Background: The use of virtual microscopy (VM) in clinical cytology has been limited due to the inability to focus through three dimensional (3D) cell clusters with a single focal plane (2D images). Limited information exists regarding the optimal scanning parameters for 3D scanning. Aims: The purpose of this study was to determine the optimal number of the focal plane levels and the optimal scanning interval to digitize gynecological (GYN) specimens prepared on SurePath^™ glass slides while maintaining a manageable file size. Subjects and Methods: The iScanCoreo Au scanner (Ventana, AZ, USA) was used to digitize 192 SurePath^™ glass slides at three focal plane levels at 1 μ interval. The digitized virtual images (VI) were annotated using BioImagene's Image Viewer. Five participants interpreted the VI and recorded the focal plane level at which they felt confident and later interpreted the corresponding glass slide specimens using light microscopy (LM). The participants completed a survey about their experiences. Inter-rater agreement and concordance between the VI and the glass slide specimens were evaluated. Results: This study determined an overall high intra-rater diagnostic concordance between glass and VI (89-97%), however, the inter-rater agreement for all cases was higher for LM (94%) compared with VM (82%). Survey results indicate participants found low grade dysplasia and koilocytes easy to diagnose using three focal plane levels, the image enhancement tool was useful and focusing through the cells helped with interpretation; however, the participants found VI with hyperchromatic crowded groups challenging to interpret. Participants reported they prefer using LM over VM. This study supports using three focal plane levels and 1 μ interval to expand the use of VM in GYN cytology. Conclusion: Future improvements in technology and appropriate training should make this format a more preferable and practical option in clinical cytology.

Background: Oral submucous fibrosis (OSF) is a pre-cancerous condition with features of chronic, inflammatory and progressive sub-epithelial fibrotic disorder of the buccal mucosa. In this study, malignant potentiality of OSF has been assessed by quantification of immunohistochemical expression of epithelial prime regulator-p63 molecule in correlation to its malignant (oral squamous cell carcinoma [OSCC] and normal counterpart [normal oral mucosa [NOM]). Attributes of spatial extent and distribution of p63 ⁺ expression in the epithelium have been investigated. Further, a correlated assessment of histopathological attributes inferred from H&E staining and their mathematical counterparts (molecular pathology of p63) have been proposed. The suggested analytical framework envisaged standardization of the immunohistochemistry evaluation procedure for the molecular marker, using computer-aided image analysis, toward enhancing its prognostic value. Subjects and Methods: In histopathologically confirmed OSF, OSCC and NOM tissue sections, p63 ⁺ nuclei were localized and segmented by identifying regional maxima in plateau-like intensity spatial profiles of nuclei. The clustered nuclei were localized and segmented by identifying concave points in the morphometry and by marker-controlled watersheds. Voronoi tessellations were constructed around nuclei centroids and mean values of spatial-relation metrics such as tessellation area, tessellation perimeter, roundness factor and disorder of the area were extracted. Morphology and extent of expression are characterized by area, diameter, perimeter, compactness, eccentricity and density, fraction of p63 ⁺ expression and expression distance of p63 ⁺ nuclei. Results: Correlative framework between histopathological features characterizing malignant potentiality and their quantitative p63 counterparts was developed. Statistical analyses of mathematical trends were evaluated between different biologically relevant combinations: (i) NOM to oral submucous fibrosis without dysplasia (OSFWT) (ii) NOM to oral submucous fibrosis with dysplasia (OSFWD) (iii) OSFWT-OSFWD (iv) OSFWD-OSCC. Significant histopathogical correlates and their corroborative mathematical features, inferred from p63 staining, were also investigated into. Conclusion: Quantitative assessment and correlative analysis identified mathematical features related to hyperplasia, cellular stratification, differentiation and maturation, shape and size, nuclear crowding and nucleocytoplasmic ratio. It is envisaged that this approach for analyzing the p63 expression and its distribution pattern may help to establish it as a quantitative bio-marker to predict the malignant potentiality and progression. The proposed work would be a value addition to the gold standard by incorporating an observer-independent framework for the associated molecular pathology.

Background: Advances in digital pathology are accelerating integration of this technology into anatomic pathology (AP). To optimize implementation and adoption of digital pathology systems within a large healthcare organization, initial assessment of both end user (pathologist) needs and organizational infrastructure are required. Contextual inquiry is a qualitative, user-centered tool for collecting, interpreting, and aggregating such detailed data about work practices that can be employed to help identify specific needs and requirements. Aim: Using contextual inquiry, the objective of this study was to identify the unique work practices and requirements in AP for the United States (US) Air Force Medical Service (AFMS) that had to be targeted in order to support their transition to digital pathology. Subjects and Methods: A pathology-centered observer team conducted 1.5 h interviews with a total of 24 AFMS pathologists and histology lab personnel at three large regional centers and one smaller peripheral AFMS pathology center using contextual inquiry guidelines. Findings were documented as notes and arranged into a hierarchal organization of common themes based on user-provided data, defined as an affinity diagram. These data were also organized into consolidated graphic models that characterized AFMS pathology work practices, structure, and requirements. Results: Over 1,200 recorded notes were grouped into an affinity diagram composed of 27 third-level, 10 second-level, and five main-level (workflow and workload distribution, quality, communication, military culture, and technology) categories. When combined with workflow and cultural models, the findings revealed that AFMS pathologists had needs that were unique to their military setting, when compared to civilian pathologists. These unique needs included having to serve a globally distributed patient population, transient staff, but a uniform information technology (IT) structure. Conclusions: The contextual inquiry method helped reveal similarities and key differences with civilian pathologists. Such an analysis helped identify specific instances that would benefit from implementing digital pathology in a military environment. Employing digital pathology to facilitate workload distribution, secondary consultations, and quality assurance (over-reads) could help the AFMS deliver more accurate, efficient, and timely AP services at a global level.

Background: In general, surgical pathology reviews report protein expression by tumors in a semi-quantitative manner, that is, -, -/+, +/-, +. At the same time, the experimental pathology literature provides multiple examples of precise expression levels determined by immunohistochemical (IHC) tissue examination of populations of tumors. Natural language processing (NLP) techniques enable the automated extraction of such information through text mining. We propose establishing a database linking quantitative protein expression levels with specific tumor classifications through NLP. Materials and Methods: Our method takes advantage of typical forms of representing experimental findings in terms of percentages of protein expression manifest by the tumor population under study. Characteristically, percentages are represented straightforwardly with the % symbol or as the number of positive findings of the total population. Such text is readily recognized using regular expressions and templates permitting extraction of sentences containing these forms for further analysis using grammatical structures and rule-based algorithms. Results: Our pilot study is limited to the extraction of such information related to lymphomas. We achieved a satisfactory level of retrieval as reflected in scores of 69.91% precision and 57.25% recall with an F-score of 62.95%. In addition, we demonstrate the utility of a web-based curation tool for confirming and correcting our findings. Conclusions: The experimental pathology literature represents a rich source of pathobiological information, which has been relatively underutilized. There has been a combinatorial explosion of knowledge within the pathology domain as represented by increasing numbers of immunophenotypes and disease subclassifications. NLP techniques support practical text mining techniques for extracting this knowledge and organizing it in forms appropriate for pathology decision support systems.

Background: Leukocyte differentials are an important component of clinical care. Morphologic assessment of peripheral blood smears (PBS) may be required to accurately classify leukocytes. However, manual microscopy is labor intensive. The CellaVision DM96 is an automated system that acquires digital images of leukocytes on PBS, pre-classifies the cell type, and displays them on screen for a Technologist or Pathologist to approve or reclassify. Our study compares the results of the DM96 with manual microscopy. Methods: Three hundred and fifty-nine PBS were selected and assessed by manual microscopy with a 200 leukocyte cell count. They were then reassessed using the CellaVision DM96 with a 115 leukocyte cell count including reclassification when necessary. Correlation between the manual microscopy results and the CellaVision DM96 results was calculated for each cell type. Results: The correlation coefficients (r² ) range from a high of 0.99 for blasts to a low of 0.72 for metamyelocytes. Conclusions: The correlation between the CellaVision DM96 and manual microscopy was as good or better than the previously published data. The accuracy of leukocyte classification depended on the cell type, and in general, there was lower correlation for rare cell types. However, the correlation is similar to previous studies on the correlation of manual microscopy with an established reference result. Therefore, the CellaVision DM96 is appropriate for clinical implementation.

Background: Algorithm evaluation provides a means to characterize variability across image analysis algorithms, validate algorithms by comparison with human annotations, combine results from multiple algorithms for performance improvement, and facilitate algorithm sensitivity studies. The sizes of images and image analysis results in pathology image analysis pose significant challenges in algorithm evaluation. We present an efficient parallel spatial database approach to model, normalize, manage, and query large volumes of analytical image result data. This provides an efficient platform for algorithm evaluation. Our experiments with a set of brain tumor images demonstrate the application, scalability, and effectiveness of the platform. Context: The paper describes an approach and platform for evaluation of pathology image analysis algorithms. The platform facilitates algorithm evaluation through a high-performance database built on the Pathology Analytic Imaging Standards (PAIS) data model. Aims: (1) Develop a framework to support algorithm evaluation by modeling and managing analytical results and human annotations from pathology images; (2) Create a robust data normalization tool for converting, validating, and fixing spatial data from algorithm or human annotations; (3) Develop a set of queries to support data sampling and result comparisons; (4) Achieve high performance computation capacity via a parallel data management infrastructure, parallel data loading and spatial indexing optimizations in this infrastructure. Materials and Methods: We have considered two scenarios for algorithm evaluation: (1) algorithm comparison where multiple result sets from different methods are compared and consolidated; and (2) algorithm validation where algorithm results are compared with human annotations. We have developed a spatial normalization toolkit to validate and normalize spatial boundaries produced by image analysis algorithms or human annotations. The validated data were formatted based on the PAIS data model and loaded into a spatial database. To support efficient data loading, we have implemented a parallel data loading tool that takes advantage of multi-core CPUs to accelerate data injection. The spatial database manages both geometric shapes and image features or classifications, and enables spatial sampling, result comparison, and result aggregation through expressive structured query language (SQL) queries with spatial extensions. To provide scalable and efficient query support, we have employed a shared nothing parallel database architecture, which distributes data homogenously across multiple database partitions to take advantage of parallel computation power and implements spatial indexing to achieve high I/O throughput. Results: Our work proposes a high performance, parallel spatial database platform for algorithm validation and comparison. This platform was evaluated by storing, managing, and comparing analysis results from a set of brain tumor whole slide images. The tools we develop are open source and available to download. Conclusions: Pathology image algorithm validation and comparison are essential to iterative algorithm development and refinement. One critical component is the support for queries involving spatial predicates and comparisons. In our work, we develop an efficient data model and parallel database approach to model, normalize, manage and query large volumes of analytical image result data. Our experiments demonstrate that the data partitioning strategy and the grid-based indexing result in good data distribution across database nodes and reduce I/O overhead in spatial join queries through parallel retrieval of relevant data and quick subsetting of datasets. The set of tools in the framework provide a full pipeline to normalize, load, manage and query analytical results for algorithm evaluation.

Digital pathology is a rapidly evolving niche in the world of pathology and is likely to increase in popularity as technology improves. We performed a questionnaire for pathologists and pathology residents across Canada, in order to determine their current experiences and attitudes towards digital pathology; which modalities digital pathology is best suited for; and to assess the need for training in digital pathology amongst pathology residents and staff. An online survey consisting of 24 yes/no, multiple choice and free text questions regarding digital pathology was sent out via E-mail to all members of the Canadian Association of Pathologists and pathology residents across Canada. Survey results showed that telepathology (TP) is used in approximately 43% of institutions, primarily for teaching purposes (65%), followed by operating room consults (46%). Seventy-one percent of respondents believe there is a need for TP in their practice; 85% use digital images in their practice. The top two favored applications for digital pathology are teaching and consultation services, with the main advantage being easier access to cases. The main limitations of using digital pathology are cost and image/diagnostic quality. Sixty-two percent of respondents would attend training courses in pathology informatics and 91% think informatics should be part of residency training. The results of the survey indicate that Pathologists and residents across Canada do see a need for TP and the use of digital images in their daily practice. Integration of an informatics component into resident training programs and courses for staff Pathologists would be welcomed.