Background: Telepathology is increasingly being employed to support diagnostic consultation services. Prior publications have addressed technology aspects for telepathology, whereas this paper will address the clinical telepathology experience of KingMed Diagnostics, the largest independent pathology medical laboratory in China. Beginning in 2012 the University of Pittsburgh Medical Center (UPMC) and KingMed Diagnostics partnered to establish an international telepathology consultation service. Materials and Methods: This is a retrospective study that summarizes the telepathology experience and diagnostic consultation results between UPMC and KingMed over a period of 3 years from January 2012 to December 2014. Results: A total of 1561 cases were submitted for telepathology consultation including 144 cases in 2012, 614 cases in 2013, and 803 in 2014. Most of the cases (61.4%) submitted were referred by pathologists, 36.9% by clinicians, and 1.7% by patients in China. Hematopathology received the most cases (23.7%), followed by bone/soft tissue (21.0%) and gynecologic/breast (20.2%) subspecialties. Average turnaround time (TAT) per case was 5.4 days, which decreased from 6.8 days in 2012 to 5.0 days in 2014. Immunostains were required for most of the cases. For some difficult cases, more than one round of immunostains was needed, which extended the TAT. Among 855 cases (54.7%) where a primary diagnosis or impression was provided by the referring local hospitals in China, the final diagnoses rendered by UPMC pathologists were identical in 25.6% of cases and significantly modified (treatment plan altered) in 50.8% of cases. Conclusion: These results indicate that international telepathology consultation can significantly improve patient care by facilitating access to pathology expertise. The success of this international digital consultation service was dependent on strong commitment and support from leadership, information technology expertise, and dedicated pathologists who understood the language and culture on both sides. Lack of clinical information, missing gross pathology descriptions, and insufficient tissue sections submitted for evaluation were the main reasons for indefinite diagnoses. The overall experience encourages international telepathology practice for second opinions.

Background: Synoptic pathology reports can serve as a rich source of cancer information, particularly when the content is available as discrete electronic data fields. Our institution generates such reports as part of a province wide program in Ontario but the resulting data is not easily extracted and analyzed at the local level. Methods: A low cost system was developed using the open sourced and freely available R scripting/data analysis environment to parse synoptic report results into a dataframe and perform basic summary statistics. Results: As a pilot project text reports from 427 prostate needle biopsies were successfully read into R and the data elements split out and converted into appropriated data classes for analysis. Conclusion: This approach provides a simple solution at minimal cost that can make discrete synoptic report data readily available for quality assurance and research activities.

Introduction: Smartphone applications (apps) have become increasingly prevalent in medicine. Due to most pathologists, pathology trainees, technicians, and medical students use smartphones; apps can be a different way for general pathology education. “General pathologist-helper (GP-HELPER)” is a novel app developed as a reference tool in general pathology and especially for general pathologists, developed for Android and iOS platforms. Materials and Methods: “GP-HELPER,” was created using Mobincube website platform. This tool also integrates “FORUM GP-HELPER,” an external website created using Miarroba website (http://forum-gp-helper.mboards.com) and “COMMUNITY GP-HELPER” a multichannel chat created using Chatango website platform. Results: The application was released in July 2015, and it is been periodically updated since then. The app has permanent information (offline data) about different pathology protocols (TNM latest edition, protocols regarding management of tumors of unknown primary origin, and flowcharts for some of the most difficult tumors to diagnose) and a database with more than 5000 immunohistochemistry results from different tumors. Online data have links to more than 1100 reference pathology video lectures, 250 antibodies information, more than 70 pathology association websites, 46 pathology providers, and 78 outstanding pathology journal websites. Besides this information, the app has two interactive places such as “FORUM GP-HELPER” and “COMMUNITY GP-HELPER” that let users to stay in touch everywhere and every time. Expert consult section is also available. Conclusions: “GP-HELPER” pretends to integrate offline and online data about pathology with two interactive external places in order to represent a reference tool for general pathologists and associate members.

Background: Histopathology is the only accepted method to measure and stage the breast tumor size. However, there is a need to find another method to measure and stage the tumor size when the pathological assessment is not available. Micro-computed tomography. (micro-CT) has the ability to measure tumor in three dimensions in an intact lumpectomy specimen. In this study, we aimed to determine the accuracy of micro-CT to measure and stage the primary tumor size in breast lumpectomy specimens, as compared to the histopathology. Materials and Methods: Seventy-two women who underwent lumpectomy surgery at the Massachusetts General Hospital Department of Surgery from June 2011 to September 2011, and from August 2013 to December 2013 participated in this study. The lumpectomy specimens were scanned using micro-CT followed by routine pathological processing. The maximum dimension of the invasive breast tumor was obtained from the micro-CT image and was compared to the corresponding pathology report for each subject. Results: The invasive tumor size measurement by micro-CT was underestimated in 24. cases. (33%), overestimated in 37. cases. (51%), and matched it exactly in 11. cases. (15%) compared to the histopathology measurement for all the cases. However, micro-CT T.stage classification differed from histopathology in only 11. (15.2%) with 6. cases. (8.3%) classified as a higher stage by micro-CT, and 5. cases. (6.9%) classified as lower compared to histopathology. In addition, micro-CT demonstrated a statically significant strong agreement (κ =0.6, P < 0.05) with pathological tumor size and staging for invasive ductal carcinoma. (IDC) group. In contrast, there was no agreement. (κ = .2, P = 0.67) between micro-CT and pathology in estimating and staging tumor size for invasive lobular carcinoma. (ILC) group. This could be explained by a small sample size. (7) for ILC group. Conclusions: Micro-CT is a promising modality for measuring and staging the IDC.

Background: DNA mixtures of two or more people are a common type of forensic crime scene evidence. A match statistic that connects the evidence to a criminal defendant is usually needed for court. Jurors rely on this strength of match to help decide guilt or innocence. However, the reliability of unsophisticated match statistics for DNA mixtures has been questioned. Materials and Methods: The most prevalent match statistic for DNA mixtures is the combined probability of inclusion (CPI), used by crime labs for over 15 years. When testing 13 short tandem repeat (STR) genetic loci, the CPI ^-1 value is typically around a million, regardless of DNA mixture composition. However, actual identification information, as measured by a likelihood ratio (LR), spans a much broader range. This study examined probability of inclusion (PI) mixture statistics for 517 locus experiments drawn from 16 reported cases and compared them with LR locus information calculated independently on the same data. The log(PI ^-1 ) values were examined and compared with corresponding log(LR) values. Results: The LR and CPI methods were compared in case examples of false inclusion, false exclusion, a homicide, and criminal justice outcomes. Statistical analysis of crime laboratory STR data shows that inclusion match statistics exhibit a truncated normal distribution having zero center, with little correlation to actual identification information. By the law of large numbers (LLN), CPI ^-1 increases with the number of tested genetic loci, regardless of DNA mixture composition or match information. These statistical findings explain why CPI is relatively constant, with implications for DNA policy, criminal justice, cost of crime, and crime prevention. Conclusions: Forensic crime laboratories have generated CPI statistics on hundreds of thousands of DNA mixture evidence items. However, this commonly used match statistic behaves like a random generator of inclusionary values, following the LLN rather than measuring identification information. A quantitative CPI number adds little meaningful information beyond the analyst's initial qualitative assessment that a person's DNA is included in a mixture. Statistical methods for reporting on DNA mixture evidence should be scientifically validated before they are relied upon by criminal justice.

Background: Identifying new and more robust assessments of proficiency/expertise (finding new "biomarkers of expertise") in histopathology is desirable for many reasons. Advances in digital pathology permit new and innovative tests such as flash viewing tests and eye tracking and slide navigation analyses that would not be possible with a traditional microscope. The main purpose of this study was to examine the usefulness of time-restricted testing of expertise in histopathology using digital images. Methods: 19 novices (undergraduate medical students), 18 intermediates (trainees), and 19 experts (consultants) were invited to give their opinion on 20 general histopathology cases after 1 s and 10 s viewing times. Differences in performance between groups were measured and the internal reliability of the test was calculated. Results: There were highly significant differences in performance between the groups using the Fisher's least significant difference method for multiple comparisons. Differences between groups were consistently greater in the 10-s than the 1-s test. The Kuder-Richardson 20 internal reliability coefficients were very high for both tests: 0.905 for the 1-s test and 0.926 for the 10-s test. Consultants had levels of diagnostic accuracy of 72% at 1 s and 83% at 10 s. Conclusions: Time-restricted tests using digital images have the potential to be extremely reliable tests of diagnostic proficiency in histopathology. A 10-s viewing test may be more reliable than a 1-s test. Over-reliance on "at a glance" diagnoses in histopathology is a potential source of medical error due to over-confidence bias and premature closure.

Background: Increasingly, as in our institution, operating rooms are located in hospitals and the pathology suite is located at a distant location because of off-site consolidation of pathology services. Telepathology is a technology which bridges the gap between pathologists and offers a means to obtain a consultation remotely. We aimed to evaluate the utility of telepathology as a means to assist the pathologist at the time of intraoperative consultation of lung nodules when a subspecialty pathologist is not available to directly review the slide. Methods: Cases of lung nodules suspicious for a neoplasm were included. Frozen sections were prepared in the usual manner. The pathologists on the intraoperative consultation service at two of our system hospitals notified the thoracic pathologist of each case after rendering a preliminary diagnosis. The consultation was performed utilizing a Nikon™ Digital Sight camera and web-based Remote Medical Technologies™ software with live video streaming directed by the host pathologist. The thoracic pathologist rendered a diagnosis without knowledge of the preliminary interpretation then discussed the interpretation with the frozen section pathologist. The interpretations were compared with the final diagnosis rendered after sign-out. Results: One hundred and three consecutive cases were included. The frozen section pathologist and a thoracic pathologist had concordant diagnoses in 93 cases (90.2%), discordant diagnoses in nine cases (8.7%), and one case in which both deferred. There was an agreement between the thoracic pathologist's diagnosis and the final diagnosis in 98% of total cases including 8/9 (88.9%) of the total discordant cases. In two cases, if the thoracic pathologist had not been consulted, the patient would have been undertreated. Conclusions: We have shown that telepathology is an excellent consultation tool in the frozen section diagnosis of lung nodules.

Background: The constant growth in the body of knowledge in medicine requires pathologists and pathology trainees to engage in continuing education. Providing them with equitable access to efficient and effective forms of education in pathology (especially in remote and rural settings) is important, but challenging. Methods: We developed three pilot cytopathology virtual microscopy adaptive tutorials (VMATs) to explore a novel adaptive E-learning platform (AeLP) which can incorporate whole slide images for pathology education. We collected user feedback to further develop this educational material and to subsequently deploy randomized trials in both pathology specialist trainee and also medical student cohorts. Cytopathology whole slide images were first acquired then novel VMATs teaching cytopathology were created using the AeLP, an intelligent tutoring system developed by Smart Sparrow. The pilot was run for Australian pathologists and trainees through the education section of Royal College of Pathologists of Australasia website over a period of 9 months. Feedback on the usability, impact on learning and any technical issues was obtained using 5-point Likert scale items and open-ended feedback in online questionnaires. Results: A total of 181 pathologists and pathology trainees anonymously attempted the three adaptive tutorials, a smaller proportion of whom went on to provide feedback at the end of each tutorial. VMATs were perceived as effective and efficient E-learning tools for pathology education. User feedback was positive. There were no significant technical issues. Conclusion: During this pilot, the user feedback on the educational content and interface and the lack of technical issues were helpful. Large scale trials of similar online cytopathology adaptive tutorials were planned for the future.

Background: Full-field optical coherence tomography (FFOCT) is a real-time imaging technique that rapidly generates images reminiscent of histology without any tissue processing, warranting its exploration for evaluation of ex vivo kidney tissue. Methods: Fresh tissue sections from tumor and adjacent nonneoplastic kidney (n = 25 nephrectomy specimens; clear cell renal cell carcinoma (CCRCC) = 12, papillary RCC (PRCC) = 4, chromophobe RCC (ChRCC) = 4, papillary urothelial carcinoma (PUC) = 1, angiomyolipoma (AML) = 2 and cystic nephroma = 2) were imaged with a commercial FFOCT device. Sections were submitted for routine histopathological diagnosis. Results: Glomeruli, tubules, interstitium, and blood vessels were identified in nonneoplastic tissue. In tumor sections, the normal architecture was completely replaced by either sheets of cells/trabeculae or papillary structures. The former pattern was seen predominantly in CCRCC/ChRCC and the latter in PRCC/PUC (as confirmed on H&E). Although the cellular details were not very prominent at this resolution, we could identify unique cytoplasmic signatures in some kidney tumors. For example, the hyper-intense punctate signal in the cytoplasm of CRCC represents glycogen/lipid, large cells with abundant hyper-intense cytoplasm representing histiocytes in PRCC, and signal-void large polygonal cell representing adipocytes in AML. According to a blinded analysis was performed by an uropathologist, all nonneoplastic tissues were differentiated from neoplastic tissues. Further, all benign tumors were called benign and malignant were called malignant. A diagnostic accuracy of 80% was obtained in subtyping the tumors. Conclusion: The ability of FFOCT to reliably differentiate nonneoplastic from neoplastic tissue and identify some tumor types makes it a valuable tool for rapid evaluation of ex vivo kidney tissue e.g. for intraoperative margin assessment and kidney biopsy adequacy. Recently, higher resolution images were achieved using an experimental FFOCT setup. This setup has the potential to further increase the diagnostic accuracy of FFOCT.

Introduction: Cervical cancer is one of the most common causes of death in women worldwide. ^[1] The introduction of cervical cytology in screening programs is an effective way for early detection and treatment of cervical precancerous lesions. Conventional screening of cervical cytology slides is still considered the current "gold standard" for the assessment of proficiency in becoming a cytotechnician, but diagnosis using digital whole slide images (WSI) may offer many advantages. Materials and Methods: In this study, we have used a selection of WSI from thin-layer specimens of the most common cervical infections and (pre) neoplastic lesions, and hypothesized that weekly WSI based case-meetings would help to obtain optimal acceptance of the new digital workflow in daily pathology practice. A questionnaire, before and after the test period was used to study the effect of our approach. Results: The participants clearly had to go through a learning curve to get accustomed to viewing WSI. In the beginning, there was a little self-confidence in recognizing classical cervical cytomorphological features in the WSI, and there were complaints about the speed of viewing and insufficient Z-resolution for cell groups. Adjusting the Z-stack settings resulted in better three-dimensional information due to better focusing options. Weekly meetings appeared to be instrumental in the implementation process, as participants had to select and present WSI from thematic cases themselves, and thereby, got used to viewing WSI. Some WSI were replaced by better ones until a final set of 45 representatives WSI remained. Eventually, the consensus was reached among all participants that cytomorphological features in WSI from thin-layers cervical specimens could comparably be appreciated in WSI as by conventional microscopy. The selection of 45 WSI was now used to create a digital WSI based reference atlas to support further studies. Conclusion: We have obtained consensus between professionals that WSI from cervical cytology can be used to identify cytomorphological features, necessary for diagnosis. In addition, we observed that active participation of professionals had a positive effect on the overall acceptance of WSI and was important in the change management.

Background: Structural reporting enables semantic understanding and prompt retrieval of clinical findings about patients. While synoptic pathology reporting provides templates for data entries, information in pathology reports remains primarily in narrative free text form. Extracting data of interest from narrative pathology reports could significantly improve the representation of the information and enable complex structured queries. However, manual extraction is tedious and error-prone, and automated tools are often constructed with a fixed training dataset and not easily adaptable. Our goal is to extract data from pathology reports to support advanced patient search with a highly adaptable semi-automated data extraction system, which can adjust and self-improve by learning from a user's interaction with minimal human effort. Methods : We have developed an online machine learning based information extraction system called IDEAL-X. With its graphical user interface, the system's data extraction engine automatically annotates values for users to review upon loading each report text. The system analyzes users' corrections regarding these annotations with online machine learning, and incrementally enhances and refines the learning model as reports are processed. The system also takes advantage of customized controlled vocabularies, which can be adaptively refined during the online learning process to further assist the data extraction. As the accuracy of automatic annotation improves overtime, the effort of human annotation is gradually reduced. After all reports are processed, a built-in query engine can be applied to conveniently define queries based on extracted structured data. Results: We have evaluated the system with a dataset of anatomic pathology reports from 50 patients. Extracted data elements include demographical data, diagnosis, genetic marker, and procedure. The system achieves F-1 scores of around 95% for the majority of tests. Conclusions: Extracting data from pathology reports could enable more accurate knowledge to support biomedical research and clinical diagnosis. IDEAL-X provides a bridge that takes advantage of online machine learning based data extraction and the knowledge from human's feedback. By combining iterative online learning and adaptive controlled vocabularies, IDEAL-X can deliver highly adaptive and accurate data extraction to support patient search.

Background: Genomic medicine has the potential to improve care by tailoring treatments to the individual. There is consensus in the literature that pharmacogenomics (PGx) may be an ideal starting point for real-world implementation, due to the presence of well-characterized drug-gene interactions. Clinical Decision Support (CDS) is an ideal avenue by which to implement PGx at the bedside. Previous literature has established theoretical models for PGx CDS implementation and discussed a number of anticipated real-world challenges. However, work detailing actual PGx CDS implementation experiences has been limited. Anticipated challenges include data storage and management, system integration, physician acceptance, and more. Methods: In this study, we analyzed the experiences of ten members of the Electronic Medical Records and Genomics (eMERGE) Network, and one affiliate, in their attempts to implement PGx CDS. We examined the resulting PGx CDS system characteristics and conducted a survey to understand the unanticipated implementation challenges sites encountered. Results: Ten sites have successfully implemented at least one PGx CDS rule in the clinical setting. The majority of sites elected to create an Omic Ancillary System (OAS) to manage genetic and genomic data. All sites were able to adapt their existing CDS tools for PGx knowledge. The most common and impactful delays were not PGx-specific issues. Instead, they were general IT implementation problems, with top challenges including team coordination/communication and staffing. The challenges encountered caused a median total delay in system go-live of approximately two months. Conclusions: These results suggest that barriers to PGx CDS implementations are generally surmountable. Moreover, PGx CDS implementation may not be any more difficult than other healthcare IT projects of similar scope, as the most significant delays encountered were not unique to genomic medicine. These are encouraging results for any institution considering implementing a PGx CDS tool, and for the advancement of genomic medicine.

Background: Whole slide imaging (WSI) using high-resolution scanners is gaining acceptance as a platform for consultation as well as for frozen section (FS) evaluation in surgical pathology. We report results of an intra-observer concordance study comparing evaluation of WSI of scanned FS microscope slides with the original interpretation of the same microscope slides after an average lag time of approximately 1-year. Methods: A total of 70 FS cases (148 microscope slides) originally interpreted by 2 pathologists were scanned at ×20 using Aperio CS2 scanner (Leica Biosystems, San Diego, CA, USA). Reports were redacted such that the study pathologists reviewed images using eSlide Manager Healthcare Network application (Leica Biosystems) accompanied by the same clinical information available at the time of original FS evaluation. Discrepancies between the original FS diagnosis and WSI diagnosis were categorized as major (impacted patient care) or minor (no impact on patient care). Results: Lymph nodes, margins for head and neck cancer resections, and arthroplasty specimens to exclude infection, were the most common FS specimens. The average wash-out interval was 380 days (range: 303–466 days). There was one major discrepancy (1.4% of 70 cases) where the original FS was interpreted as severe squamous dysplasia, and the WSI FS diagnosis was mild dysplasia. There were two minor discrepancies; one where the original FS was called focal moderate squamous dysplasia and WSI FS diagnosis was negative for dysplasia. The second case was an endometrial adenocarcinoma that was originally interpreted as Federation of Gynecology and Obstetrics (FIGO) Grade I, while the WSI FS diagnosis was FIGO Grade II. Conclusions: These findings validate and support the use of WSI to provide interpretation of FS in our network of affiliated hospitals and ambulatory surgery centers.

Background: Current histologic methods for diagnosis are limited by intra- and inter-observer variability. Immunohistochemistry (IHC) methods are frequently used to assess biomarkers to aid diagnoses, however, IHC staining is variable and nonlinear and the manual interpretation is subjective. Furthermore, the biomarkers assessed clinically are typically biomarkers of epithelial cell processes. Tumors and premalignant tissues are not composed only of epithelial cells but are interacting systems of multiple cell types, including various stromal cell types that are involved in cancer development. The complex network of the tissue system highlights the need for a systems biology approach to anatomic pathology, in which quantification of system processes is combined with informatics tools to produce actionable scores to aid clinical decision-making. Aims: Here, we describe a quantitative, multiplexed biomarker imaging approach termed TissueCypher™ that applies systems biology to anatomic pathology. Applications of TissueCypher™ in understanding the tissue system of Barrett's esophagus (BE) and the potential use as an adjunctive tool in the diagnosis of BE are described. Patients and Methods: The TissueCypher™ Image Analysis Platform was used to assess 14 epithelial and stromal biomarkers with known diagnostic significance in BE in a set of BE biopsies with nondysplastic BE with reactive atypia (RA, n = 22) and Barrett's with high-grade dysplasia (HGD, n = 17). Biomarker and morphology features were extracted and evaluated in the confirmed BE HGD cases versus the nondysplastic BE cases with RA. Results: Multiple image analysis features derived from epithelial and stromal biomarkers, including immune biomarkers and morphology, showed significant differences between HGD and RA. Conclusions: The assessment of epithelial cell abnormalities combined with an assessment of cellular changes in the lamina propria may serve as an adjunct to conventional pathology in the assessment of BE.

Background: Whole slide images (WSIs) involve digitally capturing glass slides for microscopic computer-based viewing and these are amenable to quantitative image analysis. Bile duct (BD) brushing can show morphologic features that are categorized as indeterminate for malignancy. The study aims to evaluate quantitative morphologic features of atypical categories of BD brushing by WSI analysis for the identification of criteria predictive of malignancy. Materials and Methods: Over a 3-year period, BD brush specimens with indeterminate diagnostic categorization (atypical to suspicious) were subjected to WSI analysis. Ten well-visualized groups with morphologic atypical features were selected per case and had the quantitative analysis performed for group area, individual nuclear area, the number of nuclei per group, N: C ratio and nuclear size differential. Results: There were 28 cases identified with 17 atypical and 11 suspicious. The average nuclear area was 63.7 µm² for atypical and 80.1 µm² for suspicious (+difference 16.4 µm²; P = 0.002). The nuclear size differential was 69.7 µm² for atypical and 88.4 µm² for suspicious (+difference 18.8 µm²; P = 0.009). An average nuclear area >70 µm² had a 3.2 risk ratio for suspicious categorization. Conclusion: The quantitative criteria findings as measured by image analysis on WSI showed that cases categorized as suspicious had more nuclear size pleomorphism (+18.8 µm²) and larger nuclei (+16.4 µm²) than those categorized as atypical. WSI with morphologic image analysis can demonstrate quantitative statistically significant differences between atypical and suspicious BD brushings and provide objective criteria that support the diagnosis of carcinoma.

Genomic, proteomic, epigenomic, and other "omic" data have the potential to enable precision medicine, also commonly referred to as personalized medicine. The volume and complexity of omic data are rapidly overwhelming human cognitive capacity, requiring innovative approaches to translate such data into patient care. Here, we outline a conceptual model for the application of omic data in the clinical context, called "the omic funnel." This model parallels the classic "Data, Information, Knowledge, Wisdom pyramid" and adds context for how to move between each successive layer. Its goal is to allow informaticians, researchers, and clinicians to approach the problem of translating omic data from bench to bedside, by using discrete steps with clearly defined needs. Such an approach can facilitate the development of modular and interoperable software that can bring precision medicine into widespread practice.

Background: Pathology data contained within the electronic health record (EHR), and laboratory information system (LIS) of hospitals represents a potentially powerful resource to improve clinical care. However, existing reporting tools within commercial EHR and LIS software may not be able to efficiently and rapidly mine data for quality improvement and research applications. Materials and Methods: We present experience using a data warehouse produced collaboratively between an academic medical center and a private company. The data warehouse contains data from the EHR, LIS, admission/discharge/transfer system, and billing records and can be accessed using a self-service data access tool known as Starmaker. The Starmaker software allows users to use complex Boolean logic, include and exclude rules, unit conversion and reference scaling, and value aggregation using a straightforward visual interface. More complex queries can be achieved by users with experience with Structured Query Language. Queries can use biomedical ontologies such as Logical Observation Identifiers Names and Codes and Systematized Nomenclature of Medicine. Result: We present examples of successful searches using Starmaker, falling mostly in the realm of microbiology and clinical chemistry/toxicology. The searches were ones that were either very difficult or basically infeasible using reporting tools within the EHR and LIS used in the medical center. One of the main strengths of Starmaker searches is rapid results, with typical searches covering 5 years taking only 1-2 min. A "Run Count" feature quickly outputs the number of cases meeting criteria, allowing for refinement of searches before downloading patient-identifiable data. The Starmaker tool is available to pathology residents and fellows, with some using this tool for quality improvement and scholarly projects. Conclusion: A data warehouse has significant potential for improving utilization of clinical pathology testing. Software that can access data warehouse using a straightforward visual interface can be incorporated into pathology training programs.

Introduction: Smartphone applications in medicine are becoming increasingly prevalent. Given that most pathologists and pathology trainees today use smartphones, an obvious modality for pathology education is through smartphone applications. "MyDermPath" is a novel smartphone application that was developed as an interactive reference tool for dermatology and dermatopathology, available for iOS and Android. Materials and Methods: "MyDermPath" was developed using Apple Xcode and Google Android SDK. Dermatology images (static and virtual slides) were annotated and configured into an algorithmic format. Each image comprised educational data (diagnosis, clinical information, histopathology, special stains, differential diagnosis, clinical management, linked PubMed references). Added functionality included personal note taking, pop quiz, and image upload capabilities. A website was created (http://mydermpath.com) to mirror the app. Results: The application was released in August 2011 and updated in November 2013. More than 1,100 reference diagnoses, with over 2,000 images are available via the application and website. The application has been downloaded approximately 14,000 times. The application is available for use on iOS and Android platforms. Conclusions: Smartphone applications have tremendous potential for advancing pathology education. "MyDermPath" represents an interactive reference tool for dermatology and dermatopathologists.

Background: Interest in developing more feasible and affordable applications of virtual microscopy in the field of cytology continues to grow. Aims: The aim of this study was to investigate the scanning parameters for the thyroid fine needle aspiration (FNA) cytology specimens. Subjects and Methods: A total of twelve glass slides from thyroid FNA cytology specimens were digitized at ×40 with 1 micron (μ) interval using seven focal plane (FP) levels (Group 1), five FP levels (Group 2), and three FP levels (Group 3) using iScan Coreo Au scanner (Ventana, AZ, USA) producing 36 virtual images (VI). With an average wash out period of 2 days, three participants diagnosed the preannotated cells of Groups 1, 2, and 3 using BioImagene's Image Viewer (version 3.1) (Ventana, Inc., Tucson, AZ, USA), and the corresponding 12 glass slides (Group 4) using conventional light microscopy. Results: All three raters correctly identified and showed complete agreement on the glass and VI for: 86% of the cases at FP Level 3, 83% of the cases at both the FP Levels 5 and 7. The intra-observer concordance between the glass slides and VI for all three raters was highest (97%) for Level 3 and glass, same (94%) for Level 5 and glass; and Level 7 and glass. The inter-rater reliability was found to be highest for the glass slides, and three FP levels (77%), followed by five FP levels (69.5%), and seven FP levels (69.1%). Conclusions: This pilot study found that among the three different FP levels, the VI digitized using three FP levels had slightly higher concordance, intra-observer concordance, and inter-rater reliability. Scanning additional levels above three FP levels did not improve concordance. We believe that there is no added benefit of acquiring five FP levels or more especially when considering the file size, and storage costs. Hence, this study reports that FP level three and 1 μ could be the potential scanning parameters for the thyroid FNA cytology specimens.

Context : Content-based image retrieval (CBIR) systems allow for retrieval of images from within a database that are similar in visual content to a query image. This is useful for digital pathology, where text-based descriptors alone might be inadequate to accurately describe image content. By representing images via a set of quantitative image descriptors, the similarity between a query image with respect to archived, annotated images in a database can be computed and the most similar images retrieved. Recently, non-linear dimensionality reduction methods have become popular for embedding high-dimensional data into a reduced-dimensional space while preserving local object adjacencies, thereby allowing for object similarity to be determined more accurately in the reduced-dimensional space. However, most dimensionality reduction methods implicitly assume, in computing the reduced-dimensional representation, that all features are equally important. Aims : In this paper we present boosted spectral embedding (BoSE), which utilizes a boosted distance metric to selectively weight individual features (based on training data) to subsequently map the data into a reduced-dimensional space. Settings and Design : BoSE is evaluated against spectral embedding (SE) (which employs equal feature weighting) in the context of CBIR of digitized prostate and breast cancer histopathology images. Materials and Methods : The following datasets, which were comprised of a total of 154 hematoxylin and eosin stained histopathology images, were used: (1) Prostate cancer histopathology (benign vs. malignant), (2) estrogen receptor (ER) + breast cancer histopathology (low vs. high grade), and (3) HER2+ breast cancer histopathology (low vs. high levels of lymphocytic infiltration). Statistical Analysis Used : We plotted and calculated the area under precision-recall curves (AUPRC) and calculated classification accuracy using the Random Forest classifier. Results : BoSE outperformed SE both in terms of CBIR-based (area under the precision-recall curve) and classifier-based (classification accuracy) on average across all of the dimensions tested for all three datasets: (1) Prostate cancer histopathology (AUPRC: BoSE = 0.79, SE = 0.63; Accuracy: BoSE = 0.93, SE = 0.80), (2) ER + breast cancer histopathology (AUPRC: BoSE = 0.79, SE = 0.68; Accuracy: BoSE = 0.96, SE = 0.96), and (3) HER2+ breast cancer histopathology (AUPRC: BoSE = 0.54, SE = 0.44; Accuracy: BoSE = 0.93, SE = 0.91). Conclusion : Our results suggest that BoSE could serve as an important tool for CBIR and classification of high-dimensional biomedical data.

Background: In many surgical pathology laboratories, operating room schedules are prospectively reviewed to determine specimen distribution to different subspecialty services and to predict the number and nature of potential intraoperative consultations for which prior medical records and slides require review. At our institution, such schedules were manually converted into easily interpretable, surgical pathology-friendly reports to facilitate these activities. This conversion, however, was time-consuming and arguably a non-value-added activity. Objective: Our goal was to develop a semi-automated method of generating these reports that improved their readability while taking less time to perform than the manual method. Materials and Methods: A dynamic Microsoft Excel workbook was developed to automatically convert published operating room schedules into different tabular formats. Based on the surgical procedure descriptions in the schedule, a list of linked keywords and phrases was utilized to sort cases by subspecialty and to predict potential intraoperative consultations. After two trial-and-optimization cycles, the method was incorporated into standard practice. Results: The workbook distributed cases to appropriate subspecialties and accurately predicted intraoperative requests. Users indicated that they spent 1-2 h fewer per day on this activity than before, and team members preferred the formatting of the newer reports. Comparison of the manual and semi-automatic predictions showed that the mean daily difference in predicted versus actual intraoperative consultations underwent no statistically significant changes before and after implementation for most subspecialties. Conclusions: A well-designed, lean, and simple information technology solution to determine subspecialty case distribution and prediction of intraoperative consultations in surgical pathology is approximately as accurate as the gold standard manual method and requires less time and effort to generate.

Introduction: Nucleolar changes in cancer cells are one of the cytologic features important to the tumor pathologist in cancer assessments of tissue biopsies. However, inter-observer variability and the manual approach to this work hamper the accuracy of the assessment by pathologists. In this paper, we propose a computational method for prominent nucleoli pattern detection. Materials and Methods: Thirty-five hematoxylin and eosin stained images were acquired from prostate cancer, breast cancer, renal clear cell cancer and renal papillary cell cancer tissues. Prostate cancer images were used for the development of a computer-based automated prominent nucleoli pattern detector built on a cascade farm. An ensemble of approximately 1000 cascades was constructed by permuting different combinations of classifiers such as support vector machines, eXclusive component analysis, boosting, and logistic regression. The output of cascades was then combined using the RankBoost algorithm. The output of our prominent nucleoli pattern detector is a ranked set of detected image patches of patterns of prominent nucleoli. Results: The mean number of detected prominent nucleoli patterns in the top 100 ranked detected objects was 58 in the prostate cancer dataset, 68 in the breast cancer dataset, 86 in the renal clear cell cancer dataset, and 76 in the renal papillary cell cancer dataset. The proposed cascade farm performs twice as good as the use of a single cascade proposed in the seminal paper by Viola and Jones. For comparison, a naive algorithm that randomly chooses a pixel as a nucleoli pattern would detect five correct patterns in the first 100 ranked objects. Conclusions: Detection of sparse nucleoli patterns in a large background of highly variable tissue patterns is a difficult challenge our method has overcome. This study developed an accurate prominent nucleoli pattern detector with the potential to be used in the clinical settings.

Background: Pathology reports typically require manual review to abstract research data. We developed a natural language processing (NLP) system to automatically interpret free-text breast pathology reports with limited assistance from manual abstraction. Methods: We used an iterative approach of machine learning algorithms and constructed groups of related findings to identify breast-related procedures and results from free-text pathology reports. We evaluated the NLP system using an all-or-nothing approach to determine which reports could be processed entirely using NLP and which reports needed manual review beyond NLP. We divided 3234 reports for development (2910, 90%), and evaluation (324, 10%) purposes using manually reviewed pathology data as our gold standard. Results: NLP correctly coded 12.7% of the evaluation set, flagged 49.1% of reports for manual review, incorrectly coded 30.8%, and correctly omitted 7.4% from the evaluation set due to irrelevancy (i.e. not breast-related). Common procedures and results were identified correctly (e.g. invasive ductal with 95.5% precision and 94.0% sensitivity), but entire reports were flagged for manual review because of rare findings and substantial variation in pathology report text. Conclusions: The NLP system we developed did not perform sufficiently for abstracting entire breast pathology reports. The all-or-nothing approach resulted in too broad of a scope of work and limited our flexibility to identify breast pathology procedures and results. Our NLP system was also limited by the lack of the gold standard data on rare findings and wide variation in pathology text. Focusing on individual, common elements and improving pathology text report standardization may improve performance.

Background: Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as "cell" or "image" or "tissue" or "microscope") that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical defi nitions thereby also supporting reasoning over the tagged data. Aim: This paper provides a survey of the biomedical imaging ontologies that have been developed thus far. It outlines the challenges, particularly faced by ontologies in the fields of histopathological imaging and image analysis, and suggests a strategy for addressing these challenges in the example domain of quantitative histopathology imaging. Results and Conclusions: The ultimate goal is to support the multiscale understanding of disease that comes from using interoperable ontologies to integrate imaging data with clinical and genomics data.

Background: The human protein atlas (HPA) is a powerful proteomic tool for visualizing the distribution of protein expression across most human tissues and many common malignancies. The HPA includes immunohistochemically-stained images from tissue microarrays (TMAs) that cover 48 tissue types and 20 common malignancies. The TMA data are used to provide expression information at the tissue, cellular, and occasionally, subcellular level. The HPA also provides subcellular data from confocal immunofluorescence data on three cell lines. Despite the availability of localization data, many unique patterns of cellular and subcellular expression are not documented. Materials and Methods: To get at this more granular data, we have developed a suite of Python scripts, HPASubC, to aid in subcellular, and cell-type specific classification of HPA images. This method allows the user to download and optimize specific HPA TMA images for review. Then, using a playstation-style video game controller, a trained observer can rapidly step through 10's of 1000's of images to identify patterns of interest. Results: We have successfully used this method to identify 703 endothelial cell (EC) and/or smooth muscle cell (SMCs) specific proteins discovered within 49,200 heart TMA images. This list will assist us in subdividing cardiac gene or protein array data into expression by one of the predominant cell types of the myocardium: Myocytes, SMCs or ECs. Conclusions: The opportunity to further characterize unique staining patterns across a range of human tissues and malignancies will accelerate our understanding of disease processes and point to novel markers for tissue evaluation in surgical pathology.

Background: Mobile networks and smartphones are growing in developing countries. Expert telemedicine consultation will become more convenient and feasible. We wanted to report on our experience in using a smartphone and a 3-D printed adapter for capturing microscopic images. Methods: Images and videos from a gastrointestinal biopsy teaching set of referred cases from the AFIP were captured with an iPhone 5 smartphone fitted with a 3-D printed adapter. Nine pathologists worldwide evaluated the images for quality, adequacy for telepathology consultation, and confidence rendering a diagnosis based on the images viewed on the web. Results: Average Likert scales (ordinal data) for image quality (1=poor, 5=diagnostic) and adequacy for diagnosis (1=No, 5=Yes) had modes of 3 and 4, respectively. Adding a video overview of the specimen improved diagnostic confidence. The mode of confidence in diagnosis based on the images reviewed was four. In 31 instances, reviewers' diagnoses completely agreed with AFIP diagnosis, with partial agreement in 9 and major disagreement in 5. There was strong correlation between image quality and confidence (r = 0.78), image quality and adequacy of image (r = 0.73) and whether images were found adequate when reviewers were confident (r = 0.72). Intraclass Correlation for measuring reliability among the four reviewers who finished a majority of cases was high (quality=0.83, adequacy= 0.76 and confidence=0.92). Conclusions: Smartphones allow pathologists and other image dependent disciplines in low resource areas to transmit consultations to experts anywhere in the world. Improvements in camera resolution and training may mitigate some limitations found in this study.

Aims: In Japan, cancer is the most prevalent cause of death; the number of patients suffering from cancer is increasing. Hence, there is an increased burden on pathologists to make diagnoses. To reduce pathologists' burden, researchers have developed methods of auto-pathological diagnosis. However, virtual slides, which are created when glass slides are digitally scanned, saved in a unique format, and it is difficult for researchers to work on the virtual slides for developing their own image processing method. This paper presents the support system for pathologists and researchers who use auto-pathological diagnosis (P-SSD). Main purpose of P-SSD was to support both of pathologists and researchers. P-SSD consists of several sub-functions that make it easy not only for pathologists to screen pathological images, double-check their diagnoses, and reduce unimportant image data but also for researchers to develop and apply their original image-processing techniques to pathological images. Methods: We originally developed P-SSD to support both pathologists and researchers developing auto-pathological diagnoses systems. Current version of P-SSD consists of five main functions as follows: (i) Loading virtual slides, (ii) making a supervised database, (iii) learning image features, (iv) detecting cancerous areas, (v) displaying results of detection. Results: P-SSD reduces computer memory size random access memory utilization and the processing time required to divide the virtual slides into the smaller-size images compared with other similar software. The maximum observed reduction in computer memory size and reduction in processing time is 97% and 99.94%, respectively. Conclusions: Unlike other vendor-developed software, P-SSD has interoperability and is capable of handling virtual slides in several formats. Therefore, P-SSD can support both of pathologists and researchers, and has many potential applications in both pathological diagnosis and research area.

Hematoxylin and eosin (H&E) staining is ubiquitous in pathology practice and research. As digital pathology has evolved, the reliance of quantitative methods that make use of H&E images has similarly expanded. For example, cell counting and nuclear morphometry rely on the accurate demarcation of nuclei from other structures and each other. One of the major obstacles to quantitative analysis of H&E images is the high degree of variability observed between different samples and different laboratories. In an effort to characterize this variability, as well as to provide a substrate that can potentially mitigate this factor in quantitative image analysis, we developed a technique to project H&E images into an optimized space more appropriate for many image analysis procedures. We used a decision tree-based support vector machine learning algorithm to classify 44 H&E stained whole slide images of resected breast tumors according to the histological structures that are present. This procedure takes an H&E image as an input and produces a classification map of the image that predicts the likelihood of a pixel belonging to any one of a set of user-defined structures (e.g., cytoplasm, stroma). By reducing these maps into their constituent pixels in color space, an optimal reference vector is obtained for each structure, which identifies the color attributes that maximally distinguish one structure from other elements in the image. We show that tissue structures can be identified using this semi-automated technique. By comparing structure centroids across different images, we obtained a quantitative depiction of H&E variability for each structure. This measurement can potentially be utilized in the laboratory to help calibrate daily staining or identify troublesome slides. Moreover, by aligning reference vectors derived from this technique, images can be transformed in a way that standardizes their color properties and makes them more amenable to image processing.

As a result of their practice of medicine, forensic pathologists create a wealth of data regarding the causes of and reasons for sudden, unexpected or violent deaths. This data have been effectively used to protect the health and safety of the general public in a variety of ways despite current and historical limitations. These limitations include the lack of data standards between the thousands of death investigation (DI) systems in the United States, rudimentary electronic information systems for DI, and the lack of effective communications and interfaces between these systems. Collaboration between forensic pathology and clinical informatics is required to address these shortcomings and a path forward has been proposed that will enable forensic pathology to maximize its effectiveness by providing timely and actionable information to public health and public safety agencies.

Background: Optimization of workflow for breast cancer samples with equivocal human epidermal growth factor receptors 2 (HER2)/neu score 2 ⁺ results in routine practice, remains to be a central focus of the on-going efforts to assess HER2 status. According to the College of American Pathologists/American Society of Clinical Oncology guidelines equivocal HER2/neu score 2 ⁺ cases are subject for further testing, usually by fluorescence in situ hybridization (FISH) investigations. It still remains on open question, whether quantitative digital image analysis of HER2 immunohistochemistry (IHC) stained slides can assist in further refining the HER2 score 2 ⁺ . Aim of this Work: To assess utility of quantitative digital analysis of IHC stained slides and compare its performance to FISH in cases of breast cancer with equivocal HER2 score 2 ⁺ . Materials and Methods: Fifteen specimens (previously diagnosed as breast cancer and was evaluated as HER 2^– score 2 ⁺) represented the study population. Contemporary new cuts were prepared for re-evaluation of HER2 immunohistochemical studies and FISH examination. All the cases were digitally scanned by iScan (Produced by BioImagene [Now Roche-Ventana]). The IHC signals of HER2 were measured using an automated image analyzing system (MECES, www.Diagnomx.eu/meces). Finally, a comparative study was done between the results of the FISH and the quantitative analysis of the virtual slides. Results: Three out of the 15 cases with equivocal HER2 score 2 ⁺ , turned out to be positive (3 ⁺) by quantitative digital analysis, and 12 were found to be negative in FISH too. Two of these three positive cases proved to be positive with FISH, and only one was negative. Conclusions: Quantitative digital analysis is highly sensitive and relatively specific when compared to FISH in detecting HER2/neu overexpression. Therefore, it represents a potential reliable substitute for FISH in breast cancer cases, which desire further refinement of equivocal IHC results.

Background: The pathology laboratory course at the New York College of Podiatric Medicine involves the use of Virtual Microscopy. The students can scan the whole slide, section by section, and zoom in or out. Methods: Using the advantages of digital pathology, the students can, in addition, access the slide collections from other medical schools and put up normal histology (control) slides side-by-side with the pathology. They can cut and paste and preserve the region of interest that they find. They can edit and annotate their slides. A wiki was created (http://pathlab2014.wikifoundry.com) for the Class of 2014. The students saved, edited and uploaded their slides. In the wiki format, other students could comment, further edit, and even delete the slides. Results: The students studied Basic Mechanisms and System Pathology. During this time, they saved, edited, shared, and uploaded their slides to the wiki. These were available in one full presentation and were also grouped into 16 albums. They were available to all. Student access was followed by Google analytics. At the end of the course, a questionnaire was distributed, assessing their impression of the wiki format and soliciting strengths and weaknesses. Conclusions: The use of a wiki has a number of important advantages in pathology education. It trains the students in the more sophisticated skills that they will use as professional pathologists or as clinicians: (1) Telepathology-it enables them to share slides and discuss observations. (2) Archiving and retrieval - It models the challenge faced by hospitals, diagnostic labs and physicians in maintaining a collection of slides in a form that is easily accessible. (3) Image analysis-familiarity with the wiki format allows them to jump easily to capturing and storing images found in the literature or in a pathologist's report. Experience with the use of a wiki in pathology education has been quite satisfactory from both the faculty and the student's point of view.

The digitization of images has not only led to increasingly sophisticated methods of quantitating information from those images themselves, but also to the development of new physics-based techniques for extracting information from the original specimen and presenting this as visual data in both two and three-dimensional (3D) forms. This evolution of an image-based discipline has reached maturity in Radiology, but it is only just beginning in Pathology. An historical perspective is provided both on the current state of computational imaging in pathology and of the factors that are impeding further progress in the development and application of these approaches. Emphasis is placed on barriers to the dissemination of information in this area. The value of computational imaging in basic and translational research is clear. However, while there are many examples of "virtual diagnostics" in Radiology, there are only relatively few in Pathology. Nevertheless, we can do cellular level analysis of lesions accessible by endoscopic or catheterization procedures, and a number of steps have been taken toward real-time imaging as adjuncts to traditional biopsies. Progress in computational imaging will greatly expand the role of pathologists in clinical medicine as well as research.

The way of viewing whole slide images (WSI) can be tracked and analyzed. In particular, it can be useful to learn how medical students view WSIs during exams and how their viewing behavior is correlated with correctness of the answers they give. We used software-based view path tracking method that enabled gathering data about viewing behavior of multiple simultaneous WSI users. This approach was implemented and applied during two practical exams in oral pathology in 2012 (88 students) and 2013 (91 students), which were based on questions with attached WSIs. Gathered data were visualized and analyzed in multiple ways. As a part of extended analysis, we tried to use machine learning approaches to predict correctness of students' answers based on how they viewed WSIs. We compared the results of analyses for years 2012 and 2013 - done for a single question, for student groups, and for a set of questions. The overall patterns were generally consistent across these 3 years. Moreover, viewing behavior data appeared to have certain potential for predicting answers' correctness and some outcomes of machine learning approaches were in the right direction. However, general prediction results were not satisfactory in terms of precision and recall. Our work confirmed that the view path tracking method is useful for discovering viewing behavior of students analyzing WSIs. It provided multiple useful insights in this area, and general results of our analyses were consistent across two exams. On the other hand, predicting answers' correctness appeared to be a difficult task - students' answers seem to be often unpredictable.

Visualizing tissue structures in three-dimensions (3D) is crucial to understanding normal and pathological phenomena. However, staining and imaging of thick sections and whole mount samples can be challenging. For decades, researchers have serially sectioned large tissues and painstakingly reconstructed the 3D volume. Advances in automation, from sectioning to alignment, now greatly accelerate the process. In addition, immunofluorescent staining methods allow multiple antigens to be simultaneously detected and analyzed volumetrically. The objective was to incorporate multi-channel immunofluorescent staining and automation in 3D reconstruction of serial sections for volumetric analysis. Paraffin-embedded samples were sectioned manually but were processed, stained, imaged and aligned in an automated fashion. Reconstructed stacks were quantitatively analyzed in 3D. By combining automated immunofluorescent staining and tried-and-true methods of reconstructing adjacent sections, we were able to visualize, in detail, not only the geometric structures of the sample but also the presence and interactions of multiple proteins and molecules of interest within their 3D environment. Advances in technology and software algorithms have significantly expedited the 3D reconstruction of serial sections. Automated, multi-antigen immunofluorescent staining will significantly broaden the range and complexity of scientific questions that can be answered with this methodology.

Background: Recent breakthroughs in computer vision and digital microscopy have prompted the application of such technologies in cancer diagnosis, especially in histopathological image analysis. Earlier, an attempt to classify hepatocellular carcinoma images based on nuclear and structural features has been carried out on a set of surgical resected samples. Here, we proposed methods to enhance the process and improve the classification performance. Methods: First, we segmented the histological components of the liver tissues and generated several masked images. By utilizing the masked images, some set of new features were introduced, producing three sets of features consisting nuclei, trabecular and tissue changes features. Furthermore, we extended the classification process by using biopsy resected samples in addition to the surgical samples. Results: Experiments by using support vector machine (SVM) classifier with combinations of features and sample types showed that the proposed methods improve the classification rate in HCC detection for about 1-3%. Moreover, detection rate of low-grades cancer increased when the new features were appended in the classification process, although the rate was worsen in the case of undifferentiated tumors. Conclusions: The masking process increased the reliability of extracted nuclei features. The additional of new features improved the system especially for early HCC detection. Likewise, the combination of surgical and biopsy samples as training data could also improve the classification rates. Therefore, the methods will extend the support for pathologists in the HCC diagnosis.

Background: Whole-slide imaging (WSI), while technologically mature, remains in the early adopter phase of the technology adoption lifecycle. One reason for this current situation is that current methods of visualizing and using WSI closely follow long-existing workflows for glass slides. We set out to "reimagine" the digital microscope in the era of cloud computing by combining WSI with the rich collaborative environment of the Scalable Adaptive Graphics Environment (SAGE). SAGE is a cross-platform, open-source visualization and collaboration tool that enables users to access, display and share a variety of data-intensive information, in a variety of resolutions and formats, from multiple sources, on display walls of arbitrary size. Methods: A prototype of a WSI viewer app in the SAGE environment was created. While not full featured, it enabled the testing of our hypothesis that these technologies could be blended together to change the essential nature of how microscopic images are utilized for patient care, medical education, and research. Results: Using the newly created WSI viewer app, demonstration scenarios were created in the patient care and medical education scenarios. This included a live demonstration of a pathology consultation at the International Academy of Digital Pathology meeting in Boston in November 2014. Conclusions: SAGE is well suited to display, manipulate and collaborate using WSIs, along with other images and data, for a variety of purposes. It goes beyond how glass slides and current WSI viewers are being used today, changing the nature of digital pathology in the process. A fully developed WSI viewer app within SAGE has the potential to encourage the wider adoption of WSI throughout pathology.

Digital plate reading (DPR) is increasingly being adopted as a means to facilitate the analysis and improve the quality and efficiency within the clinical microbiology laboratory. This review discusses the role of DPR in the context of total laboratory automation and explores some of the platforms currently available or in development for digital image capturing of microbial growth on media. The review focuses on the advantages and challenges of DPR. Peer-reviewed studies describing the utility and quality of these novel DPR systems are largely lacking, and professional guidelines for DPR implementation and quality management are needed. Further development and more widespread adoption of DPR is anticipated.

Background: The use of digital whole slide imaging for human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) could create improvements in workflow and performance, allowing for central archiving of specimens, distributed and remote interpretation, and the potential for additional computerized automation. Procedures: The accuracy, precision, and reproducibility of manual digital interpretation for HER2 IHC were determined by comparison to manual glass slide interpretation. Inter- and intra-pathologist reproducibility and precision between the glass slide and digital interpretations of HER2 IHC were determined in 5 studies using DAKO HercepTest-stained breast cancer slides with the Philips Digital Pathology System. In 2 inter-method studies, 3 pathologists interpreted glass and digital slides in sequence or in random order with a minimum of 7 days as a washout period. These studies also measured inter-observer reproducibility and precision. Another two studies measured intra-pathologist reproducibility on cases read 10 times by glass and digital methods. One additional study evaluated the effects of adding IHC control slides with each run, using 1 pathologist interpreting glass and digital slides randomized from the sets above along with appropriate controls for each slide in the set. Results: The overall results show that there is no statistical difference between the variance of performance when comparing glass and digital HER2 interpretations; and there were no effects noted when control tissues were evaluated in conjunction with the test slides. Conclusions: The results show that there is an equivalence of result when interpreting HER2 IHC slides in breast cancer by either glass slides or digital images. Digital interpretation can therefore be safely and effectively used for this purpose.

Background: Obtaining ground truth for pathological images is essential for various experiments, especially for training and testing image analysis algorithms. However, obtaining pathologist input is often difficult, time consuming and expensive. This leads to algorithms being over-fitted to small datasets, and inappropriate validation, which causes poor performance on real world data. There is a great need to gather data from pathologists in a simple and efficient manner, in order to maximise the amount of data obtained. Methods: We present a lightweight, web-based HTML5 system for administering and participating in data collection experiments. The system is designed for rapid input with minimal effort, and can be accessed from anywhere in the world with a reliable internet connection. Results: We present two case studies that use the system to assess how limitations on fields of view affect pathologist agreement, and to what extent poorly stained slides affect judgement. In both cases, the system collects pathologist scores at a rate of less than two seconds per image. Conclusions: The system has multiple potential applications in pathology and other domains.

Background: Liver fibrosis staging provides prognostic value, although hampered by observer variability. We used digital analysis to develop diagnostic morphometric scores for significant fibrosis, cirrhosis and fibrosis staging in chronic hepatitis C. Materials and Methods: We automated the measurement of 44 classical and new morphometric descriptors. The reference was histological METAVIR fibrosis (F) staging (F0 to F4) on liver biopsies. The derivation population included 416 patients and liver biopsies ≥20 mm-length. Two validation population included 438 patients. Results: In the derivation population, the area under the receiver operating characteristic (AUROC) for clinically significant fibrosis (F stage ≥2) of a logistic score combining 5 new descriptors (stellar fibrosis area, edge linearity, bridge thickness, bridge number, nodularity) was 0.957. The AUROC for cirrhosis of 6 new descriptors (edge linearity, nodularity, portal stellar fibrosis area, portal distance, granularity, fragmentation) was 0.994. Predicted METAVIR F staging combining 8 morphometric descriptors agreed well with METAVIR F staging by pathologists: k = 0.868. Morphometric score of clinically significant fibrosis had a higher correlation with porto-septal fibrosis area (r_s = 0.835) than METAVIR F staging (r_s = 0.756, P < 0.001) and the same correlations with fibrosis biomarkers, e.g., serum hyaluronate: r_s = 0.484 versus r_s = 0.476 for METAVIR F (P = 0.862). In the validation population, the AUROCs of clinically significant fibrosis and cirrhosis scores were, respectively: 0.893 and 0.993 in 153 patients (biopsy < 20 mm); 0.955 and 0.994 in 285 patients (biopsy ≥ 20 mm). The three morphometric diagnoses agreed with consensus expert reference as well as or better than diagnoses by first-line pathologists in 285 patients, respectively: significant fibrosis: 0.733 versus 0.733 (k), cirrhosis: 0.900 versus 0.827, METAVIR F: 0.881 versus 0.865. Conclusion: The new automated morphometric scores provide reproducible and accurate diagnoses of fibrosis stages via "virtual expert pathologist."

Background: Rapid on-site evaluation (ROSE) at the time of ultrasound-guided fine-needle aspiration (USGFNA) of head and neck lesion is essential for obtaining adequate samples and providing the preliminary diagnosis. We summarize our experience with ROSE of USGFNA on head and neck nonthyroid lesions using telecytopathology. Materials and Methods: Real-time images of Diff-Quik stained cytology smears were obtained at ultrasound suite with an Olympus DP-70 digital camera attached to an Olympus CX41 microscope, and transmitted via ethernet by a cytotechnologist to a cytopathologist in cytopathology laboratory who rendered a preliminary diagnosis. Live communication was conducted with Vocera voice communication system. The ultrasound suite was located on different floor from the cytopathology laboratory. Accuracy of ROSE via telecytopathology was compared with an equal number of cases that received ROSE, prior to introduction of telecytopathology, via conventional microscopy. Results: Rapid on-site evaluation was performed on a total of 116 USGFNA of head and neck nonthyroid lesions. The telecytopathology system and conventional microscopy was used to evaluate equal number of cases (58 each). Preliminary diagnoses of benign, atypical/suspicious for malignancy, and positive for malignancy were 72.4%, 17.2% and 10.3% for telecytopathology, and 69.0%, 10.3% and 20.7% for conventional microscopy. None of the cases were deemed unsatisfactory. The overall concordance between the preliminary and final diagnoses was 94.8% for telecytopathology and 98.3% for conventional microscopy and was not statistically significant (P = 0.309). The causes of discordant preliminary and final diagnoses were mainly attributed to availability of cell block and Papanicolaou-stained slides for review or flow cytometry results for lymphoma cases at the time of final sign out. Conclusions: Telecytopathology is comparable with conventional microscopy in ROSE of USGFNA of head and neck nonthyroid lesions.

Background: Increased workload, case complexity, financial constraints, and staffing shortages justify wider implementations of digital pathology. One of its main advantages is distance reporting. Aim: A feasibility study was conducted at our institution in order to achieve comprehensive pathology services available by distance. Methods: One senior pathologist reported 950 cases (3,650 slides) by distance during 19 weeks. Slides were scanned by ScanScope AT Turbo (Aperio) and digital images accessed through SymPathy (Tieto) on a 14" laptop. Mobile phone, mobile broadband, broadband over Wi-Fi and broadband were used for internet connections along with a virtual private network technology (VPN). Lync (Microsoft) was tested for one case consultation and resident's teaching session. Larger displays were accessed when available. Effects of ergonomics and working flexibility on the user experience were observed. Details on network speed, frequency of technical issues, data usage, scanning, and turnaround, were collected and evaluated. Turnaround was compared to in-office microscopic reporting, measured from the registration to sign off. Results: Network speeds varied 1-80 Mbps (median download speed 8-65 Mbps). 20 Mbps were satisfactory for the instant upload of digital images. VPN, image viewer, and laptop failed on two occasions each. An estimated data usage per digital image was 10 MB (1-50 MB). Two cases (15 slides) were deferred to microscopic slides (0.21/0.41%) due to scanty material and suboptimal slide quality. Additional nine cases (15 slides) needed to be rescanned for various reasons (0.95/0.41%). Average turnaround was shorter, and the percentage of cases reported up to 3 days higher (3.13 days/72.25%) comparing with in-office microscopic reporting (3.90 days/40.56%). Larger displays improved the most user experience at magnifications over Χ20. Conclusions: Existing IT solutions at our institution allow efficient and reliable distance reporting for the core pathology services in histology and cytology. Stable network speeds, fully integrated laboratory information management system, technical reliability, working flexibility, larger displays, and shorter turnaround contributed to the overall satisfaction with distance reporting. A further expansion of our pathology services available by distance, diagnostic and educational, rely on gaining experience in digital reporting and marginal IT investment. Adjustments to the organization of pathology services may follow to fully benefit from the implementation of digital pathology.

Background: Telepathology practice across international borders has become increasingly popular. Our telepathology consultation service with a laboratory in China was hampered by latency issues when viewing whole slide images. Objective: The aim was to explore data transfer solutions to improve the viewing experience of digital consult cases. Methods: Whole slide image files residing on a server in China were transferred to our data center in the USA using an open source product (Fast Data Transfer). A faster more automated commercial high speed file transfer software solution (Aspera) was also tested. Results: Transferring files with the open source product provided transfer speeds of 2-3 Mbps, but suffered from intermittent dropped connections. Employing commercial software permitted more reliable transmission of digital files with 75-100 Mbps transfer speeds. Conclusions: Successful global telepathology requires dedicated image management. Transfer of files to local servers by employing high speed data transfer tools helped overcome network latency issues, improved the overall turn-around time of digital consultations, and enhanced the viewing experience for end-user digital consultants.

Background: Computerized physician order entry (CPOE) systems are quickly becoming ubiquitous, and groups of orders ("order sets") to allow for easy order input are a common feature. This provides a streamlined mechanism to view, modify, and place groups of related orders. This often serves as an electronic equivalent of a specialty requisition. A characteristic, of these order sets is that specific orders can be predetermined to be "preselected" or "defaulted-on" whenever the order set is used while others are "optional" or "defaulted-off" (though there is typically the option is to "deselect" defaulted-on tests in a given situation). While it seems intuitive that the defaults in an order set are often accepted, additional study is required to understand the impact of these "default" settings in an order set on ordering habits. This study set out to quantify the effect of changing the default settings of an order set. Methods: For quality improvement purposes, order sets dealing with transfusions were recently reviewed and modified to improve monitoring of outcome. Initially, the order for posttransfusion hematocrits and platelet count had the default setting changed from "optional" to "preselected." The default settings for platelet count was later changed back to "optional," allowing for a natural experiment to study the effect of the default selections of an order set on clinician ordering habits. Results: Posttransfusion hematocrit values were ordered for 8.3% of red cell transfusions when the default order set selection was "off" and for 57.4% of transfusions when the default selection was "preselected" (P < 0.0001). Posttransfusion platelet counts were ordered for 7.0% of platelet transfusions when the initial default order set selection was "optional," increased to 59.4% when the default was changed to "preselected" (P < 0.0001), and then decreased to 7.5% when the default selection was returned to "optional." The posttransfusion platelet count rates during the two "optional" periods: 7.0% versus 7.5% - were not statistically different (P = 0.620). Discussion: Default settings in CPOE order sets can significantly influence physician selection of laboratory tests. Careful consideration by all stakeholders, including clinicians and pathologists, should be obtained when establishing default settings in order sets.

Introduction: Histopathological images have rich structural information, are multi-channel in nature and contain meaningful pathological information at various scales. Sophisticated image analysis tools that can automatically extract discriminative information from the histopathology image slides for diagnosis remain an area of significant research activity. In this work, we focus on automated brain cancer grading, specifically glioma grading. Grading of a glioma is a highly important problem in pathology and is largely done manually by medical experts based on an examination of pathology slides (images). To complement the efforts of clinicians engaged in brain cancer diagnosis, we develop novel image processing algorithms and systems to automatically grade glioma tumor into two categories: Low-grade glioma (LGG) and high-grade glioma (HGG) which represent a more advanced stage of the disease. Results: We propose novel image processing algorithms based on spatial domain analysis for glioma tumor grading that will complement the clinical interpretation of the tissue. The image processing techniques are developed in close collaboration with medical experts to mimic the visual cues that a clinician looks for in judging of the grade of the disease. Specifically, two algorithmic techniques are developed: (1) A cell segmentation and cell-count profile creation for identification of Pseudopalisading Necrosis, and (2) a customized operation of spatial and morphological filters to accurately identify microvascular proliferation (MVP). In both techniques, a hierarchical decision is made via a decision tree mechanism. If either Pseudopalisading Necrosis or MVP is found present in any part of the histopathology slide, the whole slide is identified as HGG, which is consistent with World Health Organization guidelines. Experimental results on the Cancer Genome Atlas database are presented in the form of: (1) Successful detection rates of pseudopalisading necrosis and MVP regions, (2) overall classification accuracy into LGG and HGG categories, and (3) receiver operating characteristic curves which can facilitate a desirable trade-off between HGG detection and false-alarm rates. Conclusion: The proposed method demonstrates fairly high accuracy and compares favorably against best-known alternatives such as the state-of-the-art WND-CHARM feature set provided by NIH combined with powerful support vector machine classifier. Our results reveal that the proposed method can be beneficial to a clinician in effectively separating histopathology slides into LGG and HGG categories, particularly where the analysis of a large number of slides is needed. Our work also reveals that MVP regions are much harder to detect than Pseudopalisading Necrosis and increasing accuracy of automated image processing for MVP detection emerges as a significant future research direction.

Background: Red blood cell (RBC) analysis is a key feature in the evaluation of hematological disorders. The gold standard light microscopy technique has high sensitivity, but is a relativity time-consuming and labor intensive procedure. This study tested the sensitivity and specificity of gold standard light microscopy manual differential to the CellaVision ^® DM96 (CCS; CellaVision, Lund, Sweden) automated image analysis system, which takes digital images of samples at high magnification and compares these images with an artificial neural network based on a database of cells and preclassified according to RBC morphology. Methods: In this study, 212 abnormal peripheral blood smears within the Calgary Laboratory Services network of hospital laboratories were selected and assessed for 15 different RBC morphologic abnormalities by manual microscopy. The same samples were reassessed as a manual addition from the instrument screen using the CellaVision ^® DM96 system with 8 microscope high power fields (×100 objective and a 22 mm ocular). The results of the investigation were then used to calculate the sensitivity and specificity of the CellaVision ^® DM96 system in reference to light microscopy. Results: The sensitivity ranged from a low of 33% (RBC agglutination) to a high of 100% (sickle cells, stomatocytes). The remainder of the RBC abnormalities tested somewhere between these two extremes. The specificity ranged from 84% (schistocytes) to 99.5% (sickle cells, stomatocytes). Conclusions: Our results showed generally high specificities but variable sensitivities for RBC morphologic abnormalities.

Background: Laboratory utilization management describes a process designed to increase healthcare value by altering requests for laboratory services. A typical approach to monitor and prioritize interventions involves audits of laboratory orders against specific criteria, defined as rule-based laboratory utilization management. This approach has inherent limitations. First, rules are inflexible. They adapt poorly to the ambiguity of medical decision-making. Second, rules judge the context of a decision instead of the patient outcome allowing an order to simultaneously save a life and break a rule. Third, rules can threaten physician autonomy when used in a performance evaluation. Methods: We developed an alternative to rule-based laboratory utilization. The core idea comes from a formula used in epidemiology to estimate disease prevalence. The equation relates four terms: the prevalence of disease, the proportion of positive tests, test sensitivity and test specificity. When applied to a laboratory utilization audit, the formula estimates the prevalence of disease (pretest probability [PTP]) in the patients tested. The comparison of PTPs among different providers, provider groups, or patient cohorts produces an objective evaluation of laboratory requests. We demonstrate the model in a review of tests for enterovirus (EV) meningitis. Results: The model identified subpopulations within the cohort with a low prevalence of disease. These low prevalence groups shared demographic and seasonal factors known to protect against EV meningitis. This suggests too many orders occurred from patients at low risk for EV. Conclusion: We introduce a new method for laboratory utilization management programs to audit laboratory services.

The clinical laboratory is a major source of health care data. Increasingly these data are being integrated with other data to inform health system-wide actions meant to improve diagnostic test utilization, service efficiency, and "meaningful use." The Academy of Clinical Laboratory Physicians and Scientists hosted a satellite meeting on clinical laboratory analytics in conjunction with their annual meeting on May 29, 2014 in San Francisco. There were 80 registrants for the clinical laboratory analytics meeting. The meeting featured short presentations on current trends in clinical laboratory analytics and several panel discussions on data science in laboratory medicine, laboratory data and its role in the larger healthcare system, integrating laboratory analytics, and data sharing for collaborative analytics. One main goal of meeting was to have an open forum of leaders that work with the "big data" clinical laboratories produce. This article summarizes the proceedings of the meeting and content discussed.

This paper describes work presented at the Nordic Symposium on Digital Pathology 2014, Linköping, Sweden. Systematic random sampling (SRS) is a stereological tool, which provides a framework to quickly build an accurate estimation of the distribution of objects or classes within an image, whilst minimizing the number of observations required. RandomSpot is a web-based tool for SRS in stereology, which systematically places equidistant points within a given region of interest on a virtual slide. Each point can then be visually inspected by a pathologist in order to generate an unbiased sample of the distribution of classes within the tissue. Further measurements can then be derived from the distribution, such as the ratio of tumor to stroma. RandomSpot replicates the fundamental principle of traditional light microscope grid-shaped graticules, with the added benefits associated with virtual slides, such as facilitated collaboration and automated navigation between points. Once the sample points have been added to the region(s) of interest, users can download the annotations and view them locally using their virtual slide viewing software. Since its introduction, RandomSpot has been used extensively for international collaborative projects, clinical trials and independent research projects. So far, the system has been used to generate over 21,000 sample sets, and has been used to generate data for use in multiple publications, identifying significant new prognostic markers in colorectal, upper gastro-intestinal and breast cancer. Data generated using RandomSpot also has significant value for training image analysis algorithms using sample point coordinates and pathologist classifications.

This paper describes work presented at the Nordic Symposium on Digital Pathology 2014, Linköping, Sweden. Quick and seamless integration between input devices and the navigation of digital slides remains a key barrier for many pathologists to "go digital." To better understand this integration, three different input device implementations were compared in terms of time to diagnose, perceived workload and users' preferences. Six pathologists reviewed in total nine cases with a computer mouse, a 6 degrees-of-freedom (6DOF) navigator and a touchpad. The participants perceived significantly less workload (P < 0.05) with the computer mouse and the 6DOF navigator, than with the touchpad, while no effect of the input device used on the time to diagnose was observed. Five out of six pathologists preferred the 6DOF navigator, while the touchpad was the least preferred device. While digital slide navigation is often designed to mimic microscope interaction, the results of this study demonstrate that in order to minimize workload there is reason to let the digital interaction move beyond the familiar microscope tradition.

Light microscopy applied to the domain of histopathology has traditionally been a two-dimensional imaging modality. Several authors, including the authors of this work, have extended the use of digital microscopy to three dimensions by stacking digital images of serial sections using image-based registration. In this paper, we give an overview of our approach, and of extensions to the approach to register multi-modal data sets such as sets of interleaved histopathology sections with different stains, and sets of histopathology images to radiology volumes with very different appearance. Our approach involves transforming dissimilar images into a multi-channel representation derived from co-occurrence statistics between roughly aligned images.

Techniques for digital pathology are envisioned to provide great benefits in clinical practice, but experiences also show that solutions must be carefully crafted. The Nordic countries are far along the path toward the use of whole-slide imaging in clinical routine. The Nordic Symposium on Digital Pathology (NDP) was created to promote knowledge exchange in this area, between stakeholders in health care, industry, and academia. This article is a summary of the NDP 2014 symposium, including conclusions from a workshop on clinical adoption of digital pathology among the 144 attendees.

Requisition storage and retrieval are an integral part of the outpatient laboratory testing process. It is frequently necessary to review an original requisition to confirm the ordering physician, patient demographics, diagnostic information, and requested tests. Manual retrieval of a paper requisition is time-consuming and tedious. Although commercial solutions exist for the scanning and archiving of barcoded paper requisitions, the tools to accomplish this are freely available from the open source software community. We present a simple dedicated piece of software, Reqscan, for scanning patient laboratory requisitions, finding all barcode information, and saving the requisition as a portable document format named according the barcode(s) found. This Python application offers a simple solution to patient requisition digitization. Reqscan has been successfully tested and implemented into routine practice for storage and retrieval of outpatient requisitions at St. Paul's Hospital, Department of Pathology and Laboratory Medicine in Vancouver, British Columbia, Canada.

Introduction: For decades cellular differentials have been generated exclusively on analog tabletop cell counters. With the advent of tablet computers, digital cell counters - in the form of mobile applications ("apps") - now represent an alternative to analog devices. However, app-based counters have not been widely adopted by clinical laboratories, perhaps owing to a presumed decrease in count accuracy related to the lack of tactile feedback inherent in a touchscreen interface. We herein provide the first systematic evidence that digital cell counters function similarly to standard tabletop units. Methods: We developed an app-based cell counter optimized for use in the clinical laboratory setting. Paired counts of 188 peripheral blood smears and 62 bone marrow aspirate smears were performed using our app-based counter and a standard analog device. Differences between paired data sets were analyzed using the correlation coefficient, Student's t-test for paired samples and Bland-Altman plots. Results: All counts showed excellent agreement across all users and touch screen devices. With the exception of peripheral blood basophils (r = 0.684), differentials generated for the measured cell categories within the paired data sets were highly correlated (all r ≥ 0.899). Results of paired t-tests did not reach statistical significance for any cell type (all P > 0.05), and Bland-Altman plots showed a narrow spread of the difference about the mean without evidence of significant outliers. Conclusions: Our analysis suggests that no systematic differences exist between cellular differentials obtained via app-based or tabletop counters and that agreement between these two methods is excellent.

Background: Little evidence is available concerning the impact of virtual microscopy (VM) in the undergraduate teaching of pathology. We aimed: (1) to determine the impact in student scores when moving from conventional microscopy (CM) to VM; (2) to assess the students' impressions and changes in study habits regarding the impact of this tool. Methods: We evaluated two groups taking the discipline of pathology in the same course, one using CM and the other VM. The same set of slides used in the CM classes was digitized in a VENTANA iScan HT (Roche Diagnostics, Sant Cugat, Spain) at ×20 and observed by the students using the Virtuoso viewer (Roche Diagnostics). We evaluated the skill level reached by the students with an online test. A voluntary survey was undertaken by the VM group to assess the students' impressions regarding the resource. The day and time of any accession to the viewer were registered. Results: There were no differences between the two groups in their marks in the online test (mean marks for the CM and the VM groups: 9.87 ± 0.34 and 9.86 ± 0.53, respectively; P = 0.880). 86.6% of the students found the software friendly, easy-to-use and effective. 71.6% of the students considered navigation easier with VM than with CM. The most appreciated feature of VM was the possibility to access the images anywhere and at any time (93.3%). 57.5% of the accesses were made on holidays and 41.9% later than 6:00 pm. Conclusions: Virtual microscopy can effectively replace the traditional methods of learning pathology, providing mobility and convenience to medical students.