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

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

J Pathol Inform 2019, 10:32 (11 November 2019)
DOI:10.4103/2153-3539.270744  
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Original Article: The California Telepathology Service: UCLA's experience in deploying a regional digital pathology subspecialty consultation network
Thomas Chong, M Fernando Palma-Diaz, Craig Fisher, Dorina Gui, Nora L Ostrzega, Geoffrey Sempa, Anthony E Sisk, Mark Valasek, Beverly Y Wang, Jonathan Zuckerman, Chris Khacherian, Scott Binder, W Dean Wallace
J Pathol Inform 2019, 10:31 (27 September 2019)
DOI:10.4103/jpi.jpi_22_19  PMID:31620310
Background: The need for extending pathology diagnostic expertise to more areas is now being met by the maturation of technology that can effectively deliver this level of care. The experience and lessons learned from our successfully deployed International Telepathology Service (ITS) to a hospital system in China guided us in starting a domestic telepathology network, the California Telepathology Service (CTS). Many of the lessons learned from the ITS project informed our decision-making for the CTS. New challenges were recognized and overcome, such as addressing the complexity and cost–benefit tradeoffs involved in setting up a digital consultation system that competes with an established conventional glass slide delivery system. Methods: The CTS is based on a hub-and-spoke telepathology network using Leica Biosystems whole-slide image scanners and the eSlide Manager (eSM Version 12.3.3.7055, Leica Biosystems) digital image management software solution. The service currently comprises six spoke sites (UC San Diego [UCSD], UC Irvine [UCI], UC Davis, Northridge Hospital Medical Center [NHMC], Olive View Medical Center [OVMC], and Children's Hospital Los Angeles) and one central hub site (UCLA Medical Center). So far, five sites have been validated for telepathology case consultations following established practice guidelines, and four sites (UCI, UCSD, NHMC, and OVMC) have activated the service. Results: For the active spoke sites, we reviewed the volume, turnaround time (TAT), and case types and evaluated for utility and value. From May 2017 to July 2018, a total of 165 cases were submitted. Of note, digital consultations were particularly advantageous for preliminary kidney biopsy diagnoses (avg TAT 0.7 day). Conclusion: For spoke sites, telepathology provided shortened TAT and significant financial savings over hiring faculty with expertise to support a potentially low-volume service. For the hub site, the value includes exposure to educationally valuable cases, additional caseload volume to support specialized services, and improved communication with referring facilities over traditional carrier mail. The creation of a hub-and-spoke telepathology network is an expensive undertaking, and careful consideration needs to be given to support the needs of the clinical services, acquisition and effective deployment of the appropriate equipment, network requirements, and laboratory workflows to ensure a successful and cost-effective system.
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Research Article: Statistical analysis of survival models using feature quantification on prostate cancer histopathological images
Jian Ren, Eric A Singer, Evita Sadimin, David J Foran, Xin Qi
J Pathol Inform 2019, 10:30 (27 September 2019)
DOI:10.4103/jpi.jpi_85_18  PMID:31620309
Background: Grading of prostatic adenocarcinoma is based on the Gleason scoring system and the more recently established prognostic grade groups. Typically, prostate cancer grading is performed by pathologists based on the morphology of the tumor on hematoxylin and eosin (H and E) slides. In this study, we investigated the histopathological image features with various survival models and attempted to study their correlations. Methods: Three texture methods (speeded-up robust features, histogram of oriented gradient, and local binary pattern) and two convolutional neural network (CNN)-based methods were applied to quantify histopathological image features. Five survival models were assessed on those image features in the context with other prostate clinical prognostic factors, including primary and secondary Gleason patterns, prostate-specific antigen levels, age, and clinical tumor stages. Results: Based on statistical comparisons among different image features with survival models, image features from CNN-based method with a recurrent neural network called CNN-long-short-term memory provided the highest hazard ratio of prostate cancer recurrence under Cox regression with an elastic net penalty. Conclusions: This approach outperformed the other image quantification methods listed above. Using this approach, patient outcomes were highly correlated with the histopathological image features of the tissue samples. In future studies, we plan to investigate the potential use of this approach for predicting recurrence in a wider range of cancer types.
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Original Article: Differentiating noninvasive follicular thyroid neoplasm with papillary-like nuclear features from classic papillary thyroid carcinoma: Analysis of cytomorphologic descriptions using a novel machine-learning approach
Sara Maleki, Amin Zandvakili, Shweta Gera, Seema D Khutti, Adam Gersten, Samer N Khader
J Pathol Inform 2019, 10:29 (18 September 2019)
DOI:10.4103/jpi.jpi_25_19  PMID:31579155
Background: Recent studies show various cytomorphologic features that can assist in the differentiation of classic papillary thyroid carcinoma (cPTC) from noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). Differentiating these two entities changes the clinical management significantly. We evaluated the performance of support vector machine (SVM), a machine learning algorithm, in differentiating cases of NIFTP and encapsulated follicular variant of papillary thyroid carcinoma with no capsular or lymphovascular invasion (EFVPTC) from cases of cPTC with the use of microscopic descriptions. SVM is a supervised learning algorithm used in classification problems. It assigns the input data to one of two categories by building a model based on a set of training examples (learning) and then using that learned model to classify new examples. Methods: Surgical pathology cases with the diagnosis of cPTC, NIFTP, and EFVPTC, were obtained from the laboratory information system. Only cases with existing fine-needle aspiration matching the tumor and available microscopic description were included. NIFTP cases with ipsilateral micro-PTC were excluded. The final cohort consisted of 59 cases (29 cPTCs and 30 NIFTP/EFVPTCs). Results: SVM successfully differentiated cPTC from NIFTP/EFVPTC 76.05 ± 0.96% of times (above chance,P < 0.05) with the sensitivity of 72.6% and specificity of 81.6% in detecting cPTC. Conclusions: This machine learning algorithm was successful in distinguishing NIFTP/EFVPTC from cPTC. Our results are compatible with the prior studies, which show cytologic features are helpful in differentiating these two entities. Furthermore, this study shows the power and potential of this approach for clinical use and in developing data-driven scoring systems, which can guide cytopathology and surgical pathology diagnosis.
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Abstracts: Abstracts

J Pathol Inform 2019, 10:28 (16 September 2019)
DOI:10.4103/2153-3539.266902  
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Editorial: 2020 vision of digital pathology in action
Sylvia L Asa, Anna C Bodén, Darren Treanor, Sofia Jarkman, Claes Lundström, Liron Pantanowitz
J Pathol Inform 2019, 10:27 (14 August 2019)
DOI:10.4103/jpi.jpi_31_19  PMID:31516758
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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  PMID:31463162
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  PMID:31463161
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 Highly accessed article
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  PMID:31523482
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  PMID:31523481
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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  PMID:31523480
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  PMID:31367473
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  PMID:31367472
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  PMID:31367471
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  PMID:31360593
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 Highly accessed article
Michael C Montalto, Robin Edwards
J Pathol Inform 2019, 10:17 (6 May 2019)
DOI:10.4103/jpi.jpi_91_18  PMID:31149368
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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  PMID:31149367
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  PMID:31198617
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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 Highly accessed article

J Pathol Inform 2019, 10:10 (1 April 2019)
DOI:10.4103/2153-3539.255259  
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