The Application, Challenges, and Advancement Toward Regulatory Acceptance of Digital Toxicologic Pathology: Results of the 7th ESTP International Expert Workshop (September 20-21, 2019)

A Minimal Approach to Demonstrate Concordance of Digital and Conventional Microscopy in Toxicologic Pathology

Digitalization of pathology workflows has undergone a rapid evolution and has been widely established in the diagnostic field but remains a challenge in the nonclinical safety context due to lack of regulatory guidance and validation experience for good laboratory practice (GLP) use. One means to demonstrate that digital slides are fit for purpose, that is, provide sufficient quality for pathologists to reach a diagnosis, is conduction of comparison studies, which have been published both, for veterinary and human diagnostic pathology, but not for toxicologic pathology. Here, we present an approach that uses study material from nonclinical safety studies and that allows for the statistical comparison of concordance rates for glass and digital slide evaluation while minimizing time and effort for the involved personnel. Using a benchmark study design, we demonstrate that evaluation of digital slides fits the purpose of nonclinical safety evaluation. These results add to reports of successful workflow validations and support the full adaptation of digital pathology in the regulatory field.

Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images

Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training, n = 245 whole-slide images (WSIs), validation (n = 35 WSIs), and test sets (n = 70 WSIs). Full annotations included the 7 tumor classes and 6 normal skin structures. The data set was used to train a convolutional neural network (CNN) for the automatic segmentation of tumor and nontumor classes. Subsequently, the detected tumor regions were classified patch-wise into 1 of the 7 tumor classes. A majority of patches-approach led to a tumor classification accuracy of the network on the slide-level of 95% (133/140 WSIs), with a patch-level precision of 85%. The same 140 WSIs were provided to 6 experienced pathologists for diagnosis, who achieved a similar slide-level accuracy of 98% (137/140 correct majority votes). Our results highlight the feasibility of artificial intelligence-based methods as a support tool in diagnostic oncologic pathology with future applications in other species and tumor types.

IT/QA and Regulatory Aspects of Digital Pathology: Results of the 8th ESTP International Workshop

Digital toxicologic histopathology has been broadly adopted in preclinical compound development for informal consultation and peer review. There is now increased interest in implementing the technology for good laboratory practice-regulated study evaluations. However, the implementation is not straightforward because systems and work processes require qualification and validation, with consideration also given to security. As a result of the high-throughput, high-volume nature of safety evaluations, computer performance, ergonomics, efficiency, and integration with laboratory information management systems are further key considerations. The European Society of Toxicologic Pathology organized an international expert workshop with participation by toxicologic pathologists, quality assurance/regulatory experts, and information technology experts to discuss qualification and validation of digital histopathology systems in a good laboratory practice environment, and to share the resulting conclusions broadly in the toxicologic pathology community.

Evaluation of a Probability-Based Predictive Tool on Pathologist Agreement Using Urinary Bladder as a Pilot Tissue

Simple Summary

There is a common joke in pathology—put three pathologists in a room and you will obtain three different answers. This saying comes from the fact that pathology can be subjective; pathologists’ diagnoses can be influenced by many different biases, and pathologists are also influenced by the presence or absence of animal information and medical history. Compared to pathology, statistics is a much more objective field. This study aimed to develop a probability-based tool using statistics obtained by analyzing 338 histopathology slides of canine and feline urinary bladders, then see if the tool affected agreement between the test pathologists. Four pathologists diagnosed 25 canine and feline bladder slides and they conducted this three times: without animal and clinical information, then with this information, and finally using the probability tool. Results showed large differences in the pathologists’ interpretation of bladder slides, with kappa agreement values (low value for digital slide images, high value for glass slides) of 7–37% without any animal or clinical information, 23–37% with animal signalment and history, and 31–42% when our probability tool was used. This study provides a starting point for the use of probability-based tools in standardizing pathologist agreement in veterinary pathology.

Abstract

Inter-pathologist variation is widely recognized across human and veterinary pathology and is often compounded by missing animal or clinical information on pathology submission forms. Variation in pathologist threshold levels of resident inflammatory cells in the tissue of interest can further decrease inter-pathologist agreement. This study applied a predictive modeling tool to bladder histology slides that were assessed by four pathologists: first without animal and clinical information, then with this information, and finally using the predictive tool. All three assessments were performed twice, using digital whole-slide images (WSI) and then glass slides. Results showed marked variation in pathologists’ interpretation of bladder slides, with kappa agreement values of 7–37% without any animal or clinical information, 23–37% with animal signalment and history, and 31–42% when our predictive tool was applied, for digital WSI and glass slides. The concurrence of test pathologists to the reference diagnosis was 60% overall. This study provides a starting point for the use of predictive modeling in standardizing pathologist agreement in veterinary pathology. It also highlights the importance of high-quality whole-slide imaging to limit the effect of digitization on inter-pathologist agreement and the benefit of continued standardization of tissue assessment in veterinary pathology.

Scientific and Regulatory Policy Committee Points to Consider: Primary Digital Histopathology Evaluation and Peer Review for Good Laboratory Practice (GLP) Nonclinical Toxicology Studies

The Society of Toxicologic Pathology's Scientific and Regulatory Policy Committee formed a working group to consider the present and future use of digital pathology in toxicologic pathology in general and specifically its use in primary evaluation and peer review in Good Laboratory Practice (GLP) environments. Digital histopathology systems can save costs by reducing travel, enhancing organizational flexibility, decreasing slide handling, improving collaboration, increasing access to historical images, and improving quality and efficiency through integration with laboratory information management systems. However, the resources to implement and operate a digital pathology system can be significant. Given the magnitude and risks involved in the decision to adopt digital histopathology, this working group used pertinent previously published survey results and its members' expertise to create a Points-to-Consider article to assist organizations with building and implementing digital pathology workflows. With the aim of providing a comprehensive perspective, the current publication summarizes aspects of digital whole-slide imaging relevant to nonclinical histopathology evaluations, and then presents points to consider applicable to both primary digital histopathology evaluation and digital peer review in GLP toxicology studies. The Supplemental Appendices provide additional tabulated resources.

Quantification of Hepatocellular Mitoses in a Toxicological Study in Rats Using a Convolutional Neural Network

Convolutional neural networks (CNNs) have been recognized as valuable tools for rapid quantitative analysis of morphological changes in toxicologic histopathology. We have assessed the performance of CNN-based (Halo-AI) mitotic figure detection in hepatocytes in comparison with detection by pathologists. In addition, we compared with Ki-67 and 5-bromodesoxyuridin (BrdU) immunohistochemistry labeling indices (LIs) obtained by image analysis. Tissues were from an exploratory toxicity study with a glycogen synthase kinase-3 (GSK-3) inhibitor. Our investigations revealed that (1) the CNN achieved similarly accurate but faster results than pathologists, (2) results of mitotic figure detection were comparable to Ki-67 and BrdU LIs, and (3) data from different methods were only moderately correlated. The latter is likely related to differences in the cell cycle component captured by each method. This highlights the importance of considering the differences of the available methods upon selection. Also, the pharmacology of our test item acting as a GSK-3 inhibitor potentially reduced the correlation. We conclude that hepatocyte cell proliferation assessment by CNNs can have several advantages when compared with the current gold standard: it relieves the pathologist of tedious routine tasks and contributes to standardization of results; the CNN algorithm can be shared and iteratively improved; it can be performed on routine histological slides; it does not require an additional animal experiment and in this way can contribute to animal welfare according to the 3R principles.

Scientific and Regulatory Policy Committee Brief Communication: 2019 Survey on Use of Digital Histopathology Systems in Nonclinical Toxicology Studies

Histopathologic evaluation and peer review using digital whole-slide images (WSIs) is a relatively new medium for assessing nonclinical toxicology studies in Good Laboratory Practice (GLP) environments. To better understand the present and future use of digital pathology in nonclinical toxicology studies, the Society of Toxicologic Pathology (STP) formed a working group to survey STP members with the goal of creating recommendations for implementation. The survey was administered in December 2019, immediately before the COVID-19 pandemic, and the results suggested that the use of digital histopathology for routine GLP histopathology assessment was not widespread. Subsequently, in follow-up correspondence during the pandemic, many responding institutions either began investigating or adopting digital WSI systems to reduce employee exposure to COVID-19. Therefore, the working group presents the survey results as a pre-pandemic baseline data set. Recommendations for use of WSI systems in GLP environments will be the subject of a separate publication.

Validation of digital microscopy: Review of validation methods and sources of bias

Digital microscopy (DM) is increasingly replacing traditional light microscopy (LM) for performing routine diagnostic and research work in human and veterinary pathology. The DM workflow encompasses specimen preparation, whole-slide image acquisition, slide retrieval, and the workstation, each of which has the potential (depending on the technical parameters) to introduce limitations and artifacts into microscopic examination by pathologists. Performing validation studies according to guidelines established in human pathology ensures that the best-practice approaches for patient care are not deteriorated by implementing DM. Whereas current publications on validation studies suggest an overall high reliability of DM, each laboratory is encouraged to perform an individual validation study to ensure that the DM workflow performs as expected in the respective clinical or research environment. With the exception of validation guidelines developed by the College of American Pathologists in 2013 and its update in 2021, there is no current review of the application of methods fundamental to validation. We highlight that there is high methodological variation between published validation studies, each having advantages and limitations. The diagnostic concordance rate between DM and LM is the most relevant outcome measure, which is influenced (regardless of the viewing modality used) by different sources of bias including complexity of the cases examined, diagnostic experience of the study pathologists, and case recall. Here, we review 3 general study designs used for previous publications on DM validation as well as different approaches for avoiding bias.

Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives

Whole slide imaging enables the use of a wide array of digital image analysis tools that are revolutionizing pathology. Recent advances in digital pathology and deep convolutional neural networks have created an enormous opportunity to improve workflow efficiency, provide more quantitative, objective, and consistent assessments of pathology datasets, and develop decision support systems. Such innovations are already making their way into clinical practice. However, the progress of machine learning - in particular, deep learning (DL) - has been rather slower in nonclinical toxicology studies. Histopathology data from toxicology studies are critical during the drug development process that is required by regulatory bodies to assess drug-related toxicity in laboratory animals and its impact on human safety in clinical trials. Due to the high volume of slides routinely evaluated, low-throughput, or narrowly performing DL methods that may work well in small-scale diagnostic studies or for the identification of a single abnormality are tedious and impractical for toxicologic pathology. Furthermore, regulatory requirements around good laboratory practice are a major hurdle for the adoption of DL in toxicologic pathology. This paper reviews the major DL concepts, emerging applications, and examples of DL in toxicologic pathology image analysis. We end with a discussion of specific challenges and directions for future research.

Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review

Since whole-slide imaging has been commercially available for over 2 decades, digital pathology has become a constantly expanding aspect of the pathology profession that will continue to significantly impact how pathologists conduct their craft. While some aspects, such as whole-slide imaging for archiving, consulting, and teaching, have gained broader acceptance, other facets such as quantitative tissue image analysis and artificial intelligence-based assessments are still met with some reservations. While most vendors in this space have focused on diagnostic applications, that is, viewing one or few slides at a time, some are developing solutions tailored more specifically to the various aspects of veterinary pathology including updated diagnostic, discovery, and research applications. This has especially advanced the use of digital pathology in toxicologic pathology and drug development, for primary reads as well as peer reviews. It is crucial that pathologists gain a deeper understanding of digital pathology and tissue image analysis technology and their applications in order to fully use these tools in a way that enhances and improves the pathologist's assessment as well as work environment. This review focuses on an updated introduction to the basics of digital pathology and image analysis and introduces emerging topics around artificial intelligence and machine learning.

Opinion on Maintaining In-House GLP Status for Toxicologic Pathology in Pharmaceutical and (Agro)Chemical Development

Many pharmaceutical companies have recently elected to stop maintaining good laboratory practices (GLP) status of their R&D sites. Similar discussions have also been engaged in the (agro)chemical industry. This opinion paper examines the pros and cons of maintaining facility GLP status for the purposes of performing the pathology interpretation or peer reviews of GLP studies internally. The toxicologic pathologist provides gross and histomorphologic evaluation and interpretation of nonclinical exploratory and regulatory studies during drug and (agro)chemical development. This assessment significantly contributes to human risk assessment by characterizing the toxicological profile and discussing the human relevance of the findings. The toxicologic pathologist is a key contributor to compound development decisions (advancement or termination) and in the development of de-risking strategies for backup compounds, thus playing a critical role in helping to reduce the late attrition of drugs and chemicals. Maintaining GLP compliance is often perceived as a costly and cumbersome process; a common and short-term strategy to reduce the costs is to outsource regulatory toxicity studies. However, there are significant advantages in maintaining the GLP status for toxicologic pathology activities in-house including the sustainable retention of internal pathology expertise that has maintained the necessary training needed to manage GLP studies. [Box: see text].

Utilizing Whole Slide Images for the Primary Evaluation and Peer Review of a GLP-Compliant Rodent Toxicology Study

The approach undertaken to deliver a Good Laboratory Practice (GLP) validation of whole slide images (WSIs) and the associated workflow for the digital primary evaluation and peer review of a GLP-compliant rodent inhalation toxicity study is described. The contract research organization (CRO) undertook validation of the slide scanner, scanner software, and associated database software. This provided a GLP validated environment within the database software for the primary histopathologic evaluation using WSI and viewed with the database software web viewer. The CRO also validated a cloud-based digital pathology platform that supported the upload and transfer of WSI and metadata to a cache within the sponsor's local area network. The sponsor undertook a separate GLP validation of the same cloud-based digital pathology platform to cover the download and review of the WSI. The establishment of a fit-for-purpose GLP-compliant workflow for WSI and successful deployment for the digital primary evaluation and peer review of a large GLP toxicology study enabled flexibility in accelerated global working and potential future reuse of digitized data for advanced artificial intelligence and machine learning image analysis.