Research Article: Development of a calculated panel reactive antibody web service with local frequencies for platelet transfusion refractoriness risk stratification
	William J Gordon, Layne Ainsworth, Samuel Aronson, Jane Baronas, Richard M Kaufman, Indira Guleria, Edgar L Milford, Michael Oates, Rory Dela Paz, Melissa Y Yeung, William J Lane J Pathol Inform 2019, 10:26 (1 August 2019) DOI:10.4103/jpi.jpi_29_19   Background: Calculated panel reactive antibody (cPRA) scoring is used to assess whether platelet refractoriness is mediated by human leukocyte antigen (HLA) antibodies in the recipient. cPRA testing uses a national sample of US kidney donors to estimate the population frequency of HLA antigens, which may be different than HLA frequencies within local platelet inventories. We aimed to determine the impact on patient cPRA scores of using HLA frequencies derived from typing local platelet donations rather than national HLA frequencies. Methods: We built an open-source web service to calculate cPRA scores based on national frequencies or custom-derived frequencies. We calculated cPRA scores for every hematopoietic stem cell transplantation (HSCT) patient at our institution based on the United Network for Organ Sharing (UNOS) frequencies and local frequencies. We compared frequencies and correlations between the calculators, segmented by gender. Finally, we put all scores into three buckets (mild, moderate, and high sensitizations) and looked at intergroup movement. Results: 2531 patients that underwent HSCT at our institution had at least 1 antibody and were included in the analysis. Overall, the difference in medians between each group's UNOS cPRA and local cPRA was statistically significant, but highly correlated (UNOS vs. local total: 0.249 and 0.243, ρ = 0.994; UNOS vs. local female: 0.474 and 0.463, ρ = 0.987, UNOS vs. local male: 0.165 and 0.141, ρ = 0.996;P< 0.001 for all comparisons). The median difference between UNOS and cPRA scores for all patients was low (male: 0.014, interquartile range [IQR]: 0.004–0.029; female: 0.0013, IQR: 0.003–0.028). Placement of patients into three groups revealed little intergroup movement, with 2.96% (75/2531) of patients differentially classified. Conclusions: cPRA scores using local frequencies were modestly but significantly different than those obtained using national HLA frequencies. We released our software as open source, so other groups can calculate cPRA scores from national or custom-derived frequencies. Further investigation is needed to determine whether a local-HLA frequency approach can improve outcomes in patients who are immune-refractory to platelets.
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	Research Article: Process variation detection using missing data in a multihospital community practice anatomic pathology laboratory
	Gretchen E Galliano J Pathol Inform 2019, 10:25 (1 August 2019) DOI:10.4103/jpi.jpi_18_19   Objectives: Barcode-driven workflows reduce patient identification errors. Missing process timestamp data frequently confound our health system's pending lists and appear as actions left undone. Anecdotally, it was noted that missing data could be found when there is procedure noncompliance. This project was developed to determine if missing timestamp data in the histology barcode drive workflow correlated with other process variations, procedure noncompliance, or is an indicator of workflows needing focus for improvement projects.Materials and Methods: Data extracts of timestamp data from January 1, 2018, to December 15, 2018 for the major histology process steps were analyzed for missing data. Case level analysis to determine the presence or absence of expected barcoding events was performed on 1031 surgical pathology cases to determine the cause of the missing data and determine if additional data variations or procedure noncompliance events were present. The data variations were classified according to a scheme defined in the study. Results: Of 70,085, there were 7218 cases (10.3%) with missing process timestamp data. Missing histology process step data was associated with other additional data variations in case-level deep dives (P < 0.0001). Of the cases missing timestamp data in the initial review, 18.4% of the cases had no identifiable cause for the missing data (all expected events took place in the case-level deep dive). Conclusions: Operationally, valuable information can be obtained by reviewing the types and causes of missing data in the anatomic pathology laboratory information system, but only in conjunction with user input and feedback.
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	Research Article: Multi-field-of-view deep learning model predicts nonsmall cell lung cancer programmed death-ligand 1 status from whole-slide hematoxylin and eosin images
	Lingdao Sha, Boleslaw L Osinski, Irvin Y Ho, Timothy L Tan, Caleb Willis, Hannah Weiss, Nike Beaubier, Brett M Mahon, Tim J Taxter, Stephen S F Yip J Pathol Inform 2019, 10:24 (23 July 2019) DOI:10.4103/jpi.jpi_24_19   Background: Tumor programmed death-ligand 1 (PD-L1) status is useful in determining which patients may benefit from programmed death-1 (PD-1)/PD-L1 inhibitors. However, little is known about the association between PD-L1 status and tumor histopathological patterns. Using deep learning, we predicted PD-L1 status from hematoxylin and eosin (H and E) whole-slide images (WSIs) of nonsmall cell lung cancer (NSCLC) tumor samples. Materials and Methods: One hundred and thirty NSCLC patients were randomly assigned to training (n = 48) or test (n = 82) cohorts. A pair of H and E and PD-L1-immunostained WSIs was obtained for each patient. A pathologist annotated PD-L1 positive and negative tumor regions on the training samples using immunostained WSIs for reference. From the H and E WSIs, over 145,000 training tiles were generated and used to train a multi-field-of-view deep learning model with a residual neural network backbone. Results: The trained model accurately predicted tumor PD-L1 status on the held-out test cohort of H and E WSIs, which was balanced for PD-L1 status (area under the receiver operating characteristic curve [AUC] =0.80, P << 0.01). The model remained effective over a range of PD-L1 cutoff thresholds (AUC = 0.67–0.81, P ≤ 0.01) and when different proportions of the labels were randomly shuffled to simulate interpathologist disagreement (AUC = 0.63–0.77, P ≤ 0.03). Conclusions: A robust deep learning model was developed to predict tumor PD-L1 status from H and E WSIs in NSCLC. These results suggest that PD-L1 expression is correlated with the morphological features of the tumor microenvironment.
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	Commentary: Commentary: Guideline for Performing Human Epidermal Growth Factor Receptor 2 Immunohistochemistry Quantitative Image Analysis well
	Bruce Beckwith J Pathol Inform 2019, 10:23 (23 July 2019) DOI:10.4103/jpi.jpi_19_19
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	Research Article: Annotations, ontologies, and whole slide images – Development of an annotated ontology-driven whole slide image library of normal and abnormal human tissue
	Karin Lindman, Jerómino F Rose, Martin Lindvall, Claes Lundstrom, Darren Treanor J Pathol Inform 2019, 10:22 (23 July 2019) DOI:10.4103/jpi.jpi_81_18   Objective: Digital pathology is today a widely used technology, and the digitalization of microscopic slides into whole slide images (WSIs) allows the use of machine learning algorithms as a tool in the diagnostic process. In recent years, “deep learning” algorithms for image analysis have been applied to digital pathology with great success. The training of these algorithms requires a large volume of high-quality images and image annotations. These large image collections are a potent source of information, and to use and share the information, standardization of the content through a consistent terminology is essential. The aim of this project was to develop a pilot dataset of exhaustive annotated WSI of normal and abnormal human tissue and link the annotations to appropriate ontological information. Materials and Methods: Several biomedical ontologies and controlled vocabularies were investigated with the aim of selecting the most suitable ontology for this project. The selection criteria required an ontology that covered anatomical locations, histological subcompartments, histopathologic diagnoses, histopathologic terms, and generic terms such as normal, abnormal, and artifact. WSIs of normal and abnormal tissue from 50 colon resections and 69 skin excisions, diagnosed 2015-2016 at the Department of Clinical Pathology in Linköping, were randomly collected. These images were manually and exhaustively annotated at the level of major subcompartments, including normal or abnormal findings and artifacts. Results: Systemized nomenclature of medicine clinical terms (SNOMED CT) was chosen, and the annotations were linked to its codes and terms. Two hundred WSI were collected and annotated, resulting in 17,497 annotations, covering a total area of 302.19 cm2, equivalent to 107,7 gigapixels. Ninety-five unique SNOMED CT codes were used. The time taken to annotate a WSI varied from 45 s to over 360 min, a total time of approximately 360 h. Conclusion: This work resulted in a dataset of 200 exhaustive annotated WSIs of normal and abnormal tissue from the colon and skin, and it has informed plans to build a comprehensive library of annotated WSIs. SNOMED CT was found to be the best ontology for annotation labeling. This project also demonstrates the need for future development of annotation tools in order to make the annotation process more efficient.
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	Review Article: The landscape of digital pathology in transplantation: From the beginning to the virtual E-slide
	Ilaria Girolami, Anil Parwani, Valeria Barresi, Stefano Marletta, Serena Ammendola, Lavinia Stefanizzi, Luca Novelli, Arrigo Capitanio, Matteo Brunelli, Liron Pantanowitz, Albino Eccher J Pathol Inform 2019, 10:21 (1 July 2019) DOI:10.4103/jpi.jpi_27_19   Background: Digital pathology has progressed over the last two decades, with many clinical and nonclinical applications. Transplantation pathology is a highly specialized field in which the majority of practicing pathologists do not have sufficient expertise to handle critical needs. In this context, digital pathology has proven to be useful as it allows for timely access to expert second-opinion teleconsultation. The aim of this study was to review the experience of the application of digital pathology to the field of transplantation. Methods: Papers on this topic were retrieved using PubMed as a search engine. Inclusion criteria were the presence of transplantation setting and the use of any type of digital image with or without the use of image analysis tools; the search was restricted to English language papers published in the 25 years until December 31, 2018. Results: Literature regarding digital transplant pathology is mostly about the digital interpretation of posttransplant biopsies (75 vs. 19), with 15/75 (20%) articles focusing on agreement/reproducibility. Several papers concentrated on the correlation between biopsy features assessed by digital image analysis (DIA) and clinical outcome (45/75, 60%). Whole-slide imaging (WSI) only appeared in recent publications, starting from 2011 (13/75, 17.3%). Papers dealing with preimplantation biopsy are less numerous, the majority (13/19, 68.4%) of which focus on diagnostic agreement between digital microscopy and light microscopy (LM), with WSI technology being used in only a small quota of papers (4/19, 21.1%). Conclusions: Overall, published studies show good concordance between digital microscopy and LM modalities for diagnosis. DIA has the potential to increase diagnostic reproducibility and facilitate the identification and quantification of histological parameters. Thus, with advancing technology such as faster scanning times, better image resolution, and novel image algorithms, it is likely that WSI will eventually replace LM.
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	Original Article: Computational algorithms that effectively reduce report defects in surgical pathology
	Jay J Ye, Michael R Tan J Pathol Inform 2019, 10:20 (1 July 2019) DOI:10.4103/jpi.jpi_17_19   Background: Pathology report defects refer to errors in the pathology reports, such as transcription/voice recognition errors and incorrect nondiagnostic information. Examples of the latter include incorrect gender, incorrect submitting physician, incorrect description of tissue blocks submitted, report formatting issues, and so on. Over the past 5 years, we have implemented computational algorithms to identify and correct these report defects. Materials and Methods: Report texts, tissue blocks submitted, and other relevant information are retrieved from the pathology information system database. Two complementary algorithms are used to identify the voice recognition errors by parsing the gross description texts to either (i) identify previously encountered error patterns or (ii) flag sentences containing previously-unused two-word sequences (bigrams). A third algorithm based on identifying conflicting information from two different sources is used to identify tissue block designation errors in the gross description; the information on actual block submission is compared with the block designation information parsed from the gross description text. Results: The computational algorithms identify voice recognition errors in approximately 8%–10% of the cases and block designation errors in approximately 0.5%–1% of all the cases. Conclusions: The algorithms described here have been effective in reducing pathology report defects. In addition to detecting voice recognition and block designation errors, these algorithms have also be used to detect other report defects, such as wrong gender, wrong provider, special stains or immunostains performed but not reported, and so on.
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	Original Article: Deep learning-based retrieval system for gigapixel histopathology cases and the open access literature
	Roger Schaer, Sebastian Otálora, Oscar Jimenez-del-Toro, Manfredo Atzori, Henning Müller J Pathol Inform 2019, 10:19 (1 July 2019) DOI:10.4103/jpi.jpi_88_18   Background: The introduction of digital pathology into clinical practice has led to the development of clinical workflows with digital images, in connection with pathology reports. Still, most of the current work is time-consuming manual analysis of image areas at different scales. Links with data in the biomedical literature are rare, and a need for search based on visual similarity within whole slide images (WSIs) exists. Objectives: The main objective of the work presented is to integrate content-based visual retrieval with a WSI viewer in a prototype. Another objective is to connect cases analyzed in the viewer with cases or images from the biomedical literature, including the search through visual similarity and text. Methods: An innovative retrieval system for digital pathology is integrated with a WSI viewer, allowing to define regions of interest (ROIs) in images as queries for finding visually similar areas in the same or other images and to zoom in/out to find structures at varying magnification levels. The algorithms are based on a multimodal approach, exploiting both text information and content-based image features. Results: The retrieval system allows viewing WSIs and searching for regions that are visually similar to manually defined ROIs in various data sources (proprietary and public datasets, e.g., scientific literature). The system was tested by pathologists, highlighting its capabilities and suggesting ways to improve it and make it more usable in clinical practice. Conclusions: The developed system can enhance the practice of pathologists by enabling them to use their experience and knowledge to control artificial intelligence tools for navigating repositories of images for clinical decision support and teaching, where the comparison with visually similar cases can help to avoid misinterpretations. The system is available as open source, allowing the scientific community to test, ideate and develop similar systems for research and clinical practice.
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	Research Article: Improving medical students' understanding of pediatric diseases through an innovative and tailored web-based digital pathology program with philips pathology Tutor (Formerly PathXL)
	Cathy P Chen, Bradley M Clifford, Matthew J O'Leary, Douglas J Hartman, Jennifer L Picarsic J Pathol Inform 2019, 10:18 (18 June 2019) DOI:10.4103/jpi.jpi_15_19   Background: Online “e-modules” integrated into medical education may enhance traditional learning. Medical students use e-modules during clinical rotations, but these often lack histopathology correlates of diseases and minimal time is devoted to pathology teaching. To address this gap, we created pediatric pathology case-based e-modules to complement the clinical pediatric curriculum and enhance students' understanding of pediatric diseases. Methods: Philips Tutor is an interactive web-based program in which pediatric pathology e-modules were created with pre-/post-test questions. Each e-module contains a clinical vignette, virtual microscopy, and links to additional resources. Topics were selected based on established learning objectives for pediatric clinical rotations. Pre- and post-tests were administered at the beginning/end of each rotation. Test group had access to the e-modules, but control group did not. Both groups completed the pre/post-tests. Posttest was followed by a feedback survey. Results: Overall, 7% (9/123) in the control group and 8% (13/164) in the test group completed both tests and were included in the analysis. Test group improved their posttest scores by about one point on a 5-point scale (P = 0.01); control group did not (P = 1.00). Students responded that test questions were helpful in assessing their knowledge of pediatric pathology (90%) and experienced relative ease of use with the technology (80%). Conclusions: Students responded favorably to the new technology, but cited time constraints as a significant barrier to study participation. Access to the e-modules suggested an improved posttest score compared to the control group, but pilot data were limited by the small sample size. Incorporating pediatric case-based e-modules with anatomic and clinical pathology topics into the clinical medical education curriculum may heighten students' understanding of important diseases. Our model may serve as a pilot for other medical education platforms.
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	Commentary: And they said it couldn't be done: Predicting known driver mutations from H&E slides
	Michael C Montalto, Robin Edwards J Pathol Inform 2019, 10:17 (6 May 2019) DOI:10.4103/jpi.jpi_91_18
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	Research Article: Burden and characteristics of unsolicited emails from medical/scientific journals, conferences, and webinars to faculty and trainees at an academic pathology department
	Matthew D Krasowski, Janna C Lawrence, Angela S Briggs, Bradley A Ford J Pathol Inform 2019, 10:16 (6 May 2019) DOI:10.4103/jpi.jpi_12_19   Background: Professionals and trainees in the medical and scientific fields may receive high e-mail volumes for conferences and journals. In this report, we analyze the amount and characteristics of unsolicited e-mails for journals, conferences, and webinars received by faculty and trainees in a pathology department at an academic medical center. Methods: With informed consent, we analyzed 7 consecutive days of e-mails from faculty and trainees who voluntarily participated in the study and saved unsolicited e-mails from their institutional e-mail address (including junk e-mail folder) for medical/scientific journals, conferences, and webinars. All e-mails were examined for characteristics such as reply receipts, domain name, and spam likelihood. Journal e-mails were specifically analyzed for claims in the message body (for example, peer review, indexing in databases/resources, rapid publication) and actual inclusion in recognized journal databases/resources. Results: A total of 17 faculty (4 assistant, 4 associate, and 9 full professors) and 9 trainees (5 medical students, 2 pathology residents, and 2 pathology fellows) completed the study. A total of 755 e-mails met study criteria (417 e-mails from 328 unique journals, 244 for conferences, and 94 for webinars). Overall, 44.4% of e-mails were flagged as potential spam by the institutional default settings, and 13.8% requested reply receipts. The highest burden of e-mails in 7 days was by associate and full professors (maximum 158 or approximately 8200 per year), although some trainees and assistant professors had over 30 e-mails in 7 days (approximately 1560 per year). Common characteristics of journal e-mails were mention of “peer review” in the message body and low rates of inclusion in recognized journal databases/resources, with 76.4% not found in any of 9 journal databases/resources. The location for conferences in e-mails included 31 different countries, with the most common being the United States (33.2%), Italy (9.8%), China (4.9%), United Kingdom (4.9%), and Canada (4.5%). Conclusions: The present study in an academic pathology department shows a high burden of unsolicited e-mails for medical/scientific journals, conferences, and webinars, especially to associate and full professors. We also demonstrate that some pathology trainees and junior faculty are receiving an estimated 1500 unsolicited e-mails per year.
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	Erratum: Erratum: Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Assoc
	J Pathol Inform 2019, 10:15 (24 April 2019) DOI:10.4103/2153-3539.259372
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	Review Article: National Society for Histotechnology and digital pathology association online self-paced digital pathology certificate of completion program
	Elizabeth A Chlipala, Traci DeGeer, Kathleen Dwyer, Shelley Ganske, David Krull, Haydee Lara, Lisa Manning, Dylan Steiner, Lisa Stephens, Diane Sterchi, Aubrey Wanner, Connie Wildeman, Liron Pantanowitz J Pathol Inform 2019, 10:14 (3 April 2019) DOI:10.4103/jpi.jpi_5_19  PMID:31057983 The field of digital pathology has rapidly expanded within the last few years with increasing adoption and growth in popularity. As digital pathology matures, it is apparent that we need well-trained individuals to manage our whole-slide imaging systems. This editorial introduces the joint National Society for Histotechnology and Digital Pathology Association online self-paced digital pathology certificate program which was launched in May 2018 that was established to meet this demand. An overview of how this program was developed, the content of the educational modules, and the way that this program is being offered is discussed.
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	Original Article: Construction and utilization of a neural network model to predict current procedural terminology codes from pathology report texts
	Jay J Ye J Pathol Inform 2019, 10:13 (3 April 2019) DOI:10.4103/jpi.jpi_3_19  PMID:31057982 Background: At our department, each specimen was assigned a tentative current procedural terminology (CPT) code at accessioning. The codes were subject to subsequent changes by pathologist assistants and pathologists. After the cases had been finalized, their CPT codes went through a final verification step by coding staff, with the aid of a keyword-based CPT code-checking web application. Greater than 97% of the initial assignments were correct. This article describes the construction of a CPT code-predicting neural network model and its incorporation into the CPT code-checking application. Materials and Methods: R programming language was used. Pathology report texts and CPT codes for the cases finalized during January 1–November 30, 2018, were retrieved from the database. The order of the specimens was randomized before the data were partitioned into training and validation set. R Keras package was used for both model training and prediction. The chosen neural network had a three-layer architecture consisting of a word-embedding layer, a bidirectional long short-term memory (LSTM) layer, and a densely connected layer. It used concatenated header-diagnosis texts as the input. Results: The model predicted CPT codes in both the validation data set and the test data set with an accuracy of 97.5% and 97.6%, respectively. Closer examination of the test data set (cases from December 1 to 27, 2018) revealed two interesting observations. First, among the specimens that had incorrect initial CPT code assignments, the model disagreed with the initial assignments in 73.6% (117/159) and agreed in 26.4% (42/159). Second, the model identified nine additional specimens with incorrect CPT codes that had evaded all steps of checking. Conclusions: A neural network model using report texts to predict CPT codes can achieve high accuracy in prediction and moderate sensitivity in error detection. Neural networks may play increasing roles in CPT coding in surgical pathology.
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	Technical Note: Dual-Personality DICOM-TIFF for whole slide images: A migration technique for legacy software
	David A Clunie J Pathol Inform 2019, 10:12 (3 April 2019) DOI:10.4103/jpi.jpi_93_18  PMID:31057981 Despite recently organized Digital Imaging and Communications in Medicine (DICOM) testing and demonstration events involving numerous participating vendors, it is still the case that scanner manufacturers, software developers, and users continue to depend on proprietary file formats rather than adopting the standard DICOM whole slide microscopic image object. Many proprietary formats are Tagged Image File Format (TIFF) based, and existing applications and libraries can read tiled TIFF files. The sluggish adoption of DICOM for whole slide image encoding can be temporarily mitigated by the use of dual-personality DICOM-TIFF files. These are compatible with the installed base of TIFF-based software, as well as newer DICOM-based software. The DICOM file format was deliberately designed to support this dual-personality capability for such transitional situations, although it is rarely used. Furthermore, existing TIFF files can be converted into dual-personality DICOM-TIFF without changing the pixel data. This paper demonstrates the feasibility of extending the dual-personality concept to multiframe-tiled pyramidal whole slide images and explores the issues encountered. Open source code and sample converted images are provided for testing.
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	Research Article: Breast cancer prognostic factors in the digital era: Comparison of Nottingham grade using whole slide images and glass slides
	Tara M Davidson, Mara H Rendi, Paul D Frederick, Tracy Onega, Kimberly H Allison, Ezgi Mercan, Tad T Brunyé, Linda G Shapiro, Donald L Weaver, Joann G Elmore J Pathol Inform 2019, 10:11 (3 April 2019) DOI:10.4103/jpi.jpi_29_18  PMID:31057980 Background: To assess reproducibility and accuracy of overall Nottingham grade and component scores using digital whole slide images (WSIs) compared to glass slides. Methods: Two hundred and eight pathologists were randomized to independently interpret 1 of 4 breast biopsy sets using either glass slides or digital WSI. Each set included 5 or 6 invasive carcinomas (22 total invasive cases). Participants interpreted the same biopsy set approximately 9 months later following a second randomization to WSI or glass slides. Nottingham grade, including component scores, was assessed on each interpretation, providing 2045 independent interpretations of grade. Overall grade and component scores were compared between pathologists (interobserver agreement) and for interpretations by the same pathologist (intraobserver agreement). Grade assessments were compared when the format (WSI vs. glass slides) changed or was the same for the two interpretations. Results:Nottingham grade intraobserver agreement was highest using glass slides for both interpretations (73%, 95% confidence interval [CI]: 68%, 78%) and slightly lower but not statistically different using digital WSI for both interpretations (68%, 95% CI: 61%, 75%; P= 0.22). The agreement was lowest when the format changed between interpretations (63%, 95% CI: 59%, 68%). Interobserver agreement was significantly higher (P < 0.001) using glass slides versus digital WSI (68%, 95% CI: 66%, 70% versus 60%, 95% CI: 57%, 62%, respectively). Nuclear pleomorphism scores had the lowest inter- and intra-observer agreement. Mitotic scores were higher on glass slides in inter- and intra-observer comparisons. Conclusions: Pathologists' intraobserver agreement (reproducibility) is similar for Nottingham grade using glass slides or WSI. However, slightly lower agreement between pathologists suggests that verification of grade using digital WSI may be more challenging.
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	ABSTRACTS: Digital and Computational Pathology: Bring the Future into Focus
	J Pathol Inform 2019, 10:10 (1 April 2019) DOI:10.4103/2153-3539.255259
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	Review Article: Introduction to digital image analysis in whole-slide imaging: A white paper from the digital pathology association
	Famke Aeffner, Mark D Zarella, Nathan Buchbinder, Marilyn M Bui, Matthew R Goodman, Douglas J Hartman, Giovanni M Lujan, Mariam A Molani, Anil V Parwani, Kate Lillard, Oliver C Turner, Venkata N P Vemuri, Ana G Yuil-Valdes, Douglas Bowman J Pathol Inform 2019, 10:9 (8 March 2019) DOI:10.4103/jpi.jpi_82_18  PMID:30984469 The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.
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	Research Article: Ki67 quantitative interpretation: Insights using image analysis
	Zoya Volynskaya, Ozgur Mete, Sara Pakbaz, Doaa Al-Ghamdi, Sylvia L Asa J Pathol Inform 2019, 10:8 (8 March 2019) DOI:10.4103/jpi.jpi_76_18  PMID:30984468 Background: Proliferation markers, especially Ki67, are increasingly important in diagnosis and prognosis. The best method for calculating Ki67 is still the subject of debate. Materials and Methods: We evaluated an image analysis tool for quantitative interpretation of Ki67 in neuroendocrine tumors and compared it to manual counts. We expanded a primary digital pathology platform to include the Leica Biosystems image analysis nuclear algorithm. Slides were digitized using a Leica Aperio AT2 Scanner and accessed through the Cerner CoPath LIS interfaced with Aperio eSlideManager through Aperio ImageScope. Selected regions of interest (ROIs) were manually defined and annotated to include tumor cells only; they were then analyzed with the algorithm and by four pathologists counting on printed images. After validation, the algorithm was used to examine the impact of the size and number of areas selected as ROIs. Results: The algorithm provided reproducible results that were obtained within seconds, compared to up to 55 min of manual counting that varied between users. Benefits of image analysis identified by users included accuracy, time savings, and ease of viewing. Access to the algorithm allowed rapid comparisons of Ki67 counts in ROIs that varied in numbers of cells and selection of fields, the outputs demonstrated that the results vary around defined cutoffs that provide tumor grade depending on the number of cells and ROIs counted. Conclusions: Digital image analysis provides accurate and reproducible quantitative data faster than manual counts. However, access to this tool allows multiple analyses of a single sample to use variable numbers of cells and selection of variable ROIs that can alter the result in clinically significant ways. This study highlights the potential risk of hard cutoffs of continuous variables and indicates that standardization of number of cells and number of regions selected for analysis should be incorporated into guidelines for Ki67 calculations.
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	Original Research: Automated detection of celiac disease on duodenal biopsy slides: A deep learning approach
	Jason W Wei, Jerry W Wei, Christopher R Jackson, Bing Ren, Arief A Suriawinata, Saeed Hassanpour J Pathol Inform 2019, 10:7 (8 March 2019) DOI:10.4103/jpi.jpi_87_18  PMID:30984467 Context: Celiac disease (CD) prevalence and diagnosis have increased substantially in recent years. The current gold standard for CD confirmation is visual examination of duodenal mucosal biopsies. An accurate computer-aided biopsy analysis system using deep learning can help pathologists diagnose CD more efficiently. Subjects and Methods: In this study, we trained a deep learning model to detect CD on duodenal biopsy images. Our model uses a state-of-the-art residual convolutional neural network to evaluate patches of duodenal tissue and then aggregates those predictions for whole-slide classification. We tested the model on an independent set of 212 images and evaluated its classification results against reference standards established by pathologists. Results: Our model identified CD, normal tissue, and nonspecific duodenitis with accuracies of 95.3%, 91.0%, and 89.2%, respectively. The area under the receiver operating characteristic curve was >0.95 for all classes. Conclusions: We have developed an automated biopsy analysis system that achieves high performance in detecting CD on biopsy slides. Our system can highlight areas of interest and provide preliminary classification of duodenal biopsies before review by pathologists. This technology has great potential for improving the accuracy and efficiency of CD diagnosis.
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	Research Article: Validation of whole-slide digitally imaged melanocytic lesions: Does z-stack scanning improve diagnostic accuracy?
	Bart Sturm, David Creytens, Martin G Cook, Jan Smits, Marcory C. R. F. van Dijk, Erik Eijken, Eline Kurpershoek, Heidi V. N Küsters-Vandevelde, Ariadne H. A. G. Ooms, Carla Wauters, Willeke A. M. Blokx, Jeroen A. W. M. van der Laak J Pathol Inform 2019, 10:6 (21 February 2019) DOI:10.4103/jpi.jpi_46_18  PMID:30972225 Background: Accurate diagnosis of melanocytic lesions is challenging, even for expert pathologists. Nowadays, whole-slide imaging (WSI) is used for routine clinical pathology diagnosis in several laboratories. One of the limitations of WSI, as it is most often used, is the lack of a multiplanar focusing option. In this study, we aim to establish the diagnostic accuracy of WSI for melanocytic lesions and investigate the potential accuracy increase of z-stack scanning. Z-stack enables pathologists to use a software focus adjustment, comparable to the fine-focus knob of a conventional light microscope. Materials and Methods: Melanocytic lesions (n = 102) were selected from our pathology archives: 35 nevi, 5 spitzoid tumors of unknown malignant potential, and 62 malignant melanomas, including 10 nevoid melanomas. All slides were scanned at a magnification comparable to use of a ×40 objective, in z-stack mode. A ground truth diagnosis was established on the glass slides by four academic dermatopathologists with a special interest in the diagnosis of melanoma. Six nonacademic surgical pathologists subspecialized in dermatopathology examined the cases by WSI. Results: An expert consensus diagnosis was achieved in 99 (97%) of cases. Concordance rates between surgical pathologists and the ground truth varied between 75% and 90%, excluding nevoid melanoma cases. Concordance rates of nevoid melanoma varied between 10% and 80%. Pathologists used the software focusing option in 7%–28% of cases, which in 1 case of nevoid melanoma resulted in correcting a misdiagnosis after finding a dermal mitosis. Conclusion:Diagnostic accuracy of melanocytic lesions based on glass slides and WSI is comparable with previous publications. A large variability in diagnostic accuracy of nevoid melanoma does exist. Our results show that z-stack scanning, in general, does not increase the diagnostic accuracy of melanocytic.
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	Research Article: Classification of melanocytic lesions in selected and whole-slide images via convolutional neural networks
	Steven N Hart, William Flotte, Andrew P Norgan, Kabeer K Shah, Zachary R Buchan, Taofic Mounajjed, Thomas J Flotte J Pathol Inform 2019, 10:5 (20 February 2019) DOI:10.4103/jpi.jpi_32_18  PMID:30972224 Whole-slide images (WSIs) are a rich new source of biomedical imaging data. The use of automated systems to classify and segment WSIs has recently come to forefront of the pathology research community. While digital slides have obvious educational and clinical uses, their most exciting potential lies in the application of quantitative computational tools to automate search tasks, assist in classic diagnostic classification tasks, and improve prognosis and theranostics. An essential step in enabling these advancements is to apply advances in machine learning and artificial intelligence from other fields to previously inaccessible pathology datasets, thereby enabling the application of new technologies to solve persistent diagnostic challenges in pathology. Here, we applied convolutional neural networks to differentiate between two forms of melanocytic lesions (Spitz and conventional). Classification accuracy at the patch level was 99.0%–2% when applied to WSI. Importantly, when the model was trained without careful image curation by a pathologist, the training took significantly longer and had lower overall performance. These results highlight the utility of augmented human intelligence in digital pathology applications, and the critical role pathologists will play in the evolution of computational pathology algorithms.
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	Original Article: Automated computational detection, quantitation, and mapping of mitosis in whole-slide images for clinically actionable surgical pathology decision support
	Munish Puri, Shelley B Hoover, Stephen M Hewitt, Bih-Rong Wei, Hibret Amare Adissu, Charles H C Halsey, Jessica Beck, Charles Bradley, Sarah D Cramer, Amy C Durham, D Glen Esplin, Chad Frank, L Tiffany Lyle, Lawrence D McGill, Melissa D Sánchez, Paula A Schaffer, Ryan P Traslavina, Elizabeth Buza, Howard H Yang, Maxwell P Lee, Jennifer E Dwyer, R Mark Simpson J Pathol Inform 2019, 10:4 (7 February 2019) DOI:10.4103/jpi.jpi_59_18  PMID:30915258 Background: Determining mitotic index by counting mitotic figures (MFs) microscopically from tumor areas with most abundant MF (hotspots [HS]) produces a prognostically useful tumor grading biomarker. However, interobserver concordance identifying MF and HS can be poorly reproducible. Immunolabeling MF, coupled with computer-automated counting by image analysis, can improve reproducibility. A computational system for obtaining MF values across digitized whole-slide images (WSIs) was sought that would minimize impact of artifacts, generate values clinically relatable to counting ten high-power microscopic fields of view typical in conventional microscopy, and that would reproducibly map HS topography. Materials and Methods: Relatively low-resolution WSI scans (0.50 μm/pixel) were imported in grid-tile format for feature-based MF segmentation, from naturally occurring canine melanomas providing a wide range of proliferative activity. MF feature extraction conformed to anti-phospho-histone H3-immunolabeled mitotic (M) phase cells. Computer vision image processing was established to subtract key artifacts, obtain MF counts, and employ rotationally invariant feature extraction to map MF topography. Results: The automated topometric HS (TMHS) algorithm identified mitotic HS and mapped select tissue tiles with greatest MF counts back onto WSI thumbnail images to plot HS topographically. Influence of dye, pigment, and extraneous structure artifacts was minimized. TMHS diagnostic decision support included image overlay graphics of HS topography, as well as a spreadsheet and plot of tile-based MF count values. TMHS performance was validated examining both mitotic HS counting and mapping functions. Significantly correlated TMHS MF mapping and metrics were demonstrated using repeat analysis with WSI in different orientation (R2 = 0.9916) and by agreement with a pathologist (R2 = 0.8605) as well as through assessment of counting function using an independently tuned object counting algorithm (OCA) (R2 = 0.9482). Limits of agreement analysis support method interchangeability. MF counts obtained led to accurate patient survival prediction in all (n = 30) except one case. By contrast, more variable performance was documented when several pathologists examined similar cases using microscopy (pair-wise correlations, rho range = 0.7597–0.9286). Conclusions: Automated TMHS MF segmentation and feature engineering performance were interchangeable with both observer and OCA in digital mode. Moreover, enhanced HS location accuracy and superior method reproducibility were achieved using the automated TMHS algorithm compared to the current practice employing clinical microscopy.
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	Technical Note: Development and implementation of real-time web-based dashboards in a multisite transfusion service
	Jennifer S Woo, Peter Suslow, Russell Thorsen, Rosaline Ma, Sara Bakhtary, Morvarid Moayeri, Ashok Nambiar J Pathol Inform 2019, 10:3 (7 February 2019) DOI:10.4103/jpi.jpi_36_18  PMID:30915257 Background: In hospital transfusion services, visualization of blood product inventory in the form of web-based dashboards has the potential to improve the workflow and efficiency of blood product inventory management. While off-the-shelf “business intelligence” solutions by external vendors may offer the ability to display and analyze blood bank inventory data, laboratories may lack resources to readily access this technology. Using in-house talent, our transfusion service developed real-time, web-based dashboards to replace manual processes for managing both blood product inventory and cooler tracking at two large academic hospital blood banks. Methods: Dashboards were developed using Hypertext Markup Language, Cascading Style Sheets, and Hypertext Preprocessor scripting/programming languages. Data are extracted in real time from Sunquest (v7.3) Laboratory Information Systems Database (InterSystems Cache) and are refreshed every 2 min. Data are hosted internally by our institution's web servers and are accessed on a webpage via Microsoft Group Policy shortcuts. Results: Dashboards were designed and implemented to provide a fully customizable, dynamic, and secure method of displaying blood product inventory and blood product cooler status. Transfusion service staff utilized dashboard data to maintain adequate blood product supply, modify blood product replacement orders to prevent excess inventory, and transfer short-dated blood products between our facilities to minimize wastage. Conclusions: Dashboard technology can be readily implemented at hospital transfusion services with minimal capital expenditure. The implementation of real-time web-based dashboards for blood product inventory and cooler management at our centers facilitated on-demand blood product monitoring and replaced a tedious, manual process with a user-friendly and intuitive electronic tool.
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	Editorial: New European union regulations related to whole slide image scanners and image analysis software
	Marcial García-Rojo, David De Mena, Pedro Muriel-Cueto, Lidia Atienza-Cuevas, Manuel Domínguez-Gómez, Gloria Bueno J Pathol Inform 2019, 10:2 (24 January 2019) DOI:10.4103/jpi.jpi_33_18  PMID:30783546 Whole slide imaging (WSI) scanners and automatic image analysis algorithms, in order to be used for clinical applications, including primary diagnosis in pathology, are subject to specific regulatory frameworks in each country. Until May 25, 2018, in the European Union (EU), in vitro diagnostic (IVD) medical devices were regulated by directive 98/79/EC (in vitro diagnostic medical device directive [IVDD]). Main scanner vendors have obtained a Conformité Européenne mark of their products that in Europe were classified as General Class IVDD, so that conformity is only based on a self-declaration of the manufacturer. This contrasts with the initial classification of the US Food and Drug Administration (FDA) of WSI system as Class III medical devices, although the first digital pathology WSI system to be cleared by FDA was classified as Class II, with special controls. Other digital pathology solutions (automated cervical cytology slide reader) are considered of higher risk by US and European regulations. There is also some disparity in the classification of image analysis solutions between Europe and the United States. All IVD-MDs must be approved under the new European regulation (in vitro diagnostic medical device regulation) 2017/746 after May 26, 2024. This means the need of a performance evaluation, including a scientific validity report, an analytical performance report, and a clinical performance report. According to its clinical use (e.g., screening, diagnosis, or staging of cancer), a WSI slide scanner can be now classified as Class C device. A special regulation is applied to companion diagnostics. The new EU regulation 2017/746 contemplates the use of standard unique identifiers for medical devices and the creation of a European database on medical devices (Eudamed). Existing validation studies and clinical guidelines already available in the literature are a sound basis to avoid that this new regulation becomes a barrier for digital pathology development in Europe.
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	Review Article: Invention and early history of telepathology (1985-2000)
	Ronald S Weinstein, Michael J Holcomb, Elizabeth A Krupinski J Pathol Inform 2019, 10:1 (24 January 2019) DOI:10.4103/jpi.jpi_71_18  PMID:30783545 This narrative-based paper provides a first-person account of the early history of telepathology (1985–2000) by the field's inventor, Ronald S. Weinstein, M. D. During the 1980s, Dr. Weinstein, a Massachusetts General Hospital-trained pathologist, was director of the Central Pathology Laboratory (CPL) for the National Cancer Institute-funded National Bladder Cancer Project, located at Rush Medical College in Chicago, IL. The CPL did post therapy revalidations of surgical pathology and cytopathology diagnoses before outcomes of the completed clinical trials were published. The CPL reported that interobserver variability was invalidating inclusion of dozens of treated bladder cancer patients in published reports on treatment outcomes. This problem seemed ripe for a technology-assisted solution. In an effort to solve the interobserver variability problem, Dr. Weinstein devised a novel solution, dynamic-robotic telepathology, that would potentially enable CPL uropathologists to consult on distant uropathology cases in real-time before their assignment to urinary bladder cancer, tumor stage, and grade-specific clinical trials. During the same period, universities were ramping up their support for faculty entrepreneurism and creating in-house technology transfer organizations. Dr. Weinstein recognized telepathology as a potential growth industry. He and his sister, Beth Newburger, were a successful brother–sister entrepreneur team. Their PC-based education software business, OWLCAT™, had just been acquired by Digital Research Inc., a leading software company, located in California. With funding from the COMSAT Corporation, a publically traded satellite communications company, the Weinstein-Newburger team brought the earliest dynamic-robotic telepathology systems to market. Dynamic-robotic telepathology became a dominant telepathology technology in the late 1990s. Dr. Weinstein, a serial entrepreneur, continued to innovate and, with a team of optical scientists at The University of Arizona's College of Optical Sciences, developed the first sub-1-min whole-slide imaging system, the DMetrix DX-40 scanner, in the early 2000s.
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	ABSTRACTS: Pathology Informatics Summit 2018
	J Pathol Inform 2018, 9:50 (31 December 2018) DOI:10.4103/2153-3539.249129
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	Research Article: A comprehensive study of telecytology using robotic digital microscope and single Z-stack digital scan for fine-needle aspiration-rapid on-site evaluation
	Keluo Yao, Rulong Shen, Anil Parwani, Zaibo Li J Pathol Inform 2018, 9:49 (24 December 2018) DOI:10.4103/jpi.jpi_75_18  PMID:30662795 Background: The current technology for remote assessment of fine-needle aspiration-rapid on-site evaluation (FNA-ROSE) is limited. Recent advances may provide solutions. This study compared the performance of VisionTek digital microscope (VDM) (Sakura, Japan) and Hamamatsu NanoZoomer C9600-12 single Z-stack digital scan (SZDS) to conventional light microscopy (CLM) for FNA-ROSE. Methods: We assembled sixty FNA cases from the thyroid (n = 16), lymph node (n = 16), pancreas (n = 9), head and neck (n = 9), salivary gland (n = 5), lung (n = 4), and rectum (n = 1) based on a single institution's routine workflow. One Diff-Quik-stained slide was selected for each case. Two board-certified cytopathologists independently evaluated the cases using VDM, SZDS, and CLM. A “washout” period of at least 14 days was placed between the reviews. The results were categorized into satisfactory versus unsatisfactory for adequacy assessment (AA) and unsatisfactory, benign, atypical, suspicious, and malignant for preliminary diagnosis (PD). Results: For AA, the Cohen's kappa statistics (CKS) scores of intermodality agreement (IMA) were 0.74–0.94 (CLM vs. VDM) and 0.86–1 (CLM vs. SZDS). The discordant rates of IMA were 3.3% (4/120) for VDM versus CLM, and 1.7% (2/120) for SZDS versus CLM. For PD, the CKS scores of IMA ranged 0.7–0.93. The overall discordant rates of IMA were 15% (18/120) for CLM versus VDM and 10.8% (13/120) for CLM versus SZDS. The discordant rates of IMA with 2 or higher degrees were 5.8% (7/120) for CLM versus VDM and 1.7% (2/120) for CLM versus SZDS. The average time spent per slide was 270 s for VDM, significantly longer than that for CLM (113 s) or for SZDS (122 s). Conclusions: Our data demonstrate that both VDM and SZDS are suitable to provide AA and reasonable PD evaluation. VDM, however, has a significantly longer turnaround time and worse diagnostic performance. The study demonstrates both the potentials and challenges of using VDM and SZDS for FNA-ROSE.
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	Research Article: Super-resolution digital pathology image processing of bone marrow aspirate and cytology smears and tissue sections
	Amol Singh, Robert S Ohgami J Pathol Inform 2018, 9:48 (24 December 2018) DOI:10.4103/jpi.jpi_56_18  PMID:30662794 Background: Accurate digital pathology image analysis depends on high-quality images. As such, it is imperative to obtain digital images with high resolution for downstream data analysis. While hematoxylin and eosin (H&E)-stained tissue section slides from solid tumors contain three-dimensional information, these data have been ignored in digital pathology. In addition, in cytology and bone marrow aspirate smears, the three-dimensional nature of the specimen has precluded efficient analysis of such morphologic data. An individual image snapshot at a single focal distance is often not sufficient for accurate diagnoses and multiple whole-slide images at different focal distances are necessary for diagnostics. Materials and Methods: We describe a novel computational pipeline and processing program for obtaining a super-resolved image from multiple static images at different z-planes in overlapping but separate frames. This program, MULTI-Z, performs image alignment, Gaussian smoothing, and Laplacian filtering to construct a final super-resolution image from multiple images. Results: We applied this algorithm and program to images of cytology and H&E-stained sections and demonstrated significant improvements in both resolution and image quality by objective data analyses (24% increase in sharpness and focus). Conclusions: With the use of our program, super-resolved images of cytology and H&E-stained tissue sections can be obtained to potentially allow for more optimal downstream computational analysis. This method is applicable to whole-slide scanned images.
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	Technical Note: Integration of cancer registry data into the text information extraction system: Leveraging the structured data import tool
	Faina Linkov, Jonathan C Silverstein, Michael Davis, Brenda Crocker, Degan Hao, Althea Schneider, Melissa Schwenk, Sharon Winters, Joyce Zelnis, Adrian V Lee, Michael J Becich J Pathol Inform 2018, 9:47 (24 December 2018) DOI:10.4103/jpi.jpi_38_18  PMID:30662793 Introduction/Background: Cancer registries in the US collect timely and systematic data on new cancer cases, extent of disease, staging, biomarker status, treatment, survival, and mortality of cancer cases. Existing methodologies for accessing local cancer registry data for research are time-consuming and often rely on the manual merging of data by staff registrars. In addition, existing registries do not provide direct access to these data nor do they routinely provide linkage to discrete electronic health record (EHR) data, reports, or imaging data. Automation of such linkage can provide an impressive data resource and make valuable data available for translational cancer research. Methods: The UPMC Network Cancer Registry collects highly structured, longitudinal data on all reportable cancer patients, from the point of the diagnosis throughout treatment and follow-up/outcomes. Using commercial registry software, we collect data in compliance with standards governed by the North American Association of Central Cancer Registries. This standardization ensures that the data are highly structured with standard coding and collection methods, which support data exchange among central cancer registries and the Centers for Disease Control and Prevention. Results: At the UPMC Hillman Cancer Center and University of Pittsburgh, we explored the feasibility of linking this well-curated, structured cancer registry data with unstructured text (i.e., pathology and radiology reports), using the Text Information Extraction System (TIES). We used the TIES platform to integrate breast cancer cases from the UPMC Network Cancer Registry system and then combine these data with other EHR data as a pilot use case that can be replicated for other cancers. Conclusions: As a result of this integration, we now have a single searchable repository of information for breast cancer patients from the UPMC registry, combined with their pathology and radiology reports. The system that we developed is easily scalable to other health systems and cancer centers.
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	Original Article: The importance of eSlide macro images for primary diagnosis with whole slide imaging
	Filippo Fraggetta, Yukako Yagi, Marcial Garcia-Rojo, Andrew J Evans, J Mark Tuthill, Alexi Baidoshvili, Douglas J Hartman, Junya Fukuoka, Liron Pantanowitz J Pathol Inform 2018, 9:46 (24 December 2018) DOI:10.4103/jpi.jpi_70_18  PMID:30662792 Introduction: A whole slide image (WSI) is typically comprised of a macro image (low-power snapshot of the entire glass slide) and stacked tiles in a pyramid structure (with the lowest resolution thumbnail at the top). The macro image shows the label and all pieces of tissue on the slide. Many whole slide scanner vendors do not readily show the macro overview to pathologists. We demonstrate that failure to do so may result in a serious misdiagnosis. Materials and Methods: Various examples of errors were accumulated that occurred during the digitization of glass slides where the virtual slide differed from the macro image of the original glass slide. Such examples were retrieved from pathology laboratories using different types of scanners in the USA, Canada, Europe, and Asia. Results: The reasons for image errors were categorized into technical problems (e.g., automatic tissue finder failure, image mismatches, and poor scan coverage) and human operator mistakes (e.g., improper manual region of interest selection). These errors were all detected because they were highlighted in the macro image. Conclusion: Our experience indicates that WSI can be subject to inadvertent errors related to glitches in scanning slides, corrupt images, or mistakes made by humans when scanning slides. Displaying the macro image that accompanies WSIs is critical from a quality control perspective in digital pathology practice as this can help detect these serious image-related problems and avoid compromised diagnoses.
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	Research Article: Computer-aided laser dissection: A microdissection workflow leveraging image analysis tools
	Jason D Hipp, Donald J Johann, Yun Chen, Anant Madabhushi, James Monaco, Jerome Cheng, Jaime Rodriguez-Canales, Martin C Stumpe, Greg Riedlinger, Avi Z Rosenberg, Jeffrey C Hanson, Lakshmi P Kunju, Michael R Emmert-Buck, Ulysses J Balis, Michael A Tangrea J Pathol Inform 2018, 9:45 (11 December 2018) DOI:10.4103/jpi.jpi_60_18  PMID:30622835 Introduction: The development and application of new molecular diagnostic assays based on next-generation sequencing and proteomics require improved methodologies for procurement of target cells from histological sections. Laser microdissection can successfully isolate distinct cells from tissue specimens based on visual selection for many research and clinical applications. However, this can be a daunting task when a large number of cells are required for molecular analysis or when a sizeable number of specimens need to be evaluated. Materials and Methods: To improve the efficiency of the cellular identification process, we describe a microdissection workflow that leverages recently developed and open source image analysis algorithms referred to as computer-aided laser dissection (CALD). CALD permits a computer algorithm to identify the cells of interest and drive the dissection process. Results: We describe several “use cases” that demonstrate the integration of image analytic tools probabilistic pairwise Markov model, ImageJ, spatially invariant vector quantization (SIVQ), and eSeg onto the ThermoFisher Scientific ArcturusXT and Leica LMD7000 microdissection platforms. Conclusions: The CALD methodology demonstrates the integration of image analysis tools with the microdissection workflow and shows the potential impact to clinical and life science applications.
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	Original Article: Laboratory computer performance in a digital pathology environment: Outcomes from a single institution
	Mark D Zarella, Adam Feldscher J Pathol Inform 2018, 9:44 (11 December 2018) DOI:10.4103/jpi.jpi_47_18  PMID:30622834 Background: In an effort to provide improved user experience and system reliability at a moderate cost, our department embarked on targeted upgrades of a total of 87 computers over a period of 3 years. Upgrades came in three forms: (i) replacement of the computer with newer architecture, (ii) replacement of the computer's hard drive with a solid-state drive (SSD), or (iii) replacement of the computer with newer architecture and a SSD. Methods: We measured the impact of each form of upgrade on a set of pathology-relevant tasks that fell into three categories: standard use, whole-slide navigation, and whole-slide analysis. We used time to completion of a task as the primary variable of interest. Results: We found that for most tasks, the SSD upgrade had a greater impact than the upgrade in architecture. This effect was especially prominent for whole-slide viewing, likely due to the way in which most whole-slide viewers cached image tiles. However, other tasks, such as whole-slide image analysis, often relied less on disk input or output and were instead more sensitive to the computer architecture. Conclusions: Based on our experience, we suggest that SSD upgrades are viewed in some settings as a viable alternative to complete computer replacement and recommend that computer replacements in a digital pathology setting are accompanied by an upgrade to SSDs.
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	Original Article: Artificial intelligence in cytopathology: A neural network to identify papillary carcinoma on thyroid fine-needle aspiration cytology smears
	Parikshit Sanyal, Tanushri Mukherjee, Sanghita Barui, Avinash Das, Prabaha Gangopadhyay J Pathol Inform 2018, 9:43 (3 December 2018) DOI:10.4103/jpi.jpi_43_18  PMID:30607310 Introduction: Fine-needle aspiration cytology (FNAC) for identification of papillary carcinoma thyroid is a moderately sensitive and specific modality. The present machine learning tools can correctly classify images into broad categories. Training software for recognition of papillary thyroid carcinoma on FNAC smears will be a decisive step toward automation of cytopathology. Aim: The aim of this study is to develop an artificial neural network (ANN) for the purpose of distinguishing papillary carcinoma thyroid and nonpapillary carcinoma thyroid on microphotographs from thyroid FNAC smears. Subjects and Methods: An ANN was developed in the Python programming language. In the training phase, 186 microphotographs from Romanowsky/Pap-stained smears of papillary carcinoma and 184 microphotographs from smears of other thyroid lesions (at ×10 and ×40 magnification) were used for training the ANN. After completion of training, performance was evaluated with a set of 174 microphotographs (66 – nonpapillary carcinoma and 21 – papillary carcinoma, each photographed at two magnifications ×10 and ×40). Results: The performance characteristics and limitations of the neural network were assessed, assuming FNAC diagnosis as gold standard. Combined results from two magnifications showed good sensitivity (90.48%), moderate specificity (83.33%), and a very high negative predictive value (96.49%) and 85.06% diagnostic accuracy. However, vague papillary formations by benign follicular cells identified wrongly as papillary carcinoma remain a drawback. Conclusion: With further training with a diverse dataset and in conjunction with automated microscopy, the ANN has the potential to develop into an accurate image classifier for thyroid FNACs.
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	Research Article: Interactive digital microscopy at the center for a cross-continent undergraduate pathology course in Mozambique
	Leonor David, Isabel Martins, Mamudo Rafik Ismail, Fabíola Fernandes, Mohsin Sidat, Mário Seixas, Elsa Fonseca, Carla Carrilho J Pathol Inform 2018, 9:42 (3 December 2018) DOI:10.4103/jpi.jpi_63_18  PMID:30607309 Background: Recent medical education trends encourage the use of teaching strategies that emphasize student centeredness and self-learning. In this context, the use of new educative technologies is stimulated at the Faculty of Medicine of Eduardo Mondlane University (FMUEM) in Mozambique. The Faculty of Medicine of University of Porto (FMUP) and FMUEM have a long-lasting record of collaborative work. Within this framework, both institutions embarked in a partnership, aimed to develop a blended learning course of pathology for undergraduates, shared between the two faculties and incorporating interactive digital microscopy as a central learning tool. Methods: A core team of faculty members from both institutions identified the existing resources and previous experiences in the two faculties. The Moodle course for students from the University of Porto was the basis to implement the current project. The objective was to develop educational modules of mutual interest, designed for e-learning, followed by a voluntary student's survey conducted in FMUEM to get their perception about the process. Results: We selected contents from the pathology curricula of FMUP and FMUEM that were of mutual interest. We next identified and produced new contents for the shared curricula. The implementation involved joint collaboration and training to prepare the new contents, together with building quizzes for self-evaluation. All the practical sessions were based on the use of interactive digital microscopy. The students have reacted enthusiastically to the incorporation of the online component that increased their performance and motivation for pathology learning. For the students in Porto, the major acquisition was the access to slides from infectious diseases as well as autopsy videos. Conclusions: Our study indicates that students benefited from high-quality educational contents, with emphasis on digital microscopy, in a platform generated in a win-win situation for FMUP and FMUEM.
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	Original Article: Parathyroid frozen section interpretation via desktop telepathology systems: A validation study
	Edward Chandraratnam, Leonardo D Santos, Shaun Chou, Jun Dai, Juan Luo, Syeda Liza, Ronald Y Chin J Pathol Inform 2018, 9:41 (3 December 2018) DOI:10.4103/jpi.jpi_57_18  PMID:30607308 Background: Telepathology can potentially be utilized as an alternative to having on-site pathology services for rural and regional hospitals. The goal of the study was to validate two small-footprint desktop telepathology systems for remote parathyroid frozen sections. Subjects and Methods: Three pathologists retrospectively diagnosed 76 parathyroidectomy frozen sections of 52 patients from three pathology services in Australia using the “live-view mode” of MikroScan D2 and Aperio LV1 and in-house direct microscopy. The final paraffin section diagnosis served as the “gold standard” for accuracy evaluation. Concordance rates of the telepathology systems with direct microscopy, inter-pathologist and intra-pathologist agreement, and the time taken to report each slide were analyzed. Results: Both telepathology systems showed high diagnostic accuracy (>99%) and high concordance (>99%) with direct microscopy. High inter-pathologist agreement for telepathology systems was demonstrated by overall kappa values of 0.92 for Aperio LV1 and 0.85 for MikroScan D2. High kappa values (from 0.85 to 1) for intra-pathologist agreement within the three systems were also observed. The time taken per slide by Aperio LV1 and MicroScan D2 within three pathologists was about 3.0 times (P < 0.001, 95% confidence interval [CI]: 2.8–3.2) and 7.7 times (P < 0.001, 95% CI: 7.1–8.3) as long as direct microscopy, respectively, while MikroScan D2 took about 2.6 times as long as Aperio LV1 (P < 0.001, 95% CI: 2.4–2.7). All pathologists evaluated Aperio LV1 as being more user-friendly. Conclusions: Telepathology diagnosis of parathyroidectomy frozen sections through small-footprint desktop systems is accurate, reliable, and comparable with in-house direct microscopy. Telepathology systems take longer than direct microscopy; however, the time taken is within clinically acceptable limits. Aperio LV1 takes shorter time than MikroScan D2 and is more user-friendly.
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	Review Article: Twenty years of digital pathology: An overview of the road travelled, what is on the horizon, and the emergence of vendor-neutral archives
	Liron Pantanowitz, Ashish Sharma, Alexis B Carter, Tahsin Kurc, Alan Sussman, Joel Saltz J Pathol Inform 2018, 9:40 (21 November 2018) DOI:10.4103/jpi.jpi_69_18  PMID:30607307 Almost 20 years have passed since the commercial introduction of whole-slide imaging (WSI) scanners. During this time, the creation of various WSI devices with the ability to digitize an entire glass slide has transformed the field of pathology. Parallel advances in computational technology and storage have permitted rapid processing of large-scale WSI datasets. This article provides an overview of important past and present efforts related to WSI. An account of how the virtual microscope evolved from the need to visualize and manage satellite data for earth science applications is provided. The article also discusses important milestones beginning from the first WSI scanner designed by Bacus to the Food and Drug Administration approval of the first digital pathology system for primary diagnosis in surgical pathology. As pathology laboratories commit to going fully digitalize, the need has emerged to include WSIs into an enterprise-level vendor-neutral archive (VNA). The different types of VNAs available are reviewed as well as how best to implement them and how pathology can benefit from participating in this effort. Differences between traditional image algorithms that extract pixel-, object-, and semantic-level features versus deep learning methods are highlighted. The need for large-scale data management, analysis, and visualization in computational pathology is also addressed.
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	Research Article: The use of screencasts with embedded whole-slide scans and hyperlinks to teach anatomic pathology in a supervised digital environment
	Mary Wong, Joseph Frye, Stacey Kim, Alberto M Marchevsky J Pathol Inform 2018, 9:39 (14 November 2018) DOI:10.4103/jpi.jpi_44_18  PMID:30607306 Background: There is an increasing interest in using digitized whole-slide imaging (WSI) for routine surgical pathology diagnoses. Screencasts are digital recordings of computer screen output with advanced interactive features that allow for the preparation of videos. Screencasts that include hyperlinks to WSIs could help teach pathology residents how to become familiar with technologies that they are likely to use in their future career. Materials and Methods: Twenty screencasts were prepared with Camtasia 2.0 software (TechSmith, Okemos, MI, USA). They included clinical history, videos of chest X-rays and/or chest computed tomography images, links to WSI digitized with an Aperio Turbo AT scanner (Leica Biosystems, Buffalo Grove, IL, USA), pre- and posttests, and faculty-narrated videos of the WSI in a manner closely resembling a slide seminar and other educational materials. Screencasts were saved in a hospital network, Screencast.com, YouTube.com, and Vimeo.com. The screencasts were viewed by 12 pathology residents and fellows who made diagnoses, answered the quizzes, and took a survey with questions designed to evaluate their perception of the quality of this technology. Quiz results were automatically e-mailed to faculty. Pre- and posttest results were compared using a paired t-test. Results: Screencasts can be viewed with Windows PC and Mac operating systems and mobile devices; only videos saved in our network and screencast.com could be used to generate quizzes. Participants' feedback was very favorable with average scores ranging from 4.5 to 4.8 (on a scale of 5). Mean posttest scores (87.0% [±21.6%]) were significantly improved over those in the pretest quizzes (48.5% [±31.2%]) (P < 0.0001). Conclusion: Screencasts with WSI that allow residents and fellows to diagnose cases using digital microscopy may prove to be a useful technology to enhance the pathology education. Future studies with larger numbers of screencasts and participants are needed to optimize various teaching strategies.
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	Review Article: Artificial intelligence and digital pathology: Challenges and opportunities
	Hamid Reza Tizhoosh, Liron Pantanowitz J Pathol Inform 2018, 9:38 (14 November 2018) DOI:10.4103/jpi.jpi_53_18  PMID:30607305 In light of the recent success of artificial intelligence (AI) in computer vision applications, many researchers and physicians expect that AI would be able to assist in many tasks in digital pathology. Although opportunities are both manifest and tangible, there are clearly many challenges that need to be overcome in order to exploit the AI potentials in computational pathology. In this paper, we strive to provide a realistic account of all challenges and opportunities of adopting AI algorithms in digital pathology from both engineering and pathology perspectives.
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	Original Article: Implementing the DICOM standard for digital pathology
	Markus D Herrmann, David A Clunie, Andriy Fedorov, Sean W Doyle, Steven Pieper, Veronica Klepeis, Long P Le, George L Mutter, David S Milstone, Thomas J Schultz, Ron Kikinis, Gopal K Kotecha, David H Hwang, Katherine P Andriole, A John Iafrate, James A Brink, Giles W Boland, Keith J Dreyer, Mark Michalski, Jeffrey A Golden, David N Louis, Jochen K Lennerz J Pathol Inform 2018, 9:37 (2 November 2018) DOI:10.4103/jpi.jpi_42_18  PMID:30533276 Background: Digital Imaging and Communications in Medicine (DICOM®) is the standard for the representation, storage, and communication of medical images and related information. A DICOM file format and communication protocol for pathology have been defined; however, adoption by vendors and in the field is pending. Here, we implemented the essential aspects of the standard and assessed its capabilities and limitations in a multisite, multivendor healthcare network. Methods: We selected relevant DICOM attributes, developed a program that extracts pixel data and pixel-related metadata, integrated patient and specimen-related metadata, populated and encoded DICOM attributes, and stored DICOM files. We generated the files using image data from four vendor-specific image file formats and clinical metadata from two departments with different laboratory information systems. We validated the generated DICOM files using recognized DICOM validation tools and measured encoding, storage, and access efficiency for three image compression methods. Finally, we evaluated storing, querying, and retrieving data over the web using existing DICOM archive software. Results: Whole slide image data can be encoded together with relevant patient and specimen-related metadata as DICOM objects. These objects can be accessed efficiently from files or through RESTful web services using existing software implementations. Performance measurements show that the choice of image compression method has a major impact on data access efficiency. For lossy compression, JPEG achieves the fastest compression/decompression rates. For lossless compression, JPEG-LS significantly outperforms JPEG 2000 with respect to data encoding and decoding speed. Conclusion: Implementation of DICOM allows efficient access to image data as well as associated metadata. By leveraging a wealth of existing infrastructure solutions, the use of DICOM facilitates enterprise integration and data exchange for digital pathology.
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	Original Article: Complete routine remote digital pathology services
	Aleksandar Vodovnik, Mohammad Reza F. Aghdam J Pathol Inform 2018, 9:36 (29 October 2018) DOI:10.4103/jpi.jpi_34_18  PMID:30505622 Background: Validation studies in digital pathology addressed so far diverse aspects of the routine work. We aimed to establish a complete remote digital pathology service. Methods: Altogether 2295 routine cases (8640 slides) were reported in our studies on digital versus microscopic diagnostics, remote reporting, diagnostic time, fine-needle aspiration cytology (FNAC) clinics, frozen sections, and diagnostic sessions with residents. The same senior pathologist was involved in all studies. Slides were scanned by ScanScope AT Turbo (Aperio). Digital images were accessed through the laboratory system (LS) on either 14” laptops or desktop computers with double 23” displays for the remote and on-site digital reporting. Larger displays were used when available for remote reporting. First diagnosis was either microscopic, digital, or remote digital only (6 months washout period). Both diagnoses were recorded separately and compared. Turnaround was measured from the registration to sign off or scanning to diagnosis. A diagnostic time was measured from the point slides were made available to the point of diagnosis or additional investigations were necessary, recorded independently in minutes/session, and compared. Jabber Video (Cisco) and Lync (Microsoft) were interchangeably used for the secure, video supervision of activities. Mobile phone, broadband, broadband over Wi-Fi, and mobile broadband were tested for internet connections. Nine autopsies were performed remotely involving three staff pathologists, one autopsy technician, and one resident over the secure video link. Remote and on-site pathologists independently interpreted and compared gross findings. Diverse benefits and technical aspects were studied using logs or information recorded in LS. Satisfaction surveys on diverse technical and professional aspects of the studies were conducted. Results: The full concordance between digital and light microscopic diagnosis was 99% (594/600 cases). A minor discordance, without clinical implications, was 1% (6/600 cases). The instant upload of digital images was achieved at 20 Mbps. Deference to microscopic slides and rescanning were under 1%. Average turnaround was shorter and percentage of cases reported up to 3 days higher for remote digital reporting. Larger displays improved the most user experience at magnifications over ×20. A digital diagnostic time was shorter than microscopic in 13 sessions. Four sessions with shorter microscopic diagnostic time included more cases requiring extensive use of magnifications over ×20. Independent interpretations of gross findings between remote and on-site pathologists yielded full agreement in the remote autopsies. Delays in reporting of frozen sections and FNAC due to scanning were clinically insignificant. Satisfaction levels with diverse technical and/or professional aspects of all studies were high. Conclusions: Complete routine remote digital pathology services are found feasible in hands of experienced staff. The introduction of digital pathology has improved provisions and organizations of our pathology services in histology, cytology, and autopsy including teaching and interdepartmental collaboration.
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	Original Article: Network analysis of autopsy diagnoses: Insights into the “cause of death” from unbiased disease clustering
	Romulo Celli, Miguel Divo, Monica Colunga, Bartolome Celli, Kisha Anne Mitchell-Richards J Pathol Inform 2018, 9:35 (9 October 2018) DOI:10.4103/jpi.jpi_20_18  PMID:30450264 Background: Autopsies usually serve to inform specific “causes of death” and associated mechanisms. However, multiple diseases can co-exist and interact leading to a final demise. We approached autopsy-produced data using network analysis in an unbiased fashion to inform about interaction among different diseases and identify possible targets of system-level health care. Methods: Reports of 261 full autopsies from one institution between 2011 and 2013 were reviewed. Comorbidities were recorded and their Spearman's association coefficients were calculated. Highly associated comorbidities (P < 0.01) were selected to construct a network in which each disease is represented by a node, and each link between the nodes represents significant co-occurrence. Results: The network comprised 140 diseases connected by 419 links. The mean number of connections per node was 6. The most highly connected nodes (“hubs”) represented infectious processes, whereas less connected nodes represented neoplasms and other chronic diseases. Eight clusters of biologically plausible associated diseases were identified. Conclusions: There is an unbiased relationship among autopsy-identified diseases. There were “hubs” (primarily infectious) with significantly more associations than others that could represent obligatory or important modulators of the final expression of other diseases. Clusters of co-occurring diseases, or “modules,” suggest the presence of clinically relevant presentations of pathobiologically related entities which are until now considered individual diseases. These modules may occur together prior to death and be amenable to interventions during life.
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	Original Article: Validation of remote digital frozen sections for cancer and transplant intraoperative services
	Luca Cima, Matteo Brunelli, Anil Parwani, Ilaria Girolami, Andrea Ciangherotti, Giulio Riva, Luca Novelli, Francesca Vanzo, Alessandro Sorio, Vito Cirielli, Mattia Barbareschi, Antonietta D'Errico, Aldo Scarpa, Chiara Bovo, Filippo Fraggetta, Liron Pantanowitz, Albino Eccher J Pathol Inform 2018, 9:34 (9 October 2018) DOI:10.4103/jpi.jpi_52_18  PMID:30450263 Introduction: Whole-slide imaging (WSI) technology can be used for primary diagnosis and consultation, including intraoperative (IO) frozen section (FS). We aimed to implement and validate a digital system for the FS evaluation of cancer and transplant specimens following recommendations of the College of American Pathologists. Materials and Methods: FS cases were routinely scanned at ×20 employing the “Navigo” scanner system. IO diagnoses using glass versus digital slides after a 3-week washout period were recorded. Intraobserver concordance was evaluated using accuracy rate and kappa statistics. Feasibility of WSI diagnoses was assessed by the way of sensitivity, specificity, as well as positive and negative predictive values. Participants also completed a survey denoting scan time, time spent viewing cases, preference for glass versus WSI, image quality, interface experience, and any problems encountered. Results: Of the 125 cases submitted, 121 (436 slides) were successfully scanned including 93 oncological and 28 donor-organ FS biopsies. Four cases were excluded because of failed digitalization due to scanning problems or sample preparation artifacts. Full agreement between glass and digital-slide diagnosis was obtained in 90 of 93 (97%, κ = 0.96) oncology and in 24 of 28 (86%, κ = 0.91) transplant cases. There were two major and one minor discrepancy for cancer cases (sensitivity 100%, specificity 96%) and two major and two minor disagreements for transplant cases (sensitivity 96%, specificity 75%). Average scan and viewing/reporting time were 12 and 3 min for cancer cases, compared to 18 and 5 min for transplant cases. A high diagnostic comfort level among pathologists emerged from the survey. Conclusions: These data demonstrate that the “Navigo” digital WSI system can reliably support an IO FS service involving complicated cancer and transplant cases.
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	Symposium: Innovation in transplantation: The digital era
	Albino Eccher, Matteo Brunelli, Liron Pantanowitz, Anil Parwani, Ilaria Girolami, Aldo Scarpa J Pathol Inform 2018, 9:33 (27 September 2018) DOI:10.4103/jpi.jpi_55_18  PMID:30294502 The international symposium entitled “Innovation in Transplantation: The Digital Era” took place on June 7 and 8, 2018 in Verona, Italy. This meeting was borne out of the productive collaboration between the Universities and Hospital Trusts of Verona and Padua in Italy, in the context of a vast regional project called Research and innovation project within the Health Technology Assessment. The project aimed to create an innovative digital platform for teleconsultation and delivering diagnostic second opinions in the field of organ transplantation within the Veneto region. This conference brought together pathologists, health informatics leaders, clinicians, researchers, vendors, and health-care planners from all around the globe. The symposium was conceived to promote the exchange of knowledge and kindle fertile discussion among the 130 attendees from 15 different countries. This article conveys the highlights of this symposium.
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	Original Article: Diagnostic performance of deep learning algorithms applied to three common diagnoses in dermatopathology
	Thomas George Olsen, B Hunter Jackson, Theresa Ann Feeser, Michael N Kent, John C Moad, Smita Krishnamurthy, Denise D Lunsford, Rajath E Soans J Pathol Inform 2018, 9:32 (27 September 2018) DOI:10.4103/jpi.jpi_31_18  PMID:30294501 Background: Artificial intelligence is advancing at an accelerated pace into clinical applications, providing opportunities for increased efficiency, improved accuracy, and cost savings through computer-aided diagnostics. Dermatopathology, with emphasis on pattern recognition, offers a unique opportunity for testing deep learning algorithms. Aims: This study aims to determine the accuracy of deep learning algorithms to diagnose three common dermatopathology diagnoses. Methods: Whole slide images (WSI) of previously diagnosed nodular basal cell carcinomas (BCCs), dermal nevi, and seborrheic keratoses were annotated for areas of distinct morphology. Unannotated WSIs, consisting of five distractor diagnoses of common neoplastic and inflammatory diagnoses, were included in each training set. A proprietary fully convolutional neural network was developed to train algorithms to classify test images as positive or negative relative to ground truth diagnosis. Results:Artificial intelligence system accurately classified 123/124 (99.45%) BCCs (nodular), 113/114 (99.4%) dermal nevi, and 123/123 (100%) seborrheic keratoses. Conclusions: Artificial intelligence using deep learning algorithms is a potential adjunct to diagnosis and may result in improved workflow efficiencies for dermatopathologists and laboratories.
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	Technical Note: Using heatmaps to identify opportunities for optimization of test utilization and care delivery
	Yonah C Ziemba, Liya Lomsadze, Yehuda Jacobs, Tylis Y Chang, Nina Haghi J Pathol Inform 2018, 9:31 (27 September 2018) DOI:10.4103/jpi.jpi_7_18  PMID:30294500 Background: When a provider orders a test in a pattern that is substantially different than their peers, it may indicate confusion in the test name or inappropriate use of the test, which can be elucidated by initiating dialog between clinicians and the laboratory. However, the analysis of ordering patterns can be challenging. We propose a utilization index (UI) as a means to quantify utilization patterns for individual providers and demonstrate the use of heatmaps to identify opportunities for improvement. Materials and Methods: Laboratory test orders by all providers were extracted from the laboratory information system. Providers were grouped into cohorts based on the specialty and patient population. A UI was calculated for each provider's use of each test using the following formula: (UI = [provider volume of specific test/provider volume of all tests]/[cohort volume of specific test/cohort volume of all tests]). A heatmap was generated to compare each provider to their cohort. Results: This method identified several hot spots and was helpful in reducing confusion and overutilization. Conclusion: The UI is a useful measure of test ordering behavior, and heatmaps provide a clear visual illustration of the utilization indices. This information can be used to identify areas for improvement and initiate meaningful dialog with providers, which will ultimately bring improvement and reduction in costs. Our method is simple and uses resources that are widely available, making this method effective convenient for many other laboratories.
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	Review Article: The human interface of biomedical informatics
	Edward C Klatt J Pathol Inform 2018, 9:30 (6 September 2018) DOI:10.4103/jpi.jpi_39_18  PMID:30237909 Biomedical informatics is the science of information, where information is defined as data with meaning. This definition identifies a fundamental challenge for informaticians: connecting with the healthcare team by enabling the acquisition, retrieval, and processing of information within the cognitive capabilities of the human brain. Informaticians can become aware of the constraints involved with cognitive processing and with workplace factors that impact how information is acquired and used to facilitate an improved user interface providing information to healthcare teams. Constraints affecting persons in the work environment include as follows: (1) cognitive processing of information; (2) cognitive load and memory capacity; (3) stress-affecting cognition; (4) cognitive distraction, attention, and multitasking; (5) cognitive bias and flexibility; (6) communication barriers; and (7) workplace environment. The human brain has a finite cognitive load capacity for processing new information. Short-term memory has limited throughput for processing of new informational items, while long-term memory supplies immediate simultaneous access to multiple informational items. Visual long-term memories can be extensive and detailed. Attention may be task dependent and highly variable among persons and requires maintaining control over distracting information. Multitasking reduces the effectiveness of working memory applied to each task. Transfer of information from person to person, or machine to person, is subject to cognitive bias and environmental stressors. High-stress levels increase emotional arousal to reduce memory formation and retrieval. The workplace environment can impact cognitive processes and stress, so maintaining civility augments cognitive abilities. Examples of human-computer interfaces employing principles of cognitive informatics inform design of systems to enhance the user interface.
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	Research Article: Conventional microscopical versus digital whole-slide imaging-based diagnosis of thin-layer cervical specimens: A validation study
	Odille Bongaerts, Carla Clevers, Marij Debets, Daniëlle Paffen, Lisanne Senden, Kim Rijks, Linda Ruiten, Daisy Sie-Go, Paul J van Diest, Marius Nap J Pathol Inform 2018, 9:29 (27 August 2018) DOI:10.4103/jpi.jpi_28_18  PMID:30197818 Background: Whole-slide imaging (WSI) has been implemented in many areas of pathology, but primary diagnostics of cytological specimens are lagging behind. One of the objectives of viewing scanned whole-slide images from histological or cytological specimens is remote exchange of knowledge and expertise of professionals to increase diagnostic accuracy. We compared the scoring results of our team obtained in double readings of two different data sets: conventional light microscopy (CLM) versus CLM and CLM versus WSI. We hypothesized that WSI is noninferior to CLM for primary diagnostics of thin-layer cervical slides. Materials and Methods: First, we determined the concordance rate at different thresholds of the participating cytotechnicians by double reading with CLM of 500 thin-layer cervical slides (Cohort 1). Next, CLM was compared with WSI examination of another 505 thin-layer cervical slides (Cohort 2) scanned at ×20 in single focus plane. Finally, all major discordant cases of Cohort 1 were evaluated by an external expert in the field of gynecological cytology and of Cohort 2 in the weekly case meetings. Results: The overall concordance rate of Cohort 1 (CLM vs. CLM) was 97.8% (95% confidence interval [CI]: 96.0%–98.7%) and of Cohort 2 was 95.3% (95% CI: 93.0%–96.9%). Conclusion: Concordance rates of WSI versus CLM were comparable with those of CLM versus CLM. We have made a step forward paving the road to implementation of WSI also in routine diagnostic cytology.
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