[Comparative pathology in oncology-Best practice]

Comparative experimental pathology is a research field at the interface of human and veterinary medicine. It is focused on the comparative study of similarities and differences between spontaneous and experimentally induced diseases in animals (animal models) compared to human diseases. The use of animal models for studying human diseases is an essential component of biomedical research. Interdisciplinary teams with species-specific expertise should collaborate wherever possible and maintain close communication. Mutual openness, cooperation, and willingness to learn form the basis for a fruitful collaboration. Research projects jointly led by or involving both animal and human pathologists make a significant contribution to high-quality biomedical research. Such approaches are promising not only in oncological research, as outlined in this article, but also in other research areas where animal models are regularly used, such as infectiology, neurology, and developmental biology.

#Pathart and cytopathology: Beauty must be shared

OBJECTIVE

There is beauty in cytopathology. That beauty and art can be transmitted and shared through fun images on social media.

METHODS

As social media becomes more popular, pathologists and related professionals have started to share the images that they capture at work on their profiles, tagging them with the hashtag #Pathart. #Pathart hashtag unites two concepts innately related: Pathology and art.

RESULTS

When groups of pathologists share concurring ideas, the result is an explosion of creativity that spreads even to new professionals and students. In addition, it attracts the attention of people, dedicated to other subjects such as journalism, who, with their interactions, give visibility to our field. This helps counteract the stereotypes and gives people a better understanding of what we do and why it is important. Therefore, the more pathologists and related professionals meet and interact with each other, the better.

CONCLUSIONS

#Pathart images raise great interest among professionals, which contributes to the creation of a united and strong community of pathologists some of whom are dedicated to cytology. Interaction and professional collaboration between these professionals can positively contribute to disseminating scientific content and creating work/research groups. This might have an impact, both direct and indirect, on improving the quality of diagnoses and treatments in our patients.

[Call attention to the overall benefit of digital pathology and promote its development]

The successful operation of the complete digital pathology(CDP) in foreign countries indicates that the full digital pathology process has entered the full implementation stage. Digital pathology in China started late and progressed slowly, so far there has not been a truly meaningful CDP. The pathologist's understanding of digital pathology is not comprehensive enough, and there are still doubts about the time efficiency and cost effectiveness of digital pathology processes. Therefore,a comprehensive analysis of the process, overall advantages and cost-effectiveness of CDP was made by drawing on international successful experience and hands-on practice experience, in order to promote the construction and development of the CDP in our country.

[Ecology: The blind spot in pathology research]

INTRODUCTION

The 2015 Paris Agreement has been the first restrictive agreement in the fight against climate change. The newer generations of pathologists, who feel more anxiety due to environmental problems than their predecessors, are asked to publish research works while they are harder and harder to and in a context of demographical tensions. We wanted to measure the rise of ecology research in pathology since the Paris Agreement.

MATERIAL & METHODS

Over a ten years study period (2013-2022), we have identified via PubMed the number of articles in which forty-three terms taken from the sustainable development vocabulary appeared in ten renowned international pathology journals, selected for their SJR index from ScimagoJr and their impact factor, plus the Annales de pathologie, and compared their means of incidence between the 2013-2015 (m1) and 2016-2022 (m2) periods. The same process has been applied for "artificial intelligence", "deep learning" and "digital pathology".

RESULTS

A total of 1336 articles have been identified. Only "digital pathology" (fromm1=8,33 to m2=23,29; p=0,010) and "deep learning" (fromm1=0 to m2=10,14; p=0,034) saw their incidence rise significantly. A significant decrease has been observed with "biological" (fromm1=70,00 to m2=56,86; p=0,020).

DISCUSSION-CONCLUSIONS

Pathology reacts to trends but research in ecology has remained in the blind spot since 2015. However there seems to be an awakening as editorials, articles and communications in congress have blossomed the last two years.

How to measure your microscope's HPF. A critical guide for residents

Counting stuff under the microscope is part of the duties of a surgical pathologist. Many textbooks and articles still report the surface area as the number of high-power fields (HPFs) counted. This is bad, since the area displayed by an HPF varies between two microscopes. It is therefore necessary to express the surface as mm2. This is a how to guide written for the resident who has to measure the HPF of the microscope for the first time. The Resident can either calibrate the microscope with a stage micrometer slide (a small ruler on a glass slide) or compute the surface area of the HPF using the numbers on the eyepiece and the magnification objective. for "10X/22" eyepiece and a "40X" objective, the diameter of the HPF is 22/40 = 0.55 (if no other magnification is present), and the surface is 0.238 mm2. The young resident might then ask: "How far off-target was I when I counted the number of HPFs that the chief resident declared to be correct?" Probably not that much: although legitimate in principle and correct in math, the size of the problem is often overstated since microscopes are not that different after all and because pathology is not just about counting.

Toxicologic Pathology Forum Opinion Piece: Use of Virtual Control Groups in Nonclinical Toxicity Studies: The Anatomic Pathology Perspective

In the last decade, numerous initiatives have emerged worldwide to reduce the use of animals in drug development, including more recently the introduction of Virtual Control Groups (VCGs) concept for nonclinical toxicity studies. Although replacement of concurrent controls (CCs) by virtual controls (VCs) represents an exciting opportunity, there are associated challenges that will be discussed in this paper with a more specific focus on anatomic pathology. Coordinated efforts will be needed from toxicologists, clinical and anatomic pathologists, and regulators to support approaches that will facilitate a staggered implementation of VCGs in nonclinical toxicity studies. Notably, the authors believe that a validated database for VC animals will need to include histopathology (digital) slides for microscopic assessment. Ultimately, the most important step lies in the validation of the concept by performing VCG and the full control group in parallel for studies of varying duration over a reasonable timespan to confirm there are no differences in outcomes (dual study design). The authors also discuss a hybrid approach, whereby control groups comprised both concurrent and VCs to demonstrate proof-of-concept. Once confidence is established by sponsors and regulators, VCs have the potential to replace some or all CC animals.

DEPAS: De-novo Pathology Semantic Masks using a Generative Model

The integration of artificial intelligence (AI) into digital pathology has the potential to automate and improve various tasks, such as image analysis and diagnostic decision-making. Yet, the inherent variability of tissues, together with the need for image labeling, lead to biased datasets that limit the generalizability of algorithms trained on them. One of the emerging solutions for this challenge is synthetic histological images. Debiasing real datasets require not only generating photorealistic images but also the ability to control the cellular features within them. A common approach is to use generative methods that perform image translation between semantic masks that reflect prior knowledge of the tissue and a histological image. However, unlike other image domains, the complex structure of the tissue prevents a simple creation of histology semantic masks that are required as input to the image translation model, while semantic masks extracted from real images reduce the process's scalability. In this work, we introduce a scalable generative model, coined as DEPAS (De-novo Pathology Semantic Masks), that captures tissue structure and generates high-resolution semantic masks with state-of-the-art quality. We demonstrate the ability of DEPAS to generate realistic semantic maps of tissue for three types of organs: skin, prostate, and lung. Moreover, we show that these masks can be processed using a generative image translation model to produce photorealistic histology images of two types of cancer with two different types of staining techniques. Finally, we harness DEPAS to generate multi-label semantic masks that capture different cell types distributions and use them to produce histological images with on-demand cellular features. Overall, our work provides a state-of-the-art solution for the challenging task of generating synthetic histological images while controlling their semantic information in a scalable way.

Class-imbalanced Unsupervised and Semi-Supervised Domain Adaptation for Histopathology Images

In dealing with the lack of sufficient annotated data and in contrast to supervised learning, unsupervised, self-supervised, and semi-supervised domain adaptation methods are promising approaches, enabling us to transfer knowledge from rich labeled source domains to different (but related) unlabeled target domains, reducing distribution discrepancy between the source and target domains. However, most existing domain adaptation methods do not consider the imbalanced nature of the real-world data, affecting their performance in practice. We propose to overcome this limitation by proposing a novel domain adaptation approach that includes two modifications to the existing models. Firstly, we leverage the focal loss function in response to class-imbalanced labeled data in the source domain. Secondly, we introduce a novel co-training approach to involve pseudo-labeled target data points in the training process. Experiments show that the proposed model can be effective in transferring knowledge from source to target domain. As an example, we use the classification of prostate cancer images into low-cancerous and high-cancerous regions.

Neuropathological images in the great pathology atlases

In the period between Morgagni's De Sedibus (1761) and Cruveilhier's Anatomie pathologique (1829-1842), six pathology atlases were published, in which neuropathological subjects were discussed and depicted. It was a period of transition in medical, technical, and publishing areas. The first three (by Matthew Baillie, Robert Hooper, and Richard Bright) were mainly atlases derived from pathological museum specimens. They were selective rather than comprehensive. Of the other three (by Jean Cruveilhier, James Hope, and Robert Carswell), most of the observations were made during autopsies. These illustrations required special arrangements so they could be executed during the autopsies. These were available in Paris rather than in London, which is the reason why Hope and Carswell made many of the drawings in France. The plates in these three were color lithographs. Baillie's book contains only figure descriptions. Bright's and Cruveilhier's atlases provide case descriptions. Hooper and Hope provide theoretical texts and figure legends. Carswell's book has 12 theoretical sections, each followed by plates. The relative cost of the atlases varied with the number of plates. Although the authors made use of artists and engravers, several were talented artists themselves. Many common neurological diseases were depicted.

VR Khanolkar

The large-scale celebration of National Pathology Day on the birth anniversary of Dr. VR Khanolkar would help pathologists to showcase their role in patient care, medical education, and research, change the image of pathology, and bring this specialty to the forefront.

What works where and how for uptake and impact of artificial intelligence in pathology: A review of theories for a realist evaluation (Preprint)

BACKGROUND

There is increasing interest in the use of artificial intelligence (AI) in pathology to increase accuracy and efficiency. To date, studies of clinicians' perceptions of AI have found only moderate acceptability, suggesting the need for further research regarding how to integrate it into clinical practice.

OBJECTIVE

The aim of the study was to determine contextual factors that may support or constrain the uptake of AI in pathology.

METHODS

To go beyond a simple listing of barriers and facilitators, we drew on the approach of realist evaluation and undertook a review of the literature to elicit stakeholders' theories of how, for whom, and in what circumstances AI can provide benefit in pathology. Searches were designed by an information specialist and peer-reviewed by a second information specialist. Searches were run on the arXiv.org repository, MEDLINE, and the Health Management Information Consortium, with additional searches undertaken on a range of websites to identify gray literature. In line with a realist approach, we also made use of relevant theory. Included documents were indexed in NVivo 12, using codes to capture different contexts, mechanisms, and outcomes that could affect the introduction of AI in pathology. Coded data were used to produce narrative summaries of each of the identified contexts, mechanisms, and outcomes, which were then translated into theories in the form of context-mechanism-outcome configurations.

RESULTS

A total of 101 relevant documents were identified. Our analysis indicates that the benefits that can be achieved will vary according to the size and nature of the pathology department's workload and the extent to which pathologists work collaboratively; the major perceived benefit for specialist centers is in reducing workload. For uptake of AI, pathologists' trust is essential. Existing theories suggest that if pathologists are able to "make sense" of AI, engage in the adoption process, receive support in adapting their work processes, and can identify potential benefits to its introduction, it is more likely to be accepted.

CONCLUSIONS

For uptake of AI in pathology, for all but the most simple quantitative tasks, measures will be required that either increase confidence in the system or provide users with an understanding of the performance of the system. For specialist centers, efforts should focus on reducing workload rather than increasing accuracy. Designers also need to give careful thought to usability and how AI is integrated into pathologists' workflow.

GPCRs Are Optimal Regulators of Complex Biological Systems and Orchestrate the Interface between Health and Disease

GPCRs arguably represent the most effective current therapeutic targets for a plethora of diseases. GPCRs also possess a pivotal role in the regulation of the physiological balance between healthy and pathological conditions; thus, their importance in systems biology cannot be underestimated. The molecular diversity of GPCR signaling systems is likely to be closely associated with disease-associated changes in organismal tissue complexity and compartmentalization, thus enabling a nuanced GPCR-based capacity to interdict multiple disease pathomechanisms at a systemic level. GPCRs have been long considered as controllers of communication between tissues and cells. This communication involves the ligand-mediated control of cell surface receptors that then direct their stimuli to impact cell physiology. Given the tremendous success of GPCRs as therapeutic targets, considerable focus has been placed on the ability of these therapeutics to modulate diseases by acting at cell surface receptors. In the past decade, however, attention has focused upon how stable multiprotein GPCR superstructures, termed receptorsomes, both at the cell surface membrane and in the intracellular domain dictate and condition long-term GPCR activities associated with the regulation of protein expression patterns, cellular stress responses and DNA integrity management. The ability of these receptorsomes (often in the absence of typical cell surface ligands) to control complex cellular activities implicates them as key controllers of the functional balance between health and disease. A greater understanding of this function of GPCRs is likely to significantly augment our ability to further employ these proteins in a multitude of diseases.

Public perceptions on pathology: a fundamental change is required

Pathology has been mostly invisible for the public. The missing recognition affects the pathologists' reputation, and efforts with recruitment and advocacy. Our survey with 387 respondents confirms that the public knowledge on the role of the pathologists has not improved despite campaigns and advocacy efforts. Pathology was identified as a medical specialty by 79.1% of the respondents. Only 34.8% assumed that it takes more than 8 years of post-high school training to become a pathologist. Most commonly, another medical specialist was identified as the ultimate diagnostician on Pap tests (gynaecologist), breast biopsies or malignant surgical excisions (oncologist), gastrointestinal biopsies (gastroenterologist) or prostate biopsies (urologist). The experience gained by undergoing these procedures had minimal impact on understanding the pathologists' role, since they were identified as ultimate diagnosis makers by the minority of these patients (13.8%-36.4%). The integration of pathologist-interactions into patient care may be a potential solution with benefits beyond improved perceptions.

Quick Annotator: an open-source digital pathology based rapid image annotation tool

Image-based biomarker discovery typically requires accurate segmentation of histologic structures (e.g. cell nuclei, tubules, and epithelial regions) in digital pathology whole slide images (WSIs). Unfortunately, annotating each structure of interest is laborious and often intractable even in moderately sized cohorts. Here, we present an open-source tool, Quick Annotator (QA), designed to improve annotation efficiency of histologic structures by orders of magnitude. While the user annotates regions of interest (ROIs) via an intuitive web interface, a deep learning (DL) model is concurrently optimized using these annotations and applied to the ROI. The user iteratively reviews DL results to either (1) accept accurately annotated regions or (2) correct erroneously segmented structures to improve subsequent model suggestions, before transitioning to other ROIs. We demonstrate the effectiveness of QA over comparable manual efforts via three use cases. These include annotating (1) 337,386 nuclei in 5 pancreatic WSIs, (2) 5,692 tubules in 10 colorectal WSIs, and (3) 14,187 regions of epithelium in 10 breast WSIs. Efficiency gains in terms of annotations per second of 102×, 9×, and 39× were, respectively, witnessed while retaining f-scores >0.95, suggesting that QA may be a valuable tool for efficiently fully annotating WSIs employed in downstream biomarker studies.

Excellence Available Everywhere: The Virtual Pathology Grand Rounds Experience

OBJECTIVES

The goal is to describe the use of a virtual platform in the delivery of Virtual Pathology Grand Rounds (VPGR) and discuss the overall experience from the perspective of hosts, speakers, and participants.

METHODS

Zoom was a natural choice for an online format because virtual platforms had been increasingly used to conduct meetings and medical education. VPGR hosted 14 speakers on a variety of topics, including subspecialty anatomic pathology material, digital pathology, molecular pathology, and medical education.

RESULTS

There were 221 registrants and 114 participants for the first lecture, reaching a maximum of 1,268 registrants for the 12th lecture and the maximum limit of 300 participants during 3 lectures. Speakers stated that VPGR conveniently provided career-building opportunities through partnerships with host universities and remote attendance. Participants identified a lack of interpersonal communication and technical challenges as downsides.

CONCLUSIONS

VPGR serves as strong proof of concept for the feasibility and demand for high-quality, remote academic pathology talks.

A fast and effective detection framework for whole-slide histopathology image analysis

Pathologists generally pan, focus, zoom and scan tissue biopsies either under microscopes or on digital images for diagnosis. With the rapid development of whole-slide digital scanners for histopathology, computer-assisted digital pathology image analysis has attracted increasing clinical attention. Thus, the working style of pathologists is also beginning to change. Computer-assisted image analysis systems have been developed to help pathologists perform basic examinations. This paper presents a novel lightweight detection framework for automatic tumor detection in whole-slide histopathology images. We develop the Double Magnification Combination (DMC) classifier, which is a modified DenseNet-40 to make patch-level predictions with only 0.3 million parameters. To improve the detection performance of multiple instances, we propose an improved adaptive sampling method with superpixel segmentation and introduce a new heuristic factor, local sampling density, as the convergence condition of iterations. In postprocessing, we use a CNN model with 4 convolutional layers to regulate the patch-level predictions based on the predictions of adjacent sampling points and use linear interpolation to generate a tumor probability heatmap. The entire framework was trained and validated using the dataset from the Camelyon16 Grand Challenge and Hubei Cancer Hospital. In our experiments, the average AUC was 0.95 in the test set for pixel-level detection.

Mycopathologia 2020: Legacy and Change to Remain Relevant for Content, Creation, and Communication

The 2020 COVID-19 pandemic had a profound impact on the publishing landscape. The 'pre-peer-review' publication model is likely to become common as a lag in publishing is not acceptable in a pandemic or other time! Mycopathologia is well placed to adopt such changes with its improved editorial processes, article formats, author engagements, and published articles' access and citation. Mycopathologia had an improved journal impact factor and article downloads in 2018-2019. A limited sampling suggested a slight decrease in the total submissions in 2019 (352 articles) compared to 2018 (371 articles). However, the acceptance rate improved to 30% in 2019 from 19% in 2018. Nearly half of all submissions in 2019 were rejected before peer-review or transferred to other Springer Nature journals. The published articles were contributed from 34 different countries, with authors from China, the USA, and Brazil among the top three contributors. An enhanced editorial oversight allowed peer-reviewers to focus on fewer articles that were well-matched to their expertise, which led to lower rejection rates post-peer-review. The introduction of MycopathologiaGENOME and MycopathologiaIMAGE article types received a good reception with notable downloads and citations.

Virtual meeting in pathology: Time to adapt to the new-norm

No abstract available.

Pathology Laboratory Policies and Procedures for Releasing Diagnostic Tissue for Cancer Research

CONTEXT.—

The Surveillance, Epidemiology, and End Results (SEER) cancer registry program is currently evaluating the use of archival, diagnostic, formalin-fixed, paraffin-embedded (FFPE) tissue obtained through SEER cancer registries, functioning as honest brokers for deidentified tissue and associated data. To determine the feasibility of this potential program, laboratory policies for sharing tissue for research needed to be assessed.

OBJECTIVE.—

To understand the willingness of pathology laboratories to share archival diagnostic tissue for cancer research and related policies.

DESIGN.—

Seven SEER registries administered a 27-item questionnaire to pathology laboratories within their respective registry catchment areas. Only laboratories that processed diagnostic FFPE specimens and completed the questionnaire were included in the analysis.

RESULTS.—

Of the 153 responding laboratories, 127 (83%) responded that they process FFPE specimens. Most (n = 88; 69%) were willing to share tissue specimens for research, which was not associated with the number of blocks processed per year by the laboratories. Most laboratories retained the specimens for at least 10 years. Institutional regulatory policies on sharing deidentified tissue varied considerably, ranging from requiring a full Institutional Review Board review to considering such use exempt from Institutional Review Board review, and 43% (55 of 127) of the laboratories did not know their terms for sharing tissue for research.

CONCLUSIONS.—

This project indicated a general willingness of pathology laboratories to participate in research by sharing FFPE tissue. Given the variability of research policies across laboratories, it is critical for each SEER registry to work with laboratories in their catchment area to understand such policies and state legislation regulating tissue retention and guardianship.

Challenges Developing Deep Learning Algorithms in Cytology

BACKGROUND

The incorporation of digital pathology into routine pathology practice is becoming more widespread. Definite advantages exist with respect to the implementation of artificial intelligence (AI) and deep learning in pathology, including cytopathology. However, there are also unique challenges in this regard.

SUMMARY

This review discusses cytology-specific challenges, including the need to implement digital cytology prior to AI; the large file sizes and increased acquisition times for whole slide images in cytology; the routine use of multiple stains, such as Papanicolaou and Romanowsky stains; the lack of high-quality annotated datasets on which to train algorithms; and the considerable computer resources required, in terms of both computer infrastructure and skilled personnel, for computing and storage of data. Global concerns regarding AI that are certainly applicable to cytology include the need for model validation and continued quality assurance, ethical issues such as the use of patient data in developing algorithms, the need to develop regulatory frameworks regarding what type of data can be utilized and ensuring cybersecurity during data collection and storage, and algorithm development. Key Messages: While AI will likely play a role in cytology practice in the future, applying this technology to cytology poses a unique set of challenges. A broad understanding of digital pathology and algorithm development is desirable to guide the development of algorithms, as well as the need to be cognizant of potential pitfalls to avoid when incorporating the technology in practice.

LifeTime and improving European healthcare through cell-based interceptive medicine

Here we describe the LifeTime Initiative, which aims to track, understand and target human cells during the onset and progression of complex diseases, and to analyse their response to therapy at single-cell resolution. This mission will be implemented through the development, integration and application of single-cell multi-omics and imaging, artificial intelligence and patient-derived experimental disease models during the progression from health to disease. The analysis of large molecular and clinical datasets will identify molecular mechanisms, create predictive computational models of disease progression, and reveal new drug targets and therapies. The timely detection and interception of disease embedded in an ethical and patient-centred vision will be achieved through interactions across academia, hospitals, patient associations, health data management systems and industry. The application of this strategy to key medical challenges in cancer, neurological and neuropsychiatric disorders, and infectious, chronic inflammatory and cardiovascular diseases at the single-cell level will usher in cell-based interceptive medicine in Europe over the next decade.

Artificial Intelligence in Medicine: Where Are We Now?

Artificial intelligence in medicine has made dramatic progress in recent years. However, much of this progress is seemingly scattered, lacking a cohesive structure for the discerning observer. In this article, we will provide an up-to-date review of artificial intelligence in medicine, with a specific focus on its application to radiology, pathology, ophthalmology, and dermatology. We will discuss a range of selected papers that illustrate the potential uses of artificial intelligence in a technologically advanced future.

Pathology and Biobanking

Biobanks are units where high quality and long-term protection of biomaterials is maintained. This system, in which biological materials and data are systematically recorded and stored, is a unique resource for the study of the pathophysiology of disease, the development of diagnostic biomarkers, and working with human tissues for the potential discovery of targeted therapeutic agents. At this point, the pathology unit plays a unifying and complementary role between the clinical and core disciplines and offers optimal management of the patients' biomaterials for diagnostic and research projects. The aim of this article is to present general information with regard to a biobank constructed for the storage of tumor tissue and blood biospecimens. Ethical issues (informed consent, protection of confidentiality and privacy, and secondary use of biospecimens) and the information technology system (collection, systematic recording, backup and protection of clinical information) are important issues in biobanking. The selection of freezers to be used in storage (mechanical freezers, liquid-vapor nitrogen tanks), and if mechanical freezers are preferred the establishment of the relevant infrastructure and support team (such as additional power units for protection from power outages), the preservation of materials by aliquoting in different freezers, ensuring financing so as to afford the cost of the infrastructure, and implementation of all these dynamics while adhering to international guidelines are of the utmost importance.