Background: Different methods have been described for data extraction from pathology reports with varying degrees of success. Here a technique for directly extracting data from relational database is described. Methods: Our department uses synoptic reports modified from College of American Pathologists (CAP) Cancer Protocol Templates to report most of our cancer diagnoses. Choosing the melanoma of skin synoptic report as an example, R scripting language extended with RODBC package was used to query the pathology information system database. Reports containing melanoma of skin synoptic report in the past 4 and a half years were retrieved and individual data elements were extracted. Using the retrieved list of the cases, the database was queried a second time to retrieve/extract the lymph node staging information in the subsequent reports from the same patients. Results: 426 synoptic reports corresponding to unique lesions of melanoma of skin were retrieved, and data elements of interest were extracted into an R data frame. The distribution of Breslow depth of melanomas grouped by year is used as an example of intra-report data extraction and analysis. When the new pN staging information was present in the subsequent reports, 82% (77/94) was precisely retrieved (pN0, pN1, pN2 and pN3). Additional 15% (14/94) was retrieved with certain ambiguity (positive or knowing there was an update). The specificity was 100% for both. The relationship between Breslow depth and lymph node status was graphed as an example of lesion-specific multi-report data extraction and analysis. Conclusions: R extended with RODBC package is a simple and versatile approach well-suited for the above tasks. The success or failure of the retrieval and extraction depended largely on whether the reports were formatted and whether the contents of the elements were consistently phrased. This approach can be easily modified and adopted for other pathology information systems that use relational database for data management.

Whole slide imaging (WSI) has the potential to be utilized in telepathology, teleconsultation, quality assurance, clinical education, and digital image analysis to aid pathologists. In this paper, the potential added benefits of computer-assisted image analysis in breast pathology are reviewed and discussed. One of the major advantages of WSI systems is the possibility of doing computer-based image analysis on the digital slides. The purpose of computer-assisted analysis of breast virtual slides can be (i) segmentation of desired regions or objects such as diagnostically relevant areas, epithelial nuclei, lymphocyte cells, tubules, and mitotic figures, (ii) classification of breast slides based on breast cancer (BCa) grades, the invasive potential of tumors, or cancer subtypes, (iii) prognosis of BCa, or (iv) immunohistochemical quantification. While encouraging results have been achieved in this area, further progress is still required to make computer-based image analysis of breast virtual slides acceptable for clinical practice.

Introduction: Cell nuclei are important indicators of cellular processes and diseases. Segmentation is an essential stage in systems for quantitative analysis of nuclei extracted from microscopy images. Given the wide variety of nuclei appearance in different organs and staining procedures, a plethora of methods have been described in the literature to improve the segmentation accuracy and robustness. Materials and Methods: In this paper, we propose an unsupervised method for cell nuclei detection and segmentation in two-dimensional microscopy images. The nuclei in the image are detected automatically using a matching-based method. Next, edge maps are generated at multiple image blurring levels followed by edge selection performed in polar space. The nuclei contours are refined iteratively in the constructed edge pyramid. The validation study was conducted over two cell nuclei datasets with manual labeling, including 25 hematoxylin and eosin-stained liver histopathology images and 35 Papanicolaou-stained thyroid images. Results: The nuclei detection accuracy was measured by miss rate, and the segmentation accuracy was evaluated by two types of error metrics. Overall, the nuclei detection efficiency of the proposed method is similar to the supervised template matching method. In comparison to four existing state-of-the-art segmentation methods, the proposed method performed the best with average segmentation error 10.34% and 0.33 measured by area error rate and normalized sum of distances (×10). Conclusion: Quantitative analysis showed that the method is automatic and accurate when segmenting cell nuclei from microscopy images with noisy background and has the potential to be used in clinic settings.