Background: Whole slide imaging (WSI) is widely used for education and research, but is increasingly being used to streamline clinical workflow. We present our experience with regard to satisfaction and time utilization using oil immersion WSI for presentation of blood/marrow aspirate smears, core biopsies, and tissue sections in hematology/oncology tumor board/treatment planning conferences (TPC). Methods: Lymph nodes and bone marrow core biopsies were scanned at ×20 magnification and blood/marrow smears at 83X under oil immersion and uploaded to an online library with areas of interest to be displayed annotated digitally via web browser. Pathologist time required to prepare slides for scanning was compared to that required to prepare for microscope projection (MP). Time required to present cases during TPC was also compared. A 10-point evaluation survey was used to assess clinician satisfaction with each presentation method. Results: There was no significant difference in hematopathologist preparation time between WSI and MP. However, presentation time was significantly less for WSI compared to MP as selection and annotation of slides was done prior to TPC with WSI, enabling more efficient use of TPC presentation time. Survey results showed a significant increase in satisfaction by clinical attendees with regard to image quality, efficiency of presentation of pertinent findings, aid in clinical decision-making, and overall satisfaction regarding pathology presentation. A majority of respondents also noted decreased motion sickness with WSI. Conclusions: Whole slide imaging, particularly with the ability to use oil scanning, provides higher quality images compared to MP and significantly increases clinician satisfaction. WSI streamlines preparation for TPC by permitting prior slide selection, resulting in greater efficiency during TPC presentation.

Context: Distinction of spindle cell melanoma (SM) and desmoplastic melanoma (DM) is clinically important due to differences in metastatic rate and prognosis; however, histological distinction is not always straightforward. During a routine review of cases, we noted differences in nuclear circularity between SM and DM. Aim: The primary aim in our study was to determine whether these differences in nuclear circularity, when assessed using a basic ImageJ-based threshold extraction, can serve as a diagnostic classifier to distinguish DM from SM. Settings and Design: Our retrospective analysis of an established patient cohort (SM n = 9, DM n = 9) was employed to determine discriminatory power. Subjects and Methods: Regions of interest (total n = 108; 6 images per case) were selected from scanned H and E-stained histological sections, and nuclear circularity was extracted and quantified by computational image analysis using open source tools (plugins for ImageJ). Statistical Analysis: Using analysis of variance, t-tests, and Fisher's exact tests, we compared extracted quantitative shape measures; statistical significance was defined as P < 0.05. Results: Classifying circularity values into four shape categories (spindled, elongated, oval, round) demonstrated significant differences in the spindled and round categories. Paradoxically, DM contained more spindled nuclei than SM (P = 0.011) and SM contained more round nuclei than DM (P = 0.026). Performance assessment using a combined shape-classification of the round and spindled fractions showed 88.9% accuracy and a Youden index of 0.77. Conclusions: Spindle cell melanoma and DM differ significantly in their nuclear morphology with respect to fractions of round and spindled nuclei. Our study demonstrates that quantifying nuclear circularity can be used as an adjunct diagnostic tool for distinction of DM and SM.

Background: Effective blood inventory management reduces outdates of blood products. Multiple strategies have been employed to reduce the rate of red blood cell (RBC) unit outdate. We designed an automated real-time web-based dashboard interfaced with our laboratory information system to effectively recycle red cell units. The objective of our approach is to decrease RBC outdate rates within our transfusion service. Methods: The dashboard was deployed in August 2011 and is accessed by a shortcut that was placed on the desktops of all blood transfusion services computers in the Capital District Health Authority region. It was designed to refresh automatically every 10 min. The dashboard provides all vital information on RBC units, and implemented a color coding scheme to indicate an RBC unit's proximity to expiration. Results: The overall RBC unit outdate rate in the 7 months period following implementation of the dashboard (September 2011-March 2012) was 1.24% (123 units outdated/9763 units received), compared to similar periods in 2010-2011 and 2009-2010: 2.03% (188/9395) and 2.81% (261/9220), respectively. The odds ratio of a RBC unit outdate postdashboard (2011-2012) compared with 2010-2011 was 0.625 (95% confidence interval: 0.497-0.786; P < 0.0001). Conclusion: Our dashboard system is an inexpensive and novel blood inventory management system which was associated with a significant reduction in RBC unit outdate rates at our institution over a period of 7 months. This system, or components of it, could be a useful addition to existing RBC management systems at other institutions.

Context: General and subject specific practice examinations for students in health sciences studying pathology were placed onto a free public internet web site entitled web path and were accessed four clicks from the home web site menu. Subjects and Methods: Multiple choice questions were coded into. html files with JavaScript functions for web browser viewing in a timed format. A Perl programming language script with common gateway interface for web page forms scored examinations and placed results into a log file on an internet computer server. The four general review examinations of 30 questions each could be completed in up to 30 min. The 17 subject specific examinations of 10 questions each with accompanying images could be completed in up to 15 min each. The results of scores and user educational field of study from log files were compiled from June 2006 to January 2014. Results: The four general review examinations had 31,639 accesses with completion of all questions, for a completion rate of 54% and average score of 75%. A score of 100% was achieved by 7% of users, ≥90% by 21%, and ≥50% score by 95% of users. In top to bottom web page menu order, review examination usage was 44%, 24%, 17%, and 15% of all accessions. The 17 subject specific examinations had 103,028 completions, with completion rate 73% and average score 74%. Scoring at 100% was 20% overall, ≥90% by 37%, and ≥50% score by 90% of users. The first three menu items on the web page accounted for 12.6%, 10.0%, and 8.2% of all completions, and the bottom three accounted for no more than 2.2% each. Conclusions: Completion rates were higher for shorter 10 questions subject examinations. Users identifying themselves as MD/DO scored higher than other users, averaging 75%. Usage was higher for examinations at the top of the web page menu. Scores achieved suggest that a cohort of serious users fully completing the examinations had sufficient preparation to use them to support their pathology education.

Background: Digital pathology offers potential improvements in workflow and interpretive accuracy. Although currently digital pathology is commonly used for research and education, its clinical use has been limited to niche applications such as frozen sections and remote second opinion consultations. This is mainly due to regulatory hurdles, but also to a dearth of data supporting a positive economic cost-benefit. Large scale adoption of digital pathology and the integration of digital slides into the routine anatomic/surgical pathology "slide less" clinical workflow will occur only if digital pathology will offer a quantifiable benefit, which could come in the form of more efficient and/or higher quality care. Aim: As a large academic-based health care organization expecting to adopt digital pathology for primary diagnosis upon its regulatory approval, our institution estimated potential operational cost savings offered by the implementation of an enterprise-wide digital pathology system (DPS). Methods: Projected cost savings were calculated for the first 5 years following implementation of a DPS based on operational data collected from the pathology department. Projected savings were based on two factors: (1) Productivity and lab consolidation savings; and (2) avoided treatment costs due to improvements in the accuracy of cancer diagnoses among nonsubspecialty pathologists. Detailed analyses of incremental treatment costs due to interpretive errors, resulting in either a false positive or false negative diagnosis, was performed for melanoma and breast cancer and extrapolated to 10 other common cancers. Results: When phased in over 5-years, total cost savings based on anticipated improvements in pathology productivity and histology lab consolidation were estimated at $12.4 million for an institution with 219,000 annual accessions. The main contributing factors to these savings were gains in pathologist clinical full-time equivalent capacity impacted by improved pathologist productivity and workload distribution. Expanding the current localized specialty sign-out model to an enterprise-wide shared general/subspecialist sign-out model could potentially reduce costs of incorrect treatment by $5.4 million. These calculations were based on annual over and under treatment costs for breast cancer and melanoma estimated to be approximately $26,000 and $11,000/case, respectively, and extrapolated to $21,500/case for other cancer types. Conclusions: The projected 5-year total cost savings for our large academic-based health care organization upon fully implementing a DPS was approximately $18 million. If the costs of digital pathology acquisition and implementation do not exceed this value, the return on investment becomes attractive to hospital administrators. Furthermore, improved patient outcome enabled by this technology strengthens the argument supporting adoption of an enterprise-wide DPS.

Background: Mortality in cancer patients is directly attributable to the ability of cancer cells to metastasize to distant sites from the primary tumor. This migration of tumor cells begins with a remodeling of the local tumor microenvironment, including changes to the extracellular matrix and the recruitment of stromal cells, both of which facilitate invasion of tumor cells into the bloodstream. In breast cancer, it has been proposed that the alignment of collagen fibers surrounding tumor epithelial cells can serve as a quantitative image-based biomarker for survival of invasive ductal carcinoma patients. Specific types of collagen alignment have been identified for their prognostic value and now these tumor associated collagen signatures (TACS) are central to several clinical specimen imaging trials. Here, we implement the semi-automated acquisition and analysis of this TACS candidate biomarker and demonstrate a protocol that will allow consistent scoring to be performed throughout large patient cohorts. Methods: Using large field of view high resolution microscopy techniques, image processing and supervised learning methods, we are able to quantify and score features of collagen fiber alignment with respect to adjacent tumor-stromal boundaries. Results: Our semi-automated technique produced scores that have statistically significant correlation with scores generated by a panel of three human observers. In addition, our system generated classification scores that accurately predicted survival in a cohort of 196 breast cancer patients. Feature rank analysis reveals that TACS positive fibers are more well-aligned with each other, are of generally lower density, and terminate within or near groups of epithelial cells at larger angles of interaction. Conclusion: These results demonstrate the utility of a supervised learning protocol for streamlining the analysis of collagen alignment with respect to tumor stromal boundaries.

Introduction: Recent technology developments have demonstrated the benefit of using whole slide imaging (WSI) in computer-aided diagnosis. In this paper, we explore the feasibility of using automatic WSI analysis to assist grading of clear cell renal cell carcinoma (RCC), which is a manual task traditionally performed by pathologists. Materials and Methods: Automatic WSI analysis was applied to 39 hematoxylin and eosin-stained digitized slides of clear cell RCC with varying grades. Kernel regression was used to estimate the spatial distribution of nuclear size across the entire slides. The analysis results were correlated with Fuhrman nuclear grades determined by pathologists. Results: The spatial distribution of nuclear size provided a panoramic view of the tissue sections. The distribution images facilitated locating regions of interest, such as high-grade regions and areas with necrosis. The statistical analysis showed that the maximum nuclear size was significantly different (P < 0.001) between low-grade (Grades I and II) and high-grade tumors (Grades III and IV). The receiver operating characteristics analysis showed that the maximum nuclear size distinguished high-grade and low-grade tumors with a false positive rate of 0.2 and a true positive rate of 1.0. The area under the curve is 0.97. Conclusion: The automatic WSI analysis allows pathologists to see the spatial distribution of nuclei size inside the tumors. The maximum nuclear size can also be used to differentiate low-grade and high-grade clear cell RCC with good sensitivity and specificity. These data suggest that automatic WSI analysis may facilitate pathologic grading of renal tumors and reduce variability encountered with manual grading.

We propose a methodology for the design of features mimicking the visual cues used by pathologists when identifying tissues in hematoxylin and eosin (H&E)-stained samples. Background: H&E staining is the gold standard in clinical histology; it is cheap and universally used, producing a vast number of histopathological samples. While pathologists accurately and consistently identify tissues and their pathologies, it is a time-consuming and expensive task, establishing the need for automated algorithms for improved throughput and robustness. Methods: We use an iterative feedback process to design a histopathology vocabulary (HV), a concise set of features that mimic the visual cues used by pathologists, e.g. "cytoplasm color" or "nucleus density." These features are based in histology and understood by both pathologists and engineers. We compare our HV to several generic texture-feature sets in a pixel-level classification algorithm. Results: Results on delineating and identifying tissues in teratoma tumor samples validate our expert knowledge-based approach. Conclusions: The HV can be an effective tool for identifying and delineating teratoma components from images of H&E-stained tissue samples.

Introduction: Digital pathology is a relatively new field. Inventors of technology in this field typically file for patents to protect their intellectual property. An understanding of the patent landscape is crucial for companies wishing to secure patent protection and market dominance for their products. To our knowledge, there has been no prior systematic review of patents related to digital pathology. Therefore, the aim of this study was to systematically identify and evaluate United States patents and patent applications related to digital pathology. Materials and Methods: Issued patents and patent applications related to digital pathology published in the United States Patent and Trademark Office (USPTO) database (www.uspto.gov) (through January 2014) were searched using the Google Patents search engine (Google Inc., Mountain View, California, USA). Keywords and phrases related to digital pathology, whole-slide imaging (WSI), image analysis, and telepathology were used to query the USPTO database. Data were downloaded and analyzed using the Papers application (Mekentosj BV, Aalsmeer, Netherlands). Results: A total of 588 United States patents that pertain to digital pathology were identified. In addition, 228 patent applications were identified, including 155 that were pending, 65 abandoned, and eight rejected. Of the 588 patents granted, 348 (59.18%) were specific to pathology, while 240 (40.82%) included more general patents also usable outside of pathology. There were 70 (21.12%) patents specific to pathology and 57 (23.75%) more general patents that had expired. Over 120 unique entities (individual inventors, academic institutions, and private companies) applied for pathology specific patents. Patents dealt largely with telepathology and image analysis. WSI related patents addressed image acquisition (scanning and focus), quality (z-stacks), management (storage, retrieval, and transmission of WSI files), and viewing (graphical user interface (GUI), workflow, slide navigation and remote control). An increasing number of recent patents focused on computer-aided diagnosis (CAD) and digital consultation networks. Conclusion: In the last 2 decades, there have been an increasing number of patents granted and patent applications filed related to digital pathology. The number of these patents quadrupled during the last decade, and this trend is predicted to intensify based on the number of patent applications already published by the USPTO.

Background: Autoverification is a process of using computer-based rules to verify clinical laboratory test results without manual intervention. To date, there is little published data on the use of autoverification over the course of years in a clinical laboratory. We describe the evolution and application of autoverification in an academic medical center clinical chemistry core laboratory. Subjects and Methods: At the institution of the study, autoverification developed from rudimentary rules in the laboratory information system (LIS) to extensive and sophisticated rules mostly in middleware software. Rules incorporated decisions based on instrument error flags, interference indices, analytical measurement ranges (AMRs), delta checks, dilution protocols, results suggestive of compromised or contaminated specimens, and 'absurd' (physiologically improbable) values. Results: The autoverification rate for tests performed in the core clinical chemistry laboratory has increased over the course of 13 years from 40% to the current overall rate of 99.5%. A high percentage of critical values now autoverify. The highest rates of autoverification occurred with the most frequently ordered tests such as the basic metabolic panel (sodium, potassium, chloride, carbon dioxide, creatinine, blood urea nitrogen, calcium, glucose; 99.6%), albumin (99.8%), and alanine aminotransferase (99.7%). The lowest rates of autoverification occurred with some therapeutic drug levels (gentamicin, lithium, and methotrexate) and with serum free light chains (kappa/lambda), mostly due to need for offline dilution and manual filing of results. Rules also caught very rare occurrences such as plasma albumin exceeding total protein (usually indicative of an error such as short sample or bubble that evaded detection) and marked discrepancy between total bilirubin and the spectrophotometric icteric index (usually due to interference of the bilirubin assay by immunoglobulin (Ig) M monoclonal gammopathy). Conclusions: Our results suggest that a high rate of autoverification is possible with modern clinical chemistry analyzers. The ability to autoverify a high percentage of results increases productivity and allows clinical laboratory staff to focus attention on the small number of specimens and results that require manual review and investigation.

Background: Color consistency in histology images is still an issue in digital pathology. Different imaging systems reproduced the colors of a histological slide differently. Materials and Methods: Color correction was implemented using the color information of the nine color patches of a color calibration slide. The inherent spectral colors of these patches along with their scanned colors were used to derive a color correction matrix whose coefficients were used to convert the pixels' colors to their target colors. Results: There was a significant reduction in the CIELAB color difference, between images of the same H & E histological slide produced by two different whole slide scanners by 3.42 units, P < 0.001 at 95% confidence level. Conclusion: Color variations in histological images brought about by whole slide scanning can be effectively normalized with the use of the color calibration slide.

Background: Whole slide imaging (WSI) offers a novel approach to digitize and review pathology slides, but the voluminous data generated by this technology demand new computational methods for image analysis. Materials and Methods: In this study, we report a method that recognizes stains in WSI data and uses kernel density estimator to calculate the stain density across the digitized pathology slides. The validation study was conducted using a rat model of acute cardiac allograft rejection and another rat model of heart ischemia/reperfusion injury. Immunohistochemistry (IHC) was conducted to label ED1 ⁺ macrophages in the tissue sections and the stained slides were digitized by a whole slide scanner. The whole slide images were tessellated to enable parallel processing. Pixel-wise stain classification was conducted to classify the IHC stains from those of the background and the density distribution of the identified IHC stains was then calculated by the kernel density estimator. Results: The regression analysis showed a correlation coefficient of 0.8961 between the number of IHC stains counted by our stain recognition algorithm and that by the manual counting, suggesting that our stain recognition algorithm was in good agreement with the manual counting. The density distribution of the IHC stains showed a consistent pattern with those of the cellular magnetic resonance (MR) images that detected macrophages labeled by ultrasmall superparamagnetic iron-oxide or micron-sized iron-oxide particles. Conclusions: Our method provides a new imaging modality to facilitate clinical diagnosis. It also provides a way to validate/correlate cellular MRI data used for tracking immune-cell infiltration in cardiac transplant rejection and cardiac ischemic injury.