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Technical Note: Use of telepathology to facilitate COVID-19 research and education through an online COVID-19 autopsy biorepository
Paul V Benson, Silvio H Litovsky, Adrie J C Steyn, Camilla Margaroli, Egiebade Iriabho, Peter G Anderson
J Pathol Inform 2021, 12:48 (1 December 2021)
DOI:10.4103/jpi.jpi_15_21  
Introduction: The coronavirus disease 2019 (COVID-19) pandemic has increased the use of technology for communication including departmental conferences, working remotely, and distance teaching. Methods to enable these activities should be developed and promulgated. Objective: To repurpose a preexisting educational website to enable the development of a COVID-19 autopsy biorepository to support distance teaching and COVID-19 research. Methods: After consent was obtained, autopsies were performed on patients with a confirmed positive severe acute respiratory syndrome coronavirus-2 reverse-transcriptase-polymerase-chain reaction test. Autopsies were performed according to a COVID-19 protocol, and all patients underwent both gross and microscopic examination. The H and E histology slides were scanned using a Leica Biosystems Aperio CS ScanScope whole slide scanner and the digital slide files were converted to deep zoom images that could be uploaded to the University of Alabama at Birmingham (UAB) Pathology Educational Instructional Resource website where virtual microscopy of the slides is available. Results: A total of 551 autopsy slides from 24 UAB COVID-19 cases, 1 influenza H1N1 case and 1 tuberculosis case were scanned and uploaded. Five separate COVID-19 research teams used the digital slides remotely with or without a pathologist on a Zoom call. The scanned slides were used to produce one published case report and one published research project. The digital COVID-19 autopsy biorepository was routinely used for educational conferences and research meetings locally, nationally and internationally. Conclusion: The repurposing of a pre-existing website enabled telepathology consultation for research and education purposes. Combined with other communication technology (Zoom) this achievement highlights what is possible using pre-existing technologies during a global pandemic.
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Technical Note: Programmed cell death ligand 1 pathologist training in the time of COVID-19: Our experience using a digital solution
Dorothy Hayden, Joseph M Herndon, James C Campion, Janine D Feng, Fangru Lian, Jessica L Baumann, Bryan K Roland, Ehab A ElGabry
J Pathol Inform 2021, 12:47 (22 November 2021)
DOI:10.4103/jpi.jpi_16_21  
The COVID-19 pandemic presented numerous challenges to the continuity of programmed cell death ligand 1 (PD-L1) assay training events conducted by our organization. Under typical conditions, these training events are face-to-face affairs, where participants are trained to assay algorithms on glass slides during multi-headed scope sessions. Social distancing measures undertaken to slow pandemic spread necessitated the adaptation of our training methods to facilitate assay training and subsequent continuation of clinical trials. The present report details the creation and use of the Roche pathology training portal (PTP) that allowed for remote training to diagnostic assay algorithms. The PTP is a web-based system comprised of a learning management system (LMS) coupled to an image management system (IMS). Whole slide images (WSIs) were produced using a DP200 instrument (Roche, Pleasanton, CA) and these scan files were then uploaded to an IMS. Courses were created on the LMS using annotated WSIs that were shared with enrolled pathologists worldwide during assay training events. These courses culminated in assay certification examinations, where pathologists evaluated test-case WSIs and evaluated these cases within the LMS. Trainee submissions were analyzed for pass/fail status by comparing user data entries with consensus scores on these test-case WSIs. To date, 47 pathologist trainings have occurred and of these, 44 have successfully passed the associated assay certification exam on the first attempt (93% 1st-try pass rate). The PTP allowed roche to continue training sites during the COVID-19 pandemic, and these early results demonstrate the capability of this digital solution regarding PD-L1 diagnostic assay training events.
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Research Article: Machine learning classification of false-positive human immunodeficiency virus screening results
Mahmoud Elkhadrawi, Bryan A Stevens, Bradley J Wheeler, Murat Akcakaya, Sarah Wheeler
J Pathol Inform 2021, 12:46 (20 November 2021)
DOI:10.4103/jpi.jpi_7_21  
Background: Human immunodeficiency virus (HIV) screening has improved significantly in the past decade as we have implemented tests that include antigen detection of p24. Incorporation of p24 detection narrows the window from 4 to 2 weeks between infection acquisition and ability to detect infection, reducing unintentional spread of HIV. The fourth- and fifth-generation HIV (HIV5G) screening tests in low prevalence populations have high numbers of false-positive screens and it is unclear if orthogonal testing improves diagnostic and public health outcomes. Methods: We used a cohort of 60,587 HIV5G screening tests with molecular and clinical correlates collected from 2016 to 2018 and applied machine learning to generate a classifier that could predict likely true and false positivity. Results: The best classification was achieved by using support vector machines and transformation of results with principle component analysis. The final classifier had an accuracy of 94% for correct classification of false-positive screens and an accuracy of 92% for classification of true-positive screens. Conclusions: Implementation of this classifier as a screening method for all HIV5G reactive screens allows for improved workflow with likely true positives reported immediately to reduce infection spread and initiate follow-up testing and treatment and likely false positives undergoing orthogonal testing utilizing the same specimen already drawn to reduce distress and follow-up visits. Application of machine learning to the clinical laboratory allows for workflow improvement and decision support to provide improved patient care and public health.
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Technical Note: A pathologist-annotated dataset for validating artificial intelligence: A project description and pilot study
Sarah N Dudgeon, Si Wen, Matthew G Hanna, Rajarsi Gupta, Mohamed Amgad, Manasi Sheth, Hetal Marble, Richard Huang, Markus D Herrmann, Clifford H Szu, Darick Tong, Bruce Werness, Evan Szu, Denis Larsimont, Anant Madabhushi, Evangelos Hytopoulos, Weijie Chen, Rajendra Singh, Steven N Hart, Ashish Sharma, Joel Saltz, Roberto Salgado, Brandon D Gallas
J Pathol Inform 2021, 12:45 (15 November 2021)
DOI:10.4103/jpi.jpi_83_20  
Purpose: Validating artificial intelligence algorithms for clinical use in medical images is a challenging endeavor due to a lack of standard reference data (ground truth). This topic typically occupies a small portion of the discussion in research papers since most of the efforts are focused on developing novel algorithms. In this work, we present a collaboration to create a validation dataset of pathologist annotations for algorithms that process whole slide images. We focus on data collection and evaluation of algorithm performance in the context of estimating the density of stromal tumor-infiltrating lymphocytes (sTILs) in breast cancer. Methods: We digitized 64 glass slides of hematoxylin- and eosin-stained invasive ductal carcinoma core biopsies prepared at a single clinical site. A collaborating pathologist selected 10 regions of interest (ROIs) per slide for evaluation. We created training materials and workflows to crowdsource pathologist image annotations on two modes: an optical microscope and two digital platforms. The microscope platform allows the same ROIs to be evaluated in both modes. The workflows collect the ROI type, a decision on whether the ROI is appropriate for estimating the density of sTILs, and if appropriate, the sTIL density value for that ROI. Results: In total, 19 pathologists made 1645 ROI evaluations during a data collection event and the following 2 weeks. The pilot study yielded an abundant number of cases with nominal sTIL infiltration. Furthermore, we found that the sTIL densities are correlated within a case, and there is notable pathologist variability. Consequently, we outline plans to improve our ROI and case sampling methods. We also outline statistical methods to account for ROI correlations within a case and pathologist variability when validating an algorithm. Conclusion: We have built workflows for efficient data collection and tested them in a pilot study. As we prepare for pivotal studies, we will investigate methods to use the dataset as an external validation tool for algorithms. We will also consider what it will take for the dataset to be fit for a regulatory purpose: study size, patient population, and pathologist training and qualifications. To this end, we will elicit feedback from the Food and Drug Administration via the Medical Device Development Tool program and from the broader digital pathology and AI community. Ultimately, we intend to share the dataset, statistical methods, and lessons learned.
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ABSTRACTS: Abstract

J Pathol Inform 2021, 12:44 (9 November 2021)
DOI:10.4103/2153-3539.330157  
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Review Article: Generative adversarial networks in digital pathology and histopathological image processing: A review
Laya Jose, Sidong Liu, Carlo Russo, Annemarie Nadort, Antonio Di Ieva
J Pathol Inform 2021, 12:43 (3 November 2021)
DOI:10.4103/jpi.jpi_103_20  
Digital pathology is gaining prominence among the researchers with developments in advanced imaging modalities and new technologies. Generative adversarial networks (GANs) are a recent development in the field of artificial intelligence and since their inception, have boosted considerable interest in digital pathology. GANs and their extensions have opened several ways to tackle many challenging histopathological image processing problems such as color normalization, virtual staining, ink removal, image enhancement, automatic feature extraction, segmentation of nuclei, domain adaptation and data augmentation. This paper reviews recent advances in histopathological image processing using GANs with special emphasis on the future perspectives related to the use of such a technique. The papers included in this review were retrieved by conducting a keyword search on Google Scholar and manually selecting the papers on the subject of H&E stained digital pathology images for histopathological image processing. In the first part, we describe recent literature that use GANs in various image preprocessing tasks such as stain normalization, virtual staining, image enhancement, ink removal, and data augmentation. In the second part, we describe literature that use GANs for image analysis, such as nuclei detection, segmentation, and feature extraction. This review illustrates the role of GANs in digital pathology with the objective to trigger new research on the application of generative models in future research in digital pathology informatics.
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Review Article: Deep learning approaches and applications in toxicologic histopathology: Current status and future perspectives
Shima Mehrvar, Lauren E Himmel, Pradeep Babburi, Andrew L Goldberg, Magali Guffroy, Kyathanahalli Janardhan, Amanda L Krempley, Bhupinder Bawa
J Pathol Inform 2021, 12:42 (1 November 2021)
DOI:10.4103/jpi.jpi_36_21  
Whole slide imaging enables the use of a wide array of digital image analysis tools that are revolutionizing pathology. Recent advances in digital pathology and deep convolutional neural networks have created an enormous opportunity to improve workflow efficiency, provide more quantitative, objective, and consistent assessments of pathology datasets, and develop decision support systems. Such innovations are already making their way into clinical practice. However, the progress of machine learning - in particular, deep learning (DL) - has been rather slower in nonclinical toxicology studies. Histopathology data from toxicology studies are critical during the drug development process that is required by regulatory bodies to assess drug-related toxicity in laboratory animals and its impact on human safety in clinical trials. Due to the high volume of slides routinely evaluated, low-throughput, or narrowly performing DL methods that may work well in small-scale diagnostic studies or for the identification of a single abnormality are tedious and impractical for toxicologic pathology. Furthermore, regulatory requirements around good laboratory practice are a major hurdle for the adoption of DL in toxicologic pathology. This paper reviews the major DL concepts, emerging applications, and examples of DL in toxicologic pathology image analysis. We end with a discussion of specific challenges and directions for future research.
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Technical Note: Advantages of using a web-based digital platform for kidney preimplantation biopsies
Flavia Neri, Albino Eccher, Paolo Rigotti, Ilaria Girolami, Gianluigi Zaza, Giovanni Gambaro, MariaGaia Mastrosimini, Giulia Bencini, Caterina Di Bella, Claudia Mescoli, Luigino Boschiero, Stefano Marletta, Paolo Angelo Dei Tos, Lucrezia Furian
J Pathol Inform 2021, 12:41 (1 November 2021)
DOI:10.4103/jpi.jpi_23_21  
Background: In the setting of kidney transplantation, histopathology of kidney biopsies is a key element in the organ assessment and allocation. Despite the broad diffusion of the Remuzzi–Karpinski score on preimplantation kidney biopsies, scientific evidence of its correlation to the transplantation outcome is controversial. The main issues affecting the prognostic value of histopathology are the referral to general on-call pathologists and the semiquantitative feature of the score, which can raise issues of interpretation. Digital pathology has shown very reliable and effective in the oncological diagnosis and treatment; however, the spread of such technologies is lagging behind in the field of transplantation. The aim of our study was to create a digital online platform where whole-slide images (WSI) of preimplantation kidney biopsies could be uploaded and stored. Methods: We included 210 kidney biopsies collected between January 2015 and December 2019 from the joint collaboration of the transplantation centers of Padua and Verona. The selected slides, stained with hematoxylin and eosin, were digitized and uploaded on a shared web platform. For each case, the on-call pathologists' Remuzzi grades were obtained from the original report, together with the clinical data and the posttransplantation follow-up. Results: The storage of WSI of preimplantation kidney biopsies would have several clinical, scientific, and educational advantages. The clinical utility relies on the possibility to consult online expert pathologists and real-time quality checks of diagnosis. From the perspective of follow-up, the archived digitized biopsies can offer a useful comparison to posttransplantation biopsies. In addition, the digital online platform is a precious tool for multidisciplinary meetings aimed both at the clinical discussion and at the design of research projects. Furthermore, this archive of readily available WSI is an important educational resource for the training of professionals. Conclusions: Finally, the web platform lays the foundation for the introduction of artificial intelligence in the field of transplantation that would help create new diagnostic algorithms and tools with the final aim of increasing the precision of organ assessment and its predictive value for transplant outcome.
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Research Article: QuPath digital immunohistochemical analysis of placental tissue
Ashley L Hein, Maheswari Mukherjee, Geoffrey A Talmon, Sathish Kumar Natarajan, Tara M Nordgren, Elizabeth Lyden, Corrine K Hanson, Jesse L Cox, Annelisse Santiago-Pintado, Mariam A Molani, Matthew Van Ormer, Maranda Thompson, Melissa Thoene, Aunum Akhter, Ann Anderson-Berry, Ana G Yuil-Valdes
J Pathol Inform 2021, 12:40 (1 November 2021)
DOI:10.4103/jpi.jpi_11_21  
Background: QuPath is an open-source digital image analyzer notable for its user-friendly design, cross-platform compatibility, and customizable functionality. Since it was first released in 2016, at least 624 publications have reported its use, and it has been applied in a wide spectrum of settings. However, there are currently limited reports of its use in placental tissue. Here, we present the use of QuPath to quantify staining of G-protein coupled receptor 18 (GPR18), the receptor for the pro-resolving lipid mediator Resolvin D2, in placental tissue. Methods: Whole slide images of vascular smooth muscle (VSM) and extravillous trophoblast (EVT) cells stained for GPR18 were annotated for areas of interest. Visual scoring was performed on these images by trained and in-training pathologists, while QuPath scoring was performed with the methodology described herein. Results: Bland–Altman analyses showed that, for the VSM category, the two methods were comparable across all staining levels. For EVT cells, the high-intensity staining level was comparable across methods, but the medium and low staining levels were not comparable. Conclusions: Digital image analysis programs offer great potential to revolutionize pathology practice and research by increasing accuracy and decreasing the time and cost of analysis. Careful study is needed to optimize this methodology further.
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Original Article: Testing of actual scanner performance in a high-loaded UNIM laboratory environment
Mikhail Yurevich Genis, Alexey Igorevich Remez, Maksim Ivanovich Untesco, Dmitrii Anatolevich Zhakota
J Pathol Inform 2021, 12:39 (1 November 2021)
DOI:10.4103/jpi.jpi_4_21  
Background: Scanners are the main tool in digital pathology. The technical abilities of scanners determine the workflow logic in the pathology laboratory. Its performance can be restricted by the divergence between the scanning time presented by the manufacturer and the actual scanning time. This could lead to critical deviations from the established business processes in a 24/7 laboratory. Aim: Our investigation is focused in exploring the performance of three main models of high-performance scanners available on the Russian market: 3DHistech, Hamamatsu и Leica. Objectives: We compared the performance of the scanners on the samples of a given size with the manufacturer's stated specifications and evaluated the speed of the scanners on the reference and routine laboratory material. Subjects and Methods: We examined 3DHistech Pannoramic 1000, Hamamatsu NanoZoomer s360 and Leica AT2 with default settings and automatic mode. Two sets of glasses were used (glass slide): Group 1 included 120 slides with 15 mm × 15 mm slices, Group 2 included 120 workflow slides. Results: The average slide scan times in Groups 1 and 2 for the C13220 (156 ± 1.25 s and 117 ± 4.17 s) and Pannoramic 1000 (210 ± 1.64 s and 183 ± 3.78 s) differ statistically significantly (P < 0.0001). Total scanning time including rack reloading was shorter for the workflow slide set group for the modern C13220 and Pannoramic 1000 scanner models. Conclusions: The scanner specifications provided by manufacturers are not sufficient to evaluate the performance. The guidelines and regulations concerning scanner selection should be consented by the digital pathology community. We suggest discussing criteria for evaluating scanner performance.
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Research Article: Browser-based data annotation, active learning, and real-time distribution of artificial intelligence models: from tumor tissue microarrays to COVID-19 radiology
Praphulla M S Bhawsar, Mustapha Abubakar, Marjanka K Schmidt, Nicola J Camp, Melissa H Cessna, Máire A Duggan, Montserrat Garcia.Closas, Jonas S Almeida
J Pathol Inform 2021, 12:38 (27 September 2021)
DOI:10.4103/jpi.jpi_100_20  
Background: Artificial intelligence (AI) is fast becoming the tool of choice for scalable and reliable analysis of medical images. However, constraints in sharing medical data outside the institutional or geographical space, as well as difficulties in getting AI models and modeling platforms to work across different environments, have led to a “reproducibility crisis” in digital medicine. Methods: This study details the implementation of a web platform that can be used to mitigate these challenges by orchestrating a digital pathology AI pipeline, from raw data to model inference, entirely on the local machine. We discuss how this federated platform provides governed access to data by consuming the Application Program Interfaces exposed by cloud storage services, allows the addition of user-defined annotations, facilitates active learning for training models iteratively, and provides model inference computed directly in the web browser at practically zero cost. The latter is of particular relevance to clinical workflows because the code, including the AI model, travels to the user's data, which stays private to the governance domain where it was acquired. Results: We demonstrate that the web browser can be a means of democratizing AI and advancing data socialization in medical imaging backed by consumer-facing cloud infrastructure such as Box.com. As a case study, we test the accompanying platform end-to-end on a large dataset of digital breast cancer tissue microarray core images. We also showcase how it can be applied in contexts separate from digital pathology by applying it to a radiology dataset containing COVID-19 computed tomography images. Conclusions: The platform described in this report resolves the challenges to the findable, accessible, interoperable, reusable stewardship of data and AI models by integrating with cloud storage to maintain user-centric governance over the data. It also enables distributed, federated computation for AI inference over those data and proves the viability of client-side AI in medical imaging. Availability: The open-source application is publicly available at https://episphere.github.io/path, with a short video demonstration at https://youtu.be/z59jToy2TxE.
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ABSTRACTS: Pathology Visions 2020: Through the Prism of Innovation

J Pathol Inform 2021, 12:37 (24 September 2021)
DOI:10.4103/2153-3539.326643  
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Research Article: State of the art cell detection in bone marrow whole slide images
Philipp Gräbel, Özcan Özkan, Martina Crysandt, Reinhild Herwartz, Melanie Baumann, Barbara Mara Klinkhammer, Peter Boor, Tim Hendrik Brümmendorf, Dorit Merhof
J Pathol Inform 2021, 12:36 (17 September 2021)
DOI:10.4103/jpi.jpi_71_20  
Context: Diseases of the hematopoietic system such as leukemia is diagnosed using bone marrow samples. The cell type distribution plays a major role but requires manual analysis of different cell types in microscopy images. Aims: Automated analysis of bone marrow samples requires detection and classification of different cell types. In this work, we propose and compare algorithms for cell localization, which is a key component in automated bone marrow analysis. Settings and Design: We research fully supervised detection architectures but also propose and evaluate several techniques utilizing weak annotations in a segmentation network. We further incorporate typical cell-like artifacts into our analysis. Whole slide microscopy images are acquired from the human bone marrow samples and annotated by expert hematologists. Subjects and Methods: We adapt and evaluate state-of-the-art detection networks. We further propose to utilize the popular U-Net for cell detection by applying suitable preprocessing steps to the annotations. Statistical Analysis Used: Evaluations are performed on a held-out dataset using multiple metrics based on the two different matching algorithms. Results: The results show that the detection of cells in hematopoietic images using state-of-the-art detection networks yields very accurate results. U-Net-based methods are able to slightly improve detection results using adequate preprocessing – despite artifacts and weak annotations. Conclusions: In this work, we propose, U-Net-based cell detection methods and compare with state-of-the-art detection methods for the localization of hematopoietic cells in high-resolution bone marrow images. We show that even with weak annotations and cell-like artifacts, cells can be localized with high precision.
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Original Article: Artificial intelligence in plasma cell myeloma: Neural networks and support vector machines in the classification of plasma cell myeloma data at diagnosis
Ashwini K Yenamandra, Caitlin Hughes, Alexander S Maris
J Pathol Inform 2021, 12:35 (16 September 2021)
DOI:10.4103/jpi.jpi_26_21  
Background: Plasma cell neoplasm and/or plasma cell myeloma (PCM) is a mature B-cell lymphoproliferative neoplasm of plasma cells that secrete a single homogeneous immunoglobulin called paraprotein or M-protein. Plasma cells accumulate in the bone marrow (BM) leading to bone destruction and BM failure. Diagnosis of PCM is based on clinical, radiologic, and pathological characteristics. The percent of plasma cells by manual differential (bone marrow morphology), the white blood cell (WBC) count, cytogenetics, fluorescence in situ hybridization (FISH), microarray, and next-generation sequencing of BM are used in the risk stratification of newly diagnosed PCM patients. The genetics of PCM is highly complex and heterogeneous with several genetic subtypes that have different clinical outcomes. National Comprehensive Cancer Network guidelines recommend targeted FISH analysis of plasma cells with specific DNA probes to detect genetic abnormalities for the staging of PCM (4.2021). Recognition of risk categories through training software for classification of high-risk PCM and a novel way of addressing the current approaches through bioinformatics will be a significant step toward automation of PCM analysis. Methods: A new artificial neural network (ANN) classification model was developed and tested in Python programming language with a first data set of 301 cases and a second data set of 176 cases for a total of 477 cases of PCM at diagnosis. Classification model was also developed with support vector machines (SVM) algorithm in R studio and interactive data visuals using Tableau. Results: The resulting ANN algorithm had 94% accuracy for the first and second data sets with a classification summary of precision (PPV): 0.97, recall (sensitivity): 0.76, f1 score: 0.83, and accuracy of logistic regression of 1.0. SVM of plasma cells versus TP53 revealed a 95% accuracy level. Conclusion: A novel classification model based only on specific morphological and genetic variables was developed using a machine learning algorithm, the ANN. ANN identified an association of WBC and BM plasma cell percentage with two of the high-risk genetic categories in the diagnostic cases of PCM. With further training and testing of additional data sets that include morphologic and additional genetic rearrangements, the newly developed ANN model has the potential to develop an accurate classification of high-risk categories of PCM.
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Research Article: An interactive pipeline for quantitative histopathological analysis of spatially defined drug effects in tumors
Sebastian W Ahn, Benjamin Ferland, Oliver H Jonas
J Pathol Inform 2021, 12:34 (16 September 2021)
DOI:10.4103/jpi.jpi_17_21  
Background: Tumor heterogeneity is increasingly being recognized as a major source of variability in the histopathological assessment of drug responses. Quantitative analysis of immunohistochemistry (IHC) and immunofluorescence (IF) images using biomarkers that capture spatialpatterns of distinct tumor biology and drug concentration in tumors is of high interest to the field. Methods: We have developed an image analysis pipeline to measure drug response using IF and IHC images along spatial gradients of local drug release from a tumor-implantable drug delivery microdevice. The pipeline utilizes a series of user-interactive python scripts and CellProfiler pipelines with custom modules to perform image and spatial analysis of regions of interest within whole-slide images. Results: Worked examples demonstrate that intratumor measurements such as apoptosis, cell proliferation, and immune cell population density can be quantitated in a spatially and drug concentration-dependent manner, establishing in vivo profiles of pharmacodynamics and pharmacokinetics in tumors. Conclusions: Spatial image analysis of tumor response along gradients of local drug release is achievable in high throughput. The major advantage of this approach is the use of spatially aware annotation tools to correlate drug gradients with drug effects in tumors in vivo.
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Original Article: Validation of a portable whole-slide imaging system for frozen section diagnosis
Rajiv Kumar Kaushal, Sathyanarayanan Rajaganesan, Vidya Rao, Akash Sali, Balaji More, Sangeeta B Desai
J Pathol Inform 2021, 12:33 (16 September 2021)
DOI:10.4103/jpi.jpi_95_20  
Background: Frozen section (FS) diagnosis is one of the promising applications of digital pathology (DP). However, the implementation of an appropriate and economically viable DP solution for FS in routine practice is challenging. The objective of this study was to establish the non-inferiority of whole-slide imaging (WSI) versus optical microscopy (OM) for FS diagnosis using a low cost and portable DP system. Materials and Methods: A validation study to investigate the technical performance and diagnostic accuracy of WSI versus OM for FS diagnosis was performed using 60 FS cases[120 slides i.e, 60 hematoxylin and eosin (H & E) and 60 toluidine blue (TOLB)]. The diagnostic concordance, inter- and intra-observer agreement for FS diagnosis by WSI versus OM were recorded. Results: The first time successful scanning rate was 89.1% (107/120). Mean scanning time per slide for H and E and TOLB slide was 1:47 min (range; 0:22–3: 21 min) and 1:46 min (range; 0:21–3: 20 min), respectively. Mean storage space per slide for H and E and TOLB slide was 0.83 GB (range: 0.12–1.73 GB) and 0.71 GB (range: 0.11–1.66 GB), respectively. Considering major discrepancies, the overall diagnostic concordance for OM and WSI, when compared with the reference standard, was 95.42% and 95.83%, respectively. There was almost perfect intra as well as inter-observer agreement (k ≥ 0.8) among 4 pathologists between WSI and OM for FS diagnosis. Mean turnaround time (TAT) of 14:58 min was observed using WSI for FS diagnosis, which was within the College of American Pathologists recommended range for FS reporting. The image quality was average to best quality in most of the cases. Conclusion: WSI was noninferior to OM for FS diagnosis across various specimen types. This portable WSI system can be safely adopted for routine FS diagnosis and provides an economically viable alternative to high-end scanners.
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Technical Note: What is essential is (no more) invisible to the eyes: The introduction of blocdoc in the digital pathology workflow
Vincenzo L’Imperio, Fabio Gibilisco, Filippo Fraggetta
J Pathol Inform 2021, 12:32 (16 September 2021)
DOI:10.4103/jpi.jpi_35_21  
Background: The implementation of a fully digital workflow in any anatomic pathology department requires a complete conversion to a tracked system. Ensuring the strict correspondence of the material submitted for the analysis, from the accessioning to the reporting phase, is mandatory in the anatomic pathology laboratory, especially when implementing the digital pathology for primary histological diagnosis. The proposed solutions, up to now, rely on the verification that all the materials present in the glass slide are also present in the whole slide images (WSIs). Although different methods have already been implemented for this purpose (e.g., the “macroimage” of the digital slide, representing the overview of the glass slide), the recent introduction of a device to capture the cut surface of paraffin blocks put the quality control of the digital workflow a step forward, allowing to match the digitized slide with the corresponding block. This system may represent a reliable, easy-to-use alternative to further reduce tissue inconsistencies between material sent to the lab and the final glass slides or WSIs. Methods: The Anatomic Pathology of the Gravina Hospital in Caltagirone, Sicily, Italy, has implemented the application of the BlocDoc devices (SPOT Imaging, Sterling Heights, USA) in its digital workflow. The instruments were positioned next to every microtome/sectioning station, with the possibility to capture the “normal” and the polarized image of the cut surface of the blocks directly by the technician. The presence of a monitor in the BlocDoc device allowed the technician to check the concordance between the cut surface of the block and the material on the corresponding slide. The link between BlocDoc and the laboratory information system, through the presence of the 2D barcode, allowed the pathologists to access the captured image of the cut surface of the block at the pathologist workstation, thus enabling the direct comparison between this image and the WSI (thumbnail and “macroimage”). Results: During the implementation period, more than 10.000 (11.248) blocks were routinely captured using the BlocDoc. The employment of this approach allowed a drastic reduction of the discordances and tissue inconsistencies. The implementation of the BlocDoc in the routine allowed the detection of two different types of “errors,” the so-called “systematic” and “occasional” ones. The first type was intrinsic of some specific specimens (e.g., transurethral resection of the prostate, nasal polypectomies, and piecemeal uterine myomectomies) characterized by the three-dimensional nature of the fragments and affected almost 100% of these samples. On the other hand, the “occasional” errors, mainly due to inexperience or extreme caution of the technicians in handling tiny specimens, affected 98 blocks (0.9%) of these samples and progressively reduced with the rising confidence with the BlocDoc. One of these cases was clinically relevant. No problems in the recognition of the 2D barcodes were encountered using a laser cassette printer. Finally, rare failures have been recorded during the period, accounting for <0.1% of all the cases, mainly due to network connection issues. Conclusions: The implementation of BlocDoc can further improve the effectiveness of the digital workflow, demonstrating its safety and robustness as a valid alternative to the traditional, nontracked analogic workflow.
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Original Article: Flextilesource: An openseadragon extension for efficient whole-slide image visualization
Peter J Schüffler, Gamze Gokturk Ozcan, Hikmat Al-Ahmadie, Thomas J Fuchs
J Pathol Inform 2021, 12:31 (14 September 2021)
DOI:10.4103/jpi.jpi_13_21  
Background: Web-based digital slide viewers for pathology commonly use OpenSlide and OpenSeadragon (OSD) to access, visualize, and navigate whole-slide images (WSI). Their standard settings represent WSI as deep zoom images (DZI), a generic image pyramid structure that differs from the proprietary pyramid structure in the WSI files. The transformation from WSI to DZI is an additional, time-consuming step when rendering digital slides in the viewer, and inefficiency of digital slide viewers is a major criticism for digital pathology. Aims: To increase efficiency of digital slide visualization by serving tiles directly from the native WSI pyramid, making the transformation from WSI to DZI obsolete. Methods: We implemented a new flexible tile source for OSD that accepts arbitrary native pyramid structures instead of DZI levels. We measured its performance on a data set of 8104 WSI reviewed by 207 pathologists over 40 days in a web-based digital slide viewer used for routine diagnostics. Results: The new FlexTileSource accelerates the display of a field of view in general by 67 ms and even by 117 ms if the block size of the WSI and the tile size of the viewer is increased to 1024 px. We provide the code of our open-source library freely on https://github.com/schuefflerlab/openseadragon. Conclusions:This is the first study to quantify visualization performance on a web-based slide viewer at scale, taking block size and tile size of digital slides into account. Quantifying performance will enable to compare and improve web-based viewers and therewith facilitate the adoption of digital pathology.
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Original Article: Improving generalization of deep learning models for diagnostic pathology by increasing variability in training data: Experiments on osteosarcoma subtypes
Haiming Tang, Nanfei Sun, Steven Shen
J Pathol Inform 2021, 12:30 (4 August 2021)
DOI:10.4103/jpi.jpi_78_20  
Background: Artificial intelligence has an emerging progress in diagnostic pathology. A large number of studies of applying deep learning models to histopathological images have been published in recent years. While many studies claim high accuracies, they may fall into the pitfalls of overfitting and lack of generalization due to the high variability of the histopathological images. Aims and Objects: Use the model training of osteosarcoma as an example to illustrate the pitfalls of overfitting and how the addition of model input variability can help improve model performance. Materials and Methods: We use the publicly available osteosarcoma dataset to retrain a previously published classification model for osteosarcoma. We partition the same set of images into the training and testing datasets differently than the original study: the test dataset consists of images from one patient while the training dataset consists images of all other patients. We also show the influence of training data variability on model performance by collecting a minimal dataset of 10 osteosarcoma subtypes as well as benign tissues and benign bone tumors of differentiation. Results: The performance of the re-trained model on the test set using the new partition schema declines dramatically, indicating a lack of model generalization and overfitting. We show the additions of more and moresubtypes into the training data step by step under the same model schema yield a series of coherent models with increasing performances. Conclusions: In conclusion, we bring forward data preprocessing and collection tactics for histopathological images of high variability to avoid the pitfalls of overfitting and build deep learning models of higher generalization abilities.
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Research Article: Improving algorithm for the alignment of consecutive, whole-slide, immunohistochemical section Images
Cher-Wei Liang, Ruey-Feng Chang, Pei-Wei Fang, Chiao-Min Chen
J Pathol Inform 2021, 12:29 (3 August 2021)
DOI:10.4103/jpi.jpi_106_20  
Background: Accurate and precise alignment of histopathology tissue sections is a key step for the interpretation of the proteome topology and cell level three-dimensional (3D) reconstruction of diseased tissues. However, the realization of an automated and robust method for aligning nonglobally stained immunohistochemical (IHC) sections is still challenging. In this study, we aim to assess the feasibility of multidimensional graph-based image registration on aligning serial-section and whole-slide IHC section images. Materials and Methods: An automated, patch graph-based registration method was established and applied to align serial, whole-slide IHC sections at ×10 magnification (average 32,947 × 27,054 pixels). The alignment began with the initial alignment of high-resolution reference and translated images (object segmentation and rigid registration) and nonlinear registration of low-resolution reference and translated images, followed by the multidimensional graph-based image registration of the segmented patches, and finally, the fusion of deformed patches for inspection. The performance of the proposed method was formulated and evaluated by the Hausdorff distance between continuous image slices. Results: Sets of average 315 patches from five serial whole slide, IHC section images were tested using 21 different IHC antibodies across five different tissue types (skin, breast, stomach, prostate, and soft tissue). The proposed method was successfully automated to align most of the images. The average Hausdorff distance was 48.93 μm with a standard deviation of 14.94 μm, showing a significant improvement from the previously published patch-based nonlinear image registration method (average Hausdorff distance of 93.89 μm with 50.85 μm standard deviation). Conclusions: Our method was effective in aligning whole-slide tissue sections at the cell-level resolution. Further advancements in the screening of the proteome topology and 3D tissue reconstruction could be expected.
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Brief Report: Digital image analysis for estimating stromal CD8+ tumor-infiltrating lymphocytes in lung adenocarcinoma
Iny Jhun, Daniel Shepherd, Yin P Hung, Emilio Madrigal, Long P Le, Mari Mino-Kenudson
J Pathol Inform 2021, 12:28 (5 July 2021)
DOI:10.4103/jpi.jpi_36_20  
Background: Stromal CD8+ tumor-infiltrating lymphocytes (TILs) are an important prognostic and predictive indicator in non-small cell lung cancer (NSCLC). In this study, we aimed to develop and test the feasibility of a digital image analysis (DIA) workflow for estimating stromal CD8+ TIL density. Methods: A DIA workflow developed in a software platform (QuPath) was applied to a specified region of interest (ROI) within the stromal compartment of dual PD-L1/CD8 immunostained slides from 50 lung adenocarcinoma patients. A random tree classifier was trained from 25 training cases and applied to 25 test cases. The DIA-estimated CD8+ TIL densities were compared to manual estimates of three pathologists, who independently quantitated the percentage of CD8+ TILs from predefined ROIs in QuPath. Results: The average estimated total stromal cell count per case was 520 (range: 282–816) by QuPath and 551 (range: 265–744) by pathologists. The DIA-estimated CD8+ TIL density (mean = 16.9%) was comparable to pathologists' manual estimates (mean = 15.9%). A paired t-test showed no statistically significant difference between DIA and pathologist estimates of CD8+ TIL density among both training (n = 25, P = 0.55) and test (n = 25, P = 0.34) cases. There was an almost perfect agreement between QuPath and each pathologist's estimates of CD8+ TIL density (κ = 0.85–0.86). Conclusions: These findings demonstrate the feasibility of applying a DIA workflow for estimating stromal CD8+ TIL density in NSCLC. DIA has the potential to provide an efficient and standardized approach for estimating stromal CD8+ TIL density.
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Research Article: Analysis on the characterization of multiphoton microscopy images for malignant neoplastic colon lesion detection under deep learning methods
Elena Terradillos, Cristina L Saratxaga, Sara Mattana, Riccardo Cicchi, Francesco S Pavone, Nagore Andraka, Benjamin J Glover, Nagore Arbide, Jacques Velasco, Mª Carmen Etxezarraga, Artzai Picon
J Pathol Inform 2021, 12:27 (30 June 2021)
DOI:10.4103/jpi.jpi_113_20  
Background: Colorectal cancer has a high incidence rate worldwide, with over 1.8 million new cases and 880,792 deaths in 2018. Fortunately, its early detection significantly increases the survival rate, reaching a cure rate of 90% when diagnosed at a localized stage. Colonoscopy is the gold standard technique for detection and removal of colorectal lesions with potential to evolve into cancer. When polyps are found in a patient, the current procedure is their complete removal. However, in this process, gastroenterologists cannot assure complete resection and clean margins which are given by the histopathology analysis of the removed tissue, which is performed at laboratory. Aims: In this paper, we demonstrate the capabilities of multiphoton microscopy (MPM) technology to provide imaging biomarkers that can be extracted by deep learning techniques to identify malignant neoplastic colon lesions and distinguish them from healthy, hyperplastic, or benign neoplastic tissue, without the need for histopathological staining. Materials and Methods: To this end, we present a novel MPM public dataset containing 14,712 images obtained from 42 patients and grouped into 2 classes. A convolutional neural network is trained on this dataset and a spatially coherent predictions scheme is applied for performance improvement. Results: We obtained a sensitivity of 0.8228 ± 0.1575 and a specificity of 0.9114 ± 0.0814 on detecting malignant neoplastic lesions. We also validated this approach to estimate the self-confidence of the network on its own predictions, obtaining a mean sensitivity of 0.8697 and a mean specificity of 0.9524 with the 18.67% of the images classified as uncertain. Conclusions: This work lays the foundations for performing in vivo optical colon biopsies by combining this novel imaging technology together with deep learning algorithms, hence avoiding unnecessary polyp resection and allowing in situ diagnosis assessment.
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Original Article: Automated cervical digitized histology whole-slide image analysis toolbox
Sudhir Sornapudi, Ravitej Addanki, R Joe Stanley, William V Stoecker, Rodney Long, Rosemary Zuna, Shellaine R Frazier, Sameer Antani
J Pathol Inform 2021, 12:26 (9 June 2021)
DOI:10.4103/jpi.jpi_52_20  
Background: Cervical intraepithelial neoplasia (CIN) is regarded as a potential precancerous state of the uterine cervix. Timely and appropriate early treatment of CIN can help reduce cervical cancer mortality. Accurate estimation of CIN grade correlated with human papillomavirus type, which is the primary cause of the disease, helps determine the patient's risk for developing the disease. Colposcopy is used to select women for biopsy. Expert pathologists examine the biopsied cervical epithelial tissue under a microscope. The examination can take a long time and is prone to error and often results in high inter-and intra-observer variability in outcomes. Methodology: We propose a novel image analysis toolbox that can automate CIN diagnosis using whole slide image (digitized biopsies) of cervical tissue samples. The toolbox is built as a four-step deep learning model that detects the epithelium regions, segments the detected epithelial portions, analyzes local vertical segment regions, and finally classifies each epithelium block with localized attention. We propose an epithelium detection network in this study and make use of our earlier research on epithelium segmentation and CIN classification to complete the design of the end-to-end CIN diagnosis toolbox. Results: The results show that automated epithelium detection and segmentation for CIN classification yields comparable results to manually segmented epithelium CIN classification. Conclusion: This highlights the potential as a tool for automated digitized histology slide image analysis to assist expert pathologists.
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Original Article: Comparative assessment of digital pathology systems for primary diagnosis
Sathyanarayanan Rajaganesan, Rajiv Kumar, Vidya Rao, Trupti Pai, Neha Mittal, Ayushi Sahay, Santosh Menon, Sangeeta Desai
J Pathol Inform 2021, 12:25 (9 June 2021)
DOI:10.4103/jpi.jpi_94_20  
Background: Despite increasing interest in whole-slide imaging (WSI) over optical microscopy (OM), limited information on comparative assessment of various digital pathology systems (DPSs) is available. Materials and Methods: A comprehensive evaluation was undertaken to investigate the technical performance–assessment and diagnostic accuracy of four DPSs with an objective to establish the noninferiority of WSI over OM and find out the best possible DPS for clinical workflow. Results: A total of 2376 digital images, 15,775 image reads (OM - 3171 + WSI - 12,404), and 6100 diagnostic reads (OM - 1245, WSI - 4855) were generated across four DPSs (coded as DPS: 1, 2, 3, and 4) using a total 240 cases (604 slides). Onsite technical evaluation revealed successful scan rate: DPS3 < DPS2 < DPS4 < DPS1; mean scanning time: DPS4 < DPS1 < DPS2 < DPS3; and average storage space: DPS3 < DPS2 < DPS1 < DPS4. Overall diagnostic accuracy, when compared with the reference standard for OM and WSI, was 95.44% (including 2.48% minor and 2.08% major discordances) and 93.32% (including 4.28% minor and 2.4% major discordances), respectively. The difference between the clinically significant discordances by WSI versus OM was 0.32%. Major discordances were observed mostly using DPS4 and least in DPS1; however, the difference was statistically insignificant. Almost perfect (κ ≥ 0.8)/substantial (κ = 0.6–0.8) inter/intra-observer agreement between WSI and OM was observed for all specimen types, except cytology. Overall image quality was best for DPS1 followed by DPS4. Mean digital artifact rate was 6.8% (163/2376 digital images) and maximum artifacts were noted in DPS2 (n = 77) followed by DPS3 (n = 36). Most pathologists preferred viewing software of DPS1 and DPS2. Conclusion: WSI was noninferior to OM for all specimen types, except for cytology. Each DPS has its own pros and cons; however, DPS1 closely emulated the real-world clinical environment. This evaluation is intended to provide a roadmap to pathologists for the selection of the appropriate DPSs while adopting WSI.
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Research Article: Digital pathology-based study of cell- and tissue-level morphologic features in serous borderline ovarian tumor and high-grade serous ovarian cancer
Jun Jiang, Burak Tekin, Ruifeng Guo, Hongfang Liu, Yajue Huang, Chen Wang
J Pathol Inform 2021, 12:24 (5 June 2021)
DOI:10.4103/jpi.jpi_76_20  
Background: Serous borderline ovarian tumor (SBOT) and high-grade serous ovarian cancer (HGSOC) are two distinct subtypes of epithelial ovarian tumors, with markedly different biologic background, behavior, prognosis, and treatment. However, the histologic diagnosis of serous ovarian tumors can be subjectively variable and labor-intensive as multiple tumor slides/blocks need to be thoroughly examined to search for these features. Materials and Methods: We developed a novel informatics system to facilitate objective and scalable diagnosis screening for SBOT and HGSOC. The system was built upon Groovy scripts and QuPath to enable interactive annotation and data exchange. Results: The system was used to successfully detect cellular boundaries and extract an expanded set of cellular features representing cell- and tissue-level characteristics. The performance of cell-level classification for both tumor and stroma cells achieved >90% accuracy. The performance of differentiating HGSOC versus SBOT achieved 91%–95% accuracy for 6485 imaging patches which have sufficient tumor and stroma cells (minimum of ten each) and 97% accuracy for classifying patients when aggregating the results to whole-slide image based on consensus. Conclusions: Cellular features digitally extracted from pathological images can be used for cell classification and SBOT v. HGSOC differentiation. Introducing digital pathology into ovarian cancer research could be beneficial to discover potential clinical implications. A larger cohort is required to further evaluate the system.
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Original Article: A synoptic reporting system to monitor bone marrow aspirate and biopsy quality
Roger S Riley, Paras Gandhi, Susan E Harley, Paulo Garcia, Justin B Dalton, Alden Chesney
J Pathol Inform 2021, 12:23 (25 May 2021)
DOI:10.4103/jpi.jpi_53_20  
Objectives: Bone marrow evaluation plays a critical role in the diagnosis, staging, and monitoring of many diseases. Although there are standardized guidelines for assessing bone marrow specimen quality, there is a lack of evidence-based tools to perform such assessments. The objective was to monitor bone marrow sample quality in real time by standardizing the basic components of a synoptic report and incorporating it into a bone marrow report template. Materials and Methods: A relational database of bone marrow quality parameters was developed and incorporated into our laboratory information system bone marrow report template, with data entry completed during specimen sign out. Data from multiple reports created within a date range were extracted by Structured Query Language query, and summarized in tabular form. Reports generated from these data were utilized in quality improvement efforts. Results: The synoptic reporting system was routinely used to record the quality of bone marrow specimens from adult patients. Data from 3189 bone marrow aspirates, 3302 biopsies, and 3183 biopsy touch imprints identified hemodilution as the principal issue affecting bone marrow aspirate quality, whereas aspiration artifact and fragmentation affected bone marrow biopsy quality. Conclusions: The bone marrow synoptic reporting process was easy to use, readily adaptable, and has proved a useful component of the overall quality assurance process to optimize bone marrow quality.
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Original Article: Three-dimensional surface imaging and printing in anatomic pathology
Melanie C Bois, Jonathan M Morris, Jennifer M Boland, Nicole L Larson, Emily F Scharrer, Marie-Christine Aubry, Joseph J Maleszewski
J Pathol Inform 2021, 12:22 (18 May 2021)
DOI:10.4103/jpi.jpi_8_21  
Three-dimensional (3D) imaging is increasingly being incorporated into a variety of medical specialties: surgery and radiology being but two prominent examples. Image-intensive disciplines, such as anatomic pathology (AP), represent excellent potential candidates for further exploration of this innovative technology. Multiple potential use cases exist within AP, involving patient care, education, and research. These use cases broadly include direct utilization of the 3D digital assets for viewing on a 2D screen, populating 3D extended reality platforms (virtual reality, augmented reality, and mixed reality) as well as generation of 3D printed photorealistic specimen models. Herein, these use cases are explored with specific regard to our experiences and yet unrealized potential. Future directions and considerations are also discussed.
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Technical Note: Dicom_wsi: A python implementation for converting whole-slide images to digital imaging and Communications in Medicine compliant files
Qiangqiang Gu, Naresh Prodduturi, Jun Jiang, Thomas J Flotte, Steven N Hart
J Pathol Inform 2021, 12:21 (11 May 2021)
DOI:10.4103/jpi.jpi_88_20  
Background: Adoption of the Digital Imaging and Communications in Medicine (DICOM) standard for whole slide images (WSIs) has been slow, despite significant time and effort by standards curators. One reason for the lack of adoption is that there are few tools which exist that can meet the requirements of WSIs, given an evolving ecosystem of best practices for implementation. Eventually, vendors will conform to the specification to ensure enterprise interoperability, but what about archived slides? Millions of slides have been scanned in various proprietary formats, many with examples of rare histologies. Our hypothesis is that if users and developers had access to easy to use tools for migrating proprietary formats to the open DICOM standard, then more tools would be developed as DICOM first implementations. Methods: The technology we present here is dicom_wsi, a Python based toolkit for converting any slide capable of being read by the OpenSlide library into DICOM conformant and validated implementations. Moreover, additional postprocessing such as background removal, digital transformations (e.g., ink removal), and annotation storage are also described. dicom_wsi is a free and open source implementation that anyone can use or modify to meet their specific purposes. Results: We compare the output of dicom_wsi to two other existing implementations of WSI to DICOM converters and also validate the images using DICOM capable image viewers. Conclusion: dicom_wsi represents the first step in a long process of DICOM adoption for WSI. It is the first open source implementation released in the developer friendly Python programming language and can be freely downloaded at https:// github.com/Steven N Hart/dicom_wsi.
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Technical Note: Remote reporting during a pandemic using digital pathology solution: Experience from a tertiary care cancer center
Veena Ramaswamy, BN Tejaswini, Sowmya B Uthaiah
J Pathol Inform 2021, 12:20 (8 April 2021)
DOI:10.4103/jpi.jpi_109_20  
Background: Remote reporting in anatomic pathology is an important advantage of digital pathology that has not been much explored. The COVID-19 pandemic has provided an opportunity to explore this important application of digital pathology system in a tertiary care cancer center to ensure patient care and staff safety. Regulatory guidelines have been described for remote reporting following the pandemic. Herein, we describe our experience of validation of digital pathology workflow for remote reporting to encourage pathologists to utilize this facility which opens door for multiple, multidisciplinary collaborations. Objective: To demonstrate the validation and the operational feasibility of remote reporting using a digital pathology system. Materials and Methods: Our retrospective validation included whole-slide images (WSIs) of 60 cases of histopathology and 20 cases each of frozen sections and a digital image-based breast algorithm after a washout period of 3 months. Three pathologists with different models of consumer-grade laptops reviewed the cases remotely to assess the diagnostic concordance and operational feasibility of the modified workflow. The slides were digitized on a USFDA-approved Philips UFS 300 scanner at ×40 resolution (0.25 μm/pixel) and viewed on the Image Management System through a web browser. All the essential parameters were reported for each case. After successful validation, 886 cases were reported remotely from March 29, 2020, to June 30, 2020, prospectively. Light microscopy formed the gold standard reference in remote reporting. Results: 100% major diagnostic concordance was observed in the validation of remote reporting in the retrospective and prospective studies using consumer-grade laptops. The deferral rate was 0.34%. 97.6% of histopathology and 100% of frozen sections were signed out within the turnaround time. Network speed and a lack of virtual private network did not significantly affect the study. Conclusion: This study of validation and reporting of complete pathology cases remotely, including their operational feasibility during a public health emergency, proves that remote sign-out using a digital pathology system is not inferior to WSIs on medical-grade monitors and light microscopy. Such studies on remote reporting open the door for the use of digital pathology for interinstitutional consultation and collaboration: Its main intended use.
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Technical Note: Use of middleware data to dissect and optimize hematology autoverification
Rachel D Starks, Anna E Merrill, Scott R Davis, Dena R Voss, Pamela J Goldsmith, Bonnie S Brown, Jeff Kulhavy, Matthew D Krasowski
J Pathol Inform 2021, 12:19 (7 April 2021)
DOI:10.4103/jpi.jpi_89_20  
Background: Hematology analysis comprises some of the highest volume tests run in clinical laboratories. Autoverification of hematology results using computer-based rules reduces turnaround time for many specimens, while strategically targeting specimen review by technologist or pathologist. Methods: Autoverification rules had been developed over a decade at an 800-bed tertiary/quarternary care academic medical central laboratory serving both adult and pediatric populations. In the process of migrating to newer hematology instruments, we analyzed the rates of the autoverification rules/flags most commonly associated with triggering manual review. We were particularly interested in rules that on their own often led to manual review in the absence of other flags. Prior to the study, autoverification rates were 87.8% (out of 16,073 orders) for complete blood count (CBC) if ordered as a panel and 85.8% (out of 1,940 orders) for CBC components ordered individually (not as the panel). Results: Detailed analysis of rules/flags that frequently triggered indicated that the immature granulocyte (IG) flag (an instrument parameter) and rules that reflexed platelet by impedance method (PLT-I) to platelet by fluorescent method (PLT-F) represented the two biggest opportunities to increase autoverification. The IG flag threshold had previously been validated at 2%, a setting that resulted in this flag alone preventing autoverification in 6.0% of all samples. The IG flag threshold was raised to 5% after detailed chart review; this was also the instrument vendor's default recommendation for the newer hematology analyzers. Analysis also supported switching to PLT-F for all platelet analysis. Autoverification rates increased to 93.5% (out of 91,692 orders) for CBC as a panel and 89.8% (out of 11,982 orders) for individual components after changes in rules and laboratory practice. Conclusions: Detailed analysis of autoverification of hematology testing at an academic medical center clinical laboratory that had been using a set of autoverification rules for over a decade revealed opportunities to optimize the parameters. The data analysis was challenging and time-consuming, highlighting opportunities for improvement in software tools that allow for more rapid and routine evaluation of autoverification parameters.
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Technical Note: Image Analysis Using Machine Learning for Automated Detection of Hemoglobin H Inclusions in Blood Smears – A Method for Morphologic Detection of Rare Cells
Shir Ying Lee, Crystal M E Chen, Elaine Y P Lim, Liang Shen, Aneesh Sathe, Aahan Singh, Jan Sauer, Kaveh Taghipour, Christina Y C Yip
J Pathol Inform 2021, 12:18 (7 April 2021)
DOI:10.4103/jpi.jpi_110_20  
Background: Morphologic rare cell detection is a laborious, operator-dependent process which has the potential to be improved by the use of image analysis using artificial intelligence. Detection of rare hemoglobin H (HbH) inclusions in red cells in the peripheral blood is a common screening method for alpha-thalassemia. This study aims to develop a convolutional neural network-based algorithm for the detection of HbH inclusions. Methods: Digital images of HbH-positive and HbH-negative blood smears were used to train and test the software. The software performance was tested on images obtained at various magnifications and on different scanning platforms. Another model was developed for total red cell counting and was used to confirm HbH cell frequency in alpha-thalassemia trait. The threshold minimum red cells to image for analysis was determined by Poisson modeling and validated on image sets. Results: The sensitivity and specificity of the software for HbH+ cells on images obtained at ×100, ×60, and ×40 objectives were close to 91% and 99%, respectively. When an AI-aided diagnostic model was tested on a pilot of 40 whole slide images (WSIs), good inter-rater reliability and high sensitivity and specificity of slide-level classification were obtained. Using the lowest frequency of HbH+ cells (1 in 100,000) observed in our study, we estimated that a minimum of 2.4 × 106 red cells would need to be analyzed to reduce misclassification at the slide level. The minimum required smear size was validated on 78 image sets which confirmed its validity. Conclusions: WSI image analysis can be utilized effectively for morphologic rare cell detection. The software can be further developed on WISs and evaluated in future clinical validation studies comparing AI-aided diagnosis with the routine diagnostic method.
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Original Article: Dissecting the business case for adoption and implementation of digital pathology: A white paper from the digital pathology association Highly accessed article
Giovanni Lujan, Jennifer C Quigley, Douglas Hartman, Anil Parwani, Brian Roehmholdt, Bryan Van Meter, Orly Ardon, Matthew G Hanna, Dan Kelly, Chelsea Sowards, Michael Montalto, Marilyn Bui, Mark D Zarella, Victoria LaRosa, Gerard Slootweg, Juan Antonio Retamero, Mark C Lloyd, James Madory, Doug Bowman
J Pathol Inform 2021, 12:17 (7 April 2021)
DOI:10.4103/jpi.jpi_67_20  
We believe the switch to a digital pathology (DP) workflow is imminent and it is essential to understand the economic implications of conversion. Many aspects of the adoption of DP will be disruptive and have a direct financial impact, both in short term costs, such as investment in equipment and personnel, and long term revenue potential, such as improved productivity and novel tests. The focus of this whitepaper is to educate pathologists, laboratorians and other stakeholders about the business and monetary considerations of converting to a digital pathology workflow. The components of a DP business plan will be thoroughly summarized, and guidance will be provided on how to build a case for adoption and implementation as well as a roadmap for transitioning from an analog to a digital pathology workflow in various laboratory settings. It is important to clarify that this publication is not intended to list prices although some financials will be mentioned as examples. The authors encourage readers who are evaluating conversion to a DP workflow to use this paper as a foundational guide for conducting a thorough and complete assessment while incorporating in current market pricing. Contributors to this paper analyzed peer-reviewed literature and data collected from various institutions, some of which are mentioned. Digital pathology will change the way we practice through facilitating patient access to expert pathology services and enabling image analysis tools and assays to aid in diagnosis, prognosis, risk stratification and therapeutic selection. Together, they will result in the delivery of valuable information from which to make better decisions and improve the health of patients.
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Guidelines: Integrating the health-care enterprise pathology and laboratory medicine guideline for digital pathology interoperability
Rajesh C Dash, Nicholas Jones, Riki Merrick, Gunter Haroske, James Harrison, Craig Sayers, Nick Haarselhorst, Mikael Wintell, Markus D Herrmann, François Macary
J Pathol Inform 2021, 12:16 (24 March 2021)
DOI:10.4103/jpi.jpi_98_20  
Integrating the health-care enterprise (IHE) is an international initiative to promote the use of standards to achieve interoperability among health information technology systems. The Pathology and Laboratory Medicine domain within IHE has brought together subject matter experts, electronic health record vendors, and digital imaging vendors, to initiate development of a series of digital pathology interoperability guidelines, called “integration profiles” within IHE. This effort begins with documentation of common use cases, followed by identification of available data and technology standards best utilized to achieve those use cases. An integration profile that describes the information flow and technology interactions is then published for trial use. Real world testing occurs in “connectathon” events, in which multiple vendors attempt to connect their products following the interoperability guidance parameters set forth in the profile. This paper describes the overarching set of integration profiles, one of which has been published, to support key digital pathology use cases.
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Original Article: Selection of representative histologic slides in interobserver reproducibility studies: Insights from expert review for ovarian carcinoma subtype classification
Marios A Gavrielides, Brigitte M Ronnett, Russell Vang, Fahime Sheikhzadeh, Jeffrey D Seidman
J Pathol Inform 2021, 12:15 (22 March 2021)
DOI:10.4103/jpi.jpi_56_20  
Background: Observer studies in pathology often utilize a limited number of representative slides per case, selected and reported in a nonstandardized manner. Reference diagnoses are commonly assumed to be generalizable to all slides of a case. We examined these issues in the context of pathologist concordance for histologic subtype classification of ovarian carcinomas (OCs). Materials and Methods: A cohort of 114 OCs consisting of 72 cases with a single representative slide (Group 1) and 42 cases with multiple representative slides (148 slides, 2-“6 sections per case, Group 2) was independently reviewed by three experts in gynecologic pathology (case-based review). In a follow-up study, each individual slide was independently reviewed in a randomized order by the same pathologists (section-based review). Results: Average interobserver concordance varied from 100% for Group 1 to 64.3% for Group 2 (86.8% across all cases). Across Group 2, 19 cases (45.2%) had at least one slide classified as a different subtype than the subtype assigned from case-based review, demonstrating the impact of intratumoral heterogeneity. Section-based concordance across individual sections from Group 2 was comparable to case-based concordance for those cases indicating diagnostic challenges at the individual section level. Findings demonstrate the increased diagnostic complexity of heterogeneous tumors that require multiple section sampling and its impact on pathologist performance. Conclusions: The proportion of cases with multiple representative slides in cohorts used in validation studies, such as those conducted to evaluate artificial intelligence/machine learning tools, can influence diagnostic performance, and if not accounted for, can cause disparities between research and real-world observations and between research studies. Case selection in validation studies should account for tumor heterogeneity to create balanced datasets in terms of diagnostic complexity.
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Commentary: Commentary: The digital fate of glomeruli in renal biopsy
Ilaria Girolami, Stefano Marletta, Albino Eccher
J Pathol Inform 2021, 12:14 (22 March 2021)
DOI:10.4103/jpi.jpi_102_20  
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Review Article: Artificial intelligence in pathology: From prototype to product Highly accessed article
André Homeyer, Johannes Lotz, Lars Ole Schwen, Nick Weiss, Daniel Romberg, Henning Höfener, Norman Zerbe, Peter Hufnagl
J Pathol Inform 2021, 12:13 (22 March 2021)
DOI:10.4103/jpi.jpi_84_20  
Modern image analysis techniques based on artificial intelligence (AI) have great potential to improve the quality and efficiency of diagnostic procedures in pathology and to detect novel biomarkers. Despite thousands of published research papers on applications of AI in pathology, hardly any research implementations have matured into commercial products for routine use. Bringing an AI solution for pathology to market poses significant technological, business, and regulatory challenges. In this paper, we provide a comprehensive overview and advice on how to meet these challenges. We outline how research prototypes can be turned into a product-ready state and integrated into the IT infrastructure of clinical laboratories. We also discuss business models for profitable AI solutions and reimbursement options for computer assistance in pathology. Moreover, we explain how to obtain regulatory approval so that AI solutions can be launched as in vitro diagnostic medical devices. Thus, this paper offers computer scientists, software companies, and pathologists a road map for transforming prototypes of AI solutions into commercial products.
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Commentary: Commentary: Leveraging edge computing technology for digital pathology
Mustafa Yousif, Ulysses G J Balis, Anil V Parwani, Liron Pantanowitz
J Pathol Inform 2021, 12:12 (22 March 2021)
DOI:10.4103/jpi.jpi_112_20  
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Editorial: Virtual mega-meetings: Here to stay?
Lewis A Hassell, Hans J G Hassell
J Pathol Inform 2021, 12:11 (15 March 2021)
DOI:10.4103/jpi.jpi_99_20  
Among the paradigms changed by the COVID-19 pandemic is the traditional academic and educational conference. In the vein of turning lemons into lemonade, many organizations and individuals have discovered ways that this public health necessitated change can be transformed into a boon to both participants and organizations. However, the question of whether this shift becomes permanent, or a component of the future of academic and educational meetings remains to be seen, and likely will depend on the solution to some of the challenges that have not been sweetened by the shift. This editorial draws on experience with a limited scope of virtual meetings in two different disciplines to make the case that the Virtual Mega-Conference is likely to continue to be a part of life in the years ahead.
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Editorial: Europe unites for the digital transformation of pathology: The role of the new ESDIP
Catarina Eloy, Norman Zerbe, Filippo Fraggetta
J Pathol Inform 2021, 12:10 (12 March 2021)
DOI:10.4103/jpi.jpi_80_20  
The European Society for Digital and Integrative Pathology (ESDIP) was formally founded in 2016 in Berlin. After a well-participated annual general meeting, ESDIP members elected a new active structure for the next term of office. The priority goals of this new and highly motivated team will be to support the digital transformation in the pathology laboratories, to build inter-institutional bridges for cooperation, to establish a solid educational program, and to increase the collaboration with industry partners.
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Original Article: Overcoming an annotation hurdle: Digitizing pen annotations from whole slide images
Peter J. Schüffler, Dig Vijay Kumar Yarlagadda, Chad Vanderbilt, Thomas J Fuchs
J Pathol Inform 2021, 12:9 (23 February 2021)
DOI:10.4103/jpi.jpi_85_20  
Background: The development of artificial intelligence (AI) in pathology frequently relies on digitally annotated whole slide images (WSI). The creation of these annotations – manually drawn by pathologists in digital slide viewers – is time consuming and expensive. At the same time, pathologists routinely annotate glass slides with a pen to outline cancerous regions, for example, for molecular assessment of the tissue. These pen annotations are currently considered artifacts and excluded from computational modeling. Methods: We propose a novel method to segment and fill hand-drawn pen annotations and convert them into a digital format to make them accessible for computational models. Our method is implemented in Python as an open source, publicly available software tool. Results: Our method is able to extract pen annotations from WSI and save them as annotation masks. On a data set of 319 WSI with pen markers, we validate our algorithm segmenting the annotations with an overall Dice metric of 0.942, Precision of 0.955, and Recall of 0.943. Processing all images takes 15 min in contrast to 5 h manual digital annotation time. Further, the approach is robust against text annotations. Conclusions: We envision that our method can take advantage of already pen-annotated slides in scenarios in which the annotations would be helpful for training computational models. We conclude that, considering the large archives of many pathology departments that are currently being digitized, our method will help to collect large numbers of training samples from those data.
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Original Article: Examining the relationship between altmetric score and traditional bibliometrics in the pathology literature
Adam R Floyd, Zachary C Wiley, Carter J Boyd, Christine G Roth
J Pathol Inform 2021, 12:8 (23 February 2021)
DOI:10.4103/jpi.jpi_81_20  
Background: Recently, research data are increasingly shared through social media and other digital platforms. Traditionally, the influence of a scientific article has been assessed by the publishing journal's impact factor (IF) and its citation count. The Altmetric scoring system, a new bibliometric that integrates research “mentions” over digital media platforms, has emerged as a metric of online research distribution. The aim of this study was to explore the relationship of the Altmetric Score with IF and citation number within the pathology literature. Methods: Citation count and Altmetric scores were obtained from the top 10 most-cited articles from the 15 pathology journals with the highest IF for 2013 and 2016. These variables were analyzed and correlated with each other, as well as the age of the publishing journal's Twitter account. Results: Three hundred articles were examined from the two cohorts. The total citation count of the articles decreased from 21,043 (2013) to 14,679 (2016), while the total Altmetric score increased from 830 (2013) to 4066 (2016). In 2013, Altmetric score weakly correlated with citation number (r = 0.284, P < 0.001) but not with journal IF (r = 0.024, P = 0.771). In 2016, there was strong correlation between citation count and Altmetric Score (r = 0.714, P < 0.0001) but not the IF (r = 0.0442, P = 0.591). Twitter was the single most important contributor to the Altmetric score; however, the age of the Twitter account was not associated with citation number nor Altmetric score. Conclusions: In the pathology literature studied, the Altmetric score correlates with article citation count, suggesting that the Altmetric score and conventional bibliometrics can be treated as complementary metrics. Given the trend towards increasing use of social media, additional investigation is warranted to evaluate the evolving role of social media metrics to assess the dissemination and impact of scientific findings in the field of pathology.
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Original Article: Experience reviewing digital pap tests using a gallery of images
Liron Pantanowitz, Sarah Harrington
J Pathol Inform 2021, 12:7 (23 February 2021)
DOI:10.4103/jpi.jpi_96_20  
Introduction: Hologic is developing a digital cytology platform. An educational website was launched for users to review these digitized Pap test cases. The aim of this study was to analyze data captured from this website. Materials and Methods: ThinPrep® Pap test slides were scanned at ×40 using a volumetric (14 focal plane) technique. Website cases consisted of an image gallery and whole slide image (WSI). Over a 13 month period data were recorded including diagnoses, time participants spent online, and number of clicks on the gallery and WSI. Results: 51,289 cases were reviewed by 918 reviewers. Cytotechnologists spent less time (M [Median] = 65.0 s) than pathologists (M = 82.2 s) reviewing cases (P < 0.001). Longer times were associated with incorrect diagnoses and cases with organisms. Cytotechnologists matched the reference diagnoses in 85% of cases compared to pathologists who matched in 79.8%. While in 62% of cases reviewers only examined the gallery, they attained the correct diagnosis 92.7% of the time. Pathologists made more clicks on the gallery and WSI than cytotechnologists (P < 0.001). Diagnostic accuracy decreased with increasing clicks. Conclusions: Website participation provided feedback about how cytologists interact with a digital platform when reviewing cases. These data suggest that digital Pap test review when comprised of an image gallery displaying diagnostically relevant objects is quick and easy to interpret. The high diagnostic concordance of digital Pap tests with reference diagnoses can be attributed to high image quality with volumetric scanning, image gallery format, and ability for users to freely navigate the entire digital slide.
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Original Article: A comparison of methods for studying the tumor microenvironment's spatial heterogeneity in digital pathology specimens
Ines Panicou Nearchou, Daniel Alexander Soutar, Hideki Ueno, David James Harrison, Ognjen Arandjelovic, Peter David Caie
J Pathol Inform 2021, 12:6 (28 January 2021)
DOI:10.4103/jpi.jpi_26_20  
Background: The tumor microenvironment is highly heterogeneous, and it is understood to affect tumor progression and patient outcome. A number of studies have reported the prognostic significance of tumor-infiltrating lymphocytes and tumor budding in colorectal cancer (CRC). However, the significance of the intratumoral heterogeneity present in the spatial distribution of these features within the tumor immune microenvironment (TIME) has not been previously reported. Evaluating this intratumoral heterogeneity may aid the understanding of the TIME's effect on patient prognosis as well as identify novel aggressive phenotypes which can be further investigated as potential targets for new treatment. Methods: In this study, we propose and apply two spatial statistical methodologies for the evaluation of the intratumor heterogeneity present in the distribution of CD3 + and CD8 + lymphocytes and tumor buds (TB) in 232 Stage II CRC cases. Getis-Ord hotspot analysis was applied to quantify the cold and hotspots, defined as regions with a significantly low or high number of each feature of interest, respectively. A novel spatial heatmap methodology for the quantification of the cold and hotspots of each feature of interest, which took into account both the interpatient heterogeneity and the intratumor heterogeneity, was further developed. Results: Resultant data from each analysis, characterizing the spatial intratumor heterogeneity of lymphocytes and TBs were used for the development of two new highly prognostic risk models. Conclusions: Our results highlight the value of applying spatial statistics for the assessment of the intratumor heterogeneity. Both Getis-Ord hotspot and our proposed spatial heatmap analysis are broadly applicable across other tissue types as well as other features of interest. Availability: The code underpinning this publication can be accessed at https://doi.org/10.17630/c2306fe9-66e2-4442-ad89-f986220053e2.
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Original Article: Effects of image quantity and image source variation on machine learning histology differential diagnosis models
Elham Vali-Betts, Kevin J Krause, Alanna Dubrovsky, Kristin Olson, John Paul Graff, Anupam Mitra, Ananya Datta-Mitra, Kenneth Beck, Aristotelis Tsirigos, Cynthia Loomis, Antonio Galvao Neto, Esther Adler, Hooman H Rashidi
J Pathol Inform 2021, 12:5 (23 January 2021)
DOI:10.4103/jpi.jpi_69_20  
Aims: Histology, the microscopic study of normal tissues, is a crucial element of most medical curricula. Learning tools focused on histology are very important to learners who seek diagnostic competency within this important diagnostic arena. Recent developments in machine learning (ML) suggest that certain ML tools may be able to benefit this histology learning platform. Here, we aim to explore how one such tool based on a convolutional neural network, can be used to build a generalizable multi-classification model capable of classifying microscopic images of human tissue samples with the ultimate goal of providing a differential diagnosis (a list of look-alikes) for each entity. Methods: We obtained three institutional training datasets and one generalizability test dataset, each containing images of histologic tissues in 38 categories. Models were trained on data from single institutions, low quantity combinations of multiple institutions, and high quantity combinations of multiple institutions. Models were tested against withheld validation data, external institutional data, and generalizability test images obtained from Google image search. Performance was measured with macro and micro accuracy, sensitivity, specificity, and f1-score. Results: In this study, we were able to show that such a model's generalizability is dependent on both the training data source variety and the total number of training images used. Models which were trained on 760 images from only a single institution performed well on withheld internal data but poorly on external data (lower generalizability). Increasing data source diversity improved generalizability, even when decreasing data quantity: models trained on 684 images, but from three sources improved generalization accuracy between 4.05' and 18.59'. Maintaining this diversity and increasing the quantity of training images to 2280 further improved generalization accuracy between 16.51' and 32.79'. Conclusions: This pilot study highlights the significance of data diversity within such studies. As expected, optimal models are those that incorporate both diversity and quantity into their platforms.s
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Research Article: Verification and validation of digital pathology (whole slide imaging) for primary histopathological diagnosis: All wales experience Highly accessed article
M Babawale, A Gunavardhan, J Walker, T Corfield, P Huey, A Savage, A Bansal, M Atkinson, H Abdelsalam, E Raweily, A Christian, I Evangelou, D Thomas, J Shannon, E Youd, P Brumwell, J Harrison, I Thompson, M Rashid, G Leopold, A Finall, S Roberts, D Housa, P Nedeva, A Davies, D Fletcher, Muhammad Aslam
J Pathol Inform 2021, 12:4 (23 January 2021)
DOI:10.4103/jpi.jpi_55_20  
Aims: The study is aimed to verify Aperio AT2 scanner for reporting on the digital pathology platform (DP) and to validate the cohort of pathologists in the interpretation of DP for routine diagnostic histopathological services in Wales, United Kingdom. Materials, Methods and Results: This was a large multicenter study involving seven hospitals across Wales and unique with 22 (largest number) pathologists participating. 7491 slides from 3001 cases were scanned on Leica Aperio AT2 scanner and reported on digital workstations with Leica software of e-slide manager. A senior pathology fellow compared DP reports with authorized reports on glass slide (GS). A panel of expert pathologists reviewed the discrepant cases under multiheader microscope to establish ground truth. 2745 out of 3001 (91%) cases showed complete concordance between DP and GS reports. Two hundred and fifty-six cases showed discrepancies in diagnosis, of which 170 (5.6%) were deemed of no clinical significance by the review panel. There were 86 (2.9%) clinically significant discrepancies in the diagnosis between DP and GS. The concordance was raised to 97.1% after discounting clinically insignificant discrepancies. Ground truth lay with DP in 28 out of 86 clinically significant discrepancies and with GS in 58 cases. Sensitivity of DP was 98.07% (confidence interval [CI] 97.57–98.56%); for GS was 99.07% (CI 98.72–99.41%). Conclusions: We concluded that Leica Aperio AT2 scanner produces adequate quality of images for routine histopathologic diagnosis. Pathologists were able to diagnose in DP with good concordance as with GS. Strengths and Limitations of this Study: Strengths of this study – This was a prospective blind study. Different pathologists reported digital and glass arms at different times giving an ambience of real-time reporting. There was standardized use of software and hardware across Wales. A strong managerial support from efficiency through the technology group was a key factor for the implementation of the study. Limitations: This study did not include Cytopathology and in situ hybridization slides. Difficulty in achieving surgical pathology practise standardization across the whole country contributed to intra-observer variations.
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Original Article: Remote reporting from home for primary diagnosis in surgical pathology: A tertiary oncology center experience during the COVID-19 pandemic Highly accessed article
Vidya Rao, Rajiv Kumar, Sathyanarayanan Rajaganesan, Swapnil Rane, Gauri Deshpande, Subhash Yadav, Asawari Patil, Trupti Pai, Santosh Menon, Aekta Shah, Katha Rabade, Mukta Ramadwar, Poonam Panjwani, Neha Mittal, Ayushi Sahay, Bharat Rekhi, Munita Bal, Uma Sakhadeo, Sumeet Gujral, Sangeeta Desai
J Pathol Inform 2021, 12:3 (8 January 2021)
DOI:10.4103/jpi.jpi_72_20  
Background: The COVID-19 pandemic accelerated the widespread adoption of digital pathology (DP) for primary diagnosis in surgical pathology. This paradigm shift is likely to influence how we function routinely in the postpandemic era. We present learnings from early adoption of DP for a live digital sign-out from home in a risk-mitigated environment. Materials and Methods: We aimed to validate DP for remote reporting from home in a real-time environment and evaluate the parameters influencing the efficiency of a digital workflow. Eighteen pathologists prospectively validated DP for remote use on 567 biopsy cases including 616 individual parts from 7 subspecialties over a duration from March 21, 2020, to June 30, 2020. The slides were digitized using Roche Ventana DP200 whole-slide scanner and reported from respective homes in a risk-mitigated environment. Results: Following re-review of glass slides, there was no major discordance and 1.2% (n = 7/567) minor discordance. The deferral rate was 4.5%. All pathologists reported from their respective homes from laptops with an average network speed of 20 megabits per second. Conclusion: We successfully validated and adopted a digital workflow for remote reporting with available resources and were able to provide our patients, an undisrupted access to subspecialty expertise during these unprecedented times.
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Technical Note: Implementation of collodion bag protocol to improve whole-slide imaging of scant gynecologic curettage specimens
Iny Jhun, David Levy, Harumi Lim, Quintina Herrera, Erika Dobo, Dominique Burns, William Hetherington, Ronald Macasaet, April J Young, Christina S Kong, Ann K Folkins, Eric Joon Yang
J Pathol Inform 2021, 12:2 (8 January 2021)
DOI:10.4103/jpi.jpi_82_20  
Background: Digital pathology has been increasingly implemented for primary surgical pathology diagnosis. In our institution, digital pathology was recently deployed in the gynecologic (GYN) pathology practice. A notable challenge encountered in the digital evaluation of GYN specimens was high rates of scanning failure of specimens with fragmented as well as scant tissue. To improve tissue detection failure rates, we implemented a novel use of the collodion bag cell block preparation method. Materials and Methods: In this study, we reviewed 108 endocervical curettage (ECC) specimens, representing specimens processed with and without the collodion bag cell block method (n = 56 without collodion bag, n = 52 with collodion bag). Results: Tissue detection failure rates were reduced from 77% (43/56) in noncollodion bag cases to 23/52 (44%) of collodion bag cases, representing a 42% reduction. The median total area of tissue detection failure per level was 0.35 mm2 (interquartile range [IQR]: 0.14, 0.70 mm2) for noncollodion bag cases and 0.08 mm2 (IQR: 0.03, 0.20 mm2) for collodion bag cases. This represents a greater than fourfold reduction in the total area of tissue detection failure per level (P < 0.001). In addition, there were no out-of-focus levels among collodion bag cases, compared to 6/56 (11%) of noncollodion bag cases (median total area = 4.9 mm2). Conclusions: The collodion bag method significantly improved the digital image quality of fragmented/scant GYN curettage specimens, increased efficiency and accuracy of diagnostic evaluation, and enhanced identification of tissue contamination during processing. The logistical challenges and labor cost of deploying the collodion bag protocol are important considerations for feasibility assessment at an institutional level.
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Letters: Digital slides as an effective tool for programmed death ligand 1 combined positive score assessment and training: Lessons learned from the “Programmed death ligand 1 key learning program in Head-and-Neck squamous cell carcinoma” Highly accessed article
Albino Eccher, Gabriella Fontanini, Nicola Fusco, Ilaria Girolami, Paolo Graziano, Elena Guerini Rocco, Maurizio Martini, Patrizia Morbini, Liron Pantanowitz, Anil Parwani, Anna Maria Pisano, Giancarlo Troncone, Elena Vigliar
J Pathol Inform 2021, 12:1 (8 January 2021)
DOI:10.4103/jpi.jpi_63_20  
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Original Article: DeepCIN: Attention-based cervical histology image classification with sequential feature modeling for pathologist-level accuracy
Sudhir Sornapudi, R Joe Stanley, William V Stoecker, Rodney Long, Zhiyun Xue, Rosemary Zuna, Shellaine R Frazier, Sameer Antani
J Pathol Inform 2020, 11:40 (24 December 2020)
DOI:10.4103/jpi.jpi_50_20  
Background: Cervical cancer is one of the deadliest cancers affecting women globally. Cervical intraepithelial neoplasia (CIN) assessment using histopathological examination of cervical biopsy slides is subject to interobserver variability. Automated processing of digitized histopathology slides has the potential for more accurate classification for CIN grades from normal to increasing grades of pre-malignancy: CIN1, CIN2, and CIN3. Methodology: Cervix disease is generally understood to progress from the bottom (basement membrane) to the top of the epithelium. To model this relationship of disease severity to spatial distribution of abnormalities, we propose a network pipeline, DeepCIN, to analyze high-resolution epithelium images (manually extracted from whole-slide images) hierarchically by focusing on localized vertical regions and fusing this local information for determining Normal/CIN classification. The pipeline contains two classifier networks: (1) a cross-sectional, vertical segment-level sequence generator is trained using weak supervision to generate feature sequences from the vertical segments to preserve the bottom-to-top feature relationships in the epithelium image data and (2) an attention-based fusion network image-level classifier predicting the final CIN grade by merging vertical segment sequences. Results: The model produces the CIN classification results and also determines the vertical segment contributions to CIN grade prediction. Conclusion: Experiments show that DeepCIN achieves pathologist-level CIN classification accuracy.
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Technical Note: A novel web application for rapidly searching the diagnostic case archive
Scott Robertson
J Pathol Inform 2020, 11:39 (24 December 2020)
DOI:10.4103/jpi.jpi_43_20  
Academic pathologists must have the ability to search their institution's archive of diagnostic case data. This ability is foundational for research, education, and other academic activities. However, the built-in search functions of commercial laboratory information systems are not always optimized for this activity, leading to delays between an initial search request, and eventual results delivery. To solve this problem, a novel web-based search platform was developed, named Pathtools, which allows our staff and trainees to directly and rapidly search our diagnostic case archive. Pathtools was built with open-source components and features a web-based user-interface. Pathtools uses an SQL database which was populated with anatomic pathology case data going back to 1980, and contains 4.2 million cases (as of July 31, 2020). Pathtools has two major modes of operation, “Preview Mode” and “Research Mode.” Since deployment in February of 2019, Pathtools carried out 33,817 searches in Preview Mode, averaging 0.72 s (standard deviation = 1.7) between search submission, and on-screen display of search results. In Research Mode, Pathtools has also been used to produce data sets for research activity, providing the data used in many abstracts and manuscripts our investigators submitted recently. Interestingly, 75% of search activity is from trainees during their preview time. In a survey of residents and fellows, 83% used Pathtools during the majority of their preview sessions, demonstrating an important role for this resource in trainee education. In conclusion, a web-based search tool can rapidly and securely provide search capability directly to end-users, which has augmented trainee education and research activity in our department.
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