorithm.Results:Multicase, multifield TMA-IMAs of follicular lymphoma and follicular hyperplasia were separately rendered, using the aforementioned tools. Each of these two IMAs contained a distinct spectrum of morphologic heterogeneity with respect to both tingible body macrophage (TBM) appearance and apoptotic body morphology. SIVQ-based pattern matching, with ring vectors selected to screen for either tingible body macrophages or apoptotic bodies, was subsequently carried out on the differing TMA-IMAs, with attainment of excellent discriminant classification between the two diagnostic classes.Conclusion:The TMA-IMA construct enables and accelerates high-throughput multicase, multifield based image feature discovery and classification, thus simplifying the development, validation, and comparison of CAD algorithms in settings where the heterogeneity of diagnostic feature morphologic is a significant factor.