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Background: Digital Imaging and Communications in Medicine (DICOM®) is the standard for the representation, storage, and communication of medical images and related information. A DICOM file format and communication protocol for pathology have been defined; however, adoption by vendors and in the field is pending. Here, we implemented the essential aspects of the standard and assessed its capabilities and limitations in a multisite, multivendor healthcare network. Methods: We selected relevant DICOM attributes, developed a program that extracts pixel data and pixel-related metadata, integrated patient and specimen-related metadata, populated and encoded DICOM attributes, and stored DICOM files. We generated the files using image data from four vendor-specific image file formats and clinical metadata from two departments with different laboratory information systems. We validated the generated DICOM files using recognized DICOM validation tools and measured encoding, storage, and access efficiency for three image compression methods. Finally, we evaluated storing, querying, and retrieving data over the web using existing DICOM archive software. Results: Whole slide image data can be encoded together with relevant patient and specimen-related metadata as DICOM objects. These objects can be accessed efficiently from files or through RESTful web services using existing software implementations. Performance measurements show that the choice of image compression method has a major impact on data access efficiency. For lossy compression, JPEG achieves the fastest compression/decompression rates. For lossless compression, JPEG-LS significantly outperforms JPEG 2000 with respect to data encoding and decoding speed. Conclusion: Implementation of DICOM allows efficient access to image data as well as associated metadata. By leveraging a wealth of existing infrastructure solutions, the use of DICOM facilitates enterprise integration and data exchange for digital pathology.

Background: Pathologists and informaticians are becoming increasingly interested in electronic clinical decision support for pathology, laboratory medicine and clinical diagnosis. Improved decision support may optimize laboratory test selection, improve test result interpretation and permit the extraction of enhanced diagnostic information from existing laboratory data. Nonetheless, the field of pathology decision support is still developing. To facilitate the exchange of ideas and preliminary studies, we convened a symposium entitled: Pathology data integration and clinical decision support. Methods: The symposium was held at the Massachusetts General Hospital, on May 10, 2013. Participants were selected to represent diverse backgrounds and interests and were from nine different institutions in eight different states. Results: The day included 16 plenary talks and three panel discussions, together covering four broad areas. Summaries of each presentation are included in this manuscript. Conclusions: A number of recurrent themes emerged from the symposium. Among the most pervasive was the dichotomy between diagnostic data and diagnostic information, including the opportunities that laboratories may have to use electronic systems and algorithms to convert the data they generate into more useful information. Differences between human talents and computer abilities were described; well-designed symbioses between humans and computers may ultimately optimize diagnosis. Another key theme related to the unique needs and challenges in providing decision support for genomics and other emerging diagnostic modalities. Finally, many talks relayed how the barriers to bringing decision support toward reality are primarily personnel, political, infrastructural and administrative challenges rather than technological limitations.

Background: In 2007, our healthcare system established a clinical fellowship program in pathology informatics. In 2011, the program benchmarked its structure and operations against a 2009 white paper "Program requirements for fellowship education in the subspecialty of clinical informatics," endorsed by the Board of the American Medical Informatics Association (AMIA) that described a proposal for a general clinical informatics fellowship program. Methods: A group of program faculty members and fellows compared each of the proposed requirements in the white paper with the fellowship program′s written charter and operations. The majority of white paper proposals aligned closely with the rules and activities in our program and comparison was straightforward. In some proposals, however, differences in terminology, approach, and philosophy made comparison less direct, and in those cases, the thinking of the group was recorded. After the initial evaluation, the remainder of the faculty reviewed the results and any disagreements were resolved. Results: The most important finding of the study was how closely the white paper proposals for a general clinical informatics fellowship program aligned with the reality of our existing pathology informatics fellowship. The program charter and operations of the program were judged to be concordant with the great majority of specific white paper proposals. However, there were some areas of discrepancy and the reasons for the discrepancies are discussed in the manuscript. Conclusions: After the comparison, we conclude that the existing pathology informatics fellowship could easily meet all substantive proposals put forth in the 2009 clinical informatics program requirements white paper. There was also agreement on a number of philosophical issues, such as the advantages of multiple fellows, the need for core knowledge and skill sets, and the need to maintain clinical skills during informatics training. However, there were other issues, such as a requirement for a 2-year fellowship and for informatics fellowships to be done after primary board certification, that pathology should consider carefully as it moves toward a subspecialty status and board certification.

Background: Stromal CD8+ tumor-infiltrating lymphocytes (TILs) are an important prognostic and predictive indicator in non-small cell lung cancer (NSCLC). In this study, we aimed to develop and test the feasibility of a digital image analysis (DIA) workflow for estimating stromal CD8+ TIL density. Methods: A DIA workflow developed in a software platform (QuPath) was applied to a specified region of interest (ROI) within the stromal compartment of dual PD-L1/CD8 immunostained slides from 50 lung adenocarcinoma patients. A random tree classifier was trained from 25 training cases and applied to 25 test cases. The DIA-estimated CD8+ TIL densities were compared to manual estimates of three pathologists, who independently quantitated the percentage of CD8+ TILs from predefined ROIs in QuPath. Results: The average estimated total stromal cell count per case was 520 (range: 282–816) by QuPath and 551 (range: 265–744) by pathologists. The DIA-estimated CD8+ TIL density (mean = 16.9%) was comparable to pathologists' manual estimates (mean = 15.9%). A paired t-test showed no statistically significant difference between DIA and pathologist estimates of CD8+ TIL density among both training (n = 25, P = 0.55) and test (n = 25, P = 0.34) cases. There was an almost perfect agreement between QuPath and each pathologist's estimates of CD8+ TIL density (κ = 0.85–0.86). Conclusions: These findings demonstrate the feasibility of applying a DIA workflow for estimating stromal CD8+ TIL density in NSCLC. DIA has the potential to provide an efficient and standardized approach for estimating stromal CD8+ TIL density.