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Synthetic data is becoming a valuable tool for computational pathologists, aiding in tasks like data augmentation and addressing data scarcity and privacy. However, its use necessitates careful planning and evaluation to prevent the creation of clinically irrelevant artifacts.This manuscript introduces a comprehensive pipeline for generating and evaluating synthetic pathology data using a diffusion model. The pipeline features a multifaceted evaluation strategy with an integrated explainability procedure, addressing two key aspects of synthetic data use in the medical domain.The evaluation of the generated data employs an ensemble-like approach. The first step includes assessing the similarity between real and synthetic data using established metrics. The second step involves evaluating the usability of the generated images in deep learning models accompanied with explainable AI methods. The final step entails verifying their histopathological realism through questionnaires answered by professional pathologists. We show that each of these evaluation steps are necessary as they provide complementary information on the generated data's quality.The pipeline is demonstrated on the public GTEx dataset of 650 Whole Slide Images (WSIs), including five different tissues. An equal number of tiles from each tissue are generated and their reliability is assessed using the proposed evaluation pipeline, yielding promising results.In summary, the proposed workflow offers a comprehensive solution for generative AI in digital pathology, potentially aiding the community in their transition towards digitalization and data-driven modeling., Competing Interests: Declaration of generative AI in scientific writing During the preparation of this work the authors used ChatGPT3.5 for grammar and spelling checks, as well as rephrasing to enhance readability. After using this tool/service, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Cytopathology represents a well established diagnostic approach because of its limited cost, reliability, and minimal invasiveness with respect to other methodologies. The evolving complexity of the different classifications systems and the implementation of ancillary techniques to refine the diagnosis is progressively helping in the risk of malignancy stratification, and the adoption of next-generation sequencing techniques contributes to enrich this valuable tool with predictive information, which is always more essential in the tailored medicine era. The recent introduction of digital and computational pathology is further boosting the potentialities of cytopathology, aiding in the interpretation of samples to improve the cost effectiveness of large screening programs and the diagnostic efficiency within intermediate/atypical categories. Moreover, the adoption of artificial intelligence tools is promising to complement molecular investigations, representing a stimulating perspective in the cytopathology field. In this work, the authors tried to summarize the multifaceted nature of this complex and evolving field of pathology, synthesizing the most recent advances and providing the young pathologists' perspective on this fascinating world., (© 2024 American Cancer Society.)

Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task 1,2 . Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations 3 . Here, to address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. We developed CHIEF using 60,530 whole-slide images spanning 19 anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for patients with cancer., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Context.—: Biomarker reporting has increasingly become a key component of pathology reporting, providing diagnostic, prognostic, and actionable therapeutic data for patient care., Objective.—: To expand and improve the College of American Pathologists (CAP) biomarker protocols., Design.—: We surveyed CAP members to better understand the limitations they experienced when reporting cancer biomarker results. A Biomarker Workgroup reviewed the survey results and developed a strategy to improve and standardize biomarker reporting. Drafts of new and revised biomarker protocols were reviewed in both print and electronic template formats during interactive webinars presented to the CAP House of Delegates. Feedback was collected, and appropriate revisions were made to finalize the protocols., Results.—: The first phase of the CAP Biomarker Workgroup saw the development of (1) a new stand-alone general Immunohistochemistry Biomarker Protocol that includes reporting for ER (estrogen receptor), PR (progesterone receptor), Ki-67, HER2 (human epidermal growth factor receptor 2), PD-L1 (programmed death ligand-1), and mismatch repair; (2) a new Head and Neck Biomarker Protocol that updates the prior 2017 paper-only version into an electronic template, adding new diagnostic and theranostic markers; (3) a major revision to the Lung Biomarker Protocol to streamline it and add in pan-cancer markers; and (4) a revision to the Colon and Rectum Biomarker Protocol to add HER2 reporting., Conclusions.—: We have taken a multipronged approach to improving biomarker reporting in the CAP cancer protocols. We continue to review current biomarker reporting protocols to reduce and eliminate unnecessary methodologic details and update with new markers as needed. The biomarker templates will serve as standardized modular units that can be inserted into cancer-reporting protocols., Competing Interests: The authors have no relevant financial interest in the products or companies described in this article., (© 2024 College of American Pathologists.)

The analysis of histopathology images with artificial intelligence aims to enable clinical decision support systems and precision medicine. The success of such applications depends on the ability to model the diverse patterns observed in pathology images. To this end, we present Virchow, the largest foundation model for computational pathology to date. In addition to the evaluation of biomarker prediction and cell identification, we demonstrate that a large foundation model enables pan-cancer detection, achieving 0.95 specimen-level area under the (receiver operating characteristic) curve across nine common and seven rare cancers. Furthermore, we show that with less training data, the pan-cancer detector built on Virchow can achieve similar performance to tissue-specific clinical-grade models in production and outperform them on some rare variants of cancer. Virchow's performance gains highlight the value of a foundation model and open possibilities for many high-impact applications with limited amounts of labeled training data., (© 2024. The Author(s).)

Objectives: To evaluate the accuracy of ChatGPT and Bard in answering pathology examination questions requiring image interpretation., Methods: The study evaluated ChatGPT-4 and Bard's performance using 86 multiple-choice questions, with 17 (19.8%) focusing on general pathology and 69 (80.2%) on systemic pathology. Of these, 62 (72.1%) included microscopic images, and 57 (66.3%) were first-order questions focusing on diagnosing the disease. The authors presented these artificial intelligence (AI) tools with questions, both with and without clinical contexts, and assessed their answers against a reference standard set by pathologists., Results: ChatGPT-4 achieved a 100% (n = 86) accuracy rate in questions with clinical context, surpassing Bard's 87.2% (n = 75). Without context, the accuracy of both AI tools declined significantly, with ChatGPT-4 at 52.3% (n = 45) and Bard at 38.4% (n = 33). ChatGPT-4 consistently outperformed Bard across various categories, particularly in systemic pathology and first-order questions. A notable issue identified was Bard's tendency to "hallucinate" or provide plausible but incorrect answers, especially without clinical context., Conclusions: This study demonstrated the potential of ChatGPT and Bard in pathology education, stressing the importance of clinical context for accurate AI interpretations of pathology images. It underlined the need for careful AI integration in medical education., (© The Author(s) 2024. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Recent research has established that the microbiome plays potential roles in the pathogenesis of numerous chronic diseases, including carcinomas. This discovery has led to significant interest in clinical microbiome testing among physicians, translational investigators, and the lay public. As novel, inexpensive methodologies to interrogate the microbiota become available, research labs and commercial vendors have offered microbial assays. However, these tests still have not infiltrated the clinical laboratory space. Here, we provide an overview of the challenges of implementing microbiome testing in clinical pathology. We discuss challenges associated with preanalytical and analytic sample handling and collection that can influence results, choosing the appropriate testing methodology for the clinical context, establishing reference ranges, interpreting the data generated by testing and its value in making patient care decisions, regulation, and cost considerations of testing. Additionally, we suggest potential solutions for these problems to expedite the establishment of microbiome testing in the clinical laboratory., (© 2024 The Author(s). The Journal of Pathology: Clinical Research published by The Pathological Society of Great Britain and Ireland and John Wiley & Sons Ltd.)

Competing Interests: The authors have no relevant financial interest in the products or companies described in this article.

Formalin is the international gold-standard fixative in pathology laboratories. However it is not the ideal one considering its deleterious effects on individuals and the environment. Complete formalin removal or even substitution does not seem possible in the near future. In this update, we present various tools allowing to integrate the use of formalin into an ecocare approach. Among them, formalin recycling according to the protocol developed by the University Hospital of Bordeaux is simple to implement and delivers rapid and significant results, allowing pathology professionals to meet the sustainable development objectives included in the France 2030 agenda., (Copyright © 2024 Elsevier Masson SAS. All rights reserved.)

Background: Technology has revolutionized not only direct patient care but also diagnostic care processes. This study evaluates the transition from glass-slide microscopy to digital pathology (DP) at a multisite academic institution, using mixed methods to understand user perceptions of digitization and key productivity metrics of practice change., Methods: Participants included dermatopathologists, pathology reporting specialists, and clinicians. Electronic surveys and individual or group interviews included questions related to technology comfort, trust in DP, and rationale for DP adoption. Case volumes and turnaround times were abstracted from the electronic health record from Qtr 4 2020 to Qtr 1 2023 (inclusive). Data were analyzed descriptively, while interviews were analyzed using methods of content analysis., Results: Thirty-four staff completed surveys and 22 participated in an interview. Case volumes and diagnostic turnaround time did not differ across the institution during or after implementation timelines (p = 0.084; p = 0.133, respectively). 82.5% (28/34) of staff agreed that DP improved the sign-out experience, with accessibility, ergonomics, and annotation features described as key factors. Clinicians reported positive perspectives of DP impact on patient safety and interdisciplinary collaboration., Conclusions: Our study demonstrates that DP has a high acceptance rate, does not adversely impact productivity, and may improve patient safety and care collaboration., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Nowadays pathology laboratories are worldwide facing a digital revolution, with an increasing number of institutions adopting digital pathology (DP) and whole slide imaging solutions. Despite indeed providing novel and helpful advantages, embracing a whole DP workflow is still challenging, especially for wide healthcare networks. The Azienda Zero of the Veneto Italian region has begun a process of a fully digital transformation of an integrated network of 12 hospitals producing nearly 3 million slides per year. In the present article, we describe the planning stages and the operative phases needed to support such a disruptive transition, along with the initial preliminary results emerging from the project. The ultimate goal of the DP program in the Veneto Italian region is to improve patients' clinical care through a safe and standardized process, encompassing a total digital management of pathology samples, easy file sharing with experienced colleagues, and automatic support by artificial intelligence tools., (© 2024. The Author(s).)

This manuscript provides a comprehensive overview of the application of artificial intelligence (AI) in lung pathology, particularly in the diagnosis of lung cancer. It discusses various AI models designed to support pathologists and clinicians. AI models supporting pathologists are to standardize diagnosis, score PD-L1 status, supporting tumor cellularity count, and indicating explainability for pathologic judgements. Several models predict outcomes beyond pathologic diagnosis and predict clinical outcomes like patients' survival and molecular alterations. The manuscript emphasizes the potential of AI to enhance accuracy and efficiency in pathology, while also addressing the challenges and future directions for integrating AI into clinical practice., Competing Interests: The authors have no funding or conflicts of interest to disclose., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Whole slide imaging (WSI) of pathology glass slides using high-resolution scanners has enabled the large-scale application of artificial intelligence (AI) in pathology, to support the detection and diagnosis of disease, potentially increasing efficiency and accuracy in tissue diagnosis. Despite the promise of AI, it has limitations. 'Brittleness' or sensitivity to variation in inputs necessitates that large amounts of data are used for training. AI is often trained on data from different scanners but not usually by replicating the same slide across scanners. The utilisation of multiple WSI instruments to produce digital replicas of the same slides will make more comprehensive datasets and may improve the robustness and generalisability of AI algorithms as well as reduce the overall data requirements of AI training. To this end, the National Pathology Imaging Cooperative (NPIC) has built the AI FORGE (Facilitating Opportunities for Robust Generalisable data Emulation), a unique multi-scanner facility embedded in a clinical site in the NHS to (1) compare scanner performance, (2) replicate digital pathology image datasets across WSI systems, and (3) support the evaluation of clinical AI algorithms. The NPIC AI FORGE currently comprises 15 scanners from nine manufacturers. It can generate approximately 4,000 WSI images per day (approximately 7 TB of image data). This paper describes the process followed to plan and build such a facility. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland., (© 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.)

The recent influx of novel renal neoplasms, particularly molecularly-defined renal carcinomas, has introduced new challenges in the daily practice of most pathology laboratories. These tumors are uncommon, they do not always have well-established morphologic features, and the expression profile of most common biomarkers is not well understood. Moreover, the diagnosis of molecularly-defined renal carcinomas requires the documentation of the disease-defining molecular alteration, with molecular studies or surrogate immunohistochemical markers. Unfortunately, most pathology laboratories lack molecular laboratories, or it is not cost-effective to maintain assays of the specific biomarkers in these unusual tumors. Pathologists should have updated knowledge about the recent changes in renal neoplasms and be aware of these limitations., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

This study aimed to evaluate and analyze the performance of a customized Chat Generative Pre-Trained Transformer (ChatGPT), known as GPT, against pathology residents in providing microscopic descriptions and diagnosing diseases from histopathological images. A dataset of representative photomicrographs from 70 diseases across 14 organ systems was analyzed by a customized version of ChatGPT-4 (GPT-4) and pathology residents. Two pathologists independently evaluated the microscopic descriptions and diagnoses using a predefined scoring system (0-4 for microscopic descriptions and 0-2 for pathological diagnoses), with higher scores indicating greater accuracy. Microscopic descriptions that received perfect scores, which included all relevant keywords and findings, were then presented to the standard version of ChatGPT to assess its diagnostic capabilities based on these descriptions. GPT-4 showed consistency in microscopic description and diagnosis scores across five rounds, accomplishing median scores of 50 % and 48.6 %, respectively. However, its performance was still inferior to junior and senior pathology residents (73.9 % and 93.9 % description scores and 63.9 % and 87.9 % diagnosis scores, respectively). When analyzing classic ChatGPT's understanding of microscopic descriptions provided by residents, it correctly diagnosed 35 (87.5 %) of cases from junior residents and 44 (68.8 %) from senior residents, given that the initial descriptions consisted of keywords and relevant findings. While GPT-4 can accurately interpret some histopathological images, its overall performance is currently inferior to that of pathology residents. However, ChatGPT's ability to accurately interpret and diagnose diseases from the descriptions provided by residents suggests that this technology could serve as a valuable support tool in pathology diagnostics., Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Traditional manual blood smear diagnosis methods are time-consuming and prone to errors, often relying heavily on the experience of clinical laboratory analysts for accuracy. As breakthroughs in key technologies such as neural networks and deep learning continue to drive digital transformation in the medical field, image recognition technology is increasingly being leveraged to enhance existing medical processes. In recent years, advancements in computer technology have led to improved efficiency in the identification of blood cells in blood smears through the use of image recognition technology. This paper provides a comprehensive summary of the methods and steps involved in utilizing image recognition algorithms for diagnosing diseases in blood smears, with a focus on malaria and leukemia. Furthermore, it offers a forward-looking research direction for the development of a comprehensive blood cell pathological detection system., (© 2024. The Author(s).)

Digital pathology (DP) has emerged as a cutting-edge technology that promises to revolutionise diagnostics in clinical laboratories. This perspective article explores the implementation planning and considerations of DP in a single multicentre institution in Canada, the University Health Network, discussing benefits, challenges, potential implications and considerations for future adopters. We examine the transition from traditional microscopy to digital slide scanning and its impact on pathology practice, patient care and medical research. Furthermore, we address the regulatory, infrastructure and change management considerations for successful integration into clinical laboratories. By highlighting the advantages and addressing concerns, we aim to shed light on the transformative potential of DP and its role in shaping the future of diagnostics., (© 2024 The Authors. Histopathology published by John Wiley & Sons Ltd.)

Integration of digital pathology (DP) into clinical diagnostic workflows is increasingly receiving attention as new hardware and software become available. To facilitate the adoption of DP, the Swiss Digital Pathology Consortium (SDiPath) organized a Delphi process to produce a series of recommendations for DP integration within Swiss clinical environments. This process saw the creation of 4 working groups, focusing on the various components of a DP system (1) scanners, quality assurance and validation of scans, (2) integration of Whole Slide Image (WSI)-scanners and DP systems into the Pathology Laboratory Information System, (3) digital workflow-compliance with general quality guidelines, and (4) image analysis (IA)/artificial intelligence (AI), with topic experts for each recruited for discussion and statement generation. The work product of the Delphi process is 83 consensus statements presented here, forming the basis for "SDiPath Recommendations for Digital Pathology". They represent an up-to-date resource for national and international hospitals, researchers, device manufacturers, algorithm developers, and all supporting fields, with the intent of providing expectations and best practices to help ensure safe and efficient DP usage., (© 2023. The Author(s).)

Context.—: In 2018 the College of American Pathologists Diagnostic Immunology and Flow Cytometry Committee designed and implemented a new plasma cell neoplasia flow cytometry proficiency testing program-PCNEO-to allow clinical flow cytometry laboratories to monitor and assess their performance compared with a peer group., Objective.—: To report the results from the first 4 years of the PCNEO program., Design.—: Program participants were sent 2 sets of challenges per year, each including 1 wet challenge and 2 dry challenges, with associated clinical and laboratory findings. The wet challenges were composed of myeloma cell line specimens (with or without dilution in preserved whole blood) for flow cytometric analysis. The dry (paper) challenges were composed of clinical case summaries and images of flow cytometric test results from various flow cytometry laboratories of committee members., Results.—: A total of 116 to 145 laboratories from 17 countries enrolled in the proficiency testing program. For the wet challenges, almost all participants (97%-100%; cumulative, 98.2%) correctly identified the presence of neoplastic plasma cell populations based on flow cytometric analysis of undiluted myeloma cell lines. Slightly fewer participants (89.0%-97.4%; cumulative, 95.2%) correctly identified the presence of neoplastic plasma cell populations based on flow cytometric analysis of diluted myeloma cell lines (10% or 50% dilutions into peripheral blood) intended to better represent a typical clinical sample. There was generally agreement among 80% or more of participants for positive or negative staining for CD38, CD138, CD19, CD20, and surface and cytoplasmic κ and λ light chains. Similarly, 84% to 100% of participants were able to correctly identify the presence of neoplastic plasma cell populations in paper challenges, including the presence of small, neoplastic plasma cell populations (0.01%-5.0% clonal plasma cells) and the presence of nonneoplastic plasma cell populations (correctly identified by 91%-96% of participants)., Conclusions.—: Participant performance in the new proficiency testing program was excellent overall, with the vast majority of participants able to perform flow cytometric analysis and identify neoplastic plasma cell populations and to identify small plasma cell clones or expanded populations of reactive plasma cells in dry challenge flow cytometry results. This program will allow laboratories to verify the accuracy of their testing program and test interpretations for the assessment of patients suspected of having a plasma cell neoplasm., Competing Interests: All authors are current or past members of the College of American Pathologists Diagnostic Immunology and Flow Cytometry Committee, except for Bashleben, who is an employee of the College of American Pathologists., (© 2024 College of American Pathologists.)

Context: Computational pathology is a new interdisciplinary field that combines traditional pathology with modern technologies such as digital imaging and machine learning to better understand the diagnosis, prognosis, and natural history of many diseases., Objective: To provide an overview of digital and computational pathology and its current and potential applications in renal cell carcinoma (RCC)., Evidence Acquisition: A systematic review of the English-language literature was conducted using the PubMed, Web of Science, and Scopus databases in December 2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PROSPERO ID: CRD42023389282). Risk of bias was assessed according to the Prediction Model Study Risk of Bias Assessment Tool., Evidence Synthesis: In total, 20 articles were included in the review. All the studies used a retrospective design, and all digital pathology techniques were implemented retrospectively. The studies were classified according to their primary objective: detection, tumor characterization, and patient outcome. Regarding the transition to clinical practice, several studies showed promising potential. However, none presented a comprehensive assessment of clinical utility and implementation. Notably, there was substantial heterogeneity for both the strategies used for model building and the performance metrics reported., Conclusions: This review highlights the vast potential of digital and computational pathology for the detection, classification, and assessment of oncological outcomes in RCC. Preliminary work in this field has yielded promising results. However, these models have not yet reached a stage where they can be integrated into routine clinical practice., Patient Summary: Computational pathology combines traditional pathology and technologies such as digital imaging and artificial intelligence to improve diagnosis of disease and identify prognostic factors and new biomarkers. The number of studies exploring its potential in kidney cancer is rapidly increasing. However, despite the surge in research activity, computational pathology is not yet ready for widespread routine use., (Copyright © 2023 European Association of Urology. Published by Elsevier B.V. All rights reserved.)

Objectives: This study aimed to evaluate the accuracy and interobserver reliability of diagnosing and subtyping gastric intestinal metaplasia (IM) among general pathologists and pathology residents at a university hospital in Thailand, focusing on the challenges in the histopathologic evaluation of gastric IM for less experienced practitioners., Methods: The study analyzed 44 non-neoplastic gastric biopsies, using a consensus diagnosis of gastrointestinal pathologists as the reference standard. Participants included 6 general pathologists and 9 pathology residents who assessed gastric IM and categorized its subtype (complete, incomplete, or mixed) on digital slides. After initial evaluations and receiving feedback, participants reviewed specific images of gastric IM, as agreed by experts. Following a one-month washout period, a reevaluation of the slides was conducted., Results: Diagnostic accuracy, interobserver reliability, and time taken for diagnosis improved following training, with general pathologists showing higher accuracies than residents (median accuracy of gastric IM detection: 100 % vs. 97.7 %). Increased years of experience were associated with more IM detection accuracy (p-value<0.05). However, the overall median accuracy for diagnosing incomplete IM remained lower than for complete IM (86.4 % vs. 97.7 %). After training, diagnostic errors occurred in 6 out of 44 specimens (13.6 %), reported by over 40 % of participants. Errors involved omitting 5 slides with incomplete IM and 1 with complete IM, all showing a subtle presence of IM., Conclusions: The study highlights the diagnostic challenges in identifying incomplete gastric IM, showing notable discrepancies in accuracy and interobserver agreement. It underscores the need for better diagnostic protocols and training to enhance detection and management outcomes., Competing Interests: Declaration of competing interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Context.—: The College of American Pathologists (CAP) surveys provide national benchmarks of pathology practice., Objective.—: To investigate pancreaticobiliary cytology practice in domestic and international laboratories in 2021., Design.—: We analyzed data from the CAP Pancreaticobiliary Cytology Practice Supplemental Questionnaire that was distributed to laboratories participating in the 2021 CAP Nongynecologic Cytopathology Education Program., Results.—: Ninety-three percent (567 of 612) of respondent laboratories routinely evaluated pancreaticobiliary cytology specimens. Biliary brushing (85%) was the most common pancreaticobiliary cytology specimen evaluated, followed by pancreatic fine-needle aspiration (79%). The most used sampling methods reported by 235 laboratories were 22-gauge needle for fine-needle aspiration (62%) and SharkCore needle for fine-needle biopsy (27%). Cell block was the most used slide preparation method (76%), followed by liquid-based cytology (59%) for pancreatic cystic lesions. Up to 95% (303 of 320) of laboratories performed rapid on-site evaluation (ROSE) on pancreatic solid lesions, while 56% (180 of 320) performed ROSE for cystic lesions. Thirty-six percent (193 of 530) of laboratories used the Papanicolaou Society of Cytopathology System for Reporting Pancreaticobiliary Cytology in 2021. Among all institution types, significant differences in specimen volume, specimen type, ROSE practice, and case sign-out were identified. Additionally, significant differences in specimen type, slide preparation, and ROSE practice were found., Conclusions.—: This is the first survey from the CAP to investigate pancreaticobiliary cytology practice. The findings reveal significant differences among institution types and between domestic and international laboratories. These data provide a baseline for future studies in a variety of practice settings., Competing Interests: The authors are or were members of the College of American Pathologists Cytopathology Committee. Nwosu and Souers are employees of the College of American Pathologists., (© 2024 College of American Pathologists.)

Context.—: In 2014, the College of American Pathologists developed an evidence-based guideline to address analytic validation of immunohistochemical assays. Fourteen recommendations were offered. Per the National Academy of Medicine standards for developing trustworthy guidelines, guidelines should be updated when new evidence suggests modifications., Objective.—: To assess evidence published since the release of the original guideline and develop updated evidence-based recommendations., Design.—: The College of American Pathologists convened an expert panel to perform a systematic review of the literature and update the original guideline recommendations using the Grading of Recommendations Assessment, Development and Evaluation approach., Results.—: Two strong recommendations, 1 conditional recommendation, and 12 good practice statements are offered in this updated guideline. They address analytic validation or verification of predictive and nonpredictive assays, and recommended revalidation procedures following changes in assay conditions., Conclusions.—: While many of the original guideline statements remain similar, new recommendations address analytic validation of assays with distinct scoring systems, such as programmed death receptor-1 and analytic verification of US Food and Drug Administration approved/cleared assays; more specific guidance is offered for validating immunohistochemistry performed on cytology specimens., Competing Interests: Authors’ disclosures of potential conflicts of interest and author contributions are found in the Appendix at the end of this article., (© 2024 College of American Pathologists.)

Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles 1-3 . Prior models have often resorted to subsampling a small portion of tiles for each slide, thus missing the important slide-level context 4 . Here we present Prov-GigaPath, a whole-slide pathology foundation model pretrained on 1.3 billion 256 × 256 pathology image tiles in 171,189 whole slides from Providence, a large US health network comprising 28 cancer centres. The slides originated from more than 30,000 patients covering 31 major tissue types. To pretrain Prov-GigaPath, we propose GigaPath, a novel vision transformer architecture for pretraining gigapixel pathology slides. To scale GigaPath for slide-level learning with tens of thousands of image tiles, GigaPath adapts the newly developed LongNet 5 method to digital pathology. To evaluate Prov-GigaPath, we construct a digital pathology benchmark comprising 9 cancer subtyping tasks and 17 pathomics tasks, using both Providence and TCGA data 6 . With large-scale pretraining and ultra-large-context modelling, Prov-GigaPath attains state-of-the-art performance on 25 out of 26 tasks, with significant improvement over the second-best method on 18 tasks. We further demonstrate the potential of Prov-GigaPath on vision-language pretraining for pathology 7,8 by incorporating the pathology reports. In sum, Prov-GigaPath is an open-weight foundation model that achieves state-of-the-art performance on various digital pathology tasks, demonstrating the importance of real-world data and whole-slide modelling., (© 2024. The Author(s).)

In the fully digital Caltagirone pathology laboratory, a reverse shift from a digital to a manual workflow occurred due to a server outage in September 2023. Here, insights gained from this unplanned transition are explored. Surveying the affected pathologists and technicians revealed unanimous preferences for the time-saving and error-reducing capabilities of the digital methodology. Conversely, the return to manual methods highlighted increased dissatisfaction and reduced efficiency, emphasising the superiority of digital workflows. This case study underscores that transition challenges are not inherent to digital workflows but to transitioning itself, advocating for the adoption of digital technologies in all pathology practices., Competing Interests: Competing interests: AC, FM and FFraggetta report ad hoc advisory board membership with Roche Diagnostics Italia unrelated to the current work. FFraggetta is one of the inventors of 'Sample imaging and imagery archiving for imagery comparison Merlo, P.T. et al. US patent 16/688/613 2020'. The remaining authors have no conflicts of interest to declare., (© Author(s) (or their employer(s)) 2024. No commercial re-use. See rights and permissions. Published by BMJ.)

Interpretability in machine learning has become increasingly important as machine learning is being used in more and more applications, including those with high-stakes consequences such as healthcare where Interpretability has been regarded as a key to the successful adoption of machine learning models. However, using confounding/irrelevant information in making predictions by deep learning models, even the interpretable ones, poses critical challenges to their clinical acceptance. That has recently drawn researchers' attention to issues beyond the mere interpretation of deep learning models. In this paper, we first investigate application of an inherently interpretable prototype-based architecture, known as ProtoPNet, for breast cancer classification in digital pathology and highlight its shortcomings in this application. Then, we propose a new method that uses more medically relevant information and makes more accurate and interpretable predictions. Our method leverages the clustering concept and implicitly increases the number of classes in the training dataset. The proposed method learns more relevant prototypes without any pixel-level annotated data. To have a more holistic assessment, in addition to classification accuracy, we define a new metric for assessing the degree of interpretability based on the comments of a group of skilled pathologists. Experimental results on the BreakHis dataset show that the proposed method effectively improves the classification accuracy and interpretability by respectively 8 % and 18 % . Therefore, the proposed method can be seen as a step toward implementing interpretable deep learning models for the detection of breast cancer using histopathology images., (© 2024. The Author(s).)

The initial enthusiasm about computational pathology (CP) and artificial intelligence (AI) was that they will replace pathologists entirely on the way to fully automated diagnostics. It is becoming clear that currently this is not the immediate model to pursue. On top of the legal and regulatory complexities surrounding its implementation, the majority of tested machine learning (ML)-based predictive algorithms do not display the exquisite performance needed to render them unequivocal, standalone decision makers for matters with direct implications to human health. We are thus moving into a different model of "computer-assisted diagnostics", where AI is there to provide support, rather than replacing, the pathologist. Herein we focus on the practical aspects of CP, from a pathologist perspective. There is a wide range of potential applications where CP can enhance precision of pathology diagnosis, tailor prognostic and predictive information, as well as save time. There are, however, a number of potential limitations for CP that currently hinder their wider adoption in the clinical setting. We address the key necessary steps towards clinical implementation of computational pathology, discuss the significant obstacles that hinders its adoption in the clinical context and summarize some proposed solutions. We conclude that the advancement of CP in the clinic is a promising resource-intensive endeavour that requires broad and inclusive collaborations between academia, industry, and regulatory bodies., (© 2024 Walter de Gruyter GmbH, Berlin/Boston.)

Aims: To conduct a definitive multicentre comparison of digital pathology (DP) with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples., Methods: A total of 2024 cases (608 breast, 607 GI, 609 skin, 200 renal) were studied, including 207 breast and 250 bowel cancer screening samples. Cases were examined by four pathologists (16 study pathologists across the four speciality groups), using both LM and DP, with the order randomly assigned and 6 weeks between viewings. Reports were compared for clinical management concordance (CMC), meaning identical diagnoses plus differences which do not affect patient management. Percentage CMCs were computed using logistic regression models with crossed random-effects terms for case and pathologist. The obtained percentage CMCs were referenced to 98.3% calculated from previous studies., Results: For all cases LM versus DP comparisons showed the CMC rates were 99.95% [95% confidence interval (CI) = 99.90-99.97] and 98.96 (95% CI = 98.42-99.32) for cancer screening samples. In speciality groups CMC for LM versus DP showed: breast 99.40% (99.06-99.62) overall and 96.27% (94.63-97.43) for cancer screening samples; [gastrointestinal (GI) = 99.96% (99.89-99.99)] overall and 99.93% (99.68-99.98) for bowel cancer screening samples; skin 99.99% (99.92-100.0); renal 99.99% (99.57-100.0). Analysis of clinically significant differences revealed discrepancies in areas where interobserver variability is known to be high, in reads performed with both modalities and without apparent trends to either., Conclusions: Comparing LM and DP CMC, overall rates exceed the reference 98.3%, providing compelling evidence that pathologists provide equivalent results for both routine and cancer screening samples irrespective of the modality used., (© 2024 The Authors. Histopathology published by John Wiley & Sons Ltd.)

One of the goals of pathology is to standardize laboratory practices to increase the precision and effectiveness of diagnostic testing, which will ultimately enhance patient care and results. Standardization is crucial in the domains of tissue processing, analysis, and reporting. To enhance diagnostic testing, innovative technologies are also being created and put into use. Furthermore, although problems like algorithm training and data privacy issues still need to be resolved, digital pathology and artificial intelligence are emerging in a structured manner. Overall, for the field of pathology to advance and for patient care to be improved, standard laboratory practices and innovative technologies must be adopted. In this paper, we describe the state-of-the-art of automation in pathology laboratories in order to lead technological progress and evolution. By anticipating laboratory needs and demands, the aim is to inspire innovation tools and processes as positively transformative support for operators, organizations, and patients., (© 2023. The Author(s).)

Aims: Mesothelioma is a rare malignancy of the serosal membranes that is commonly related to exposure to asbestos. Despite extensive research and clinical trials, prognosis to date remains poor. Consistent, comprehensive and reproducible pathology reporting form the basis of all future interventions for an individual patient, but also ensures that meaningful data are collected to identify predictive and prognostic markers., Methods and Results: This article details the International Collaboration on Cancer Reporting (ICCR) process and the development of the international consensus mesothelioma reporting data set. It describes the 'core' and 'non-core' elements to be included in pathology reports for mesothelioma of all sites, inclusive of clinical, macroscopic, microscopic and ancillary testing considerations. An international expert panel consisting of pathologists and a medical oncologist produced a set of data items for biopsy and resection specimens based on a critical review and discussion of current evidence, and in light of the changes in the 2021 WHO Classification of Tumours. The commentary focuses particularly upon new entities such as mesothelioma in situ and provides background on relevant and essential ancillary testing as well as implementation of the new requirement for tumour grading., Conclusion: We recommend widespread and consistent implementation of this data set, which will facilitate accurate reporting and enhance the consistency of data collection, improve the comparison of epidemiological data, support retrospective research and ultimately help to improve clinical outcomes. To this end, all data sets are freely available worldwide on the ICCR website (www.iccr-cancer.org/data-sets)., (© 2023 The Authors. Histopathology published by John Wiley & Sons Ltd.)

Digital pathology (DP) is increasingly entering routine clinical pathology diagnostics. As digitization of the routine caseload advances, implementation of digital image analysis algorithms and artificial intelligence tools becomes not only attainable, but also desirable in daily sign out. The Swiss Digital Pathology Consortium (SDiPath) has initiated a Delphi process to generate best-practice recommendations for various phases of the process of digitization in pathology for the local Swiss environment, encompassing the following four topics: i) scanners, quality assurance, and validation of scans; ii) integration of scanners and systems into the pathology laboratory information system; iii) the digital workflow; and iv) digital image analysis (DIA)/artificial intelligence (AI). The current article focuses on the DIA-/AI-related recommendations generated and agreed upon by the working group and further verified by the Delphi process among the members of SDiPath. Importantly, they include the view and the currently perceived needs of practicing pathologists from multiple academic and cantonal hospitals as well as private practices., (© 2023. The Author(s).)

Digital pathology workflows can improve pathology operations by allowing reliable and fast retrieval of digital images, digitally reviewing pathology slides, enabling remote work and telepathology, use of computer-aided tools, and sharing of digital images for research and educational purposes. The need for quality systems is a prerequisite for successful clinical-grade digital pathology adoption and patient safety. In this article, we describe the development of a structured digital pathology laboratory quality management system (QMS) for clinical digital pathology operations at Memorial Sloan Kettering Cancer Center (MSK). This digital pathology-specific QMS development stemmed from the gaps that were identified when MSK integrated digital pathology into its clinical practice. The digital scan team in conjunction with the Department of Pathology and Laboratory Medicine quality team developed a QMS tailored to the scanning operation to support departmental and institutional needs. As a first step, systemic mapping of the digital pathology operations identified the prescan, scan, and postscan processes; instrumentation; and staffing involved in the digital pathology operation. Next, gaps identified in quality control and quality assurance measures led to the development of standard operating procedures and training material for the different roles and workflows in the process. All digital pathology-related documents were subject to regulatory review and approval by departmental leadership. The quality essentials were developed into an extensive Digital Pathology Quality Essentials framework to specifically address the needs of the growing clinical use of digital pathology technologies. Using the unique digital experience gained at MSK, we present our recommendations for QMS for large-scale digital pathology operations in clinical settings., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

As digital pathology replaces conventional glass slide microscopy as a means of reporting cellular pathology samples, the annotation of digital pathology whole slide images is rapidly becoming part of a pathologist's regular practice. Currently, there is no recognizable organization of these annotations, and as a result, pathologists adopt an arbitrary approach to defining regions of interest, leading to irregularity and inconsistency and limiting the downstream efficient use of this valuable effort. In this study, we propose a Standardized Annotation Reporting Style for digital whole slide images. We formed a list of 167 commonly annotated entities (under 12 specialty subcategories) based on review of Royal College of Pathologists and College of American Pathologists documents, feedback from reporting pathologists in our NHS department, and experience in developing annotation dictionaries for PathLAKE research projects. Each entity was assigned a suitable annotation shape, SNOMED CT (SNOMED International) code, and unique color. Additionally, as an example of how the approach could be expanded to specific tumor types, all lung tumors in the fifth World Health Organization of thoracic tumors 2021 were included. The proposed standardization of annotations increases their utility, making them identifiable at low power and searchable across and between cases. This would aid pathologists reporting and reviewing cases and enable annotations to be used for research. This structured approach could serve as the basis for an industry standard and be easily adopted to ensure maximum functionality and efficiency in the use of annotations made during routine clinical examination of digital slides., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Despite recent advances in digital imaging, the adoption of digital cytology is challenging due to technical limitations. This study describes our 5-year institutional experience with the implementation of digital cytology. The routine cytology workflow included conventional two-step screening by cytotechnologists, followed by sign out by pathologists. We introduced sign out of cytologic cases using a microscopic digital imaging platform operated by cytotechnologists, which allowed for remote review of slides by cytopathologists via video streaming. We also provided cytologic correlation to support the virtual slide-based sign out of histopathological specimens and for a weekly pathology-radiology conference. In addition, positive cytology cases were archived for integration into the laboratory information system and for prospective computational pathology studies. We also summarized lessons learned over the years and outlined our vision for future developments. This unique experience may serve as a role model for other institutions., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Self-supervised representation learning (SSL) has achieved remarkable success in its application to natural images while falling behind in performance when applied to whole-slide pathological images (WSIs). This is because the inherent characteristics of WSIs in terms of gigapixel resolution and multiple objects in training patches are fundamentally different from natural images. Directly transferring the state-of-the-art (SOTA) SSL methods designed for natural images to WSIs will inevitably compromise their performance. We present a novel scheme SGCL: Spatial Guided Contrastive Learning, to fully explore the inherent properties of WSIs, leveraging the spatial proximity and multi-object priors for stable self-supervision. Beyond the self-invariance of instance discrimination, we expand and propagate the spatial proximity for the intra-invariance from the same WSI and inter-invariance from different WSIs, as well as propose the spatial-guided multi-cropping for inner-invariance within patches. To adaptively explore such spatial information without supervision, we propose a new loss function and conduct a theoretical analysis to validate it. This novel scheme of SGCL is able to achieve additional improvements over the SOTA pre-training methods on diverse downstream tasks across multiple datasets. Extensive ablation studies have been carried out and visualizations of these results have been presented to aid understanding of the proposed SGCL scheme. As open science, all codes and pre-trained models are available at https://github.com/HHHedo/SGCL., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Aims: With increasing utility of digital pathology (DP), it is important to consider the experiences of histopathologists in training, particularly in view of the varied access to DP across a training region and the consequent need to remain competent in reporting on glass slides (GS), which is also relevant for the Fellowship of the Royal College of Pathologists part 2 examination. Understanding the impact of DP on training is limited but could aid development of guidance to support the transition. We sought to investigate the perceptions of histopathologists in training around the introduction of DP for clinical diagnosis within a training region, and the potential training benefits and challenges., Methods: An anonymous online survey was circulated to 24 histopathologists in training within a UK training region, including a hospital which has been fully digitised since summer 2020., Results: 19 of 24 histopathologists in training responded (79%). The results indicate that DP offers many benefits to training, including ease of access to cases to enhance individual learning and teaching in general. Utilisation of DP for diagnosis appears variable; almost half of the (10 of 19) respondents with DP experience using it only for ancillary purposes such as measurements, reporting varying levels of confidence in using DP clinically. For those yet to undergo the transition, there was a perceived anxiety regarding digital reporting despite experience with DP in other contexts., Conclusions: The survey evidences the need for provision of training and support for histopathologists in training during the transition to DP, and for consideration of their need to maintain competence and confidence with GS reporting., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ.)

Digital pathological scanners transform traditional glass slides into whole slide images (WSIs), which significantly improve the efficiency of pathological diagnosis and promote the development of digital pathology. However, the huge economic burden limits the spread and application of general WSI scanners in relatively remote and backward regions. In this paper, we develop an automatic portable cytopathology scanner based on mobile internet, Landing-Smart, to avert the above problems. Landing-Smart is a tiny device with a size of 208 mm × 107 mm × 104 mm and a weight of 1.8 kg, which integrates four main components including a smartphone, a glass slide carrier, an electric controller, and an optical imaging unit. By leveraging a simple optical imaging unit to substitute the sophisticated but complex conventional light microscope, the cost of Landing-Smart is less than $3000, much cheaper than general WSI scanners. On the one hand, Landing-Smart utilizes the built-in camera of the smartphone to acquire field of views (FoVs) in the section one by one. On the other hand, it uploads the images to the cloud server in real time via mobile internet, where the image processing and stitching method is implemented to generate the WSI of the cytological sample. The practical assessment of 209 cervical cytological specimens has demonstrated that Landing-Smart is comparable to general digital scanners in cytopathology diagnosis. Landing-Smart provides an effective tool for preliminary cytological screening in underdeveloped areas., (© 2023. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Objective: An international panel in the field of body fluid cytology, supported by the International Academy of Cytology and the American Society of Cytopathology, conducted a survey to identify opinions and explore existing practice patterns regarding body fluid cytopathology., Methods: The study group, formed during the 2018 European Congress of Cytology in Madrid, generated a survey of 54 questions related to the practice and taxonomy of body fluid cytology. The survey was available online from 28 August 2018 until 10 December 2018. Participants were invited through the websites and listserves of the professional societies., Results: The survey collected 593 international participant responses. Questions pertained to practice patterns and diagnostic language. Information was collected regarding credentials, work setting, work volume (4-10,000 samples) and years in practice (0-60 years). The responses revealed variations in diagnostic practice and sample management. Direct smears and ThinPrep® preparations are the most popular methods, followed by Cytospin® and SurePath®. Most (70%) respondents perform ancillary studies on their material, with over 50% preferring a cell block preparation. Approximately 32% indicated that they are capable of performing genetic studies on the samples. Nearly 78% of participants would accept a two-stage cytology report, with a preliminary assessment followed by a final diagnosis that accounts for ancillary studies to generate a more precise cytological interpretation. Approximately one-third (36%) never report adequacy on body fluid samples. Most (78%) report a general category result (negative, atypical, suspicious, or positive) and 22% provide a detailed surgical pathology type report. Most (73.6%) participants believe that both Papanicolaou stains and a modified Giemsa stain (eg Diff Quik) should be standard preparations for all serous fluid cytology., Conclusions: The results of the survey demonstrated strong support for the development of a unified system for reporting body fluid cytopathology among respondents., (© 2023 The Authors. Cytopathology published by John Wiley & Sons Ltd.)

Objectives: Accurate evaluation of residual cancer burden remains challenging because of the lack of appropriate techniques for tumor bed sampling. This study evaluated the application of a white light imaging system to help pathologists differentiate the components and location of tumor bed in specimens., Methods: The high dynamic range dual-mode white light imaging (HDR-DWI) system was developed to capture antiglare reflection and multiexposure HDR transmission images. It was tested in 60 specimens of modified radical mastectomy after neoadjuvant therapy. We observed the differential transmittance among tumor tissue, fibrosis tissue, and adipose tissue., Results: The sensitivity and specificity of HDR-DWI were compared with x-ray or visual examination to determine whether HDR-DWI was superior in identifying tumor beds. We found that tumor tissue had lower transmittance (0.12 ± 0.03) than fibers (0.15 ± 0.04) and fats (0.27 ± 0.07) (P < .01)., Conclusions: HDR-DWI was more sensitive in identifying fiber and tumor tissues than cabinet x-ray and visual observation (P < .01). In addition, HDR-DWI could identify more fibrosis areas than the currently used whole slide imaging did in 12 samples (12/60). We have determined that HDR-DWI can provide more in-depth tumor bed information than x-ray and visual examination do, which will help prevent diagnostic errors in tumor bed sampling., (© The Author(s) 2023. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Accurate and complete pathology reports are critical for the optimal management of cancer patients. Protocols for the pathologic reporting of Merkel cell carcinoma (MCC) have been developed independently by the Royal College of Pathologists (UK) and the College of American Pathologists. In this study, data elements for pathologic reporting of MCC were analyzed by an international panel of pathologists and clinicians with the aim of developing a common, internationally agreed upon dataset useful for clinical practice. The International Collaboration on Cancer Reporting expert review panel developed a protocol containing "core" (required) and "noncore" (recommended) elements. Core elements were defined as those that had evidentiary support and were unanimously agreed upon by the review panel as essential for the clinical management, staging, and/or assessment of prognosis in patients with MCC. Noncore elements were those considered to be clinical of interest, but with lesser degrees of supportive evidence or nonactionable implications. Ten core data elements for pathology reports on primary MCC were defined. Development and agreement on this evidence-based protocol at an international level was accomplished in a timely and efficient manner. The template developed for melanoma reporting was used as a structural base for this initiative. It is applicable to, and may facilitate the development of, protocols for other tumor types. Widespread utilization of an internationally agreed upon structured pathology dataset for MCC can be expected to lead to improved patient management. It should also facilitate collaborative clinical research., Competing Interests: Conflicts of Interest and Source of Funding: R.A.S. is supported by an NHMRC Practitioner Fellowship (APP1141295). Support from Deborah McMurtrie and John McMurtrie AM is also gratefully acknowledged. R.A.S. has received fees for professional services from F. Hoffmann-La Roche Ltd, Evaxion, Provectus Biopharmaceuticals Australia, Qbiotics, Novartis, Merck Sharp & Dohme, NeraCare, AMGEN Inc., Bristol-Myers Squibb, Myriad Genetics, GlaxoSmithKline. For the remaining authors none were declared., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Objective: To assess accuracy of whole slide imaging (WSI) in the interpretation of permanent and frozen sections in surgical pathology and the identification of tumors in cutaneous en face frozen sections., Methods: Twenty glass slides containing cutaneous en face frozen sections were selected from twenty cases of keratinocyte carcinomas treated with Mohs micrographic surgery. Ten slides contained tumor and ten did not. A blinded dermatologic surgeon used traditional light microscopy (LM) to assess physical slides for tumor presence and type, while noting the confidence (scale 1-10) and time (min) in making the determination. After a seven-day washout period, the surgeon repeated this process using WSI of the same slides, each de-identified and scanned at 20x using the Aperio AT2 (Leica Biosystems)., Results: Percent agreement between LM and WSI was 100%, with Cohen's kappa of 1.0. The average time taken to determine tumor presence was significantly greater using WSI than LM. Similarly, the surgeon was significantly more confident using LM than WSI., Conclusion: This proof-of-concept study suggests that diagnostic concordance is excellent between LM and WSI in the evaluation of Mohs frozen sections. However, WSI was cumbersome to use, not ergonomic, and required significantly more time., (Copyright © 2022 by the Association of Clinical Scientists, Inc.)

Objectives: To learn what color vision-deficient pathologists and cytotechnologists consider their most significant problems and advantages as well as any accommodations., Methods: An anonymous online survey developed for practicing pathologists and cytotechnologists regarding their experiences with stains was sent to the members of 4 national societies., Results: We received 377 responses. Twenty-three people, all men, identified themselves as color vision deficient, with 22 reporting red-green color vision deficiency and 1 reporting uncertain type. Eight pathologists and cytotechnologists indicated that they thought that their color vision deficiency conferred advantages to them, including a greater appreciation of morphology, with less confusion resulting from variations in stain quality or intensity. Nineteen pathologists and cytotechnologists thought that their color vision deficiency conferred disadvantages; the most common disadvantages stated were the identification of eosinophils and acid-fast bacilli. Other difficulties included interpretation of RBCs and nucleoli and sometimes Alcian blue, Brown and Brenn, Congo red, crystal violet, Fite, Giemsa, mucicarmine, periodic acid-Schiff, and fluorescence in situ hybridization stains. Only 2 of the color vision-deficient pathologists and cytotechnologists found digital slides more difficult than glass slides., Conclusions: Color vision-deficient pathologists and cytotechnologists report that they have developed approaches to viewing slides that do not compromise their interpretations. Digital pathology may provide several approaches for aiding color vision-deficient pathologists with the interpretation of certain stains., (© The Author(s) 2022. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Integrating clinical pathology data with anatomic pathology data is a common practice when reporting findings in the context of nonclinical toxicity studies and aids in understanding and communicating the nonclinical safety profile of test articles in development. Appropriate pathology data integration requires knowledge of analyte and tissue biology, species differences, methods of specimen acquisition and analysis, study procedures, and an understanding of the potential causes and effects of a variety of pathophysiologic processes. Neglecting these factors can lead to inappropriate data integration or a missed opportunity to enhance understanding and communication of observed changes. In such cases, nonclinical safety information relevant to human safety risk assessment may be misrepresented or misunderstood. This "Points to Consider" manuscript presents general concepts regarding pathology data integration in nonclinical studies, considerations for avoiding potential oversights and errors in data integration, and focused discussion on topics relevant to data integration for several key organ systems, including liver, kidney, and cardiovascular systems., (© 2022 American Society for Veterinary Clinical Pathology.)

Integrating clinical pathology data with anatomic pathology data is a common practice when reporting findings in the context of nonclinical toxicity studies and aids in understanding and communicating the nonclinical safety profile of test articles in development. Appropriate pathology data integration requires knowledge of analyte and tissue biology, species differences, methods of specimen acquisition and analysis, study procedures, and an understanding of the potential causes and effects of a variety of pathophysiologic processes. Neglecting these factors can lead to inappropriate data integration or a missed opportunity to enhance understanding and communication of observed changes. In such cases, nonclinical safety information relevant to human safety risk assessment may be misrepresented or misunderstood. This "Points to Consider" manuscript presents general concepts regarding pathology data integration in nonclinical studies, considerations for avoiding potential oversights and errors in data integration, and focused discussion on topics relevant to data integration for several key organ systems including liver, kidney, and cardiovascular system.

Colorectal cancer is one of the most common cancers worldwide, accounting for an annual estimated 1.8 million incident cases. With the increasing number of colonoscopies being performed, colorectal biopsies make up a large proportion of any histopathology laboratory workload. We trained and validated a unique artificial intelligence (AI) deep learning model as an assistive tool to screen for colonic malignancies in colorectal specimens, in order to improve cancer detection and classification; enabling busy pathologists to focus on higher order decision-making tasks. The study cohort consists of Whole Slide Images (WSI) obtained from 294 colorectal specimens. Qritive's unique composite algorithm comprises both a deep learning model based on a Faster Region Based Convolutional Neural Network (Faster-RCNN) architecture for instance segmentation with a ResNet-101 feature extraction backbone that provides glandular segmentation, and a classical machine learning classifier. The initial training used pathologists' annotations on a cohort of 66,191 image tiles extracted from 39 WSIs. A subsequent application of a classical machine learning-based slide classifier sorted the WSIs into 'low risk' (benign, inflammation) and 'high risk' (dysplasia, malignancy) categories. We further trained the composite AI-model's performance on a larger cohort of 105 resections WSIs and then validated our findings on a cohort of 150 biopsies WSIs against the classifications of two independently blinded pathologists. We evaluated the area under the receiver-operator characteristic curve (AUC) and other performance metrics. The AI model achieved an AUC of 0.917 in the validation cohort, with excellent sensitivity (97.4%) in detection of high risk features of dysplasia and malignancy. We demonstrate an unique composite AI-model incorporating both a glandular segmentation deep learning model and a classical machine learning classifier, with excellent sensitivity in picking up high risk colorectal features. As such, AI plays a role as a potential screening tool in assisting busy pathologists by outlining the dysplastic and malignant glands., (© 2022. The Author(s).)