Search

Machine learning has seen slow but steady uptake in diagnostic pathology over the past decade to assess digital whole-slide images. Machine learning tools have incredible potential to standardise, and likely even improve, histopathologic diagnoses, but they are not yet widely used in clinical practice. We describe the principles of these tools and technologies and some successful preclinical and pretranslational efforts in cardiovascular pathology, as well as a roadmap for moving forward. In nonhuman animal models, one proof-of-principle application is in rodent progressive cardiomyopathy, which is of particular significance to drug toxicity studies. Basic science successes include screening the quality of differentiated stem cells and characterising cardiomyocyte developmental stages, with potential applications for research and toxicology/drug safety screening using derived or native human pluripotent stem cells differentiated into cardiomyocytes. Translational studies of particular note include those with success in diagnosing the various forms of heart allograft rejection. For fully realising the value of these tools in clinical cardiovascular pathology, we identify 3 essential challenges. First is image quality standardisation to ensure that algorithms can be developed and implemented on robust, consistent data. The second is consensus diagnosis; experts don't always agree, and thus "truth" may be difficult to establish, but the algorithms themselves may provide a solution. The third is the need for large-enough data sets to facilitate robust algorithm development, necessitating large cross-institutional shared image databases. The power of histopathology-based machine learning technologies is tremendous, and we outline the next steps needed to capitalise on this power., (Copyright © 2021 Canadian Cardiovascular Society. Published by Elsevier Inc. All rights reserved.)

The field of anatomic pathology has been evolving in the last few decades and the advancements have been largely fostered by innovative technology. Immunohistochemistry enabled a paradigm shift in discovery and diagnostic evaluation, followed by booming genomic advancements which allowed for submicroscopic pathologic characterization, and now the field of digital pathology coupled with machine learning and big data acquisition is paving the way to revolutionize the pathology medical domain. Whole slide imaging (WSI) is a disruptive technology where glass slides are digitized to produce on-screen whole slide images. Specifically, in the past decade, there have been significant advances in digital pathology systems that have allowed this technology to promote integration into clinical practice. Whole slide images (WSI), or digital slides, can be viewed and navigated comparable to glass slides on a microscope, as digital files. Whole slide imaging has increased in adoption among pathologists, pathology departments, and scientists for clinical, educational, and research initiatives. Integration of digital pathology systems requires a coordinated effort with numerous stakeholders, not only within the pathology department, but across the entire enterprise. Each pathology department has distinct needs, use cases and blueprints, however the framework components and variables for successful clinical integration can be generalized across any organization seeking to undergo a digital transformation at any scale. This article will review those components and considerations for integrating digital pathology systems into clinical practice., (© 2021. The Author(s), under exclusive licence to United States & Canadian Academy of Pathology.)

This study aimed to clarify whether the pattern recognition involved in scoring proliferation fractions can be trained by abstract computerized images of virtual tissues. Twenty computer-generated images with randomly distributed blue or red dots were scored by 12 probands (all co-workers or collaborators of the Institute of Pathology, University of Bonn). Afterward, the probands underwent a training phase during which they received an immediate feedback on the actual rate of positivity after each image. Finally, the initial testing series was rescored. In a second round with 15 different probands, 20 Ki-67 immunohistochemistry images of tonsil tissue were scored, followed by the same training phase with computer-generated images, before the immunohistochemistry slides were scored again. Paired t-tests were used to compare the differences in mean rates pre- and post-training. Concerning computerized images, untrained probands scored the percentages of positive dots with a mean deviation from the true rates of 8.2%. Following training, the same testing series was scored significantly better with a mean deviation of 4.9% (mean improvement 3.3%, p < 0.001). Scoring real immunohistochemistry slides, the training with computerized images also improved correct estimations, albeit to a lesser degree (mean improvement 1%, p = 0.03). Abstract computerized images of virtual tissues may be a useful tool to train and improve the accuracy of pattern recognition involved in semiquantitative scoring of immunohistochemistry slides. As a side results, this study highlights the value of computer-generated images to verify the performance of image-analysis software., (© 2021 The Authors. APMIS published by John Wiley & Sons Ltd on behalf of Scandinavian Societies for Medical Microbiology and Pathology.)

The Ki-67 proliferation index (PI) is a prognostic factor in neuroendocrine tumors (NETs) and defines tumor grade. Analysis of Ki-67 PI requires calculation of Ki-67-positive and Ki-67-negative tumor cells, which is highly subjective. To overcome this, we developed a deep learning-based Ki-67 PI algorithm (KAI) that objectively calculates Ki-67 PI. Our study material consisted of NETs divided into training (n = 39), testing (n = 124), and validation (n = 60) series. All slides were digitized and processed in the Aiforia ® Create (Aiforia Technologies, Helsinki, Finland) platform. The ICC between the pathologists and the KAI was 0.89. In 46% of the tumors, the Ki-67 PIs calculated by the pathologists and the KAI were the same. In 12% of the tumors, the Ki-67 PI calculated by the KAI was 1% lower and in 42% of the tumors on average 3% higher. The DL-based Ki-67 PI algorithm yields results similar to human observers. While the algorithm cannot replace the pathologist, it can assist in the laborious Ki-67 PI assessment of NETs. In the future, this approach could be useful in, for example, multi-center clinical trials where objective estimation of Ki-67 PI is crucial., (© 2021 The Authors. APMIS published by John Wiley & Sons Ltd on behalf of Scandinavian Societies for Medical Microbiology and Pathology.)

Digital pathology provides a possibility for computational analysis of histological slides and automatization of routine pathological tasks. Histological slides are very heterogeneous concerning staining, sections' thickness, and artifacts arising during tissue processing, cutting, staining, and digitization. In this study, we digitally reproduce major types of artifacts. Using six datasets from four different institutions digitized by different scanner systems, we systematically explore artifacts' influence on the accuracy of the pre-trained, validated, deep learning-based model for prostate cancer detection in histological slides. We provide evidence that any histological artifact dependent on severity can lead to a substantial loss in model performance. Strategies for the prevention of diagnostic model accuracy losses in the context of artifacts are warranted. Stress-testing of diagnostic models using synthetically generated artifacts might be an essential step during clinical validation of deep learning-based algorithms., (© 2021. The Author(s).)

Aim: We aimed to determine the hormone receptor status in breast cancers and to investigate the relationship between single hormone receptor-positive, double hormone receptor-positive, double hormone receptor negativity, and human epidermal growth factor receptor 2 (HER2) status and some clinicopathological features., Materials and Methods: The study includes 85 patients who were diagnosed in our center between 2018 and 2019 and having surgical specimens were included in the study. Data of the cases, such as estrogen receptor (ER), progesterone receptor (PR), HER2 status, silver in situ hybridization (SISH) evaluation results, age distribution, histopathological findings were recorded., Results and Conclusions: We investigated the relationship between age, grade, tumor size, lymph node metastases and ER, PR, and HER2. However, there was not a significant association between ER, PR, and HER2 and age, tumor size, lymph node metastases (P > 0.05). On the other hand, we found a significant association between grades and ER (P = 0.02) and PR (P = 0.004), but not between grades and HER2 (P > 0.05). High-grade tumors were tumors with the lowest ER, PR positivity rate. Considering the four subtypes, cases aged above 45 years were at most double hormone receptor-positive (75%) and ER-positive/PR-negative (56%), respectively (P < 0.001). High-grade tumors were mostly double hormone receptor-negative and at least double hormone receptor positive. The ER-positive/PR-negative subtype was between these two groups (P < 0.001). The increased tumor size (T3) and increased metastatic lymph node number (N2 and N3) were observed at least in the ER-positive/PR-negative subtype. The majority of cases are in the older age group and invasive ductal carcinoma (IDC) is the most common tumor type. Older cases are most frequently double hormone receptor-positive and ER-positive/PR-negative, respectively. The ER, PR positivity rate is low in high-grade tumors. ER-positive/PR-negative tumors are of a higher grade than double hormone receptor-positive tumors, but they are of a lower grade than double hormone receptor-negative tumors. The increased tumor size and increased lymph node metastasis number are at most in the double hormone negative subtype and at least in the ER-positive/PR-negative subtype. The ER-negative/PR-positive subtype is observed very rarely, which raises the question of whether ER-negative/PR-positive tumors really exist. Further studies are needed to investigate this subtype and its properties.

Aims: Machine learning in digital pathology can improve efficiency and accuracy via prescreening with automated feature identification. Studies using uniform histological material have shown promise. Generalised application requires validation on slides from multiple institutions. We used machine learning to identify glomeruli on renal biopsies and compared performance between single and multiple institutions., Methods and Results: Randomly selected, adequately sampled renal core biopsy cases (71) consisting of four stains each (haematoxylin and eosin, trichrome, silver, periodic acid Schiff) from three institutions were digitised at ×40. Glomeruli were manually annotated by three renal pathologists using a digital tool. Cases were divided into training/validation (n = 52) and evaluation (n = 19) cohorts. An algorithm was trained to develop three convolutional neural network (CNN) models which tested case cohorts intra- and inter-institutionally. Raw CNN search data from each of the four slides per case were merged into composite regions of interest containing putative glomeruli. The sensitivity and modified specificity of glomerulus detection (versus annotated truth) were calculated for each model/cohort. Intra-institutional (3) sensitivity ranged from 90 to 93%, with modified specificity from 86 to 98%. Interinstitutional (1) sensitivity was 77%, with modified specificity 97%. Combined intra- and inter-institutional (1) sensitivity was 86%, with modified specificity 92%., Conclusions: Feature detection sensitivity degrades when training and test material originate from different sites. Training using a combined set of digital slides from three institutions improves performance. Differing histology methods probably account for algorithm performance contrasts. Our data highlight the need for diverse training sets for the development of generalisable machine learning histology algorithms., (© 2021 John Wiley & Sons Ltd.)

Digital pathology has become an integral part of pathology education in recent years, particularly during the COVID-19 pandemic, for its potential utility as a teaching tool that augments the traditional 1-to-1 sign-out experience. Herein, we evaluate the utility of whole slide imaging (WSI) in reducing diagnostic errors in pigmented cutaneous lesions by pathology fellows without subspecialty training in dermatopathology. Ten cases of 4 pigmented cutaneous lesions commonly encountered by general pathologists were selected. Corresponding whole slide images were distributed to our fellows, along with two sets of online surveys, each composed of 10 multiple-choice questions with 4 answers. Identical cases were used for both surveys to minimize variability in trainees' scores depending on the perceived level of difficulty, with the second set being distributed after random shuffling. Brief image-based teaching slides as self-assessment tool were provided to trainees between each survey. Pre- and post-self-assessment scores were analyzed. 61% (17/28) and 39% (11/28) of fellows completed the first and second surveys, respectively. The mean score in the first survey was 5.2/10. The mean score in the second survey following self-assessment increased to 7.2/10. 64% (7/11) of trainees showed an improvement in their scores, with 1 trainee improving his/her score by 8 points. No fellow scored less post-self-assessment than on the initial assessment. The difference in individual scores between two surveys was statistically significant (p = 0.003). Our study demonstrates the utility of WSI-based self-assessment learning as a source of improving diagnostic skills of pathology trainees in a short period of time., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Aims: The nuclear proliferation biomarker Ki67 plays potential prognostic and predictive roles in breast cancer treatment. However, the lack of interpathologist consistency in Ki67 assessment limits the clinical use of Ki67. The aim of this article was to report a solution utilising an artificial intelligence (AI)-empowered microscope to improve Ki67 scoring concordance., Methods and Results: We developed an AI-empowered microscope in which the conventional microscope was equipped with AI algorithms, and AI results were provided to pathologists in real time through augmented reality. We recruited 30 pathologists with various experience levels from five institutes to assess the Ki67 labelling index on 100 Ki67-stained slides from invasive breast cancer patients. In the first round, pathologists conducted visual assessment on a conventional microscope; in the second round, they were assisted with reference cards; and in the third round, they were assisted with an AI-empowered microscope. Experienced pathologists had better reproducibility and accuracy [intraclass correlation coefficient (ICC) = 0.864, mean error = 8.25%] than inexperienced pathologists (ICC = 0.807, mean error = 11.0%) in visual assessment. Moreover, with reference cards, inexperienced pathologists (ICC = 0.836, mean error = 10.7%) and experienced pathologists (ICC = 0.875, mean error = 7.56%) improved their reproducibility and accuracy. Finally, both experienced pathologists (ICC = 0.937, mean error = 4.36%) and inexperienced pathologists (ICC = 0.923, mean error = 4.71%) improved the reproducibility and accuracy significantly with the AI-empowered microscope., Conclusion: The AI-empowered microscope allows seamless integration of the AI solution into the clinical workflow, and helps pathologists to obtain higher consistency and accuracy for Ki67 assessment., (© 2021 John Wiley & Sons Ltd.)

Context.—: Pathology practices have begun integrating digital pathology tools into their routine workflow. During 2020, the coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged as a pandemic, causing a global health crisis that significantly affected the world population in several areas, including medical practice, and pathology was no exception., Objective.—: To summarize our experience in implementing digital pathology for remote primary diagnosis, education, and research during this pandemic., Design.—: We surveyed our pathologists (all subspecialized) and trainees to gather information about their use of digital pathology tools before and during the pandemic. Quality assurance and slide distribution data were also examined., Results.—: During the pandemic, the widespread use of digital tools in our institution allowed a smooth transition of most clinical and academic activities into remote with no major disruptions. The number of pathologists using whole slide imaging (WSI) for primary diagnosis increased from 20 (62.5%) to 29 (90.6%) of a total of 32 pathologists, excluding renal pathology and hematopathology, during the pandemic. Furthermore, the number of pathologists exclusively using whole slide imaging for primary diagnosis also increased from 2 (6.3%) to 5 (15.6%) during the pandemic. In 35 (100%) survey responses from attending pathologists, 21 (60%) reported using whole slide imaging for remote primary diagnosis following the Centers for Medicare and Medicaid Services waiver. Of these 21 pathologists, 18 (86%) responded that if allowed, they will continue using whole slide imaging for remote primary diagnosis after the pandemic., Conclusions.—: The pandemic served as a catalyst to pathologists adopting a digital workflow into their daily practice and realizing the logistic and technical advantages of such tools.

The use of immunohistochemistry in the reporting of prostate biopsies is an important adjunct when the diagnosis is not definite on haematoxylin and eosin (H&E) morphology alone. The process is however inherently inefficient with delays while waiting for pathologist review to make the request and duplicated effort reviewing a case more than once. In this study, we aimed to capture the workflow implications of immunohistochemistry requests and demonstrate a novel artificial intelligence tool to identify cases in which immunohistochemistry (IHC) is required and generate an automated request. We conducted audits of the workflow for prostate biopsies in order to understand the potential implications of automated immunohistochemistry requesting and collected prospective cases to train a deep neural network algorithm to detect tissue regions that presented ambiguous morphology on whole slide images. These ambiguous foci were selected on the basis of the pathologist requesting immunohistochemistry to aid diagnosis. A gradient boosted trees classifier was then used to make a slide-level prediction based on the outputs of the neural network prediction. The algorithm was trained on annotations of 219 immunohistochemistry-requested and 80 control images, and tested by threefold cross-validation. Validation was conducted on a separate validation dataset of 222 images. Non IHC-requested cases were diagnosed in 17.9 min on average, while IHC-requested cases took 33.4 min over multiple reporting sessions. We estimated 11 min could be saved on average per case by automated IHC requesting, by removing duplication of effort. The tool attained 99% accuracy and 0.99 Area Under the Curve (AUC) on the test data. In the validation, the average agreement with pathologists was 0.81, with a mean AUC of 0.80. We demonstrate the proof-of-principle that an AI tool making automated immunohistochemistry requests could create a significantly leaner workflow and result in pathologist time savings., (© 2021. The Author(s).)

Pathology is practiced by visual inspection of histochemically stained tissue slides. While the hematoxylin and eosin (H&E) stain is most commonly used, special stains can provide additional contrast to different tissue components. Here, we demonstrate the utility of supervised learning-based computational stain transformation from H&E to special stains (Masson's Trichrome, periodic acid-Schiff and Jones silver stain) using kidney needle core biopsy tissue sections. Based on the evaluation by three renal pathologists, followed by adjudication by a fourth pathologist, we show that the generation of virtual special stains from existing H&E images improves the diagnosis of several non-neoplastic kidney diseases, sampled from 58 unique subjects (P = 0.0095). A second study found that the quality of the computationally generated special stains was statistically equivalent to those which were histochemically stained. This stain-to-stain transformation framework can improve preliminary diagnoses when additional special stains are needed, also providing significant savings in time and cost., (© 2021. The Author(s).)

Introduction: Racial/ethnic diversity in prostate cancer (CaP) clinical trials (CTs) is essential to address CaP disparities. California Cancer Registry mandated electronic reporting (e-path) of structured data elements from pathologists diagnosing cancer thereby creating an opportunity to identify and approach patients rapidly. This study tested the utility of an online CT matching tool (called Trial Library) used in combination with e-path to improve matching of underrepresented CaP patients into CTs at time of diagnosis., Methods: This was a nonrandomized, single-arm feasibility study among patients with a new pathologic diagnosis of high-risk CaP (Gleason Score ≥8). Eligible patients were sent recruitment materials and enrolled patients were introduced to Trial Library., Results: A total of 419 case listings were assessed. Patients were excluded due to physician contraindication, not meeting baseline eligibility, or unable to be reached. Final participants (N = 52) completed a baseline survey. Among study participants, 77% were White, 10% were Black/Hispanic/Missing, and 14% were Asian. The majority of the study participants were over 65 years of age (81%) and Medicare insured (62%). Additionally, 81% of participants reported using the Internet to learn about CaP. The majority (62%) of participants reported that Trial Library increased their interest in CT participation., Conclusions: The current study demonstrated that leveraging structured e-path data reporting to a population-based cancer registry to recruit men with high risk CaP to clinical research is feasible and acceptable. We observed that e-path may be linked with an online CT matching tool, Trial Library. Future studies will prioritize recruitment from reporting facilities that serve more racially/ethnically diverse patient populations., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Objectives: The role of transfusion medicine consultative services in prospectively auditing (PA) orders for four-factor prothrombin complex concentrate (4F-PCC) was evaluated at an academic medical center., Methods: Data from 4 years of 4F-PCC orders were obtained from the laboratory information system, and electronic health records of patients receiving concentrate were reviewed., Results: 4F-PCC was ordered for 427 patients with warfarin-, apixaban-, or rivaroxaban-associated hemorrhage. Turnaround time (TAT) to prepare 4F-PCC was longer when PA-recommended dose adjustments were needed (85 vs 66 minutes, P = .03). There was no difference in TAT between patients who died and those who were ultimately discharged (60 vs 70, P = .22). TAT was shortest for orders originating in the emergency department (ED) compared with other locations (64 vs 85, P < .001), and ED TAT was not associated with patient outcomes in ED patients. PA and dose adjustments reduced amounts of concentrate issued by 27 IU per dose (P = .01). Median international normalized ratio less than 1.3 after 4F-PCC transfusion was achieved for all anticoagulants after dose adjustments. PA did not affect order cancellation or product wastage rates., Conclusions: PA can ensure 4F-PCC is dosed appropriately without affecting patient outcomes., (© American Society for Clinical Pathology, 2021. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Deep Learning (DL) algorithms are a set of techniques that exploit large and/or complex real-world datasets for cross-domain and cross-discipline prediction and classification tasks. DL architectures excel in computer vision tasks, and in particular image processing and interpretation. This has prompted a wave of disruptingly innovative applications in medical imaging, where DL strategies have the potential to vastly outperform human experts. This is particularly relevant in the context of histopathology, where whole slide imaging (WSI) of stained tissue in conjuction with DL algorithms for their interpretation, selection and cancer staging are beginning to play an ever increasing role in supporting human operators in visual assessments. This has the potential to reduce everyday workload as well as to increase precision and reproducibility across observers, centers, staining techniques and even pathologies. In this paper we introduce the most common DL architectures used in image analysis, with a focus on histopathological image analysis in general and in breast histology in particular. We briefly review how, state-of-art DL architectures compare to human performance on across a number of critical tasks such as mitotic count, tubules analysis and nuclear pleomorphism analysis. Also, the development of DL algorithms specialized to pathology images have been enormously fueled by a number of world-wide challenges based on large, multicentric image databases which are now publicly available. In turn, this has allowed most recent efforts to shift more and more towards semi-supervised learning methods, which provide greater flexibility and applicability. We also review all major repositories of manually labelled pathology images in breast cancer and provide an in-depth discussion of the challenges specific to training DL architectures to interpret WSI data, as well as a review of the state-of-the-art methods for interpretation of images generated from immunohistochemical analysis of breast lesions. We finally discuss the future challenges and opportunities which the adoption of DL paradigms is most likely to pose in the field of pathology for breast cancer detection, diagnosis, staging and prognosis. This review is intended as a comprehensive stepping stone into the field of modern computational pathology for a transdisciplinary readership across technical and medical disciplines., (Copyright © 2020. Published by Elsevier Ltd.)

Background: Whole slide imaging (WSI) has diverse applications in modern pathology practice, including providing histopathology services to remote locations., Materials and Methods: Utilising an existing contractual partnership with a Northern Ontario group of hospitals, the feasibility of using WSI for primary diagnostic services from Toronto was explored by the dedicated working group. All aspects explored from information technology (IT), laboratory information system (LIS) integration, scanning needs, laboratory workflow and pathologist needs and training, were taken into account in the developing the rationale and business case., Results: The financial outlay for a scanner was $CA180K (approximately £105.6 k) after discounts. There were no human resource requirements as staff were reorganised to cater for slide scanning. Additional IT/LIS costs were not incurred as existing connectivity was adapted to allow two site groups (gastrointestinal and skin) to pilot this study. Scanned slides were available for pathologist review 24-96 hours sooner than glass slides; there was a 2-day improvement for final authorised cases, and per annum savings were: $CA26 000 (£15.2 k) in courier costs, $CA60 000 (£35.2 k) travel and $CA45 000 (£26.4 k) in accommodation, meals and car rental expense., Conclusion: WSI is a viable solution to provide timely, high-quality and cost efficient histopathology services to underserviced, remote areas., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: To validate the Modified Determinant-Based Classification (MDBC) system, and compare it with the Revised Atlanta Classification (RAC) and the Determinant-Based Classification (DBC)., Methods: Prospective observational research was conducted in 35 international intensive care units, on patients with acute pancreatitis, and at least 1 organ failure (OF). Patient classification according to the MDBC was as follows: group 1 (transient OF, without local complications [LCs]), group 2 (transient OF and LC), group 3 (persistent OF, without LC), and group 4 (persistent OF and LC)., Results: A total of 316 patients were enrolled (mortality of 25.6%). In group 1, patients presented with low mortality (3.31%) and low morbidity (13.68%); in group 2, low mortality (5.26%) and moderate morbidity (55.56%); in group 3, high mortality (32.18%) and moderate morbidity (54.24%); and in group 4, high mortality (53.93%) and high morbidity (97.56%). The area under the receiver operator characteristic curve for mortality was 0.80 (95% confidence interval [CI], 0.75-0.84), with significant differences in comparison to RAC (0.76; 95% CI, 0.70-0.80) and DBC (0.79; 95% CI, 0.74-0.83) (P < 0.01)., Conclusions: The MDBC identified 4 groups with differentiated clinical evolutions. Its tiered mortality rating provided it with better discriminatory power than the DBC and the RAC., Competing Interests: The authors declare no conflict of interest., (Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved.)

Since 2007, we have gradually been building up infrastructure for digital pathology, starting with a whole slide scanner park to build up a digital archive to streamline doing multidisciplinary meetings, student teaching and research, culminating in a full digital diagnostic workflow where we are currently integrating artificial intelligence algorithms. In this paper, we highlight the different steps in this process towards digital diagnostics, which was at times a rocky road with definitely issues in implementation, but eventually an exciting new way to practice pathology in a more modern and efficient way where patient safety has clearly gone up., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: Digital pathology (DP) has the potential to fundamentally change the way that histopathology is practised, by streamlining the workflow, increasing efficiency, improving diagnostic accuracy and facilitating the platform for implementation of artificial intelligence-based computer-assisted diagnostics. Although the barriers to wider adoption of DP have been multifactorial, limited evidence of reliability has been a significant contributor. A meta-analysis to demonstrate the combined accuracy and reliability of DP is still lacking in the literature., Objectives: We aimed to review the published literature on the diagnostic use of DP and to synthesise a statistically pooled evidence on safety and reliability of DP for routine diagnosis (primary and secondary) in the context of validation process., Methods: A comprehensive literature search was conducted through PubMed, Medline, EMBASE, Cochrane Library and Google Scholar for studies published between 2013 and August 2019. The search protocol identified all studies comparing DP with light microscopy (LM) reporting for diagnostic purposes, predominantly including H&E-stained slides. Random-effects meta-analysis was used to pool evidence from the studies., Results: Twenty-five studies were deemed eligible to be included in the review which examined a total of 10 410 histology samples (average sample size 176). For overall concordance (clinical concordance), the agreement percentage was 98.3% (95% CI 97.4 to 98.9) across 24 studies. A total of 546 major discordances were reported across 25 studies. Over half (57%) of these were related to assessment of nuclear atypia, grading of dysplasia and malignancy. These were followed by challenging diagnoses (26%) and identification of small objects (16%)., Conclusion: The results of this meta-analysis indicate equivalent performance of DP in comparison with LM for routine diagnosis. Furthermore, the results provide valuable information concerning the areas of diagnostic discrepancy which may warrant particular attention in the transition to DP., Competing Interests: Competing interests: DRJS has worked in the past as a member of the Philips Digital Pathology advisory board., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Objectives: This study aimed to develop and validate a deep learning algorithm to screen digitized acid fast-stained (AFS) slides for mycobacteria within tissue sections., Methods: A total of 441 whole-slide images (WSIs) of AFS tissue material were used to develop a deep learning algorithm. Regions of interest with possible acid-fast bacilli (AFBs) were displayed in a web-based gallery format alongside corresponding WSIs for pathologist review. Artificial intelligence (AI)-assisted analysis of another 138 AFS slides was compared to manual light microscopy and WSI evaluation without AI support., Results: Algorithm performance showed an area under the curve of 0.960 at the image patch level. More AI-assisted reviews identified AFBs than manual microscopy or WSI examination (P < .001). Sensitivity, negative predictive value, and accuracy were highest for AI-assisted reviews. AI-assisted reviews also had the highest rate of matching the original sign-out diagnosis, were less time-consuming, and were much easier for pathologists to perform (P < .001)., Conclusions: This study reports the successful development and clinical validation of an AI-based digital pathology system to screen for AFBs in anatomic pathology material. AI assistance proved to be more sensitive and accurate, took pathologists less time to screen cases, and was easier to use than either manual microscopy or viewing WSIs., (© American Society for Clinical Pathology, 2021. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The Indian Association of Pathologists and Microbiologists (IAPM) and Indian Society of Gastroenterology (ISG) decided to make a joint consensus recommendation for handling, processing, and interpretation of SI biopsies for the diagnosis and management of celiac disease (CD) recognizing the inhomogeneous practice of biopsy sampling, orientation, processing, and interpretation. A modified Delphi process was used to develop this consensus document containing a total of 42 statements and recommendations, which were generated by sharing the document draft, incorporating expert's opinion, followed by three cycles of electronic voting as well as a full-day face-to-face virtual ZOOM meeting and review of supporting literature. Of the 42 statements, 7 statements are on small intestinal (SI) biopsy in suspected patients of CD, site and the number of biopsies; 7 on handling, fixative, orientation, processing, and sectioning in pathology laboratories; 2 on histological orientation; 13 statements on histological interpretation and histological grading; 3 on the assessment of follow-up biopsies; 2 statements on gluten-free diet (GFD)-nonresponsive CD; 4 on challenges in the diagnosis of CD; 2 statements each on pathology reporting protocol and training and infrastructure in this area. The goal of this guideline document is to formulate a uniform protocol agreed upon both by the experienced pathologists and gastroenterologists to standardize the practice, improve the yield of small bowel biopsy interpretation, patients' compliance, overall management in CD, and generate unified data for patient care and research in the related field., Competing Interests: None

Objectives: To study the concordance between pathologists in the diagnosis of follicular patterned thyroid lesions using both digital and conventional optical settings., Material and Methods: Five pathologists reviewed 50 hematoxylin and eosin-stained slides of follicular patterned thyroid lesions using both digital (the D-Sight 2.0 scanner and navigator viewer) and conventional optical instruments with washout interval time., Results: The mean concordance rate with the ground truth (GT) was similar between conventional optical and digital observation (83.2 and 85.2%, respectively). The most frequent reason for diagnostic discordance with GT on both systems was the evaluation of nuclear features (69.1% for conventional optical observation and 59.4% for digital observation). The intraobserver diagnostic concordance mean was 86.8%. Time for digital observation (mean time per case = 2.9 ± 0.8 min) was higher than that for conventional optical observation (mean time per case = 2.0 ± 0.7 min). Interobserver correlation of measurements was higher in the digital observation than the conventional optical observation., Conclusion: Conventional optical and digital observation settings showed a comparable accuracy for the diagnosis of follicular patterned thyroid nodules, as well as substantial intraobserver agreement and a significant improvement in the reproducibility of the measurements that support the use of digital diagnosis in thyroid pathology. The origins underlying the variability of the diagnosis were the same in both conventional optical microscopy and digital pathology systems.

Context.—: Endometrial serous carcinoma is an aggressive subtype of endometrial cancer with the highest rate of recurrence and mortality among all histotypes. A recent clinical trial showed prolonged progression-free survival in advanced-stage and recurrent human epidermal growth factor receptor 2 (HER2)-positive endometrial serous carcinoma when trastuzumab was added to the standard chemotherapy regimen. This targeted therapeutic approach was recently endorsed by the National Comprehensive Cancer Network clinical guidelines. There is a growing interest among clinicians to obtain HER2 testing in endometrial serous carcinoma, and pathologists need to be prepared to recognize the unique characteristics of HER2 protein expression and gene amplification in these tumors and apply specific HER2 scoring criteria., Objective.—: To provide a historical overview of targeted HER2 therapy in endometrial serous carcinoma and to summarize key findings from recent studies on the specific features of HER2 protein expression and gene amplification relative to other tumor types. Endometrial carcinoma-specific HER2 testing criteria are proposed based on evidence in the existing literature., Data Sources.—: Sources comprise review of the literature and personal experience of the author., Conclusions.—: HER2 protein overexpression and/or gene amplification is present in approximately 25% to 30% of endometrial serous carcinomas, providing an opportunity for targeted therapy. Pathologists play a key role in tumor HER2 testing and scoring to ensure appropriate patient selection and successful clinical outcome.

Context.—: Studies on the adoption of voice recognition in health care have mostly focused on turnaround time and error rate, with less attention paid to the impact on the efficiency of the providers., Objective.—: To study the impact of voice recognition on the efficiency of grossing biopsy specimens., Design.—: Timestamps corresponding to barcode scanning for biopsy specimen bottles and cassettes were retrieved from the pathology information system database. The time elapsed between scanning a specimen bottle and the corresponding first cassette was the length of time spent on the gross processing of that specimen and is designated as the specimen time. For the first specimen of a case, the specimen time additionally included the time spent on dictating the clinical information. Therefore, the specimen times were divided into the following 2 categories: first-specimen time and subsequent-specimen time. The impact of voice recognition on specimen times was studied using both univariate and multivariate analyses., Results.—: Specimen complexity, prosector variability, length of clinical information text, and the number of biopsies the prosector grossed that day were the major determinants of specimen times. Adopting voice recognition had a negligible impact on specimen times., Conclusions.—: Adopting voice recognition in the gross room removes the need to hire transcriptionists without negatively impacting the efficiency of the prosectors, resulting in an overall cost saving. Using computer scripting to automatically enter clinical information (received through the electronic order interface) into report templates may potentially increase the grossing efficiency in the future., (© 2021 College of American Pathologists.)

In order to understand the structure and effectiveness of national cancer control systems, the International Cancer Control Partnership, the World Health Organization, the National Cancer Institute and the Union for International Cancer Control underwent a review of available national cancer health plans (NCCPs) and noncommunicable diseases plans (NCDPs) worldwide. Pathology and Laboratory Medicine (PALM) plays a major role in cancer management, from prevention and screening to patient care (diagnosis and treatment) and population-level cancer surveillance. This review concentrates on the analysis of elements in national cancer care plans pertaining to PALM. Of 157 countries surveyed, 90 (57%) had a NCCP and 123 (78%) had a NCDP. While 54% of plans included guidelines on cancer diagnosis or plans to develop standards protocols for diagnosis, only 14% included PALM as a component of the plan. PALM-related variables such as synoptic pathology reporting, cancer staging guidelines and cancer genetics programs were similarly underrepresented (being mentioned in only 6%, 17% and 16% of plans, respectively). Absence of PALM-related variables tended to be more frequent in lower-income countries. Our analysis highlights an important gap in national cancer control initiatives worldwide represented by the overall lack of inclusion of PALM resources. Cancer control will only be effective if laboratory sciences are placed as a priority. Based on the data presented herein, there is a need to increase awareness about the importance of PALM in cancer care, and to incorporate this discipline in the design and implementation of multilevel cancer control strategies., (© 2020 UICC.)

Morphologic examination of tissue biopsies is essential for histopathological diagnosis. However, accurate and scalable cellular quantification in human samples remains challenging. Here, we present a deep learning-based approach for antigen-specific cellular morphometrics in human kidney biopsies, which combines indirect immunofluorescence imaging with U-Net-based architectures for image-to-image translation and dual segmentation tasks, achieving human-level accuracy. In the kidney, podocyte loss represents a hallmark of glomerular injury and can be estimated in diagnostic biopsies. Thus, we profiled over 27,000 podocytes from 110 human samples, including patients with antineutrophil cytoplasmic antibody-associated glomerulonephritis (ANCA-GN), an immune-mediated disease with aggressive glomerular damage and irreversible loss of kidney function. We identified previously unknown morphometric signatures of podocyte depletion in patients with ANCA-GN, which allowed patient classification and, in combination with routine clinical tools, showed potential for risk stratification. Our approach enables robust and scalable molecular morphometric analysis of human tissues, yielding deeper biological insights into the human kidney pathophysiology.

Purpose of Review: Breast cancer is a collection of diseases including the more common invasive ductal and lobular carcinomas and rarer subtypes of breast cancer. This review summarizes the features of rare breast cancers., Recent Findings: Each of the rare tumors has defined pathological and clinical features that impact treatment recommendations. In this review, we summarize these for each rare type of breast cancer and where available we include molecular features of each tumor. Rare subtypes of breast cancer each have unique features. In many cases, data is limited for the optimal treatment approaches.

Background: Transition to digital pathology usually takes months or years to be completed. We were familiarizing ourselves with digital pathology solutions at the time when the COVID-19 outbreak forced us to embark on an abrupt transition to digital pathology., Objective: The aim of this study was to quantitatively describe how the abrupt transition to digital pathology might affect the quality of diagnoses, model possible causes by probabilistic modeling, and qualitatively gauge the perception of this abrupt transition., Methods: A total of 17 pathologists and residents participated in this study; these participants reviewed 25 additional test cases from the archives and completed a final psychologic survey. For each case, participants performed several different diagnostic tasks, and their results were recorded and compared with the original diagnoses performed using the gold standard method (ie, conventional microscopy). We performed Bayesian data analysis with probabilistic modeling., Results: The overall analysis, comprising 1345 different items, resulted in a 9% (117/1345) error rate in using digital slides. The task of differentiating a neoplastic process from a nonneoplastic one accounted for an error rate of 10.7% (42/392), whereas the distinction of a malignant process from a benign one accounted for an error rate of 4.2% (11/258). Apart from residents, senior pathologists generated most discrepancies (7.9%, 13/164). Our model showed that these differences among career levels persisted even after adjusting for other factors., Conclusions: Our findings are in line with previous findings, emphasizing that the duration of transition (ie, lengthy or abrupt) might not influence the diagnostic performance. Moreover, our findings highlight that senior pathologists may be limited by a digital gap, which may negatively affect their performance with digital pathology. These results can guide the process of digital transition in the field of pathology., (©Simone Giaretto, Salvatore Lorenzo Renne, Daoud Rahal, Paola Bossi, Piergiuseppe Colombo, Paola Spaggiari, Sofia Manara, Mauro Sollai, Barbara Fiamengo, Tatiana Brambilla, Bethania Fernandes, Stefania Rao, Abubaker Elamin, Marina Valeri, Camilla De Carlo, Vincenzo Belsito, Cesare Lancellotti, Miriam Cieri, Angelo Cagini, Luigi Terracciano, Massimo Roncalli, Luca Di Tommaso. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 22.02.2021.)

The COVID-19 pandemic has challenged our diagnostic services at a time when many histopathology departments already faced a diminishing workforce and increasing workload. Digital pathology (DP) has been hailed as a potential solution to at least some of the challenges faced. We present a survey of pathologists within a UK National Health Service cellular pathology department with access to DP, in which we ascertain the role of DP in clinical services during this current pandemic and explore challenges encountered. This survey indicates an increase in uptake of diagnostic DP during this period, with increased remote access. Half of respondents agreed that DP had facilitated maintenance of diagnostic practice. While challenges have been encountered, these are remediable, and none have impacted on the uptake of DP during this period. We conclude that in our institution, DP has demonstrated current and future potential to increase resilience in diagnostic practice and have highlighted some of the challenges that need to be considered., Competing Interests: Competing interests: Oxford is part of the PathLAKE digital pathology consortium. PathLAKE is one of the UK Government’s 5 AI Centres of Excellence and has received in kind industry investment from Philips for digital pathology equipment, software and other services. In this context, we have also received research support from InnovateUK for the PathLAKE consortium project – grant ref:File Ref: 104689/Application number: 18181. Personal conflicts: JR is a cofounder of Ground Truth Labs., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.)

Background: This study evaluated the consequences in Europe of the COVID-19 outbreak on pathology laboratories orientated toward the diagnosis of thoracic diseases., Materials and Methods: A survey was sent to 71 pathology laboratories from 21 European countries. The questionnaire requested information concerning the organization of biosafety, the clinical and molecular pathology, the biobanking, the workload, the associated research into COVID-19, and the organization of education and training during the COVID-19 crisis, from 15 March to 31 May 2020, compared with the same period in 2019., Results: Questionnaires were returned from 53/71 (75%) laboratories from 18 European countries. The biosafety procedures were heterogeneous. The workload in clinical and molecular pathology decreased dramatically by 31% (range, 3%-55%) and 26% (range, 7%-62%), respectively. According to the professional category, between 28% and 41% of the staff members were not present in the laboratories but did teleworking. A total of 70% of the laboratories developed virtual meetings for the training of residents and junior pathologists. During the period of study, none of the staff members with confirmed COVID-19 became infected as a result of handling samples., Conclusions: The COVID-19 pandemic has had a strong impact on most of the European pathology laboratories included in this study. Urgent implementation of several changes to the organization of most of these laboratories, notably to better harmonize biosafety procedures, was noted at the onset of the pandemic and maintained in the event of a new wave of infection occurring in Europe., Competing Interests: Disclosure The authors have declared no conflicts of interest. Data sharing Anonymized participant-level data are available for investigators. Ethical approval The study complied with the law of January 1978 (78-17) relating to computing, data, and freedom known as ‘computing and freedoms’ and with rules 2016/679 of the European Parliament (of 25 May 2018) concerning the general data protection regulation (GDPR; MR004). The study was registered by the Nice University Hospital (R04-39) and filed on the Health Data Hub website (7 July 2020) (www.health-data-hub.fr/depot)., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The pathologist's assessment of tumor tissue plays a critical role in therapeutic decision-making in early-stage invasive breast cancer. In daily practice, however, there appears to be considerable variation in grading between the different Dutch pathology laboratories and between individual pathologists within the same laboratory. This underlines the need to standardize grading by pathologists as much as possible in order to minimize the risk of a worse outcome for patients due to under-treatment and of unnecessary toxicity from over-treatment. Therefore, two initiatives were launched, i.e. laboratory-specific feedback reports and an e-learning module in which pathologists were trained in grading of invasive breast cancer. While these initiatives have yielded encouraging results, the overall variation in grading remains significant. Awareness of this variation, and of the inherent difficulties of subjective grading, among the various clinicians involved in breast cancer management, is therefore of utmost importance to improve clinical decision-making for patients.

Introduction: Early diagnosis of paucibacillary tuberculosis represents a challenge, even with direct tissue examination. Digital pathology allows the digital analysis of tissues to identify microorganisms. We aim to develop a program to detect and quantify typical and atypical mycobacteria in paraffin-embedded Ziehl-Neelsen-stained tissues., Material and Methods: Program development: The building of the program, named Pat-Scan, included pathology, systems engineering, and scientific applications. The iScan Coreo Au scanner® was used, and 9 variables were adjusted. Ten Ziehl-Neelsen-stained samples were fragmented into 2000 images and analyzed to validate the reproducibility of the bacilli images in the tissue, as detected by the software., Results: Pat-Scan included software and a scanner that were used to detect and quantify bacilli in paraffin-embedded Ziehl-Neelsen-stained tissues. All samples containing mycobacteria were successfully analyzed by the scanner, and the bacilli could be detected; these results were validated by expert pathologists by microscopy examination, and the presence of bacilli was confirmed in all cases., Conclusions: Pat-Scan allowed the identification and quantification of mycobacteria in paraffin-embedded Ziehl-Neelsen-stained tissues, offering a reproducible diagnostic method that reduces the time for diagnosis and does not affect precision. Further validation is needed for application in the clinical setting., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The magnitude of the diagnostic benefit conferred by performing histopathological examinations after medico-legal/forensic autopsies remains debatable. We have tried to address this issue by reviewing a series of histopathology referrals concerning medico-legal autopsies in real-world routine practice. We present an audit of the consultations provided to forensics by clinical pathologists at our institute between 2015 and 2018. Over this period, 493 post-mortem examinations were performed by forensic pathologists. Of these cases, 52 (11%) were referred for histopathology. Gross assessment was requested in 22/52 (42%) cases. Histopathology examination was performed on single organs in 15/52 (29%) cases, primarily on the lung and heart, whereas parenchymatous multi-organ analysis was carried out in 14/52 (27%) cases. Bone-marrow sampling was studied in 4/52 (8%) cases. Immunohistochemistry was needed in 16/52 (31%) cases, special stains in 9/52 (21%) cases and molecular analysis in 4/52 (8%) cases. Focusing on technical processes, standard methodology on pre-analytical procedures was changed in 10/52 (19%) cases in order to answer specific diagnostic questions. We showed that although most of the time the diagnosis is clear by the end of dissection on the basis of the macroscopic findings, histopathology can provide, modify or confirm the cause of death in many medico-legal/forensic cases. Therefore, it is desirable that forensic pathologists and clinical pathologists establish robust working relationships in a cooperative environment. We conclude that it is important to implement guidelines based on real-world routine practice in order to identify cases where histopathology can provide useful contributions, which in our experience applied to 11% of forensic cases.

Context: Whole slide imaging (WSI) is an important component of digital pathology which includes digitization of glass slides and their storage as digital images. Implementation of WSI for primary surgical pathology diagnosis is evolving, following various studies which have evaluated the feasibility of WSI technology for primary diagnosis., Aims, Settings and Design: The present study was a single-center, observational study which included evaluation by three pathologists and aimed at assessing concordance on specialty-specific diagnosis and comparison of time taken for diagnosis on WSI and conventional light microscopy (CLM)., Materials and Methods: Seventy prostate core biopsy slides (reported between January 2016 and December 2016) were scanned using Pannoramic MIDI II scanner, 3DHISTECH, Budapest, Hungary, at 20× and 40×. Sixty slides were used for validation study following training with 10 slides., Statistical Analysis Used: Intraobserver concordance for diagnosis between the two platforms of evaluation was analyzed using Cohen's κ statistics and intraclass correlation coefficient (ICC); observation time for diagnosis was compared by Wilcoxon signed-rank test., Results: Interpretation on WSI using 20× and 40× was comparable with no major discordance. A high level of intraobserver agreement was observed between CLM and WSI for all three observers, both for primary diagnosis (κ = 0.9) and Grade group (κ = 0.7-0.8) in cases of prostatic adenocarcinoma. The major discordance rate between CLM and WSI was 3.3%-8.3%, which reflected the expertise of the observers. The time spent for diagnosis using WSI was variable for the three pathologists., Conclusion: WSI is comparable to CLM and can be safely incorporated for primary histological diagnosis of prostate core biopsies.

Clinical practice is most efficient when physicians have the right information, including pathology and laboratory results, at the point of contact with the patient. In downstream workflows, subsequent groups using lab data want to have it available in a format that is easy to manipulate. With the complexity of electronic medical records, hospital information systems, and the need to accommodate data from outside systems, this is not easy to accomplish. By utilizing a group of concepts from clinical and pathology informatics, system implementations may be improved to achieve relevant laboratory data in a format that is usable by healthcare entities to improve patient care and forward endeavors in precision medicine.

COVID-19 is a zoonotic viral infection that originated in Wuhan, China, in late 2019. WHO classified the resulting pandemic as a 'global health emergency' due to its virulence and propensity to cause acute respiratory distress syndrome. The COVID-19 pandemic has had a major impact on diagnostic laboratories, particularly those handling cell and tissue specimens. This development carries serious implications for laboratory practice in that safety of personnel has to be balanced against high-quality analysis and timely reporting of results. The aim of this article is to present some recommendations for the handling of such specimens in the preanalytical, analytical and postanalytical phases of laboratory testing and analysis in an era of high COVID-19 prevalence, such as that seen, for example, in the UK, Spain, Italy and France., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

The coronavirus disease 2019 pandemic has affected business on numerous fronts in unprecedented and abrupt ways. From site closures and local "stay-at-home orders" to travel advisories and restrictions, the day-to-day practice of toxicologic pathology has been impacted dramatically and rapidly. A critical function of Toxicologic Pathologists is performing pathology peer review for nonclinical studies. Traditionally, corroborating the findings of histological assessment could be achieved through shipment of histopathological slides to the peer review pathologist, or by the peer review pathologist traveling to the location of the slides (eg, the test facility). Since early 2020, many pathologists have been unable to perform the latter due to local, regional, national, test facility, company, and/or personal restrictions. The disruption for some has been minimal, while others are working from home for the first time. We recommend that contingency plans for all peer review procedures and personnel should be in-place to accommodate sudden and unexpected workflow transitions. Now, more than ever, approaching peer reviews with enhanced adaptability will help ensure success.

Objective: With the unprecedented rise of patient access to clinical documentation through electronic health records, there is a need for health systems to understand best practices for redesigning clinical documentation to support patient needs. This study used an experience-based co-design approach to inform the redesign of cancer pathology reports to improve their patient-centeredness and impact on patient engagement., Materials and Methods: Multiple methods for data collection and stakeholder engagement were used, including Delphi prioritisation with breast and colorectal cancer experts (n=78) and focus groups with patients with cancer (n=23) in the Seattle area. Iterative rounds of consensus generation and reflection were used to elicit themes and design recommendations for the development of patient-centred pathology reports on cancer care., Results: Although each cancer type had nuanced elements to consider, common design requirements emerged around two key themes: (1) clinical documentation language should be framed in a way that informs and engages patients, and (2) clinical documentation format should be leveraged to enhance readability and information flow. Study activities illuminated detailed recommendations to improve the patient-centeredness of pathology reports based on patients' and clinicians' lived experience., Discussion: The design requirements that emerged from this study provide a framework that can guide the rapid development of patient-centred pathology reports for all cancer types. Even further, health systems can replicate these methods to guide experience-based co-design of clinical documentation for contexts beyond cancer care., Conclusion: This work offers practice-based learnings that can more effectively guide health systems in their clinical documentation redesign efforts., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Competing Interests: Competing interests: None declared.

Pathology Autopsy and Mortuary Services have been front and center in the severe acute respiratory syndrome coronavirus 2 (SARS-Co-V-2) pandemic. The sheer number of fatalities from the pandemic have been unlike any other in recent memory and needed the rapid creation of new protocols and paradigms to manage the situation. This required rapidly escalating mortuary capacity to manage the increased fatalities from the pandemic with the establishment of lines of communication and networking with governmental entities, institution of new policies for patient flow, and implementation of worker infection control and well-being plans. Autopsies also assumed a crucial role, both to provide insight into the pathomechanisms of a novel disease and to allow tissue retrieval necessary to power research directed towards finding a vaccine. We here outline the plan adopted by the Yale Autopsy and Mortuary Services, in alignment with the institutional mission of high-quality patient care, education, research and health care worker safety and well-being, as the Corona Virus Disease of 2019 (COVID-19) pandemic surged in Connecticut. In the early response phase, ensuring sufficient mortuary capacity necessarily took center stage. As we enter the recovery and plateau phase of the pandemic, setting up a process for a rapid and safe autopsy, that will meet educational and research needs while ensuring the safety of our workforce is being implemented.

The 2020 COVID-19 crisis has had and will have many implications for healthcare, including pathology. Rising number of infections create staffing shortages and other hospital departments might require pathology employees to fill more urgent positions. Furthermore, lockdown measures and social distancing cause many people to work from home. During this crisis, it became clearer than ever what an asset digital diagnostics is to keep pathologists, residents, molecular biologists and pathology assistants engaged in the diagnostic process, allowing social distancing and a 'need to be there' on-the-premises policy, while working effectively from home. This paper provides an overview of our way of working during the 2020 COVID-19 crisis with emphasis on the virtues of digital pathology., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Context.—: Mitotic count is an important histologic criterion for grading and prognostication in phyllodes tumors (PTs). Counting mitoses is a routine practice for pathologists evaluating neoplasms, but different microscopes, variable field selection, and areas have led to possible misclassification., Objective.—: To determine whether 10 high-power fields (HPFs) or whole slide mitotic counts correlated better with PT clinicopathologic parameters using digital pathology (DP). We also aimed to find out whether this study might serve as a basis for an artificial intelligence (AI) protocol to count mitosis., Design.—: Representative slides were chosen from 93 cases of PTs diagnosed between 2014 and 2015. The slides were scanned and viewed with DP. Mitotic counting was conducted on the whole slide image, before choosing 10 HPFs and demarcating the tumor area in DP. Values of mitoses per millimeter squared were used to compare results between 10 HPFs and the whole slide. Correlations with clinicopathologic parameters were conducted., Results.—: Both whole slide counting of mitoses and 10 HPFs had similar statistically significant correlation coefficients with grade, stromal atypia, and stromal hypercellularity. Neither whole slide mitotic counts nor mitoses per 10 HPFs showed statistically significant correlations with patient age and tumor size., Conclusions.—: Accurate mitosis counting in breast PTs is important for grading. Exploring machine learning on digital whole slides may influence approaches to training, testing, and validation of a future AI algorithm., (© 2020 College of American Pathologists.)