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Technical Note: (Re) Defining the high-power field for digital pathology
David Kim, Liron Pantanowitz, Peter Schüffler, Dig Vijay Kumar Yarlagadda, Orly Ardon, Victor E Reuter, Meera Hameed, David S Klimstra, Matthew G Hanna
J Pathol Inform 2020, 11:33 (9 October 2020)
DOI:10.4103/jpi.jpi_48_20  
Background: The microscope high-power field (HPF) is the cornerstone for histopathology diagnostic evaluation such as the quantification of mitotic figures, lymphocytes, and tumor grading. With traditional light microscopy, HPFs are typically evaluated by quantifying histologic events in 10 fields of view at × 400 magnification. In the era of digital pathology, new variables are introduced that may affect HPF evaluation. The aim of this study was to determine the parameters that influence HPF in whole slide images (WSIs). Materials and Methods: Glass slides scanned on various devices (Leica's Aperio GT450, AT2, and ScanScope XT; Philips UltraFast Scanner; Hamamatsu's Nanozoomer 2.0HT; and 3DHistech's P1000) were compared to acquired digital slides reviewed on each vendor's respective WSI viewer software (e.g., Aperio ImageScope, ImageScope DX, Philips IMS, 3DHistech CaseViewer, and Hamamatsu NDP.view) and an in-house developed vendor-agnostic viewer. WSIs were reviewed at “×40” equivalent HPF on different sized monitors with varying display resolutions (1900 × 1080–4500 × 3000) and aspect ratios (e.g., Food and Drug Administration [FDA]-cleared 27” Philips PS27QHDCR, FDA-cleared 24” Dell MR2416, 24” Hewlett Packard Z24n G2, and 28” Microsoft Surface Studio). Digital and microscopic HPF areas were calculated and compared. Results: A significant variation of HPF area occurred between differing monitor size and display resolutions with minor differences between WSI viewers. No differences were identified by scanner or WSIs scanned at different resolutions (e.g., 0.5, 0.25, 0.24, and 0.12 μm/pixel). Conclusion: Glass slide HPF at × 400 magnification with conventional light microscopy was not equivalent to “×40” digital HPF areas. Digital HPF quantification may vary due to differences in the tissue area displayed by monitor sizes, display resolutions, and WSI viewers but not by scanner or scanning resolution. These findings will need to be further clinically validated with potentially new digital metrics for evaluation.
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Original Article: Comparing deep learning and immunohistochemistry in determining the site of origin for well-differentiated neuroendocrine tumors
Jordan Redemann, Fred A Schultz, Cathy Martinez, Michael Harrell, Douglas P Clark, David R Martin, Joshua A Hanson
J Pathol Inform 2020, 11:32 (9 October 2020)
DOI:10.4103/jpi.jpi_37_20  
Background: Determining the site of origin for metastatic well-differentiated neuroendocrine tumors (WDNETs) is challenging, and immunohistochemical (IHC) profiles do not always lead to a definitive diagnosis. We sought to determine if a deep-learning convolutional neural network (CNN) could improve upon established IHC profiles in predicting the site of origin in a cohort of WDNETs from the common primary sites. Materials and Methods: Hematoxylin and eosin (H&E)-stained tissue microarrays (TMAs) were created using 215 WDNETs arising from the known primary sites. A CNN trained and tested on 60% (n = 130) and 40% (n = 85) of these cases, respectively. One hundred and seventy-nine cases had TMA tissue remaining for the IHC analysis. These cases were stained with IHC markers pPAX8, CDX2, SATB2, and thyroid transcription factor-1 (markers of pancreas/duodenum, ileum/jejunum/duodenum, colorectum/appendix, and lung WDNET sites of origin, respectively). The CNN diagnosis was deemed correct if it designated a majority or plurality of the tumor area as the known site of origin. The IHC diagnosis was deemed correct if the most specific marker for a particular site of origin met an H-score threshold determined by two pathologists. Results: When all cases were considered, the CNN correctly identified the site of origin at a lower rate compared to IHC (72% vs. 82%, respectively). Of the 85 cases in the CNN test set, 66 had sufficient TMA material for IHC stains, thus 66 cases were available for a direct case-by-case comparison of IHC versus CNN. The CNN correctly identified 70% of these cases, while IHC correctly identified 76%, a finding that was not statistically significant (P = 0.56). Conclusion: A CNN can identify WDNET site of origin at an accuracy rate close to the current gold standard IHC methods.
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Original Article: UniTwain: A cost-effective solution for lean gross imaging
Hansen Lam, Ricky Kwan, Mark Tuthill, Mehrvash Haghighi
J Pathol Inform 2020, 11:31 (5 October 2020)
DOI:10.4103/jpi.jpi_42_20  
Background: Gross imaging of surgical specimens is paramount for the accurate gross examination and diagnosis of disease. Optimized imaging workflow can facilitate consistently high-quality gross photographs, especially in high-volume, metropolitan hospitals such as ours. Most commercial medical gross imaging technology provides ergonomically well-designed hardware, remotely operated cameras, intuitive software interfaces, and automation of workflow. However, these solutions are usually cost-prohibitive and require a large sum of capital budget. Materials and Methods: We applied lean techniques such as value stream mapping (VSM) to design a streamlined and error-free workflow for gross imaging process. We implemented a cost-effective technology, UniTwain, combined with high-resolution webcam to achieve the ideal results. Results: We reduced the mean process time from 600 min to 4.0 min (99.3% decrease in duration); the median process time was reduced from 580 min to 3.0 min. The process efficiency increased from 20% to 100%. The implemented solution has a comparable durability, scalability, and archiving feasibility to commercial medical imaging systems and costs four times less. The only limitations are manual operation of the webcam and lower resolution. The webcam sensors have 8.2 megapixel (MP) resolution, approximately 12 MP less than medical imaging devices. However, we believe that this difference is not visually significant and the effect on gross diagnosis with the naked eye is minimal. Conclusions: To our knowledge, this is the first study that utilized UniTwain as a viable, low-cost solution to streamline the gross imaging workflow. The UniTwain combined with high-resolution webcam could be a suitable alternative for our institution that does not plan to heavily invest in medical imaging.
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ABSTRACTS: What did we expect from Porto's ECDP2020

J Pathol Inform 2020, 11:30 (18 September 2020)
DOI:10.4103/2153-3539.295359  
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Original Article: ImageBox 2 – Efficient and rapid access of image tiles from whole-slide images using serverless HTTP range requests
Erich Bremer, Joel Saltz, Jonas S Almeida
J Pathol Inform 2020, 11:29 (10 September 2020)
DOI:10.4103/jpi.jpi_31_20  
Background: Whole-slide images (WSI) are produced by a high-resolution scanning of pathology glass slides. There are a large number of whole-slide imaging scanners, and the resulting images are frequently larger than 100,000 × 100,000 pixels which typically image 100,000 to one million cells, ranging from several hundred megabytes to many gigabytes in size. Aims and Objectives: Provide HTTP access over the web to Whole Slide Image tiles that do not have localized tiling servers but only basic HTTP access. Move all image decode and tiling functions to calling agent (ImageBox). Methods: Current software systems require tiling image servers to be installed on systems providing local disk access to these images. ImageBox2 breaks this requirement by accessing tiles from remote HTTP source via byte-level HTTP range requests. This method does not require changing the client software as the operation is relegated to the ImageBox2 server which is local (or remote) to the client and can access tiles from remote images that have no server of their own such as Amazon S3 hosted images. That is, it provides a data service [on a server that does not need to be managed], the definition of serverless execution model increasingly favored by cloud computing infrastructure. Conclusions: The specific methodology described and assessed in this report preserves normal client connection semantics by enabling cloud-friendly tiling, promoting a web of http connected whole-slide images from a wide-ranging number of sources, and providing tiling where local tiling servers would have been otherwise unavailable.
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Original Article: Colorectal cancer detection based on deep learning
Lin Xu, Blair Walker, Peir-In Liang, Yi Tong, Cheng Xu, Yu Chun Su, Aly Karsan
J Pathol Inform 2020, 11:28 (21 August 2020)
DOI:10.4103/jpi.jpi_68_19  
Introduction: The initial point in the diagnostic workup of solid tumors remains manual, with the assessment of hematoxylin and eosin (H&E)-stained tissue sections by microscopy. This is a labor-intensive step that requires attention to detail. In addition, diagnoses are influenced by an individual pathologist's knowledge and experience and may not always be reproducible between pathologists. Methods: We introduce a deep learning-based method in colorectal cancer detection and segmentation from digitized H&E-stained histology slides. Results: In this study, we demonstrate that this neural network approach produces median accuracy of 99.9% for normal slides and 94.8% for cancer slides compared to pathologist-based diagnosis on H&E-stained slides digitized from clinical samples. Conclusion: Given that our approach has very high accuracy on normal slides, use of neural network algorithms may provide a screening approach to save pathologist time in identifying tumor regions. We suggest that this new method may be a powerful assistant for colorectal cancer diagnostics.
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Research Article: TissueWand, a rapid histopathology annotation tool
Martin Lindvall, Alexander Sanner, Fredrik Petré, Karin Lindman, Darren Treanor, Claes Lundström, Jonas Löwgren
J Pathol Inform 2020, 11:27 (21 August 2020)
DOI:10.4103/jpi.jpi_5_20  
Background: Recent advancements in machine learning (ML) bring great possibilities for the development of tools to assist with diagnostic tasks within histopathology. However, these approaches typically require a large amount of ground truth training data in the form of image annotations made by human experts. As such annotation work is a very time-consuming task, there is a great need for tools that can assist in this process, saving time while not sacrificing annotation quality. Methods: In an iterative design process, we developed TissueWand – an interactive tool designed for efficient annotation of gigapixel-sized histopathological images, not being constrained to a predefined annotation task. Results: Several findings regarding appropriate interaction concepts were made, where a key design component was semi-automation based on rapid interaction feedback in a local region. In a user study, the resulting tool was shown to cause substantial speed-up compared to manual work while maintaining quality. Conclusions: The TissueWand tool shows promise to replace manual methods for early stages of dataset curation where no task-specific ML model yet exists to aid the effort.
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Original Article: LibMI: An open source library for efficient histopathological image processing
Yuxin Dong, Pargorn Puttapirat, Jingyi Deng, Xiangrong Zhang, Chen Li
J Pathol Inform 2020, 11:26 (21 August 2020)
DOI:10.4103/jpi.jpi_11_20  
Background: Whole-slide images (WSIs) as a kind of image data are rapidly growing in the digital pathology domain. With unusual high resolution, these images make them hard to be supported by conventional tools or file formats. Thus, it obstructs data sharing and automated analysis. Here, we propose a library, LibMI, along with its open and standardized image file format. They can be used together to efficiently read, write, modify, and annotate large images. Materials and Methods: LibMI utilizes the concept of pyramid image structure and lazy propagation from a segment tree algorithm to support reading and modifying and to guarantee that both operations have linear time complexity. Further, a cache mechanism was introduced to speed up the program. Results: LibMI is an open and efficient library for histopathological image processing. To demonstrate its functions, we applied it to several tasks including image thresholding, microscopic color correction, and storing pixel-wise information on WSIs. The result shows that libMI is particularly suitable for modifying large images. Furthermore, compared with congeneric libraries and file formats, libMI and modifiable multiscale image (MMSI) run 18.237 times faster on read-only tasks. Conclusions: The combination of libMI library and MMSI file format enables developers to efficiently read and modify WSIs, thus can assist in pixel-wise image processing on extremely large images to promote building image processing pipeline. The library together with the data schema is freely available on GitLab: https://gitlab.com/BioAI/libMI.
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Editorial: A synoptic electronic order set for placental pathology: A framework extensible to nonneoplastic pathology
Adela Cimic, Maria Mironova, Scarlett Karakash, Sahussapont Joseph Sirintrapun
J Pathol Inform 2020, 11:25 (21 August 2020)
DOI:10.4103/jpi.jpi_24_20  
Accurate pathologic assessment in placental pathology is mostly dependent on a complete clinical history provided by a clinical team. However, often, the necessary clinical information is lacking, and electronic order sets (EOSs), if implemented correctly, create an opportunity for entering consistent and accurate clinical data. In this viewpoint piece, we describe a framework for synoptic EOS in placental pathology. We outline the necessary data and create optional clinical data that get entered as a dropdown menu of free text. While EOSs are the best way to approach and diagnose placenta and other nonneoplastic pathologic specimens, the barriers for implementation include paper requisitions and a cultural mindset resistance. The aspiration for our synoptic EOS is to become an effective tool for communication between proceduralists and pathologists for proper diagnosis of placental specimens. Through our EOS, the appropriate and complete clinical context is conveyed from the clinical teams to the pathologist. The pathologist can easily and rapidly extract the necessary information to render an accurate and precise diagnosis. The captured data likewise become a valuable research resource.
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Letters: Digital pathology during a pandemic
Aleksandar Vodovnik, Tonje Bøyum Riste, Bjørn Ståle Sund
J Pathol Inform 2020, 11:24 (11 August 2020)
DOI:10.4103/jpi.jpi_44_20  
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Review Article: Display characteristics and their impact on digital pathology: A current review of pathologists' future “microscope”
Jacob T Abel, Peter Ouillette, Christopher L Williams, John Blau, Jerome Cheng, Keluo Yao, Winston Y Lee, Toby C Cornish, Ulysses G J Balis, David S McClintock
J Pathol Inform 2020, 11:23 (10 August 2020)
DOI:10.4103/jpi.jpi_38_20  
Digital displays (monitors) are an indispensable component of a pathologists' daily workflow, from writing reports, viewing whole-slide images, or browsing the Internet. Due to a paucity of literature and experience surrounding display use and standardization in pathology, the Food and Drug Administration's (FDA) has currently restricted FDA-cleared whole-slide imaging systems to a specific model of display for each system, which at this time consists of only medical-grade (MG) displays. Further, given that a pathologists' display will essentially become their new surrogate “microscope,” it becomes exceedingly important that all pathologists have a basic understanding of fundamental display properties and their functional consequences. This review seeks to: (a) define and summarize the current and emerging display technology, terminology, features, and regulation as they pertain to pathologists and review the current literature on the impact of different display types (e.g. MG vs. consumer off the shelf vs. professional grade) on pathologists' diagnostic performance and (b) discuss the impact of the recent digital pathology device componentization and the coronavirus disease 2019 public emergency on the pixel pathway and display use for remote digital pathology. Display technology has changed dramatically over the past 20 years and continues to change at a rapid rate. There is a paucity of published studies to date that investigate how display type affects pathologist performance, with more research necessary in order to develop standards and minimum specifications for displays in digital pathology. Given the complexity of modern displays, pathologists must become better informed regarding display technology if they wish to have more choice over their future “microscopes.”
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Original Article: A regulatory science initiative to harmonize and standardize digital pathology and machine learning processes to speed up clinical innovation to patients
Hetal Desai Marble, Richard Huang, Sarah Nixon Dudgeon, Amanda Lowe, Markus D Herrmann, Scott Blakely, Matthew O Leavitt, Mike Isaacs, Matthew G Hanna, Ashish Sharma, Jithesh Veetil, Pamela Goldberg, Joachim H Schmid, Laura Lasiter, Brandon D Gallas, Esther Abels, Jochen K Lennerz
J Pathol Inform 2020, 11:22 (6 August 2020)
DOI:10.4103/jpi.jpi_27_20  
Unlocking the full potential of pathology data by gaining computational access to histological pixel data and metadata (digital pathology) is one of the key promises of computational pathology. Despite scientific progress and several regulatory approvals for primary diagnosis using whole-slide imaging, true clinical adoption at scale is slower than anticipated. In the U.S., advances in digital pathology are often siloed pursuits by individual stakeholders, and to our knowledge, there has not been a systematic approach to advance the field through a regulatory science initiative. The Alliance for Digital Pathology ( the Alliance) is a recently established, volunteer, collaborative, regulatory science initiative to standardize digital pathology processes to speed up innovation to patients. The purpose is: (1) to account for the patient perspective by including patient advocacy; (2) to investigate and develop methods and tools for the evaluation of effectiveness, safety, and quality to specify risks and benefits in the precompetitive phase; (3) to help strategize the sequence of clinically meaningful deliverables; (4) to encourage and streamline the development of ground-truth data sets for machine learning model development and validation; and (5) to clarify regulatory pathways by investigating relevant regulatory science questions. The Alliance accepts participation from all stakeholders, and we solicit clinically relevant proposals that will benefit the field at large. The initiative will dissolve once a clinical, interoperable, modularized, integrated solution (from tissue acquisition to diagnostic algorithm) has been implemented. In times of rapidly evolving discoveries, scientific input from subject-matter experts is one essential element to inform regulatory guidance and decision-making. The Alliance aims to establish and promote synergistic regulatory science efforts that will leverage diverse inputs to move digital pathology forward and ultimately improve patient care.
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Original Article: Improving critical value notification through secure text messaging
Terrance James Lynn, Jordan Erik Olson
J Pathol Inform 2020, 11:21 (6 August 2020)
DOI:10.4103/jpi.jpi_19_20  
Background: To improve communication between clinical providers and the laboratory, we recently implemented secure text messaging for our critical value notifications. This was done to communicate laboratory critical values (CV) to providers faster so changes to patient care could be done faster. Our previous method of communicating CV to providers was paging and relied on a call back to receive the critical value. Methods: We implemented delivery of CV through a secure texting application in which the CV was directly communicated to the provider on their smart phone device. Results: The mean pre-implementation turnaround time (TAT) was 11.3 minutes (median: 7 minutes, range: 0 - 210 minutes). The mean post- secure text messaging implementation TAT was 3.03 minutes (median: 0.89 minutes, range: < 1 - 95 minutes).When comparing pre- and post-implementation, there was a significant reduction in the TAT from using secure text messaging (p < 0.001). Of the 234 surveys sent out, 81 providers responded (35%). Of these responses, 85% reported that critical value notification by secure text messaging has increased their efficiency and 95% reported that critical value notification is more effective than a pager-phone-call based system. 83% of providers reported that they were able to provide better, faster care to their patients. Conclusions: Using secure text messaging (STM) to deliver critical values significantly reduces the CV TAT. Furthermore, providers noted they preferred to receive CV notifications through STM and reported that they were able to provide more effective care to their patients.
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Original Article: Whole-slide imaging allows pathologists to work remotely in regions with severe logistical constraints due to Covid-19 pandemic
Daniel S Liscia, Donata Bellis, Elena Biletta, Mariangela D’Andrea, Giorgio A Croci, Umberto Dianzani
J Pathol Inform 2020, 11:20 (28 July 2020)
DOI:10.4103/jpi.jpi_32_20  
Introduction: In this study, we report on our experience using digital pathology to overcome the severe limitations imposed on health care by the Covid-19 outbreak in Northern Italy. Social distancing had a major impact on public transportation, causing it to run with reduced timetables. This resulted in a major challenge for hospital commuters. To limit the presence in our hospital of no more than two pathologists at a time out of four, a web-based digital pathology system (DPS) was employed to work remotely. Subjects and Methods: We used a DPS in which a scanner, a laboratory information system, a storage device, and a web server were interfaced so that tissue slides could be viewed over the Internet by whole-slide imaging (WSI). After a brief internal verification test, the activity on the DPS was recorded, taking track of a set of performance and efficiency indicators. At the end of the study, 405 cases were signed out remotely. Results: Of 693 cases, 58.4% were signed out remotely by WSI, while 8.4% needed to be kept on hold to return to the original microscope slide. In three cases, at least one slide had to be rescanned. In eight cases, one slide was recut. Panel discussion by WSI was necessary in 34 cases, a condition in which all pathologists were asked for their opinion. A consultation with a more experienced colleague was necessary in 17 cases. Conclusions: We show that WSI easily allows pathologists to overcome the problems caused by the severe social distancing measures imposed by the Covid-19 pandemic. Our experience shows that soon there will not be alternatives to digital pathology, given that there is no assurance that other similar outbreaks will not occur.
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Research Article: Deep learning to estimate human epidermal growth factor receptor 2 status from hematoxylin and eosin-stained breast tissue images
Deepak Anand, Nikhil Cherian Kurian, Shubham Dhage, Neeraj Kumar, Swapnil Rane, Peter H Gann, Amit Sethi
J Pathol Inform 2020, 11:19 (24 July 2020)
DOI:10.4103/jpi.jpi_10_20  
Context: Several therapeutically important mutations in cancers are economically detected using immunohistochemistry (IHC), which highlights the overexpression of specific antigens associated with the mutation. However, IHC panels can be imprecise and relatively expensive in low-income settings. On the other hand, although hematoxylin and eosin (H&E) staining used to visualize the general tissue morphology is a routine and low cost, it does not highlight any specific antigen or mutation. Aims: Using the human epidermal growth factor receptor 2 (HER2) mutation in breast cancer as an example, we strengthen the case for cost-effective detection and screening of overexpression of HER2 protein in H&E-stained tissue. Settings and Design: We use computational methods that reliably detect subtle morphological changes associated with the over-expression of mutation-specific proteins directly from H&E images. Subjects and Methods: We trained a classification pipeline to determine HER2 overexpression status of H&E stained whole slide images. Our training dataset was derived from a single hospital containing 26 (11 HER2+ and 15 HER2–) cases. We tested the classification pipeline on 26 (8 HER2+ and 18 HER2–) held-out cases from the same hospital and 45 independent cases (23 HER2+ and 22 HER2–) from the TCGA-BRCA cohort. The pipeline was composed of a stain separation module and three deep neural network modules in tandem for robustness and interpretability. Statistical Analysis Used: We evaluate our trained model through area under the curve (AUC)-receiver operating characteristic. Results: Our pipeline achieved an AUC of 0.82 (confidence interval [CI]: 0. 65–0. 98) on held-out cases and an AUC of 0.76 (CI: 0. 61–0. 89) on the independent dataset from TCGA. We also demonstrate the region-level correspondence of HER2 overexpression between a patient's IHC and H&E serial sections. Conclusions: Our work strengthens the case for automatically quantifying the overexpression of mutation-specific proteins in H&E-stained digital pathology, and it highlights the importance of multi-stage machine learning pipelines for added robustness and interpretability.
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Commentary: Commentary: Impact of digital pathology in the field of intraoperative neuropathology: Master the tool
Albino Eccher, Ilaria Girolami
J Pathol Inform 2020, 11:18 (16 July 2020)
DOI:10.4103/jpi.jpi_39_20  
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Technical Note: A point-of-use quality assurance tool for digital pathology remote working
Alexander I Wright, Emily L Clarke, Catriona M Dunn, Bethany J Williams, Darren E Treanor, David S Brettle
J Pathol Inform 2020, 11:17 (16 July 2020)
DOI:10.4103/jpi.jpi_25_20  
Pathology services are facing pressures due to the COVID-19 pandemic. Digital pathology has the capability to meet some of these unprecedented challenges by allowing remote diagnoses to be made at home, during periods of social distancing or self-isolation. However, while digital pathology allows diagnoses to be made on standard computer screens, unregulated home environments may not be conducive for optimal viewing conditions. There is also a paucity of experimental evidence available to support the minimum display requirements for digital pathology. This study presents a Point-of-Use Quality Assurance (POUQA) tool for remote assessment of viewing conditions for reporting digital pathology slides. The tool is a psychophysical test combining previous work from successfully implemented quality assurance tools in both pathology and radiology to provide a minimally intrusive display screen validation task, before viewing digital slides. The test is specific to pathology assessment in that it requires visual discrimination between colors derived from hematoxylin and eosin staining, with a perceptual difference of ±1 delta E (dE). This tool evaluates the transfer of a 1 dE signal through the digital image display chain, including the observers' contrast and color responses within the test color range. The web-based system has been rapidly developed and deployed as a response to the COVID-19 pandemic and may be used by anyone in the world to help optimize flexible working conditions at: http://www.virtualpathology.leeds.ac.uk/res earch/systems/pouqa/.
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Editorial: Is the time right to start using digital pathology and artificial intelligence for the diagnosis of lymphoma?
Mohamed E Salama, William R Macon, Liron Pantanowitz
J Pathol Inform 2020, 11:16 (26 June 2020)
DOI:10.4103/jpi.jpi_16_20  
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Editorial: The future of pathology: What can we learn from the COVID-19 pandemic?
Bethany J Williams, Filippo Fraggetta, Matthew G Hanna, Richard Huang, Jochen Lennerz, Roberto Salgado, S Joseph Sirintrapun, Liron Pantanowitz, Anil Parwani, Mark Zarella, Darren E Treanor
J Pathol Inform 2020, 11:15 (9 June 2020)
DOI:10.4103/jpi.jpi_29_20  
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Research Article: Bridging the collaboration gap: Real-time identification of clinical specimens for biomedical research
Thomas J S Durant, Guannan Gong, Nathan Price, Wade L Schulz
J Pathol Inform 2020, 11:14 (20 May 2020)
DOI:10.4103/jpi.jpi_15_20  
Introduction: Biomedical and translational research often relies on the evaluation of patients or specimens that meet specific clinical or laboratory criteria. The typical approach used to identify biospecimens is a manual, retrospective process that exists outside the clinical workflow. This often makes biospecimen collection cost prohibitive and prevents the collection of analytes with short stability times. Emerging data architectures offer novel approaches to enhance specimen-identification practices. To this end, we present a new tool that can be deployed in a real-time environment to automate the identification and notification of available biospecimens for biomedical research. Methods: Real-time clinical and laboratory data from Cloverleaf (Infor, NY, NY) were acquired within our computational health platform, which is built on open-source applications. Study-specific filters were developed in NiFi (Apache Software Foundation, Wakefield, MA, USA) to identify the study-appropriate specimens in real time. Specimen metadata were stored in Elasticsearch (Elastic N. V., Mountain View, CA, USA) for visualization and automated alerting. Results: Between June 2018 and December 2018, we identified 2992 unique specimens belonging to 2815 unique patients, split between two different use cases. Based on laboratory policy for specimen retention and study-specific stability requirements, secure E-mail notifications were sent to investigators to automatically notify of availability. The assessment of throughput on commodity hardware demonstrates the ability to scale to approximately 2000 results per second. Conclusion: This work demonstrates that real-world clinical data can be analyzed in real time to increase the efficiency of biospecimen identification with minimal overhead for the clinical laboratory. Future work will integrate additional data types, including the analysis of unstructured data, to enable more complex cases and biospecimen identification.
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Original Article: The next generation robotic microscopy for intraoperative teleneuropathology consultation
Swikrity Upadhyay Baskota, Clayton Wiley, Liron Pantanowitz
J Pathol Inform 2020, 11:13 (23 April 2020)
DOI:10.4103/jpi.jpi_2_20  
Introduction: Teleneuropathology at our institution evolved over the last 17 years from using static to dynamic robotic microscopy. Historically (2003–2007), using older technology, the deferral rate was 19.7%, and the concordance was 81% with the final diagnosis. Two years ago, we switched to use hybrid robotic devices to perform these intraoperative (IO) consultations because our older devices were obsolete. The aim of this study was to evaluate the impact this change had on our deferral and concordance rates with teleneuropathology using this newer instrument. Materials and Methods: Aperio LV1 4-slide capacity hybrid robotic scanners with an attached desktop console (Leica Biosystems, Vista, CA, USA) and GoToAssist (v4.5.0.1620, Boston, MA, USA) were used for IO telepathology cases. A cross-sectional comparative study was conducted comparing teleneuropathology from three remote hospitals (193 cases) to IO neuropathology consultation performed by conventional glass slide examination at a light microscope (310 cases) from the host hospital. Deferral and concordance rates were compared to final histopathological diagnoses. Results: The deferral rate for IO teleneuropathology was 26% and conventional glass slide 24.24% (P = 0.58). The concordance rate for teleneuropathology was 93.94%, which was slightly higher than 89.09% for conventional glass slides (P = 0.047). Conclusion: The new hybrid robotic device for performing IO teleneuropathology interpretations at our institution was as effective as conventional glass slide interpretation. While we did observe a noticeable change in the deferral rate compared to prior years, we did appreciate the marked improvement of the concordance rate using this new hybrid scanner.
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Guidelines: Guidance for remote reporting of digital pathology slides during periods of exceptional service pressure: An emergency response from the UK royal college of pathologists
Bethany Jill Williams, David Brettle, Muhammad Aslam, Paul Barrett, Gareth Bryson, Simon Cross, David Snead, Clare Verrill, Emily Clarke, Alexander Wright, Darren Treanor
J Pathol Inform 2020, 11:12 (17 April 2020)
DOI:10.4103/jpi.jpi_23_20  
Pathology departments must rise to new staffing challenges caused by the coronavirus disease-19 pandemic and may need to work more flexibly for the foreseeable future. In light of this, many pathologists and departments are considering the merits of remote or home reporting of digital cases. While some individuals have experience of this, little work has been done to determine optimum conditions for home reporting, including technical and training considerations. In this publication produced in response to the pandemic, we provide information regarding risk assessment of home reporting of digital slides, summarize available information on specifications for home reporting computing equipment, and share access to a novel point-of-use quality assurance tool for assessing the suitability of home reporting screens for digital slide diagnosis. We hope this study provides a useful starting point and some practical guidance in a difficult time. This study forms the basis of the guidance issued by the Royal College of Pathologists, available at: https://www.rcpath.org/uploads/assets/626ead77-d7dd-42e1-949988e43dc84c97/RCPath-guidance-for-remote-digital-pathology.pdf.
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Erratum: Erratum: Guidelines from the Canadian Association of Pathologists for Establishing a Telepathology Service for Anatomic Pathology Using Whole-slide Imaging

J Pathol Inform 2020, 11:11 (10 April 2020)
DOI:10.4103/2153-3539.282276  
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Original Article: EpithNet: Deep regression for epithelium segmentation in cervical histology images
Sudhir Sornapudi, Jason Hagerty, R Joe Stanley, William V Stoecker, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, Shellaine R Frazier
J Pathol Inform 2020, 11:10 (30 March 2020)
DOI:10.4103/jpi.jpi_53_19  
Background: Automated pathology techniques for detecting cervical cancer at the premalignant stage have advantages for women in areas with limited medical resources. Methods: This article presents EpithNet, a deep learning approach for the critical step of automated epithelium segmentation in digitized cervical histology images. EpithNet employs three regression networks of varying dimensions of image input blocks (patches) surrounding a given pixel, with all blocks at a fixed resolution, using varying network depth. Results: The proposed model was evaluated on 311 digitized histology epithelial images and the results indicate that the technique maximizes region-based information to improve pixel-wise probability estimates. EpithNet-mc model, formed by intermediate concatenation of the convolutional layers of the three models, was observed to achieve 94% Jaccard index (intersection over union) which is 26.4% higher than the benchmark model. Conclusions: EpithNet yields better epithelial segmentation results than state-of-the-art benchmark methods.
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Original Article: Individualized bayesian risk assessment for cervical squamous neoplasia
Lama F Farchoukh, Agnieszka Onisko, R Marshall Austin
J Pathol Inform 2020, 11:9 (30 March 2020)
DOI:10.4103/jpi.jpi_66_19  
Background: Cervical screening could potentially be improved by better stratifying individual risk for the development of cervical cancer or precancer, possibly even allowing follow-up of individual patients differently than proposed under current guidelines that focus primarily on recent screening test results. We explore the use of a Bayesian decision science model to quantitatively stratify individual risk for the development of cervical squamous neoplasia. Materials and Methods: We previously developed a dynamic multivariate Bayesian network model that uses cervical screening and histopathologic data collected over 13 years in our system to quantitatively estimate the risk of individuals for the development of cervical precancer or invasive cervical cancer. The database includes 1,126,048 liquid-based cytology test results belonging to 389,929 women. From-the-vial, high risk human papilloma virus (HPV) test results and follow-up gynecological surgical procedures were available on 33.6% and 12% of these results (378,896 and 134,727), respectively. Results: Historical data impacted 5-year cumulative risk for both histopathologic cervical intraepithelial neoplasia 3 (CIN3) and squamous cell carcinoma (SCC) diagnoses. The risk was highest in patients with prior high grade squamous intraepithelial lesion cytology results. Persistent abnormal cervical screening test results, either cytologic or HPV results, were associated with variable increasing risk for squamous neoplasia. Risk also increased with prior histopathologic diagnoses of precancer, including CIN2, CIN3, and adenocarcinoma in situ. Conclusions: Bayesian modeling allows for individualized quantitative risk assessments of system patients for histopathologic diagnoses of significant cervical squamous neoplasia, including very rare outcomes such as SCC.
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Brief Report: Value-Based intervention with hospital and pathology laboratory informatics: A case of analytics and outreach at the veterans affairs
Gregory D Scott, Thomas F Osborne, Sang P Gross, Dean Fong
J Pathol Inform 2020, 11:8 (9 March 2020)
DOI:10.4103/jpi.jpi_67_19  
Background: Laboratory tests are among the most ordered tests and account for a large portion of wasted health-care spending. Meta-analyses suggest that the most promising interventions at improving health-care value and reducing cost are low investment strategies involving simple changes to ordering systems. The veterans affairs (VA) has a 2018–2024 strategic objective to reduce wasted spending through data- and performance-focused decision-making. Methods: VA Palo Alto Healthcare System laboratory utilization data were obtained from multiple sources, including the VA Corporate Data Warehouse and utilization reports from reference laboratory. Ordering volume, test results, and follow-up clinical impact data were collected and evaluated in partnership with the treating physicians and hospital informatics in order to optimize ordering sets. Results: Dextromethorphan (Dext) and synthetic cannabinoid testing were identified as the lowest value tests based on a three-tier score of negativity rate, volume, and cost. In partnership with the ordering physicians and hospital informatics, reflexive testing was eliminated, resulting in persistent decreases in the volume of Dext (162–10 tests/month) and synthetic cannabinoid tests (155–19 tests/month) ordered. The proportion of unnecessary repeat tests also dropped from 71.5% to 5.5%, the test positivity rate increased from 0.87% to 3.49%, and the approximate monthly cost of both tests decreased ten-fold from $21,250 to $2087 for a yearly savings of $229,000 at a single VA. Conclusions: Improved laboratory utilization is central to the VA' strategic objective to reduce waste. A relatively simple intervention involving partnership with the treating physicians and hospital informatics in combination with data- and performance-focused decision-making can yield substantial reductions in health-care waste.
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Editorial: Value of public challenges for the development of pathology deep learning algorithms
Douglas Joseph Hartman, Jeroen A. W. M. Van Der Laak, Metin N Gurcan, Liron Pantanowitz
J Pathol Inform 2020, 11:7 (26 February 2020)
DOI:10.4103/jpi.jpi_64_19  
The introduction of digital pathology is changing the practice of diagnostic anatomic pathology. Digital pathology offers numerous advantages over using a physical slide on a physical microscope, including more discriminative tools to render a more precise diagnostic report. The development of these tools is being facilitated by public challenges related to specific diagnostic tasks within anatomic pathology. To date, 24 public challenges related to pathology tasks have been published. This article discusses these public challenges and briefly reviews the underlying characteristics of public challenges and why they are helpful to the development of digital tools.
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Original Article: Payment reform in the era of advanced diagnostics, artificial intelligence, and machine learning
James Sorace
J Pathol Inform 2020, 11:6 (21 February 2020)
DOI:10.4103/jpi.jpi_63_19  
Health care is undergoing a profound transformation driven by an increase in new types of diagnostic data, increased data sharing enabled by interoperability, and improvements in our ability to interpret data through the application of artificial intelligence and machine learning. Paradoxically, we are also discovering that our current paradigms for implementing electronic health-care records and our ability to create new models for reforming the health-care system have fallen short of expectations. This article traces these shortcomings to two basic issues. The first is a reliance on highly centralized quality improvement and measurement strategies that fail to account for the high level of variation and complexity found in human disease. The second is a reliance on legacy payment systems that fail to reward the sharing of data and knowledge across the health-care system. To address these issues, and to better harness the advances in health care noted above, the health-care system must undertake a phased set of reforms. First, efforts must focus on improving both the diagnostic process and data sharing at the local level. These efforts should include the formation of diagnostic management teams and increased collaboration between pathologists and radiologists. Next, building off current efforts to develop national federated research databases, providers must be able to query national databases when information is needed to inform the care of a specific complex patient. In addition, providers, when treating a specific complex patient, should be enabled to consult nationally with other providers who have experience with similar patient issues. The goal of these efforts is to build a health-care system that is funded in part by a novel fee-for-knowledge-sharing paradigm that fosters a collaborative decentralized approach to patient care and financially incentivizes large-scale data and knowledge sharing.
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Original Article: Limited number of cases may yield generalizable models, a proof of concept in deep learning for colon histology
Lorne Holland, Dongguang Wei, Kristin A Olson, Anupam Mitra, John Paul Graff, Andrew D Jones, Blythe Durbin-Johnson, Ananya Datta Mitra, Hooman H Rashidi
J Pathol Inform 2020, 11:5 (21 February 2020)
DOI:10.4103/jpi.jpi_49_19  
Background: Little is known about the effect of a minimum number of slides required in generating image datasets used to build generalizable machine-learning (ML) models. In addition, the assumption within deep learning is that the increased number of training images will always enhance accuracy and that the initial validation accuracy of the models correlates well with their generalizability. In this pilot study, we have been able to test the above assumptions to gain a better understanding of such platforms, especially when data resources are limited. Methods: Using 10 colon histology slides (5 carcinoma and 5 benign), we were able to acquire 1000 partially overlapping images (Dataset A) that were then trained and tested on three convolutional neural networks (CNNs), ResNet50, AlexNet, and SqueezeNet, to build a large number of unique models for a simple task of classifying colon histopathology into benign and malignant. Different quantities of images (10–1000) from Dataset A were used to construct >200 unique CNN models whose performances were individually assessed. The performance of these models was initially assessed using 20% of Dataset A's images (not included in the training phase) to acquire their initial validation accuracy (internal accuracy) followed by their generalization accuracy on Dataset B (a very distinct secondary test set acquired from public domain online sources).Results: All CNNs showed similar peak internal accuracies (>97%) from the Dataset A test set. Peak accuracies for the external novel test set (Dataset B), an assessment of the ability to generalize, showed marked variation (ResNet50: 98%; AlexNet: 92%; and SqueezeNet: 80%). The models with the highest accuracy were not generated using the largest training sets. Further, a model's internal accuracy did not always correlate with its generalization accuracy. The results were obtained using an optimized number of cases and controls. Conclusions: Increasing the number of images in a training set does not always improve model accuracy, and significant numbers of cases may not always be needed for generalization, especially for simple tasks. Different CNNs reach peak accuracy with different training set sizes. Further studies are required to evaluate the above findings in more complex ML models prior to using such ancillary tools in clinical settings.
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Original Article: Artificial intelligence-driven structurization of diagnostic information in free-text pathology reports
Pericles S Giannaris, Zainab Al-Taie, Mikhail Kovalenko, Nattapon Thanintorn, Olha Kholod, Yulia Innokenteva, Emily Coberly, Shellaine Frazier, Katsiarina Laziuk, Mihail Popescu, Chi-Ren Shyu, Dong Xu, Richard D Hammer, Dmitriy Shin
J Pathol Inform 2020, 11:4 (11 February 2020)
DOI:10.4103/jpi.jpi_30_19  
Background: Free-text sections of pathology reports contain the most important information from a diagnostic standpoint. However, this information is largely underutilized for computer-based analytics. The vast majority of NLP-based methods lack a capacity to accurately extract complex diagnostic entities and relationships among them as well as to provide an adequate knowledge representation for downstream data-mining applications. Methods: In this paper, we introduce a novel informatics pipeline that extends open information extraction (openIE) techniques with artificial intelligence (AI) based modeling to extract and transform complex diagnostic entities and relationships among them into Knowledge Graphs (KGs) of relational triples (RTs). Results: Evaluation studies have demonstrated that the pipeline's output significantly differs from a random process. The semantic similarity with original reports is high (Mean Weighted Overlap of 0.83). The precision and recall of extracted RTs based on experts' assessment were 0.925 and 0.841 respectively (P <0.0001). Inter-rater agreement was significant at 93.6% and inter-rated reliability was 81.8%. Conclusion: The results demonstrated important properties of the pipeline such as high accuracy, minimality and adequate knowledge representation. Therefore, we conclude that the pipeline can be used in various downstream data-mining applications to assist diagnostic medicine.
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Original Article: Precise identification of cell and tissue features important for histopathologic diagnosis by a whole slide imaging system
Thomas W Bauer, Cynthia Behling, Dylan V Miller, Bernard S Chang, Elena Viktorova, Robert Magari, Perry E Jensen, Keith A Wharton, Jinsong Qiu
J Pathol Inform 2020, 11:3 (6 February 2020)
DOI:10.4103/jpi.jpi_47_19  
Background: Previous studies have demonstrated the noninferiority of pathologists' interpretation of whole slide images (WSIs) compared to microscopic slides in diagnostic surgical pathology; however, to our knowledge, no published studies have tested analytical precision of an entire WSI system. Methods: In this study, five pathologists at three locations tested intra-system, inter-system/site, and intra- and inter-pathologist precision of the Aperio AT2 DX System (Leica Biosystems, Vista, CA, USA). Sixty-nine microscopic slides containing 23 different morphologic features suggested by the Digital Pathology Association as important to diagnostic pathology were identified and scanned. Each of 202 unique fields of view (FOVs) had 1–3 defined morphologic features, and each feature was represented in three different tissues. For intra-system precision, each site scanned 23 slides at three different times and one pathologist interpreted all FOVs. For inter-system/site precision, all 69 slides were scanned once at each of three sites, and FOVs from each site were read by one pathologist. To test intra- and inter-pathologist precision, all 69 slides were scanned at one site, FOVs were saved in three different orientations, and the FOVs were transferred to a different site. Three different pathologists then interpreted FOVs from all 69 slides. Wildcard (unscored) slides and washout intervals were included in each study. Agreement estimates with 95% confidence intervals were calculated. Results: Combined precision from all three studies, representing 606 FOVs in each of the three studies, showed overall intra-system agreement of 97.9%; inter-system/site agreement was 96%, intra-pathologist agreement was 95%, and inter-pathologist agreement was 94.2%. Conclusions: Pathologists using the Aperio AT2 DX System identified histopathological features with high precision, providing increased confidence in using WSI for primary diagnosis in surgical pathology.
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Research Article: A validation study of human epidermal growth factor receptor 2 immunohistochemistry digital imaging analysis and its correlation with human epidermal growth factor receptor 2 fluorescence In situ hybridization results in breast carcinoma
Ramon Hartage, Aidan C Li, Scott Hammond, Anil V Parwani
J Pathol Inform 2020, 11:2 (4 February 2020)
DOI:10.4103/jpi.jpi_52_19  
Background: The Visiopharm human epidermal growth factor receptor 2 (HER2) digital imaging analysis (DIA) algorithm assesses digitized HER2 immunohistochemistry (IHC) by measuring cell membrane connectivity. We aimed to validate this algorithm for clinical use by comparing with pathologists' scoring and correlating with HER2 fluorescence in situ hybridization (FISH) results. Materials and Methods: The study cohort consisted of 612 consecutive invasive breast carcinoma specimens including 395 biopsies and 217 resections. HER2 IHC slides were scanned using Philips IntelliSite Scanners, and the digital images were analyzed using Visiopharm HER2-CONNECT App to obtain the connectivity values (0–1) and scores (0, 1+, 2+, and 3+). HER2 DIA scores were compared with Pathologists' manual scores, and HER2 connectivity values were correlated with HER2 FISH results. Results: The concordance between HER2 DIA scores and pathologists' scores was 87.3% (534/612). All discordant cases (n = 78) were only one-step discordant (negative to equivocal, equivocal to positive, or vice versa). Five cases (0.8%) showed discordant HER2 IHC DIA and HER2 FISH results, but all these cases had relatively low HER2 copy numbers (between 4 and 6). HER2 IHC connectivity showed significantly better correlation with HER2 copy number than HER2/CEP17 ratio. Conclusions: HER2 IHC DIA demonstrates excellent concordance with pathologists' scores and accurately discriminates between HER2 FISH positive and negative cases. HER2 IHC connectivity has better correlation with HER2 copy number than HER2/CEP17 ratio, suggesting HER2 copy number may be more important in predicting HER2 protein expression, and response to anti-HER2-targeted therapy.
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ABSTRACTS: Pathology Vision 2019

J Pathol Inform 2020, 11:1 (20 January 2020)
DOI:10.4103/2153-3539.276115  
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Commentary: Commentary: Automated diagnosis and gleason grading of prostate cancer – are artificial intelligence systems ready for prime time?
Anil V Parwani
J Pathol Inform 2019, 10:41 (23 December 2019)
DOI:10.4103/jpi.jpi_56_19  
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Research Article: A digital pathology-based shotgun-proteomics approach to biomarker discovery in colorectal cancer
Stefan Zahnd, Sophie Braga-Lagache, Natasha Buchs, Alessandro Lugli, Heather Dawson, Manfred Heller, Inti Zlobec
J Pathol Inform 2019, 10:40 (12 December 2019)
DOI:10.4103/jpi.jpi_65_18  PMID:31921488
Background: Biomarkers in colorectal cancer are scarce, especially for patients with Stage 2 disease. The aim of our study was to identify potential prognostic biomarkers from colorectal cancers using a novel combination of approaches, whereby digital pathology is coupled to shotgun proteomics followed by validation of candidates by immunohistochemistry (IHC) using digital image analysis (DIA). Methods and Results: Tissue cores were punched from formalin-fixed paraffin-embedded colorectal cancers from patients with Stage 2 and 3 disease (n = 26, each). Protein extraction and liquid chromatography-mass spectrometry (MS) followed by analysis using three different methods were performed. Fold changes were evaluated. The candidate biomarker was validated by IHC on a series of 413 colorectal cancers from surgically treated patients using a next-generation tissue microarray. DIA was performed by using a pan-cytokeratin serial alignment and quantifying staining within the tumor and normal tissue epithelium. Analysis was done in QuPath and Brightness_Max scores were used for statistical analysis and clinicopathological associations. MS identified 1947 proteins with at least two unique peptides. To reinforce the validity of the biomarker candidates, only proteins showing a significant (P < 0.05) fold-change using all three analysis methods were considered. Eight were identified, and of these, cathepsin B was selected for further validation. DIA revealed strong associations between higher cathepsin B expression and less aggressive tumor features, including tumor node metastasis stage and lymphatic vessel and venous vessel invasion (P < 0.001, all). Cathepsin B was associated with more favorable survival in univariate analysis only. Conclusions: Our results present a novel approach to biomarker discovery that includes MS and digital pathology. Cathepsin B expression analyzed by DIA within the tumor epithelial compartment was identified as a strong feature of less aggressive tumor behavior and favorable outcome, a finding that should be further investigated on a more functional level.
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Research Article: Whole-slide image focus quality: Automatic assessment and impact on ai cancer detection Highly accessed article
Timo Kohlberger, Yun Liu, Melissa Moran, Po-Hsuan Cameron Chen, Trissia Brown, Jason D Hipp, Craig H Mermel, Martin C Stumpe
J Pathol Inform 2019, 10:39 (12 December 2019)
DOI:10.4103/jpi.jpi_11_19  PMID:31921487
Background: Digital pathology enables remote access or consults and powerful image analysis algorithms. However, the slide digitization process can create artifacts such as out-of-focus (OOF). OOF is often only detected on careful review, potentially causing rescanning, and workflow delays. Although scan time operator screening for whole-slide OOF is feasible, manual screening for OOF affecting only parts of a slide is impractical. Methods: We developed a convolutional neural network (ConvFocus) to exhaustively localize and quantify the severity of OOF regions on digitized slides. ConvFocus was developed using our refined semi-synthetic OOF data generation process and evaluated using seven slides spanning three different tissue and three different stain types, each of which were digitized using two different whole-slide scanner models ConvFocus's predictions were compared with pathologist-annotated focus quality grades across 514 distinct regions representing 37,700 35 μm × 35 μm image patches, and 21 digitized “z-stack” WSIs that contain known OOF patterns. Results: When compared to pathologist-graded focus quality, ConvFocus achieved Spearman rank coefficients of 0.81 and 0.94 on two scanners and reproduced the expected OOF patterns from z-stack scanning. We also evaluated the impact of OOF on the accuracy of a state-of-the-art metastatic breast cancer detector and saw a consistent decrease in performance with increasing OOF. Conclusions: Comprehensive whole-slide OOF categorization could enable rescans before pathologist review, potentially reducing the impact of digitization focus issues on the clinical workflow. We show that the algorithm trained on our semi-synthetic OOF data generalizes well to real OOF regions across tissue types, stains, and scanners. Finally, quantitative OOF maps can flag regions that might otherwise be misclassified by image analysis algorithms, preventing OOF-induced errors.
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Commentary: Clinical-grade Computational Pathology: Alea Iacta Est
Filippo Fraggetta
J Pathol Inform 2019, 10:38 (11 December 2019)
DOI:10.4103/jpi.jpi_54_19  PMID:31921486
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Original Article: On the edge of a digital pathology transformation: Views from a cellular pathology laboratory focus group
Casmir Turnquist, Sharon Roberts-Gant, Helen Hemsworth, Kieron White, Lisa Browning, Gabrielle Rees, Derek Roskell, Clare Verrill
J Pathol Inform 2019, 10:37 (2 December 2019)
DOI:10.4103/jpi.jpi_38_19  PMID:31897354
Introduction: Digital pathology has the potential to revolutionize the way clinical diagnoses are made while improving safety and quality. With a few notable exceptions in the UK, few National Health Service (NHS) departments have deployed digital pathology platforms. Thus, in the next few years, many departments are anticipated to undergo the transition to digital pathology. In this period of transition, capturing attitudes and experiences can elucidate issues to be addressed and foster collaboration between NHS Trusts. This study aims to qualitatively ascertain the benefits and challenges of transitioning to digital pathology from the perspectives of pathologists and biomedical scientists in a department about to undergo the transition from diagnostic reporting via traditional microscopy to digital pathology. Methods:A focus group discussion was held in the setting of a large NHS teaching hospital's cellular pathology department which was on the brink of transitioning to digital pathology. A set of open questions were developed and posed to a group of pathologists and biomedical scientists in a focus group setting. Notes of the discussion were made along with an audio recording with permission. The discussion was subsequently turned into a series of topic headings and analyzed using content analysis. Results:Identified benefits of digital pathology included enhanced collaboration, teaching, cost savings, research, growth of specialty, multidisciplinary teams, and patient-centered care. Barriers to transitioning to digital pathology included standardization, validation, national implementation, storage and backups, training, logistical implementation, cost-effectiveness, privacy, and legality. Conclusion:Many benefits of digital pathology were identified, but key barriers need to be addressed in order to fully implement digital pathology on a trust and national level.
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Original Article: Order Indication Solicitation to Assess Clinical Laboratory Test Utilization: D-Dimer Order Patterns as an Illustrative Case
Joseph W Rudolf, Jason M Baron, Anand S Dighe
J Pathol Inform 2019, 10:36 (2 December 2019)
DOI:10.4103/jpi.jpi_46_19  PMID:31897353
Background:A common challenge in the development of laboratory clinical decision support (CDS) and laboratory utilization management (UM) initiatives stems from the fact that many laboratory tests have multiple potential indications, limiting the ability to develop context-specific alerts. As a potential solution, we designed a CDS alert that asks the ordering clinician to provide the indication for testing, using D-dimer as an exemplar. Using data collected over a nearly 3-year period, we sought to determine whether the indication capture was a useful feature within the CDS alert and whether it provided actionable intelligence to guide the development of an UM strategy. Methods: We extracted results and ordering data for D-dimer testing performed in our laboratory over a 35-month period. We analyzed order patterns by clinical indication, hospital service, and length of hospitalization. Results: Our final data set included 13,971 result-order combinations and indeed provided actionable intelligence regarding test utilization patterns. For example, pulmonary embolism was the most common emergency department indication (86%), while disseminated intravascular coagulation was the most common inpatient indication (56%). D-dimer positivity rates increased with the duration of hospitalization and our data suggested limited utility for ordering this test in the setting of suspected venous thromboembolic disease in admitted patients. In addition, we found that D-dimer was ordered for unexpected indications including the assessment of stroke, dissection, and extracorporeal membrane oxygenation. Conclusions: Indication capture within a CDS alert and correlation with result data can provide insight into order patterns which can be used to develop future CDS strategies to guide appropriate test use by clinical indication.
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Review Article: Role of Telemedicine in Multidisciplinary Team Meetings Highly accessed article
Mohammad Reza F. Aghdam, Aleksandar Vodovnik, Rania Adel Hameed
J Pathol Inform 2019, 10:35 (18 November 2019)
DOI:10.4103/jpi.jpi_20_19  PMID:31799021
We reviewed the role of telemedicine in multidisciplinary team (MDT) meetings, which play an important role in the provision of effective and tailored patient care in diverse clinical settings. This article is based on conducted search in PubMed. Search terms included “telemedicine,” “multidisciplinary team,” and “(telemedicine) and (multidisciplinary team).” Telemedicine provides an important advantage in the provision of MDT meeting comparing with traditional settings. Those include improved access to and collaboration of medical experts. This resulted in increased levels of medical competence and improved provisions of diagnosis, treatment, and follow-up to patients irrespective of location.
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Original Article: Diagnosis of pancreatic cystic lesions by virtual slicing: Comparison of diagnostic potential of needle-based confocal laser endomicroscopy versus endoscopic ultrasound-guided fine-needle aspiration
Mehrvash Haghighi, Amrita Sethi, Iman Tavassoly, Tamas A Gonda, John M Poneros, Russell B McBride
J Pathol Inform 2019, 10:34 (13 November 2019)
DOI:10.4103/jpi.jpi_32_19  PMID:31799020
Background: Pancreatic cystic lesions are often challenging entities for diagnosis and management. EUS-FNA diagnostic accuracy is limited by paucicellularity of cytology specimens and sampling errors. Needle-based confocal laser endomicroscopy (nCLE) provides real-time imaging of the microscopic structure of the cystic lesion and could result in a more accurate diagnosis. Aims and Objectives: To determine the diagnostic utility of in vivo nCLE and EUS-FNA in the diagnosis and histologic characterization of pancreatic cystic lesions (PCL). Materials and Methods: All patients diagnosed with PCL who had undergone nCLE and FNA over a 10-year period within a major urban teaching hospital were included in this study. All gastroenterology reports of the nCLE images and corresponding pathologist findings from the EUS-FNA were collected and compared with, a final diagnosis prospectively collected from clinicopathological and imaging data. Results: A total of n=32 patients were included in this study, which consisted of n=13 serous cystadenoma (SCA), n=7 intraductal papillary mucinous neoplasms (IPMN), n=2 mucinous cystic neoplasms (MCN), n=3 well-differentiated neuroendocrine tumors, n=2 cysts, n=2 benign pancreatic lesions, n=1 adenocarcinoma, n=1 gastrointestinal stromal tumor (GIST) and n=1 lymphangioma. The overall diagnostic rate was higher in nCLE (87.5%) vs. EUS-FNA (71.9%) While the diagnostic accuracy of nCLE and EUS-FNA were comparable in characterization of benign vs. malignant lesions, the nCLE diagnosis demonstrated higher accuracy rate in identifying mucinous cystic neoplasms compared to EUS-FNA. Conclusion: nCLE is a useful companion diagnostic tool for pancreatic cystic lesions and could assist the cytopathologist to better triage the sample for required ancillary testing and treatment planning. The combination of nCLE and EUS-FNA may be especially helpful in reducing the proportion of cases categorized as non-diagnostic.
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Original Article: A taxonomic index for retrieval of digitized whole slide images from an electronic database for medical school and pathology residency education
Agnes G Loeffler, Mark Smith, Elizabeth Way, Michelle Stoffel, Daniel F I Kurtycz
J Pathol Inform 2019, 10:33 (12 November 2019)
DOI:10.4103/jpi.jpi_34_19  PMID:31799019
Since the advent of whole slide imaging, the utility of digitized slides for education in medical school and residency has been amply documented. Pathology departments at most major academic medical centers have made digitized slides available to pathology residents for study, even before the use of digitized slides for clinical purposes (i.e., primary diagnosis) has become commonplace. This article describes the experience of one academic medical center with the storage and indexing of large volumes of digitized slides. Our goal was to be able to retrieve scanned slides for a variety of educational applications and thereby maximize the heuristic value of the slides. This posed a formidable challenge in terms of development and deployment of an index system that would allow exemplary slides to be identified and retrieved irrespective of the purpose for which the slide was scanned. We used the structure inherent in Aperio's image management software (eSlide Manager) to build an educational database that allowed each image to be appended with a unique taxonomic identifier so that the individual files could be retrieved in a flexible and utilitarian manner.
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ABSTRACTS: 14th European congress on digital pathology Highly accessed article

J Pathol Inform 2019, 10:32 (11 November 2019)
DOI:10.4103/2153-3539.270744  
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