Context: The quality of images produced by whole slide imaging (WSI) scanners has a direct influence on the readers' performance and reliability of the clinical diagnosis. Therefore, WSI scanners should produce not only high quality but also consistent quality images. Aim: We aim to evaluate reproducibility of WSI scanners based on the quality of images produced over time and among multiple scanners. The evaluation is independent of content or context of test specimen. Methods: The ultimate judge of image quality is a pathologist, however, subjective evaluations are heavily influenced by the complexity of a case and subtle variations introduced by a scanner can be easily overlooked. Therefore, we employed a quantitative image quality assessment method based on clinically relevant parameters, such as sharpness and brightness, acquired in a survey of pathologists. The acceptable level of quality per parameter was determined in a subjective study. The evaluation of scanner reproducibility was conducted with Philips Ultra-Fast Scanners. A set of 36 HercepTest™ slides were used in three sub-studies addressing variations due to systems and time, producing 8640 test images for evaluation. Results: The results showed that the majority of images in all the sub-studies are within the acceptable quality level; however, some scanners produce higher quality images more often than others. The results are independent of case types, and they match our perception of quality. Conclusion: The quantitative image quality assessment method was successfully applied in the HercepTest™ slides to evaluate WSI scanner reproducibility. The proposed method is generic and applicable to any other types of slide stains and scanners.

Background: The use of electronic health records (EHRs) has continued to increase within healthcare systems in the developed and developing nations. EHRs allow for increased patient safety, grant patients easier access to their medical records, and offer a wealth of data to researchers. However, various bioethical, financial, logistical, and information security considerations must be addressed while transitioning to an EHR system. The need to encrypt private patient information for data sharing is one of the foremost challenges faced by health information technology. Method: We describe the usage of the message digest-5 (MD5) and secure hashing algorithm (SHA) as methods for encrypting electronic medical data. In particular, we present an application of the MD5 and SHA-1 algorithms in encrypting a composite message from private patient information. Results: The results show that the composite message can be used to create a unique one-way encrypted ID per patient record that can be used for data sharing. Conclusion: The described software tool can be used to share patient EMRs between practitioners without revealing patients identifiable data.

Context: The format of a synoptic report can significantly affect the accuracy, speed, and preference with which a reader can retrieve information. Objective: The objective of this study is to compare different formats of Gleason grading/score in synoptic reports of radical prostatectomies. Methods: The performance of 16 nonpathologists (cancer registrars, MDs, medical non-MDs, and nonmedical) at identifying specific information in various formatted synoptic reports using a computerized quiz that measured both accuracy and speed. Results: Compared to the standard format (primary, secondary, tertiary grades, and total score on separate lines), omitting tertiary grade when "Not applicable" reduced accuracy (72 vs. 97%, P < 0.001) and increased time to retrieve information 63% (P < 0.001). No user preferred to have tertiary grade omitted. Both the biopsy format (primary + secondary = total score, tertiary on a separate line) and the single line format (primary + secondary + (tertiary) -> total score) were associated with increased speed of data extraction (18 and 24%, respectively, P < 0.001). The single line format was more accurate (100% vs. 97%, P = 0.02). No user preferred the biopsy format, and only 7/16 users preferred the single line format. Conclusions : Different report formats for Gleason grading significantly affect users speed, accuracy, and preference; users do not always prefer either speed or accuracy.

Background: The rapid acquisition of biological data and development of computationally intensive analyses has led to a need for novel approaches to software deployment. In particular, the complexity of common analytic tools for genomics makes them difficult to deploy and decreases the reproducibility of computational experiments. Methods: Recent technologies that allow for application virtualization, such as Docker, allow developers and bioinformaticians to isolate these applications and deploy secure, scalable platforms that have the potential to dramatically increase the efficiency of big data processing. Results: While limitations exist, this study demonstrates a successful implementation of a pipeline with several discrete software applications for the analysis of next-generation sequencing (NGS) data. Conclusions: With this approach, we significantly reduced the amount of time needed to perform clonal analysis from NGS data in acute myeloid leukemia.

Background: In previous research, we introduced an automated, localized, fusion-based approach for classifying uterine cervix squamous epithelium into Normal, CIN1, CIN2, and CIN3 grades of cervical intraepithelial neoplasia (CIN) based on digitized histology image analysis. As part of the CIN assessment process, acellular and atypical cell concentration features were computed from vertical segment partitions of the epithelium region to quantize the relative distribution of nuclei. Methods: Feature data was extracted from 610 individual segments from 61 images for epithelium classification into categories of Normal, CIN1, CIN2, and CIN3. The classification results were compared against CIN labels obtained from two pathologists who visually assessed abnormality in the digitized histology images. In this study, individual vertical segment CIN classification accuracy improvement is reported using the logistic regression classifier for an expanded data set of 118 histology images. Results: We analyzed the effects on classification using the same pathologist labels for training and testing versus using one pathologist labels for training and the other for testing. Based on a leave-one-out approach for classifier training and testing, exact grade CIN accuracies of 81.29% and 88.98% were achieved for individual vertical segment and epithelium whole-image classification, respectively. Conclusions: The Logistic and Random Tree classifiers outperformed the benchmark SVM and LDA classifiers from previous research. The Logistic Regression classifier yielded an improvement of 10.17% in CIN Exact grade classification results based on CIN labels for training-testing for the individual vertical segments and the whole image from the same single expert over the baseline approach using the reduced features. Overall, the CIN classification rates tended to be higher using the training-testing labels for the same expert than for training labels from one expert and testing labels from the other expert. The Exact class fusion- based CIN discrimination results obtained in this study are similar to the Exact class expert agreement rate.

Background: Classical statistics is a well-established approach in the analysis of medical data. While the medical community seems to be familiar with the concept of a statistical analysis and its interpretation, the Bayesian approach, argued by many of its proponents to be superior to the classical frequentist approach, is still not well-recognized in the analysis of medical data. Aim: The goal of this study is to encourage data analysts to use the Bayesian approach, such as modeling with graphical probabilistic networks, as an insightful alternative to classical statistical analysis of medical data. Materials and Methods: This paper offers a comparison of two approaches to analysis of medical time series data: (1) classical statistical approach, such as the Kaplan-Meier estimator and the Cox proportional hazards regression model, and (2) dynamic Bayesian network modeling. Our comparison is based on time series cervical cancer screening data collected at Magee-Womens Hospital, University of Pittsburgh Medical Center over 10 years. Results: The main outcomes of our comparison are cervical cancer risk assessments produced by the three approaches. However, our analysis discusses also several aspects of the comparison, such as modeling assumptions, model building, dealing with incomplete data, individualized risk assessment, results interpretation, and model validation. Conclusion : Our study shows that the Bayesian approach is (1) much more flexible in terms of modeling effort, and (2) it offers an individualized risk assessment, which is more cumbersome for classical statistical approaches.

Context: Text-based reporting and manual arbitration for whole slide imaging (WSI) validation studies are labor intensive and do not allow for consistent, scalable, and repeatable data collection or analysis. Objective: The objective of this study was to establish a method of data capture and analysis using standardized codified checklists and predetermined synoptic discordance tables and to use these methods in a pilot multisite validation study. Methods and Study Design: Fifteen case report form checklists were generated from the College of American Pathology cancer protocols. Prior to data collection, all hypothetical pairwise comparisons were generated, and a level of harm was determined for each possible discordance. Four sites with four pathologists each generated 264 independent reads of 33 cases. Preestablished discordance tables were applied to determine site by site and pooled accuracy, intrareader/intramodality, and interreader intramodality error rates. Results: Over 10,000 hypothetical pairwise comparisons were evaluated and assigned harm in discordance tables. The average difference in error rates between WSI and glass, as compared to ground truth, was 0.75% with a lower bound of 3.23% (95% confidence interval). Major discordances occurred on challenging cases, regardless of modality. The average inter-reader agreement across sites for glass was 76.5% (weighted kappa of 0.68) and for digital it was 79.1% (weighted kappa of 0.72). Conclusion: These results demonstrate the feasibility and utility of employing standardized synoptic checklists and predetermined discordance tables to gather consistent, comprehensive diagnostic data for WSI validation studies. This method of data capture and analysis can be applied in large-scale multisite WSI validations.

Background: Endometrial carcinoma (EC) is the most common extracolonic malignant neoplasm associated with Lynch syndrome (LS). LS is caused by autosomal dominant germline mutations in DNA mismatch repair (MMR) genes. Screening for LS in EC is often evaluated by loss of immunohistochemical (IHC) expression of DNA MMR enzymes MLH1, MSH2, MSH6, and PMS2 (MMR IHC). In July 2013, our clinicians asked that we screen all EC in patients ≤60 for loss of MMR IHC expression. Despite this policy, several cases were not screened or screening was delayed. We implemented an informatics-based approach to ensure that all women who met criteria would have timely screening. Subjects and Methods: Reports are created in PowerPath (Sunquest Information Systems, Tucson, AZ) with custom synoptic templates. We implemented an algorithm on March 6, 2014 requiring pathologists to address MMR IHC in patients ≤60 with EC before sign out (S/O). Pathologists must answer these questions: is patient ≤60 (yes/no), if yes, follow-up questions (IHC done previously, ordered with addendum to follow, results included in report, N/A, or not ordered), if not ordered, one must explain. We analyzed cases from July 18, 2013 to August 31, 2016 preimplementation (PreImp) and postimplementation (PostImp) that met criteria. Data analysis was performed using the standard data package included with GraphPad Prism^® 7.00 (GraphPad Software, Inc., La Jolla, CA, USA). Results: There were 147 patients who met criteria (29 PreImp and 118 PostImp). IHC was ordered in a more complete and timely fashion PostImp than PreImp. PreImp, 4/29 (13.8%) cases did not get any IHC, but PostImp, only 4/118 (3.39%) were missed (P = 0.0448). Of cases with IHC ordered, 60.0% (15/25) were ordered before or at S/O PreImp versus 91.2% (104/114) PostImp (P = 0.0004). Relative to day of S/O, the mean days of order delay were longer and more variable PreImp versus PostImp (12.9 ± 40.7 vs. -0.660 ± 1.15; P = 0.0227), with the average being before S/O PostImp. Conclusion: This algorithm ensures MMR IHC ordering in women ≤60 with EC and can be applied to similar scenarios. Ancillary tests for management are increasing, especially genetic and molecular-based methods. The burden of managing orders and results remains with the pathologist and relying on human intervention alone is ineffective. Ordering IHC before or at S/O prevents oversight and the additional work of retrospective ordering and reporting.

Background: Measures of spatial intratumor heterogeneity are potentially important diagnostic biomarkers for cancer progression, proliferation, and response to therapy. Spatial relationships among cells including cancer and stromal cells in the tumor microenvironment (TME) are key contributors to heterogeneity. Methods: We demonstrate how to quantify spatial heterogeneity from immunofluorescence pathology samples, using a set of 3 basic breast cancer biomarkers as a test case. We learn a set of dominant biomarker intensity patterns and map the spatial distribution of the biomarker patterns with a network. We then describe the pairwise association statistics for each pattern within the network using pointwise mutual information (PMI) and visually represent heterogeneity with a two-dimensional map. Results: We found a salient set of 8 biomarker patterns to describe cellular phenotypes from a tissue microarray cohort containing 4 different breast cancer subtypes. After computing PMI for each pair of biomarker patterns in each patient and tumor replicate, we visualize the interactions that contribute to the resulting association statistics. Then, we demonstrate the potential for using PMI as a diagnostic biomarker, by comparing PMI maps and heterogeneity scores from patients across the 4 different cancer subtypes. Estrogen receptor positive invasive lobular carcinoma patient, AL13-6, exhibited the highest heterogeneity score among those tested, while estrogen receptor negative invasive ductal carcinoma patient, AL13-14, exhibited the lowest heterogeneity score. Conclusions: This paper presents an approach for describing intratumor heterogeneity, in a quantitative fashion (via PMI), which departs from the purely qualitative approaches currently used in the clinic. PMI is generalizable to highly multiplexed/hyperplexed immunofluorescence images, as well as spatial data from complementary in situ methods including FISSEQ and CyTOF, sampling many different components within the TME. We hypothesize that PMI will uncover key spatial interactions in the TME that contribute to disease proliferation and progression.

Background: Celiac disease (CD) is a common autoimmune disorder. Efficient identification of patients may improve chronic management of the disease. Prior studies have shown searching International Classification of Diseases-9 (ICD-9) codes alone is inaccurate for identifying patients with CD. In this study, we developed automated classification algorithms leveraging pathology reports and other clinical data in Electronic Health Records (EHRs) to refine the subset population preselected using ICD-9 code (579.0). Materials and Methods: EHRs were searched for established ICD-9 code (579.0) suggesting CD, based on which an initial identification of cases was obtained. In addition, laboratory results for tissue transglutaminse were extracted. Using natural language processing we analyzed pathology reports from upper endoscopy. Twelve machine learning classifiers using different combinations of variables related to ICD-9 CD status, laboratory result status, and pathology reports were experimented to find the best possible CD classifier. Ten-fold cross-validation was used to assess the results. Results: A total of 1498 patient records were used including 363 confirmed cases and 1135 false positive cases that served as controls. Logistic model based on both clinical and pathology report features produced the best results: Kappa of 0.78, F1 of 0.92, and area under the curve (AUC) of 0.94, whereas in contrast using ICD-9 only generated poor results: Kappa of 0.28, F1 of 0.75, and AUC of 0.63. Conclusion: Our automated classification system presented an efficient and reliable way to improve the performance of CD patient identification.

Objective: Within the information technology (IT) industry, best practices and standards are constantly evolving and being refined. In contrast, computer technology utilized within the healthcare industry often evolves at a glacial pace, with reduced opportunities for justified innovation. Although the use of timely technology refreshes within an enterprise's overall technology stack can be costly, thoughtful adoption of select technologies with a demonstrated return on investment can be very effective in increasing productivity and at the same time, reducing the burden of maintenance often associated with older and legacy systems. In this brief technical communication, we introduce the concept of microservices as applied to the ecosystem of data analysis pipelines. Microservice architecture is a framework for dividing complex systems into easily managed parts. Each individual service is limited in functional scope, thereby conferring a higher measure of functional isolation and reliability to the collective solution. Moreover, maintenance challenges are greatly simplified by virtue of the reduced architectural complexity of each constitutive module. This fact notwithstanding, rendered overall solutions utilizing a microservices-based approach provide equal or greater levels of functionality as compared to conventional programming approaches. Bioinformatics, with its ever-increasing demand for performance and new testing algorithms, is the perfect use-case for such a solution. Moreover, if promulgated within the greater development community as an open-source solution, such an approach holds potential to be transformative to current bioinformatics software development. Context: Bioinformatics relies on nimble IT framework which can adapt to changing requirements. Aims: To present a well-established software design and deployment strategy as a solution for current challenges within bioinformatics Conclusions: Use of the microservices framework is an effective methodology for the fabrication and implementation of reliable and innovative software, made possible in a highly collaborative setting.

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.

Background: The skin biopsy requisition form (RF) serves as a key communication tool for transfer of relevant information related to skin biopsy between clinicians and pathologists. Clinical information in the skin biopsy RF is frequently missing or incomplete. Objective: To determine the rates of provision of critical clinical information necessary for histopathologic interpretation in the skin biopsy RF and encounter visit note (EVN). Methods: A retrospective review of 300 RFs and corresponding EVNs from May 1 to 7, 2012, in a tertiary care dermatology practice. Results: Age (100%), lesion location (100%), and clinical impression (93%) were the most commonly supplied elements in the RF and EVN. Clinical elements that were commonly not provided in the RF but present in the EVN included sampling method - partial versus complete (46%), duration of lesion (54%), morphology of lesion (97%), clinical symptoms (63%), clinical photos (63%), previous clinical (97%), and dermatopathologic diagnoses (82%). Limitations: Retrospective study design. Conclusions: These data suggest that while missing critical clinical information in the RF is often present in the EVN, some information is still not present in either source.

Background: Immune cell infiltration in tumor is an emerging prognostic biomarker in breast cancer. The gold standard for quantification of immune cells in tissue sections is visual assessment through a microscope, which is subjective and semi-quantitative. In this study, we propose and evaluate an approach based on antibody-guided annotation and deep learning to quantify immune cell-rich areas in hematoxylin and eosin (H&E) stained samples. Methods: Consecutive sections of formalin-fixed parafin-embedded samples obtained from the primary tumor of twenty breast cancer patients were cut and stained with H&E and the pan-leukocyte CD45 antibody. The stained slides were digitally scanned, and a training set of immune cell-rich and cell-poor tissue regions was annotated in H&E whole-slide images using the CD45-expression as a guide. In analysis, the images were divided into small homogenous regions, superpixels, from which features were extracted using a pretrained convolutional neural network (CNN) and classified with a support of vector machine. The CNN approach was compared to texture-based classification and to visual assessments performed by two pathologists. Results: In a set of 123,442 labeled superpixels, the CNN approach achieved an F-score of 0.94 (range: 0.92-0.94) in discrimination of immune cell-rich and cell-poor regions, as compared to an F-score of 0.88 (range: 0.87-0.89) obtained with the texture-based classification. When compared to visual assessment of 200 images, an agreement of 90% (k = 0.79) to quantify immune infiltration with the CNN approach was achieved while the inter-observer agreement between pathologists was 90% (k = 0.78). Conclusions: Our findings indicate that deep learning can be applied to quantify immune cell infiltration in breast cancer samples using a basic morphology staining only. A good discrimination of immune cell-rich areas was achieved, well in concordance with both leukocyte antigen expression and pathologists' visual assessment.

Laboratory methodology websites are specialized niche websites. The visibility of a niche website transforms it into an authority site on a particular "niche of knowledge." This article presents some ways in which a laboratory methodology website can maintain its sustainability. The optimal composition of the website includes a basic content, a blog, and an ancillary part. This article discusses experimenting with the search engine optimization query results page. Strategic placement of keywords and even phrases, as well as fragmentation of the post's material, can improve the website's visibility to search engines. Hyperlinks open a chain reaction of additional links and draw attention to the previous posts. Publications in printed periodicals are a substantial part of a niche website presence on the Internet. Although this article explores a laboratory website on the basis of our hands-on expertise maintaining "Grossing Technology in Surgical Pathology" (www.grossing-technology.com) website with a high volume of traffic for more than a decade, the recommendations presented here for developing an authority website can be applied to other professional specialized websites. The authority websites visibility and sustainability are preconditions for aggregating them in a specialized educational laboratory portal.

Background: Recent developments in molecular pathology and genetic/epigenetic analysis of cancer tissue have resulted in a marked increase in objective and measurable data. In comparison, the traditional morphological analysis approach to pathology diagnosis, which can connect these molecular data and clinical diagnosis, is still mostly subjective. Even though the advent and popularization of digital pathology has provided a boost to computer-aided diagnosis, some important pathological concepts still remain largely non-quantitative and their associated data measurements depend on the pathologist's sense and experience. Such features include pleomorphism and heterogeneity. Methods and Results: In this paper, we propose a method for the objective measurement of pleomorphism and heterogeneity, using the cell-level co-occurrence matrix. Our method is based on the widely used Gray-level co-occurrence matrix (GLCM), where relations between neighboring pixel intensity levels are captured into a co-occurrence matrix, followed by the application of analysis functions such as Haralick features. In the pathological tissue image, through image processing techniques, each nucleus can be measured and each nucleus has its own measureable features like nucleus size, roundness, contour length, intra-nucleus texture data (GLCM is one of the methods). In GLCM each nucleus in the tissue image corresponds to one pixel. In this approach the most important point is how to define the neighborhood of each nucleus. We define three types of neighborhoods of a nucleus, then create the co-occurrence matrix and apply Haralick feature functions. In each image pleomorphism and heterogeneity are then determined quantitatively. For our method, one pixel corresponds to one nucleus feature, and we therefore named our method Cell Feature Level Co-occurrence Matrix (CFLCM). We tested this method for several nucleus features. Conclusion: CFLCM is showed as a useful quantitative method for pleomorphism and heterogeneity on histopathological image analysis.

The field of pathology has used light microscopy (LM) extensively since the mid-19 ^th century for examination of histological tissue preparations. This technology has remained the foremost tool in use by pathologists even as other fields have undergone a great change in recent years through new technologies. However, as new microscopy techniques are perfected and made available, this reliance on the standard LM will likely begin to change. Advanced imaging involving both diffraction-limited and subdiffraction techniques are bringing nondestructive, high-resolution, molecular-level imaging to pathology. Some of these technologies can produce three-dimensional (3D) datasets from sampled tissues. In addition, block-face/tissue-sectioning techniques are already providing automated, large-scale 3D datasets of whole specimens. These datasets allow pathologists to see an entire sample with all of its spatial information intact, and furthermore allow image analysis such as detection, segmentation, and classification, which are impossible in standard LM. It is likely that these technologies herald a major paradigm shift in the field of pathology.

Background: Today, pathology reporting consists of many separate tasks, carried out by multiple people. Common tasks include dictation during case review, transcription, verification of the transcription, report distribution, and report the key findings to follow-up registries. Introduction of digital workstations makes it possible to remove some of these tasks and simplify others. This study describes the work presented at the Nordic Symposium on Digital Pathology 2015, in Linköping, Sweden. Methods: We explored the possibility to have a digital tool that simplifies image review by assisting note-taking, and with minimal extra effort, populates a structured report. Thus, our prototype sees reporting as an activity interleaved with image review rather than a separate final step. We created an interface to collect, sort, and display findings for the most common reporting needs, such as tumor size, grading, and scoring. Results: The interface was designed to reduce the need to retain partial findings in the head or on paper, while at the same time be structured enough to support automatic extraction of key findings for follow-up registry reporting. The final prototype was evaluated with two pathologists, diagnosing complicated partial mastectomy cases. The pathologists experienced that the prototype aided them during the review and that it created a better overall workflow. Conclusions: These results show that it is feasible to simplify the reporting tasks in a way that is not distracting, while at the same time being able to automatically extract the key findings. This simplification is possible due to the realization that the structured format needed for automatic extraction of data can be used to offload the pathologists' working memory during the diagnostic review.

Context: Whole-slide images (WSIs) present a rich source of information for education, training, and quality assurance. However, they are often used in a fashion similar to glass slides rather than in novel ways that leverage the advantages of WSI. We have created a pipeline to transform annotated WSI into pattern recognition training, and quality assurance web application called novel diagnostic electronic resource (NDER). Aims: Create an efficient workflow for extracting annotated WSI for use by NDER, an attractive web application that provides high-throughput training. Materials and Methods: WSI were annotated by a resident and classified into five categories. Two methods of extracting images and creating image databases were compared. Extraction Method 1: Manual extraction of still images and validation of each image by four breast pathologists. Extraction Method 2: Validation of annotated regions on the WSI by a single experienced breast pathologist and automated extraction of still images tagged by diagnosis. The extracted still images were used by NDER. NDER briefly displays an image, requires users to classify the image after time has expired, then gives users immediate feedback. Results: The NDER workflow is efficient: annotation of a WSI requires 5 min and validation by an expert pathologist requires An additional one to 2 min. The pipeline is highly automated, with only annotation and validation requiring human input. NDER effectively displays hundreds of high-quality, high-resolution images and provides immediate feedback to users during a 30 min session. Conclusions: NDER efficiently uses annotated WSI to rapidly increase pattern recognition and evaluate for diagnostic proficiency.

Background: The quality and limitations of digital slides are not fully known. We aimed to estimate intrapathologist discrepancy in detecting specific microscopic features on glass slides and digital slides created by scanning at ×20. Methods: Hematoxylin and eosin and periodic acid-Schiff glass slides were digitized using the Mirax Scan (Carl Zeiss Inc., Germany). Six pathologists assessed 50-71 digital slides. We recorded objective magnification, total time, and detection of the following: Mast cells; eosinophils; plasma cells; pigmented macrophages; melanin in the epidermis; fungal bodies; neutrophils; civatte bodies; parakeratosis; and sebocytes. This process was repeated using the corresponding glass slides after 3 weeks. The diagnosis was not required. Results: The mean time to assess digital slides was 176.77 s and 137.61 s for glass slides (P < 0.001, 99% confidence interval [CI]). The mean objective magnification used to detect features using digital slides was 18.28 and 14.07 for glass slides (P < 0.001, 99.99% CI). Parakeratosis, civatte bodies, pigmented macrophages, melanin in the epidermis, mast cells, eosinophils, plasma cells, and neutrophils, were identified at lower objectives on glass slides (P = 0.023-0.001, 95% CI). Average intraobserver concordance ranged from κ = 0.30 to κ = 0.78. Features with poor to fair average concordance were: Melanin in the epidermis (κ = 0.15-0.58); plasma cells (κ = 0.15-0.49); and neutrophils (κ = 0.12-0.48). Features with moderate average intrapathologist concordance were: parakeratosis (κ = 0.21-0.61); civatte bodies (κ = 0.21-0.71); pigment-laden macrophages (κ = 0.34-0.66); mast cells (κ = 0.29-0.78); and eosinophils (κ = 0.31-0.79). The average intrapathologist concordance was good for sebocytes (κ = 0.51-1.00) and fungal bodies (κ = 0.47-0.76). Conclusions: Telepathology using digital slides scanned at ×20 is sufficient for detection of histopathologic features routinely encountered in dermatitis cases, though less efficient than glass slides.

Background: Deep learning (DL) is a representation learning approach ideally suited for image analysis challenges in digital pathology (DP). The variety of image analysis tasks in the context of DP includes detection and counting (e.g., mitotic events), segmentation (e.g., nuclei), and tissue classification (e.g., cancerous vs. non-cancerous). Unfortunately, issues with slide preparation, variations in staining and scanning across sites, and vendor platforms, as well as biological variance, such as the presentation of different grades of disease, make these image analysis tasks particularly challenging. Traditional approaches, wherein domain-specific cues are manually identified and developed into task-specific "handcrafted" features, can require extensive tuning to accommodate these variances. However, DL takes a more domain agnostic approach combining both feature discovery and implementation to maximally discriminate between the classes of interest. While DL approaches have performed well in a few DP related image analysis tasks, such as detection and tissue classification, the currently available open source tools and tutorials do not provide guidance on challenges such as (a) selecting appropriate magnification, (b) managing errors in annotations in the training (or learning) dataset, and (c) identifying a suitable training set containing information rich exemplars. These foundational concepts, which are needed to successfully translate the DL paradigm to DP tasks, are non-trivial for (i) DL experts with minimal digital histology experience, and (ii) DP and image processing experts with minimal DL experience, to derive on their own, thus meriting a dedicated tutorial. Aims: This paper investigates these concepts through seven unique DP tasks as use cases to elucidate techniques needed to produce comparable, and in many cases, superior to results from the state-of-the-art hand-crafted feature-based classification approaches. Results : Specifically, in this tutorial on DL for DP image analysis, we show how an open source framework (Caffe), with a singular network architecture, can be used to address: (a) nuclei segmentation (F-score of 0.83 across 12,000 nuclei), (b) epithelium segmentation (F-score of 0.84 across 1735 regions), (c) tubule segmentation (F-score of 0.83 from 795 tubules), (d) lymphocyte detection (F-score of 0.90 across 3064 lymphocytes), (e) mitosis detection (F-score of 0.53 across 550 mitotic events), (f) invasive ductal carcinoma detection (F-score of 0.7648 on 50 k testing patches), and (g) lymphoma classification (classification accuracy of 0.97 across 374 images). Conclusion: This paper represents the largest comprehensive study of DL approaches in DP to date, with over 1200 DP images used during evaluation. The supplemental online material that accompanies this paper consists of step-by-step instructions for the usage of the supplied source code, trained models, and input data.

Context: It has been shown that ovarian carcinoma subtypes are distinct pathologic entities with differing prognostic and therapeutic implications. Histotyping by pathologists has good reproducibility, but occasional cases are challenging and require immunohistochemistry and subspecialty consultation. Motivated by the need for more accurate and reproducible diagnoses and to facilitate pathologists' workflow, we propose an automatic framework for ovarian carcinoma classification. Materials and Methods: Our method is inspired by pathologists' workflow. We analyse imaged tissues at two magnification levels and extract clinically-inspired color, texture, and segmentation-based shape descriptors using image-processing methods. We propose a carefully designed machine learning technique composed of four modules: A dissimilarity matrix, dimensionality reduction, feature selection and a support vector machine classifier to separate the five ovarian carcinoma subtypes using the extracted features. Results: This paper presents the details of our implementation and its validation on a clinically derived dataset of eighty high-resolution histopathology images. The proposed system achieved a multiclass classification accuracy of 95.0% when classifying unseen tissues. Assessment of the classifier's confusion (confusion matrix) between the five different ovarian carcinoma subtypes agrees with clinician's confusion and reflects the difficulty in diagnosing endometrioid and serous carcinomas. Conclusions: Our results from this first study highlight the difficulty of ovarian carcinoma diagnosis which originate from the intrinsic class-imbalance observed among subtypes and suggest that the automatic analysis of ovarian carcinoma subtypes could be valuable to clinician's diagnostic procedure by providing a second opinion.

Context: Recognition of the importance of informatics to the practice of pathology has surged. Training residents in pathology informatics have been a daunting task for most residency programs in the United States because faculty often lacks experience and training resources. Nevertheless, developing resident competence in informatics is essential for the future of pathology as a specialty. Objective: The objective of the study is to develop and deliver a pathology informatics curriculum and instructional framework that guides pathology residency programs in training residents in critical pathology informatics knowledge and skills and meets Accreditation Council for Graduate Medical Education Informatics Milestones. Design: The College of American Pathologists, Association of Pathology Chairs, and Association for Pathology Informatics formed a partnership and expert work group to identify critical pathology informatics training outcomes and to create a highly adaptable curriculum and instructional approach, supported by a multiyear change management strategy. Results: Pathology Informatics Essentials for Residents (PIER) is a rigorous approach for educating all pathology residents in important pathology informatics knowledge and skills. PIER includes an instructional resource guide and toolkit for incorporating informatics training into residency programs that vary in needs, size, settings, and resources. PIER is available at http://www.apcprods.org/PIER (accessed April 6, 2016). Conclusions: PIER is an important contribution to informatics training in pathology residency programs. PIER introduces pathology trainees to broadly useful informatics concepts and tools that are relevant to practice. PIER provides residency program directors with a means to implement a standardized informatics training curriculum, to adapt the approach to local program needs, and to evaluate resident performance and progress over time.

Introduction: Whole slide imaging (WSI) permits intraoperative consultations (frozen sections) to be performed remotely. However, WSI files are large and can be problematic if there are tissue artifacts (e.g., tissue folds) or when slides are scanned without multiplanes (Z-stacks) to permit focusing. The Panoptiq dynamic imaging system allows users to create their own digital files that combine low power panoramic digital images with regions of interest that can be imaged using high power Z-stacks. The aim of this study was to determine the utility of the Panoptiq dynamic imaging system for frozen section telepathology. Materials and Methods: Twenty archival randomly selected genitourinary surgical pathology frozen sectional cases were evaluated using conventional light microscopy (glass slides), panoramic images, and whole slide images. To create panoramic images glass slides were digitized using a Prosilica GT camera (model GT1920C, Allied Vision Technologies) attached to an Olympus B × 45 microscope and Dell Precision Tower 810 computer (Dell). Panoptiq 3 version 3.1.2 software was used for image acquisition and Panoptiq View version 3.1.2 to view images (ViewsIQ, Richmond, BC, Canada). Image acquisition using Panoptiq software involved a pathology resident, who manually created digital maps (×4 objective) and then selected representative regions of interest to generate Z-stacks at higher magnification (×40 objective). Whole slide images were generated using an Aperio XT Scanscope (Leica) and viewed using ImageScope Software (Aperio ePathology, Leica). Three pathologists were asked to render diagnoses and rate image quality (1-10) and their diagnostic confidence (1-10) for each modality. Results: The diagnostic concordance with glass slides was 98.3% for panoramic images and 100% for WSI. Panoptiq images were comparable to the glass slide viewing experience in terms of image quality and diagnostic confidence. Complaints regarding WSI included poor focus near tissue folds and air bubbles. Panoptiq permitted fine focusing of tissue folds and air bubbles. Issues with panoramic images included difficulty interpreting low-resolution ×4 image maps and the presence of tiling artifacts. In some cases, Z-stacked areas of Panoptiq images were limited or not representative of diagnostic regions. The image file size of Panoptiq was more than 14 times smaller than that of WSI files. Conclusions: The Panoptiq imaging system is a novel tool that can be used for frozen section telepathology. Panoramic digital images were easy to generate and navigate, of relatively small file size, and offered a mechanism to overcome focusing problems commonly encountered with WSI of frozen sections. However, the acquisition of representative Panoptiq images was operator dependent with the individual creating files that may impact the final diagnosis.

Background: Whole slide imaging (WSI) finds increasingly higher value in everyday surgical pathology in addition to its well-established use for educational and research purposes. However, its diagnostic utility, especially in subspecialty settings such as neuropathology, is not fully validated. Neuropathology practice is unique with smaller overall tissue size and frequent need for high-power evaluation. In addition, tumor grade is an integral part of the initial diagnosis. The purpose of this study is to assess the feasibility of primary pathology diagnosis of surgical neuropathology specimens using WSI. Materials and Methods: We reviewed consecutive surgical neuropathology cases diagnosed in our institution during a 2-month period and identified a single diagnostic slide, which was scanned at 40× magnification. Two neuropathologists who were blinded to the original diagnoses reviewed the whole slide image and rendered a diagnosis including tumor grade when applicable. They reviewed the single diagnostic slide after a wash-out period. Intra- and inter-observer discrepancies, as well as reasons for discrepancies, were evaluated. Results: The concordance rates were 94.9% and 88% for two neuropathologists. Two critical issues leading to discrepancies were identified: (1) identification of mitoses and (2) recognition of nuclear details. Conclusions: Given the current study is exclusively for surgical neuropathology cases, an all-encompassing conclusion about the utility of WSI for diagnostic purposes may not be available. Nevertheless, pathologists should be aware of the potential pitfalls due to identification of mitotic figures and nuclear details. We recommend independent validation for each subspecialty of pathology to identify subspecialty-specific concerns, so they can be properly addressed.

Context: The Eastern Ontario Regional Laboratory Association (EORLA) is a newly established association of all the laboratory and pathology departments of Eastern Ontario that currently includes facilities from eight hospitals. All surgical specimens for EORLA are processed in one central location, the Department of Pathology and Laboratory Medicine (DPLM) at The Ottawa Hospital (TOH), where the rapid growth and influx of surgical and cytology specimens has created many challenges in ensuring the timely processing of cases and reports. Although the entire process is maintained and tracked in a clinical information system, this system lacks pre-emptive warnings that can help management address issues as they arise. Aims: Dashboard technology provides automated, real-time visual clues that could be used to alert management when a case or specimen is not being processed within predefined time frames. We describe the development of a dashboard helping pathology clinical management to make informed decisions on specimen allocation and tracking. Methods: The dashboard was designed and developed in two phases, following a prototyping approach. The first prototype of the dashboard helped monitor and manage pathology processes at the DPLM. Results: The use of this dashboard helped to uncover operational inefficiencies and contributed to an improvement of turn-around time within The Ottawa Hospital's DPML. It also allowed the discovery of additional requirements, leading to a second prototype that provides finer-grained, real-time information about individual cases and specimens. Conclusion: We successfully developed a dashboard that enables managers to address delays and bottlenecks in specimen allocation and tracking. This support ensures that pathology reports are provided within time frame standards required for high-quality patient care. Given the importance of rapid diagnostics for a number of diseases, the use of real-time dashboards within pathology departments could contribute to improving the quality of patient care beyond EORLA's.

Background: The adoption of digital pathology offers benefits over labor-intensive, time-consuming, and error-prone manual processes. However, because most workflow and laboratory transactions are centered around the anatomical pathology laboratory information system (APLIS), adoption of digital pathology ideally requires integration with the APLIS. A digital pathology system (DPS) integrated with the APLIS was recently implemented at our institution for diagnostic use. We demonstrate how such integration supports digital workflow to sign-out anatomical pathology cases. Methods: Workflow begins when pathology cases get accessioned into the APLIS (CoPathPlus). Glass slides from these cases are then digitized (Omnyx VL120 scanner) and automatically uploaded into the DPS (Omnyx^; Integrated Digital Pathology (IDP) software v.1.3). The APLIS transmits case data to the DPS via a publishing web service. The DPS associates scanned images with the correct case using barcode labels on slides and information received from the APLIS. When pathologists remotely open a case in the DPS, additional information (e.g. gross pathology details, prior cases) gets retrieved from the APLIS through a query web service. Results: Following validation of this integration, pathologists at our institution have signed out more than 1000 surgical pathology cases in a production environment. Integration between the APLIS and DPS enabled pathologists to review digital slides while simultaneously having access to pertinent case metadata. The introduction of a digital workflow eliminated costly manual tasks involving matching of glass slides and avoided delays waiting for glass slides to be delivered. Conclusion: Integrating the DPS and APLIS were instrumental for successfully implementing a digital solution at our institution for pathology sign-out. The integration streamlined our digital sign-out workflow, diminished the potential for human error related to matching slides, and improved the sign-out experience for pathologists.

Background: Digital slides obtained from whole slide imaging (WSI) platforms are typically viewed in two dimensions using desktop personal computer monitors or more recently on mobile devices. To the best of our knowledge, we are not aware of any studies viewing digital pathology slides in a virtual reality (VR) environment. VR technology enables users to be artificially immersed in and interact with a computer-simulated world. Oculus Rift is among the world's first consumer-targeted VR headsets, intended primarily for enhanced gaming. Our aim was to explore the use of the Oculus Rift for examining digital pathology slides in a VR environment. Methods: An Oculus Rift Development Kit 2 (DK2) was connected to a 64-bit computer running Virtual Desktop software. Glass slides from twenty randomly selected lymph node cases (ten with benign and ten malignant diagnoses) were digitized using a WSI scanner. Three pathologists reviewed these digital slides on a 27-inch 5K display and with the Oculus Rift after a 2-week washout period. Recorded endpoints included concordance of final diagnoses and time required to examine slides. The pathologists also rated their ease of navigation, image quality, and diagnostic confidence for both modalities. Results: There was 90% diagnostic concordance when reviewing WSI using a 5K display and Oculus Rift. The time required to examine digital pathology slides on the 5K display averaged 39 s (range 10-120 s), compared to 62 s with the Oculus Rift (range 15-270 s). All pathologists confirmed that digital pathology slides were easily viewable in a VR environment. The ratings for image quality and diagnostic confidence were higher when using the 5K display. Conclusion: Using the Oculus Rift DK2 to view and navigate pathology whole slide images in a virtual environment is feasible for diagnostic purposes. However, image resolution using the Oculus Rift device was limited. Interactive VR technologies such as the Oculus Rift are novel tools that may be of use in digital pathology.

Background: Virtual microscopy and automated processing of cytological slides are more challenging compared to histological slides. Since cytological slides exhibit a three-dimensional surface and the required microscope objectives with high resolution have a low depth of field, these cannot capture all objects of a single field of view in focus. One solution would be to scan multiple focal planes; however, the increase in processing time and storage requirements are often prohibitive for clinical routine. Materials and Methods: In this paper, we show that it is a reasonable trade-off to scan a single focal plane and automatically reject defocused objects from the analysis. To this end, we have developed machine learning solutions for the automated identification of defocused objects. Our approach includes creating novel features, systematically optimizing their parameters, selecting adequate classifier algorithms, and identifying the correct decision boundary between focused and defocused objects. We validated our approach for computer-assisted DNA image cytometry. Results and Conclusions: We reach an overall sensitivity of 96.08% and a specificity of 99.63% for identifying defocused objects. Applied on ninety cytological slides, the developed classifiers automatically removed 2.50% of the objects acquired during scanning, which otherwise would have interfered the examination. Even if not all objects are acquired in focus, computer-assisted DNA image cytometry still identified more diagnostically or prognostically relevant objects compared to manual DNA image cytometry. At the same time, the workload for the expert is reduced dramatically.

Introduction: Detection of MYC translocations using fluorescence in situ hybridization (FISH) is important in the evaluation of lymphomas, in particular, Burkitt lymphoma and diffuse large B-cell lymphoma. Our aim was to validate a digital FISH capture and imaging system for the detection of MYC 8q24 translocations using LSI-MYC (a break-apart probe) and MYC 8;14 translocation using IGH-MYC (a fusion probe). Materials and Methods: LSI-MYC probe was evaluated using tissue sections from 35 patients. IGH-MYC probe was evaluated using tissue sections from forty patients. Sections were processed for FISH and analyzed using traditional methods. FISH slides were then analyzed using the GenASIs capture and analysis system. Results: Results for LSI-MYC had a high degree of correlation between traditional method of FISH analysis and digital FISH analysis. Results for IGH-MYC had a 100% concordance between traditional method of FISH analysis and digital FISH analysis. Conclusion: Annotated whole slide images of H and E and FISH sections can be digitally aligned, so that areas of tumor within a section can be matched and evaluated with a greater degree of accuracy. Images can be archived permanently, providing a means for examining the results retrospectively. Digital FISH imaging of the MYC translocations provides a better diagnostic tool compared to traditional methods for evaluating lymphomas.

This manuscript is an adaptation of the closing keynote presentation of the Digital Pathology Association Pathology Visions Conference 2015 in Boston, MA, USA. In this presentation, analogies are drawn between the adoption of whole slide imaging (WSI) and other mainstream digital technologies, including digital music and books. In doing so, it is revealed that the adoption of seemingly similar digital technologies does not follow the same adoption profiles and that understanding the unique aspects of value for each customer segment is critical. Finally, a call to action is given to academia and industry to study the value that WSI brings to the global healthcare community.

Context: Color normalization techniques for histology have not been empirically tested for their utility for computational pathology pipelines. Aims: We compared two contemporary techniques for achieving a common intermediate goal - epithelial-stromal classification. Settings and Design: Expert-annotated regions of epithelium and stroma were treated as ground truth for comparing classifiers on original and color-normalized images. Materials and Methods: Epithelial and stromal regions were annotated on thirty diverse-appearing H and E stained prostate cancer tissue microarray cores. Corresponding sets of thirty images each were generated using the two color normalization techniques. Color metrics were compared for original and color-normalized images. Separate epithelial-stromal classifiers were trained and compared on test images. Main analyses were conducted using a multiresolution segmentation (MRS) approach; comparative analyses using two other classification approaches (convolutional neural network [CNN], Wndchrm) were also performed. Statistical Analysis: For the main MRS method, which relied on classification of super-pixels, the number of variables used was reduced using backward elimination without compromising accuracy, and test - area under the curves (AUCs) were compared for original and normalized images. For CNN and Wndchrm, pixel classification test-AUCs were compared. Results: Khan method reduced color saturation while Vahadane reduced hue variance. Super-pixel-level test-AUC for MRS was 0.010-0.025 (95% confidence interval limits ± 0.004) higher for the two normalized image sets compared to the original in the 10-80 variable range. Improvement in pixel classification accuracy was also observed for CNN and Wndchrm for color-normalized images. Conclusions: Color normalization can give a small incremental benefit when a super-pixel-based classification method is used with features that perform implicit color normalization while the gain is higher for patch-based classification methods for classifying epithelium versus stroma.

Background: The opportunity offered by whole slide scanners of automated histological analysis implies an ever increasing importance of digital pathology. To go beyond the importance of conventional pathology, however, digital pathology may need a basic histological starting point similar to that of hematoxylin and eosin staining in conventional pathology. This study presents an automated fluorescence-based microscopy approach providing highly detailed morphological data from unstained microsections. This data may provide a basic histological starting point from which further digital analysis including staining may benefit. Methods: This study explores the inherent tissue fluorescence, also known as autofluorescence, as a mean to quantitate cardiac tissue components in histological microsections. Data acquisition using a commercially available whole slide scanner and an image-based quantitation algorithm are presented. Results: It is shown that the autofluorescence intensity of unstained microsections at two different wavelengths is a suitable starting point for automated digital analysis of myocytes, fibrous tissue, lipofuscin, and the extracellular compartment. The output of the method is absolute quantitation along with accurate outlines of above-mentioned components. The digital quantitations are verified by comparison to point grid quantitations performed on the microsections after Van Gieson staining. Conclusion: The presented method is amply described as a prestain multicomponent quantitation and outlining tool for histological sections of cardiac tissue. The main perspective is the opportunity for combination with digital analysis of stained microsections, for which the method may provide an accurate digital framework.

Background: Although pathology informatics (PI) is essential to modern pathology practice, the field is often poorly understood. Pathologists who have received little to no exposure to informatics, either in training or in practice, may not recognize the roles that informatics serves in pathology. The purpose of this study was to characterize perceptions of PI by noninformatics-oriented pathologists and to do so at two large centers with differing informatics environments. Methods: Pathology trainees and staff at Cleveland Clinic (CC) and Massachusetts General Hospital (MGH) were surveyed. At MGH, pathology department leadership has promoted a pervasive informatics presence through practice, training, and research. At CC, PI efforts focus on production systems that serve a multi-site integrated health system and a reference laboratory, and on the development of applications oriented to department operations. The survey assessed perceived definition of PI, interest in PI, and perceived utility of PI. Results: The survey was completed by 107 noninformatics-oriented pathologists and trainees. A majority viewed informatics positively. Except among MGH trainees, confusion of PI with information technology (IT) and help desk services was prominent, even in those who indicated they understood informatics. Attendings and trainees indicated desire to learn more about PI. While most acknowledged that having some level of PI knowledge would be professionally useful and advantageous, only a minority plan to utilize it. Conclusions: Informatics is viewed positively by the majority of noninformatics pathologists at two large centers with differing informatics orientations. Differences in departmental informatics culture can be attributed to the varying perceptions of PI by different individuals. Incorrect perceptions exist, such as conflating PI with IT and help desk services, even among those who claim to understand PI. Further efforts by the PI community could address such misperceptions, which could help enable a better understanding of what PI is and is not, and potentially lead to increased acceptance by non-informaticist pathologists.

Background: Imaging mass spectrometry (IMS) generates molecular images directly from tissue sections to provide better diagnostic insights and expand the capabilities of clinical anatomic pathology. Although IMS technology has matured over recent years, the link between microscopy imaging currently used by pathologists and MS-based molecular imaging has not been established. Methods: We adapted the Vanderbilt University Tissue Core workflow for IMS into a web-based system that facilitates remote collaboration. The platform was designed to perform within acceptable web response times for viewing, annotating, and processing high resolution microscopy images. Results: We describe a microscopy-driven approach to tissue analysis by IMS. Conclusion: The Pathology Interface for Mass Spectrometry is designed to provide clinical access to IMS technology and deliver enhanced diagnostic value.

Cross-disciplinary and cross-sectorial collaboration is a key success factor for turning the promise of digital pathology into actual clinical benefits. The Nordic symposium on digital pathology (NDP) was created to promote knowledge exchange in this area, among stakeholders in health care, industry, and academia. This article is a summary of the third NDP symposium in Linkφping, Sweden. The Nordic experiences, including several hospitals using whole-slide imaging for substantial parts of their primary reviews, formed a fertile base for discussions among the 190 NDP attendees originating from 15 different countries. This summary also contains results from a survey on adoption and validation aspects of clinical digital pathology use.

Introduction: Centralization of cancer treatment entails a reassessment of the diagnostic tissue specimens. Packaging and shipment of glass slides from the local to the central pathology unit means that the standard procedure is time-consuming and that it is difficult to comply with governmental requirements. The aim was to evaluate whether real-time digital microscopy for urological cancer specimens during the primary diagnostic process can replace subsequent physical slide referral and reassessment without compromising diagnostic safety. Methods: From May to October 2014, tissue specimens from 130 patients with urological cancer received at Næstved Hospital's Pathology Department, and expected to be referred for further treatment at cancer unit of a university hospital, were diagnosed using standard light microscopy. In the event of diagnostic uncertainty, the VisionTek digital microscope (Sakura Finetek) was employed. The Pathology Department at Næstved Hospital was equipped with a digital microscope and three consultant pathologists were stationed at Rigshospitalet with workstations optimized for digital microscopy. Representative slides for each case were selected for consultation and live digital consultation took place over the telephone using remote access software. Time of start and finish for each case was logged. For the physically referred cases, time from arrival to sign-out was logged in the national pathology information system, and time spent on microscopy and reporting was noted manually. Diagnosis, number of involved biopsies, grade, and stage were compared between digital microscopy and conventional microscopy. Results: Complete data were available for all 130 cases. Standard procedure with referral of urological cancer specimens took a mean of 8 min 56 s for microscopy, reporting and sign-out per case. For live digital consultations, a mean of 18 min 37 s was spent on each consultation with 4 min 43 s for each case, depending on the number of digital slides included. Only in two cases could a consensus regarding the diagnosis not be reached during live consultation; this did not, it should be noted, affect patient treatment. Complete agreement between conventional and digital histopathology diagnosis was reached in all the 53 patients referred to central pathology units. The participating pathologists were in general comfortable using live digital microscopy, but they emphasized that a fast internet connection was essential for a smooth consultation. Discussion and Conclusion: An almost perfect agreement between live digital and conventional microscopy was observed in this study. Live digital consultation allowed cases to be referred from local hospitals to central cancer units without the standard delay caused by shipment. Only a few preselected specimen slides for each patient were presented in live consultation, which reduced the time spent on diagnosis compared to using the conventional method. Implementation of real-time digital microscopy would result in quicker turnaround and patient referral time, and with careful selection of relevant specimen slides for consultation, diagnostic safety would not be compromised.

Introduction: Telemicrobiology is a growing component of clinical microbiology informatics. However, few studies have been performed to assess the diagnostic utility of telemicroscopy systems in evaluating infectious agents. Objective: Evaluate multiple contemporary digital pathology platforms for use in diagnostic telemicrobiology. Materials and Methods: A mix of thirty cases that included viral, bacterial, fungal, and parasitological findings were evaluated by four experts using ×40 whole slide imaging (WSI) scans, ×83 oil-immersion WSI scans, ×100 oil-immersion WSI scans, digital photomicrographs, and glass slides. Results: The ×83 WSI, ×100 WSI, and photomicrograph interpretations were not significantly different in quality and accuracy when compared to glass slide interpretations. The ×40 WSI interpretations were of lower quality and were more likely to be incorrect when compared to glass slide interpretations. Conclusions: In this study, high magnification, oil-immersion digital pathology platforms are better suited to support telemicrobiology applications and yield interpretations on par with glass slide evaluations.

Background: Our cytotechnology (CT) program has been utilizing virtual microscopy (VM) as an adjunct educational resource since 2011. Aims: The aim of this study was to identify the utilization of VM in other CT programs across the United States (US). Subjects and Methods: A cover letter was sent to the program directors of all accredited CT programs in the US (excluding our program), requesting their participation in an online survey. After 2 days, the participants were sent an online link to the survey. The survey results were analyzed using descriptive statistics. Results: There were a total of 25 respondents to the survey. Among the 25, three CT programs use VM. Two of the three programs have been using VM for <2 years while another program for "2-4" years. The respondents found that VM's side-by-side comparison feature helped to demonstrate differences between diagnoses and preparation methods, and VM helped to preserve the important slides by digitizing them. Respondents believed that teaching with glass slides was very important. The reasons for not using VM were that VM is expensive and time-consuming to incorporate into the program, and lack of manpower resources to create digitized teaching files. Conclusions: The CT programs that use VM found it to be a valuable educational tool. Even though many were not using VM, responses from the survey indicated they will likely use it in the future.

Background: Epic Beaker Clinical Pathology (CP) is a relatively new laboratory information system (LIS) operating within the Epic suite of software applications. To date, there have not been any publications describing implementation of Beaker CP. In this report, we describe our experience in implementing Beaker CP version 2012 at a state academic medical center with a go-live of August 2014 and a subsequent upgrade to Beaker version 2014 in May 2015. The implementation of Beaker CP was concurrent with implementations of Epic modules for revenue cycle, patient scheduling, and patient registration. Methods: Our analysis covers approximately 3 years of time (2 years preimplementation of Beaker CP and roughly 1 year after) using data summarized from pre- and post-implementation meetings, debriefings, and the closure document for the project. Results: We summarize positive aspects of, and key factors leading to, a successful implementation of Beaker CP. The early inclusion of subject matter experts in the design and validation of Beaker workflows was very helpful. Since Beaker CP does not directly interface with laboratory instrumentation, the clinical laboratories spent extensive preimplementation effort establishing middleware interfaces. Immediate challenges postimplementation included bar code scanning and nursing adaptation to Beaker CP specimen collection. The most substantial changes in laboratory workflow occurred with microbiology orders. This posed a considerable challenge with microbiology orders from the operating rooms and required intensive interventions in the weeks following go-live. In postimplementation surveys, pathology staff, informatics staff, and end-users expressed satisfaction with the new LIS. Conclusions: Beaker CP can serve as an effective LIS for an academic medical center. Careful planning and preparation aid the transition to this LIS.

The advent of next-generation sequencing (NGS) brings with it a need to manage large volumes of patient data in a manner that is compliant with both privacy laws and long-term archival needs. Outside of the realm of genomics there is a need in the broader medical community to store data, and although radiology aside the volume may be less than that of NGS, the concepts discussed herein are similarly relevant. The relation of so-called "privacy principles" to data protection and cryptographic techniques is explored with regards to the archival and backup storage of health data in Australia, and an example implementation of secure management of genomic archives is proposed with regards to this relation. Readers are presented with sufficient detail to have informed discussions - when implementing laboratory data protocols - with experts in the fields.

This paper describes work presented at the Nordic Symposium on Digital Pathology 2015, in Linköping, Sweden. Prostatic intraepithelial neoplasia (PIN) represents premalignant tissue involving epithelial growth confined in the lumen of prostatic acini. In the attempts to understand oncogenesis in the human prostate, early neoplastic changes can be modeled in the mouse with genetic manipulation of certain tumor suppressor genes or oncogenes. As with many early pathological changes, the PIN lesions in the mouse prostate are macroscopically small, but microscopically spanning areas often larger than single high magnification focus fields in microscopy. This poses a challenge to utilize full potential of the data acquired in histological specimens. We use whole prostates fixed in molecular fixative PAXgene™, embedded in paraffin, sectioned through and stained with H&E. To visualize and analyze the microscopic information spanning whole mouse PIN (mPIN) lesions, we utilize automated whole slide scanning and stacked sections through the tissue. The region of interests is masked, and the masked areas are processed using a cascade of automated image analysis steps. The images are normalized in color space, after which exclusion of secretion areas and feature extraction is performed. Machine learning is utilized to build a model of early PIN lesions for determining the probability for histological changes based on the calculated features. We performed a feature-based analysis to mPIN lesions. First, a quantitative representation of over 100 features was built, including several features representing pathological changes in PIN, especially describing the spatial growth pattern of lesions in the prostate tissue. Furthermore, we built a classification model, which is able to align PIN lesions corresponding to grading by visual inspection to more advanced and mild lesions. The classifier allowed both determining the probability of early histological changes for uncategorized tissue samples and interpretation of the model parameters. Here, we develop quantitative image analysis pipeline to describe morphological changes in histological images. Even subtle changes in mPIN lesion characteristics can be described with feature analysis and machine learning. Constructing and using multidimensional feature data to represent histological changes enables richer analysis and interpretation of early pathological lesions.

Background: Numerous validation studies in digital pathology confirmed its value as a diagnostic tool. However, a longer time to diagnosis than traditional microscopy has been seen as a significant barrier to the routine use of digital pathology. As a part of our validation study, we compared a digital and microscopic diagnostic time in the routine diagnostic setting. Materials and Methods: One senior staff pathologist reported 400 consecutive cases in histology, nongynecological, and fine needle aspiration cytology (20 sessions, 20 cases/session), over 4 weeks. Complex, difficult, and rare cases were excluded from the study to reduce the bias. A primary diagnosis was digital, followed by traditional microscopy, 6 months later, with only request forms available for both. Microscopic slides were scanned at ×20, digital images accessed through the fully integrated laboratory information management system (LIMS) and viewed in the image viewer on double 23” displays. A median broadband speed was 299 Mbps. A diagnostic time was measured from the point slides were made available to the point diagnosis was made or additional investigations were deemed necessary, recorded independently in minutes/session and compared. Results: A digital diagnostic time was 1841 and microscopic 1956 min; digital being shorter than microscopic in 13 sessions. Four sessions with shorter microscopic diagnostic time included more cases requiring extensive use of magnifications over ×20. Diagnostic time was similar in three sessions. Conclusions: A diagnostic time in digital pathology can be shorter than traditional microscopy in the routine diagnostic setting, with adequate and stable network speeds, fully integrated LIMS and double displays as default parameters. This also related to better ergonomics, larger viewing field, and absence of physical slide handling, with effects on both diagnostic and nondiagnostic time. Differences with previous studies included a design, image size, number of cases, specimen type, network speed, and participant's level of confidence and experience in digital reporting. Further advancements in working stations and gained experience in digital reporting are expected to improve diagnostic time and widen routine applications of digital pathology.

Background: Intratumoral hypoxia plays an important role with regard to tumor biology and susceptibility to radio. and chemotherapy. For further investigation of hypoxia.related changes, areas of certain hypoxia must be reliably detected within cancer tissues. Pimonidazole, a 2.nitroimindazole, accumulates in hypoxic tissue and can be easily visualized using immunohistochemistry. Materials and Methods: To improve detection of highly hypoxic versus normoxic areas in prostate cancer, immunoreactivity of pimonidazole and a combination of known hypoxia.related proteins was used to create computational oxygen supply maps of prostate cancer. Pimonidazole was intravenously administered before radical prostatectomy in n = 15 patients, using the da Vinci robot.assisted surgical system. Prostatectomy specimens were immediately transferred into buffered formaldehyde, fixed overnight, and completely embedded in paraffin. Pimonidazole accumulation and hypoxia.related protein expression were visualized by immunohistochemistry. Oxygen supply maps were created using the normalized information from pimonidazole and hypoxia.related proteins. Results: Based on pimonidazole staining and other hypoxia.related proteins (osteopontin, hypoxia.inducible factor 1.alpha, and glucose transporter member 1) oxygen supply maps in prostate cancer were created. Overall, oxygen supply maps consisting of information from all hypoxia.related proteins showed high correlation and mutual information to the golden standard of pimonidazole. Here, we describe an improved computer.based ex vivo model for an accurate detection of oxygen supply in human prostate cancer tissue. Conclusions: This platform can be used for precise colocalization of novel candidate hypoxia.related proteins in a representative number of prostate cancer cases, and improve issues of single marker correlations. Furthermore, this study provides a source for further in situ tests and biochemical investigations

The Computer Science, Biology, and Biomedical Informatics (CoSBBI) program was initiated in 2011 to expose the critical role of informatics in biomedicine to talented high school students.^[1] By involving them in Science, Technology, Engineering, and Math (STEM) training at the high school level and providing mentorship and research opportunities throughout the formative years of their education, CoSBBI creates a research infrastructure designed to develop young informaticians. Our central premise is that the trajectory necessary to be an expert in the emerging fields of biomedical informatics and pathology informatics requires accelerated learning at an early age.In our 4^th year of CoSBBI as a part of the University of Pittsburgh Cancer Institute (UPCI) Academy (http://www.upci.upmc.edu/summeracademy/), and our 2nd year of CoSBBI as an independent informatics-based academy, we enhanced our classroom curriculum, added hands-on computer science instruction, and expanded research projects to include clinical informatics. We also conducted a qualitative evaluation of the program to identify areas that need improvement in order to achieve our goal of creating a pipeline of exceptionally well-trained applicants for both the disciplines of pathology informatics and biomedical informatics in the era of big data and personalized medicine.

Background: Intraductal proliferative lesions (IDPLs) of the breast are recognized as a risk factor for subsequent invasive carcinoma development. Although opportunities for IDPL diagnosis have increased, these lesions are difficult to diagnose correctly, especially atypical ductal hyperplasia (ADH) and low-grade ductal carcinoma in situ (LG-DCIS). In order to define the difference between these lesions, many molecular pathological approaches have been performed. However, still we do not have a molecular marker and objective histological index about IDPLs of the breast. Methods: We generated full digital pathology archives from 175 female IDPL patients, including usual ductal hyperplasia (UDH), ADH, LG-DCIS, intermediate-grade (IM)-DCIS, and high-grade (HG)-DCIS. After total 2,035,807 nucleic segmentations were extracted, we evaluated nuclear features using step-wise linear discriminant analysis (LDA) and a support vector machine. Results: High diagnostic accuracy (81.8–99.3%) was achieved between pathologists' diagnoses and two-group LDA predictions from nucleic features for IDPL discrimination. Grouping of nuclear features as size and shape-related or intranuclear texture-related revealed that the latter group was more important when distinguishing between normal duct, UDH, ADH, and LG-DCIS. However, these two groups were equally important when discriminating between LG-DCIS and HG-DCIS. The Mahalanobis distances between each group showed that the smallest distance values occurred between LG-DCIS and IM-DCIS and between ADH and Normal. On the other hand, the distance value between ADH and LG-DCIS was larger than this distance. Conclusions: In this study, we have presented a practical and useful digital pathological method that incorporates nuclear morphological and textural features for IDPL prediction. We expect that this novel algorithm is used for the automated diagnosis assisting system for breast cancer.