Whole slide imaging (WSI) scanner differences influence optical and computed properties of digitized prostate cancer histology

Purpose

Digital pathology is becoming an increasingly popular area of advancement in both research and clinically. Pathologists are now able to manage and interpret slides digitally, as well as collaborate with external pathologists with digital copies of slides. Differences in slide scanners include variation in resolution, image contrast, and optical properties, which may influence downstream image processing. This study tested the hypothesis that varying slide scanners would result in differences in computed pathomic features on prostate cancer whole mount slides.

Design

This study collected 192 unique tissue slides from 30 patients following prostatectomy. Tissue samples were paraffin-embedded, stained for hematoxylin and eosin (H&E), and digitized using 3 different scanning microscopes at the highest available magnification rate, for a total of 3 digitized slides per tissue slide. These scanners included a (S1) Nikon microscope equipped with an automated sliding stage, an (S2) Olympus VS120 slide scanner, and a (S3) Huron TissueScope LE scanner. A color deconvolution algorithm was then used to optimize contrast by projecting the RGB image into color channels representing optical stain density. The resulting intensity standardized images were then computationally processed to segment tissue and calculate pathomic features including lumen, stroma, epithelium, and epithelial cell density, as well as second-order features including lumen area and roundness; epithelial area, roundness, and wall thickness; and cell fraction. For each tested feature, mean values of that feature per digitized slide were collected and compared across slide scanners using mixed effect models, fit to compare differences in the tested feature associated with all slide scanners for each slide, including a random effect of subject with a nested random effect of slide to account for repeated measures. Similar models were also computed for tissue densities to examine how differences in scanner impact downstream processing.

Results

Each mean color channel intensity (i.e., Red, Green, Blue) differed between slide scanners (all P<.001). Of the color deconvolved images, only the hematoxylin channel was similar in all 3 scanners (all P>.05). Lumen and stroma densities between S3 and S1 slides, and epithelial cell density between S3 and S2 (P>.05) were comparable but all other comparisons were significantly different (P<.05). The second-order features were found to be comparable for all scanner comparisons, except for lumen area and epithelium area.

Conclusion

This study demonstrates that both optical and computed properties of digitized histological samples are impacted by slide scanner differences. Future research is warranted to better understand which scanner properties influence the tissue segmentation process and to develop harmonization techniques for comparing data across multiple slide scanners.

European Society of Toxicologic Pathology (Pathology 2.0 Molecular Pathology Special Interest Group): Review of In Situ Hybridization Techniques for Drug Research and Development

In situ hybridization (ISH) is used for the localization of specific nucleic acid sequences in cells or tissues by complementary binding of a nucleotide probe to a specific target nucleic acid sequence. In the last years, the specificity and sensitivity of ISH assays were improved by innovative techniques like synthetic nucleic acids and tandem oligonucleotide probes combined with signal amplification methods like branched DNA, hybridization chain reaction and tyramide signal amplification. These improvements increased the application spectrum for ISH on formalin-fixed paraffin-embedded tissues. ISH is a powerful tool to investigate DNA, mRNA transcripts, regulatory noncoding RNA, and therapeutic oligonucleotides. ISH can be used to obtain spatial information of a cell type, subcellular localization, or expression levels of targets. Since immunohistochemistry and ISH share similar workflows, their combination can address simultaneous transcriptomics and proteomics questions. The goal of this review paper is to revisit the current state of the scientific approaches in ISH and its application in drug research and development.

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.

Advances in Digital Pathology: From Artificial Intelligence to Label-Free Imaging

BACKGROUND

Digital pathology, in its primary meaning, describes the utilization of computer screens to view scanned histology slides. Digitized tissue sections can be easily shared for a second opinion. In addition, it allows tissue image analysis using specialized software to identify and measure events previously observed by a human observer. These tissue-based readouts were highly reproducible and precise. Digital pathology has developed over the years through new technologies. Currently, the most discussed development is the application of artificial intelligence to automatically analyze tissue images. However, even new label-free imaging technologies are being developed to allow imaging of tissues by means of their molecular composition.

SUMMARY

This review provides a summary of the current state-of-the-art and future digital pathologies. Developments in the last few years have been presented and discussed. In particular, the review provides an outlook on interesting new technologies (e.g., infrared imaging), which would allow for deeper understanding and analysis of tissue thin sections beyond conventional histopathology.

KEY MESSAGES

In digital pathology, mathematical methods are used to analyze images and draw conclusions about diseases and their progression. New innovative methods and techniques (e.g., label-free infrared imaging) will bring significant changes in the field in the coming years.

Artificial Intelligence in Toxicological Pathology: Quantitative Evaluation of Compound-Induced Follicular Cell Hypertrophy in Rat Thyroid Gland Using Deep Learning Models

Digital pathology has recently been more broadly deployed, fueling artificial intelligence (AI) application development and more systematic use of image analysis. Here, two different AI models were developed to evaluate follicular cell hypertrophy in hematoxylin and eosin-stained whole-slide-images of rat thyroid gland, using commercial AI-based-software. In the first, mean cytoplasmic area measuring approach (MCA approach), mean cytoplasmic area was calculated via several sequential deep learning (DL)-based algorithms including segmentation in microanatomical structures (separation of colloid and stroma from thyroid follicular epithelium), nuclear detection, and area measurements. With our additional second, hypertrophy area fraction predicting approach (HAF approach), we present for the first time DL-based direct detection of the histopathological change follicular cell hypertrophy in the thyroid gland with similar results. For multiple studies, increased output parameters (mean cytoplasmic area and hypertrophic area fraction) were shown in groups given different hypertrophy-inducing reference compounds in comparison to control groups. Quantitative results correlated with the gold standard of board-certified veterinary pathologists' diagnoses and gradings as well as thyroid hormone dependent gene expressions. Accuracy and repeatability of diagnoses and grading by pathologists are expected to be improved by additional evaluation of mean cytoplasmic area or direct detection of hypertrophy, combined with standard histopathological observations.

Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images

Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

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.

International Guidelines for Veterinary Tumor Pathology: A Call to Action

Standardization of tumor assessment lays the foundation for validation of grading systems, permits reproducibility of oncologic studies among investigators, and increases confidence in the significance of study results. Currently, there is minimal methodological standardization for assessing tumors in veterinary medicine, with few attempts to validate published protocols and grading schemes. The current article attempts to address these shortcomings by providing standard guidelines for tumor assessment parameters and protocols for evaluating specific tumor types. More detailed information is available in the Supplemental Files, the intention of which is 2-fold: publication as part of this commentary, but more importantly, these will be available as "living documents" on a website (www.vetcancerprotocols.org), which will be updated as new information is presented in the peer-reviewed literature. Our hope is that veterinary pathologists will agree that this initiative is needed, and will contribute to and utilize this information for routine diagnostic work and oncologic studies. Journal editors and reviewers can utilize checklists to ensure publications include sufficient detail and standardized methods of tumor assessment. To maintain the relevance of the guidelines and protocols, it is critical that the information is periodically updated and revised as new studies are published and validated with the intent of providing a repository of this information. Our hope is that this initiative (a continuation of efforts published in this journal in 2011) will facilitate collaboration and reproducibility between pathologists and institutions, increase case numbers, and strengthen clinical research findings, thus ensuring continued progress in veterinary oncologic pathology and improving patient care.

An Image Analysis Solution For Quantification and Determination of Immunohistochemistry Staining Reproducibility

Supplemental Digital Content is available in the text.

With immunohistochemical (IHC) staining increasingly being used to guide clinical decisions, variability in staining quality and reproducibility are becoming essential factors in generating diagnoses using IHC tissue preparations. The current study tested a method to track and quantify the interrun, intrarun, and intersite variability of IHC staining intensity. Our hypothesis was that staining precision between laboratory sites, staining runs, and individual slides may be verified quantitatively, efficiently and effectively utilizing algorithm-based, automated image analysis. To investigate this premise, we tested the consistency of IHC staining in 40 routinely processed (formalin-fixed, paraffin-embedded) human tissues using 10 common antibiomarker antibodies on 2 Dako Omnis instruments at 2 locations (Carpinteria, CA: 30 m above sea level and Longmont, CO: 1500 m above sea level) programmed with identical, default settings and sample pretreatments. Digital images of IHC-labeled sections produced by a whole slide scanner were analyzed by a simple commercially available algorithm and compared with a board-certified veterinary pathologist’s semiquantitative scoring of staining intensity. The image analysis output correlated well with pathology scores but had increased sensitivity for discriminating subtle variations and providing reproducible digital quantification across sites as well as within and among staining runs at the same site. Taken together, our data indicate that digital image analysis offers an objective and quantifiable means of verifying IHC staining parameters as a part of laboratory quality assurance systems.

In Vivo Aortic Magnetic Resonance Elastography in Abdominal Aortic Aneurysm: A Validation in an Animal Model

OBJECTIVES

Using maximum diameter of an abdominal aortic aneurysm (AAA) alone for management can lead to delayed interventions or unnecessary urgent repairs. Abdominal aortic aneurysm stiffness plays an important role in its expansion and rupture. In vivo aortic magnetic resonance elastography (MRE) was developed to spatially measure AAA stiffness in previous pilot studies and has not been thoroughly validated and evaluated for its potential clinical value. This study aims to evaluate noninvasive in vivo aortic MRE-derived stiffness in an AAA porcine model and investigate the relationships between MRE-derived AAA stiffness and (1) histopathology, (2) uniaxial tensile test, and (3) burst testing for assessing MRE's potential in evaluating AAA rupture risk.

MATERIALS AND METHODS

Abdominal aortic aneurysm was induced in 31 Yorkshire pigs (n = 226 stiffness measurements). Animals were randomly divided into 3 cohorts: 2-week, 4-week, and 4-week-burst. Aortic MRE was sequentially performed. Histopathologic analyses were performed to quantify elastin, collagen, and mineral densities. Uniaxial tensile test and burst testing were conducted to measure peak stress and burst pressure for assessing the ultimate wall strength.

RESULTS

Magnetic resonance elastography-derived AAA stiffness was significantly higher than the normal aorta. Significant reduction in elastin and collagen densities as well as increased mineralization was observed in AAAs. Uniaxial tensile test and burst testing revealed reduced ultimate wall strength. Magnetic resonance elastography-derived aortic stiffness correlated to elastin density (ρ = -0.68; P < 0.0001; n = 60) and mineralization (ρ = 0.59; P < 0.0001; n = 60). Inverse correlations were observed between aortic stiffness and peak stress (ρ = -0.32; P = 0.0495; n = 38) as well as burst pressure (ρ = -0.55; P = 0.0116; n = 20).

CONCLUSIONS

Noninvasive in vivo aortic MRE successfully detected aortic wall stiffening, confirming the extracellular matrix remodeling observed in the histopathologic analyses. These mural changes diminished wall strength. Inverse correlation between MRE-derived aortic stiffness and aortic wall strength suggests that MRE-derived stiffness can be a potential biomarker for clinically assessing AAA wall status and rupture potential.

Microscope-Based Automated Quantification of Liver Fibrosis in Mice Using a Deep Learning Algorithm

In preclinical studies that involve animal models for hepatic fibrosis, accurate quantification of the fibrosis is of utmost importance. The use of digital image analysis based on deep learning artificial intelligence (AI) algorithms can facilitate accurate evaluation of liver fibrosis in these models. In the present study, we compared the quantitative evaluation of collagen proportionate area in the carbon tetrachloride model of liver fibrosis in the mouse by a newly developed AI algorithm to the semiquantitative assessment of liver fibrosis performed by a board-certified toxicologic pathologist. We found an excellent correlation between the 2 methods of assessment, most evident in the higher magnification (×40) as compared to the lower magnification (×10). These findings strengthen the confidence of using digital tools in the toxicologic pathology field as an adjunct to an expert toxicologic pathologist.

Artificial Intelligence in Toxicologic Pathology: Quantitative Evaluation of Compound-Induced Hepatocellular Hypertrophy in Rats

Digital pathology evolved rapidly, enabling more systematic usage of image analysis and development of artificial intelligence (AI) applications. Here, combined AI models were developed to evaluate hepatocellular hypertrophy in rat liver, using commercial AI-based software on hematoxylin and eosin-stained whole slide images. In a first approach, deep learning-based identification of critical tissue zones (centrilobular, midzonal, and periportal) enabled evaluation of region-specific cell size. Mean cytoplasmic area of hepatocytes was calculated via several sequential algorithms including segmentation in microanatomical structures (separation of sinusoids and vessels from hepatocytes), nuclear detection, and area measurements. An increase in mean cytoplasmic area could be shown in groups given phenobarbital, known to induce hepatocellular hypertrophy when compared to control groups, in multiple studies. Quantitative results correlated with the gold standard: observation and grading performed by board-certified veterinary pathologists, liver weights, and gene expression. Furthermore, as a second approach, we introduce for the first time deep learning-based direct detection of hepatocellular hypertrophy with similar results. Cell hypertrophy is challenging to pick up, particularly in milder cases. Additional evaluation of mean cytoplasmic area or direct detection of hypertrophy, combined with histopathological observations and liver weights, is expected to increase accuracy and repeatability of diagnoses and grading by pathologists.

Developing a Qualification and Verification Strategy for Digital Tissue Image Analysis in Toxicological Pathology

Digital tissue image analysis is a computational method for analyzing whole-slide images and extracting large, complex, and quantitative data sets. However, as with any analysis method, the quality of generated results is dependent on a well-designed quality control system for the entire digital pathology workflow. Such system requires clear procedural controls, appropriate user training, and involvement of specialists to oversee key steps of the workflow. The toxicologic pathologist is responsible for reporting data obtained by digital image analysis and therefore needs to ensure that it is correct. To accomplish that, they must understand the main parameters of the quality control system and should play an integral part in its conception and implementation. This manuscript describes the most common digital tissue image analysis end points and potential sources of analysis errors. In addition, it outlines recommended approaches for ensuring quality and correctness of results for both classical and machine-learning based image analysis solutions, as adapted from a recently proposed Food and Drug Administration regulatory framework for modifications to artificial intelligence/machine learning-based software as a medical device. These approaches are beneficial for any type of toxicopathologic study which uses the described end points and can be adjusted based on the intended use of the image analysis solution.

Deep Learning in Toxicologic Pathology: A New Approach to Evaluate Rodent Retinal Atrophy

Quantification of retinal atrophy, caused by therapeutics and/or light, by manual measurement of retinal layers is labor intensive and time-consuming. In this study, we explored the role of deep learning (DL) in automating the assessment of retinal atrophy, particularly of the outer and inner nuclear layers, in rats. Herein, we report our experience creating and employing a hybrid approach, which combines conventional image processing and DL to quantify rodent retinal atrophy. Utilizing a DL approach based upon the VGG16 model architecture, models were trained, tested, and validated using 10,746 image patches scanned from whole slide images (WSIs) of hematoxylin-eosin stained rodent retina. The accuracy of this computational method was validated using pathologist annotated WSIs throughout and used to separately quantify the thickness of the outer and inner nuclear layers of the retina. Our results show that DL can facilitate the evaluation of therapeutic and/or light-induced atrophy, particularly of the outer retina, efficiently in rodents. In addition, this study provides a template which can be used to train, validate, and analyze the results of toxicologic pathology DL models across different animal species used in preclinical efficacy and safety studies.

Immune contexture analysis in immuno-oncology: applications and challenges of multiplex fluorescent immunohistochemistry

The tumor microenvironment is an integral player in cancer initiation, tumor progression, response and resistance to anti-cancer therapy. Understanding the complex interactions of tumor immune architecture (referred to as 'immune contexture') has therefore become increasingly desirable to guide our approach to patient selection, clinical trial design, combination therapies, and patient management. Quantitative image analysis based on multiplexed fluorescence immunohistochemistry and deep learning technologies are rapidly developing to enable researchers to interrogate complex information from the tumor microenvironment and find predictive insights into treatment response. Herein, we discuss current developments in multiplexed fluorescence immunohistochemistry for immune contexture analysis, and their application in immuno-oncology, and discuss challenges to effectively use this technology in clinical settings. We also present a multiplexed image analysis workflow to analyse fluorescence multiplexed stained tumor sections using the Vectra Automated Digital Pathology System together with FCS express flow cytometry software. The benefit of this strategy is that the spectral unmixing accurately generates and analyses complex arrays of multiple biomarkers, which can be helpful for diagnosis, risk stratification, and guiding clinical management of oncology patients.

Introduction to digital pathology and computer-aided pathology

Digital pathology (DP) is no longer an unfamiliar term for pathologists, but it is still difficult for many pathologists to understand the engineering and mathematics concepts involved in DP. Computer-aided pathology (CAP) aids pathologists in diagnosis. However, some consider CAP a threat to the existence of pathologists and are skeptical of its clinical utility. Implementation of DP is very burdensome for pathologists because technical factors, impact on workflow, and information technology infrastructure must be considered. In this paper, various terms related to DP and computer-aided pathologic diagnosis are defined, current applications of DP are discussed, and various issues related to implementation of DP are outlined. The development of computer-aided pathologic diagnostic tools and their limitations are also discussed.

Digital Microscopy, Image Analysis, and Virtual Slide Repository

Advancements in technology and digitization have ushered in novel ways of enhancing tissue-based research via digital microscopy and image analysis. Whole slide imaging scanners enable digitization of histology slides to be stored in virtual slide repositories and to be viewed via computers instead of microscopes. Easier and faster sharing of histologic images for teaching and consultation, improved storage and preservation of quality of stained slides, and annotation of features of interest in the digital slides are just a few of the advantages of this technology. Combined with the development of software for digital image analysis, digital slides further pave the way for the development of tools that extract quantitative data from tissue-based studies. This review introduces digital microscopy and pathology, and addresses technical and scientific considerations in slide scanning, quantitative image analysis, and slide repositories. It also highlights the current state of the technology and factors that need to be taken into account to insure optimal utility, including preanalytical considerations and the importance of involving a pathologist in all major steps along the digital microscopy and pathology workflow.

Pathology Principles and Practices for Analysis of Animal Models

Over 60% of NIH extramural funding involves animal models, and approximately 80% to 90% of these are mouse models of human disease. It is critical to translational research that animal models are accurately characterized and validated as models of human disease. Pathology analysis, including histopathology, is essential to animal model studies by providing morphologic context to in vivo, molecular, and biochemical data; however, there are many considerations when incorporating pathology endpoints into an animal study. Mice, and in particular genetically modified models, present unique considerations because these modifications are affected by background strain genetics, husbandry, and experimental conditions. Comparative pathologists recognize normal pathobiology and unique phenotypes that animals, including genetically modified models, may present. Beyond pathology, comparative pathologists with research experience offer expertise in animal model development, experimental design, optimal specimen collection and handling, data interpretation, and reporting. Critical pathology considerations in the design and use of translational studies involving animals are discussed, with an emphasis on mouse models.

Computational pathology definitions, best practices, and recommendations for regulatory guidance: a white paper from the Digital Pathology Association

In this white paper, experts from the Digital Pathology Association (DPA) define terminology and concepts in the emerging field of computational pathology, with a focus on its application to histology images analyzed together with their associated patient data to extract information. This review offers a historical perspective and describes the potential clinical benefits from research and applications in this field, as well as significant obstacles to adoption. Best practices for implementing computational pathology workflows are presented. These include infrastructure considerations, acquisition of training data, quality assessments, as well as regulatory, ethical, and cyber-security concerns. Recommendations are provided for regulators, vendors, and computational pathology practitioners in order to facilitate progress in the field. © 2019 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

Introduction to Digital Image Analysis in Whole-slide Imaging: A White Paper from the Digital Pathology Association

The advent of whole-slide imaging in digital pathology has brought about the advancement of computer-aided examination of tissue via digital image analysis. Digitized slides can now be easily annotated and analyzed via a variety of algorithms. This study reviews the fundamentals of tissue image analysis and aims to provide pathologists with basic information regarding the features, applications, and general workflow of these new tools. The review gives an overview of the basic categories of software solutions available, potential analysis strategies, technical considerations, and general algorithm readouts. Advantages and limitations of tissue image analysis are discussed, and emerging concepts, such as artificial intelligence and machine learning, are introduced. Finally, examples of how digital image analysis tools are currently being used in diagnostic laboratories, translational research, and drug development are discussed.

Toxicologic Pathology Forum*: Opinion on Integrating Innovative Digital Pathology Tools in the Regulatory Framework

Digital pathology is defined as the ability to examine digitized microscopic slides and to generate qualitative and quantitative data. The field of digital pathology is rapidly evolving and has the potential to revolutionize toxicologic pathology. Techniques such as automated 2-D image analysis, whole slide imaging, and telepathology are already considered “mature” technologies and have been used for decades in exploratory studies; however, many organizations are reluctant to use digital pathology in regulatory toxicology studies. Innovative technologies using digitized slides including high-content imaging modalities and artificial intelligence are still under development but are increasingly used in toxicologic pathology. While software validation requirements are already described, clear guidance for application of these rules to the digital pathology field are few and the acceptance of these technologies by regulatory authorities remains necessary for successful adoption of digital pathology into the mainstream of toxicologic pathology. This topic was discussed during a roundtable at the 2018 Annual Congress of the French Society of Toxicologic Pathology. This opinion article summarizes the discussion regarding the current questions and challenges on the integration of innovative digital pathology tools within a good laboratory practice framework and is meant to stimulate further discussion among the toxicologic pathology community.

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Beyond the H&E: Advanced Technologies for in situ Tissue Biomarker Imaging

For decades, histopathology with routine hematoxylin and eosin staining has been and remains the gold standard for reaching a morphologic diagnosis in tissue samples from humans and veterinary species. However, within the past decade, there has been exponential growth in advanced techniques for in situ tissue biomarker imaging that bridge the divide between anatomic and molecular pathology. It is now possible to simultaneously observe localization and expression magnitude of multiple protein, nucleic acid, and molecular targets in tissue sections and apply machine learning to synthesize vast, image-derived datasets. As these technologies become more sophisticated and widely available, a team-science approach involving subspecialists with medical, engineering, and physics backgrounds is critical to upholding quality and validity in studies generating these data. The purpose of this manuscript is to detail the scientific premise, tools and training, quality control, and data collection and analysis considerations needed for the most prominent advanced imaging technologies currently applied in tissue sections: immunofluorescence, in situ hybridization, laser capture microdissection, matrix-assisted laser desorption ionization imaging mass spectrometry, and spectroscopic/optical methods. We conclude with a brief overview of future directions for ex vivo and in vivo imaging techniques.

Keeping Score: Semiquantitative and Quantitative Scoring Approaches to Genetically Engineered and Xenograft Mouse Models of Cancer

There is a growing need to quantitate or "score" lesions in mouse models of human disease, for correlation with human disease and to establish their clinical relevance. Several standard semiquantitative scoring schemes have been adapted for nonneoplastic lesions; similarly, the pathologist must carefully select an approach to score mouse models of cancer. Genetically engineered mouse models with a continuum of precancerous and cancerous lesions and xenogeneic models of various derivations present unique challenges for the pathologist. Important considerations include experimental design, understanding of the human disease being modeled, standardized classification of lesions, and approaches for semiquantitative and/or quantitative scoring in the model being evaluated. Quantification should be considered for measuring the extent of neoplasia and expression of tumor biomarkers. Semiquantitative scoring schemes have been devised that include severity, frequency, and distribution of lesions. Although labor-intensive, scoring mouse models of cancer provides numerical data that enable statistical analysis and greater translational impact.

Comparative Pathologists: Ultimate Control Freaks Seeking Validation!

Definable, reproducible, and meaningful are elemental features of grading/scoring systems, while thoroughness, accuracy, and consistency are quality indicators of pathology reports. The expertise of pathologists is significantly underutilized when it is limited to rendering diagnoses. The opportunity to provide guidance on animal model development, experimental design, optimal sample collection, and data interpretation not only contributes to job satisfaction but also, more importantly, promotes validation of the pathology data. Keys to validation include standard operating procedures, experimental controls, and standardized nomenclature applied throughout the experimental design and execution, tissue sampling, and slide preparation, as well as the creation or adaptation and application of semiquantitative grading/scoring systems. Diagnostic drift, thresholds, mental noise, and various diurnal fluctuations strongly influence the repeatability of grading/scoring systems used by the same or different pathologists. Quantitative image analyses are not plagued by the visual and cognitive traps that affect manual semiquantitative grading schemes but may still be affected by technical variables associated with necropsy, tissue sampling, and slide preparation. The validity of a grading scheme is ultimately assessed by its repeatability and biologic relevance, so it is important to correlate scores with comprehensive pathobiology data such as results of antemortem imaging, clinical pathology data, body and organ weights, and histopathologic evaluation of full tissue sets.

A Practical Guide to Whole Slide Imaging: A White Paper From the Digital Pathology Association

Context.—

Whole slide imaging (WSI) represents a paradigm shift in pathology, serving as a necessary first step for a wide array of digital tools to enter the field. Its basic function is to digitize glass slides, but its impact on pathology workflows, reproducibility, dissemination of educational material, expansion of service to underprivileged areas, and intrainstitutional and interinstitutional collaboration exemplifies a significant innovative movement with far-reaching effects. Although the benefits of WSI to pathology practices, academic centers, and research institutions are many, the complexities of implementation remain an obstacle to widespread adoption. In the wake of the first regulatory clearance of WSI for primary diagnosis in the United States, some barriers to adoption have fallen. Nevertheless, implementation of WSI remains a difficult prospect for many institutions, especially those with stakeholders unfamiliar with the technologies necessary to implement a system or who cannot effectively communicate to executive leadership and sponsors the benefits of a technology that may lack clear and immediate reimbursement opportunity.

Objectives.—

To present an overview of WSI technology—present and future—and to demonstrate several immediate applications of WSI that support pathology practice, medical education, research, and collaboration.

Data Sources.—

Peer-reviewed literature was reviewed by pathologists, scientists, and technologists who have practical knowledge of and experience with WSI.

Conclusions.—

Implementation of WSI is a multifaceted and inherently multidisciplinary endeavor requiring contributions from pathologists, technologists, and executive leadership. Improved understanding of the current challenges to implementation, as well as the benefits and successes of the technology, can help prospective users identify the best path for success.

Meeting Report: Tissue-based Image Analysis

Quantitative image analysis (IA) is a rapidly evolving area of digital pathology. Although not a new concept, the quantification of histological features on photomicrographs used to be cumbersome, resource-intensive, and limited to specialists and specialized laboratories. Recent technological advances like highly efficient automated whole slide digitizer (scanner) systems, innovative IA platforms, and the emergence of pathologist-friendly image annotation and analysis systems mean that quantification of features on histological digital images will become increasingly prominent in pathologists' daily professional lives. The added value of quantitative IA in pathology includes confirmation of equivocal findings noted by a pathologist, increasing the sensitivity of feature detection, quantification of signal intensity, and improving efficiency. There is no denying that quantitative IA is part of the future of pathology; however, there are also several potential pitfalls when trying to estimate volumetric features from limited 2-dimensional sections. This continuing education session on quantitative IA offered a broad overview of the field; a hands-on toxicologic pathologist experience with IA principles, tools, and workflows; a discussion on how to apply basic stereology principles in order to minimize bias in IA; and finally, a reflection on the future of IA in the toxicologic pathology field.

An Automated Image Analysis Method to Quantify Veterinary Bone Marrow Cellularity on H&E Sections

Bone marrow toxicity is a common finding when assessing safety of drug candidate molecules. Standard hematoxylin and eosin (H&E) marrow tissue sections are typically manually evaluated to provide a semiquantitative assessment of overall cellularity. Here, we developed an automated image analysis method that allows quantitative assessment of changes in bone marrow cell population in sternal bone. In order to test whether the method was repeatable and sensitive, we compared the automated method with manual subjective histopathology scoring of total cellularity in rat sternal bone marrow samples across 17 independently run studies. The automated method was consistent with manual scoring methodology for detecting altered bone marrow cellularity and, in multiple cases, identified changes at lower doses. The image analysis method allows rapid and more quantitative assessment of bone marrow toxicity compared to manual examination of H&E slides, making it an excellent tool to aid detection of bone marrow cell depletion in preclinical toxicologic studies.

An update on the validation of whole slide imaging systems following FDA approval of a system for a routine pathology diagnostic service in the United States

Pathologists have used light microscopes and glass slides to interpret the histologic appearance of normal and diseased tissues for more than 150 years. The quality of both microtomes used to cut tissue sections and microscopes has improved significantly during the past few decades, but the process of rendering diagnoses has changed little. By contrast, major advances in digital technology have occurred since the introduction of hand held electronic devices, including the development of whole slide imaging (WSI) systems with software packages that can convert microscope images into virtual (digital) slides that can be viewed on computer monitors and via the internet. To date, however, these technological developments have had minimal impact on the way pathologists perform their daily work, with the exception of using computers to access electronic medical records and scholarly web sites for pertinent information to assist interpretation of cases. Traditional practice is likely to change significantly during the next decade, especially since the Federal Drug Administration in the USA has approved the first WSI system for routine diagnostic practice. I review here the development and slow acceptance of WSI by pathology departments. I focus on recent advances in validation of WSI systems that is required for routine diagnostic reporting of pathology cases using this technology.

Quantitative analysis of histopathological findings using image processing software

In evaluating pathological changes in drug efficacy and toxicity studies, morphometric analysis can be quite robust. In this experiment, we examined whether morphometric changes of major pathological findings in various tissue specimens stained with hematoxylin and eosin could be recognized and quantified using image processing software. Using Tissue Studio, hypertrophy of hepatocytes and adrenocortical cells could be quantified based on the method of a previous report, but the regions of red pulp, white pulp, and marginal zones in the spleen could not be recognized when using one setting condition. Using Image-Pro Plus, lipid-derived vacuoles in the liver and mucin-derived vacuoles in the intestinal mucosa could be quantified using two criteria (area and/or roundness). Vacuoles derived from phospholipid could not be quantified when small lipid deposition coexisted in the liver and adrenal cortex. Mononuclear inflammatory cell infiltration in the liver could be quantified to some extent, except for specimens with many clustered infiltrating cells. Adipocyte size and the mean linear intercept could be quantified easily and efficiently using morphological processing and the macro tool equipped in Image-Pro Plus. These methodologies are expected to form a base system that can recognize morphometric features and analyze quantitatively pathological findings through the use of information technology.

A Primer for Oncoimmunology (Immunooncology)

Oncoimmunology (or immunooncology) is a burgeoning specialty of precision (“personalized”) medicine designed to heighten the antitumor response of the immune system against molecules expressed excessively or only by tumor cells. This focus is necessary, as cancers are polyclonal tissues comprised of antigenically heterogeneous cells, the exact composition of which is shaped by the balance between antitumor immunity and tumor-promoting inflammation. Key targets include enhancing immune system (especially T cell) reactivity, inhibiting immune checkpoints, and promoting tumor cytolysis. Therapeutic modalities to address these targets include administering antibodies, cytokines, or small molecules that directly stimulate the immune system, attack tumor-associated antigens, or interfere with tumor–stroma interactions; adoptive transfer of autologous T cells following ex vivo selection/expansion/activation (typically after lymphoid-depleting regimens and in conjunction with immunostimulatory therapy); and vaccination (against tumor antigens). Pathology involvement in oncoimmunology product development is critical to assess expression of target molecules in tumor cells, stromal cells, and tumor-infiltrating leukocytes.

The Gold Standard Paradox in Digital Image Analysis: Manual Versus Automated Scoring as Ground Truth

CONTEXT

- Novel therapeutics often target complex cellular mechanisms. Increasingly, quantitative methods like digital tissue image analysis (tIA) are required to evaluate correspondingly complex biomarkers to elucidate subtle phenotypes that can inform treatment decisions with these targeted therapies. These tIA systems need a gold standard, or reference method, to establish analytical validity. Conventional, subjective histopathologic scores assigned by an experienced pathologist are the gold standard in anatomic pathology and are an attractive reference method. The pathologist's score can establish the ground truth to assess a tIA solution's analytical performance. The paradox of this validation strategy, however, is that tIA is often used to assist pathologists to score complex biomarkers because it is more objective and reproducible than manual evaluation alone by overcoming known biases in a human's visual evaluation of tissue, and because it can generate endpoints that cannot be generated by a human observer.

OBJECTIVE

- To discuss common visual and cognitive traps known in traditional pathology-based scoring paradigms that may impact characterization of tIA-assisted scoring accuracy, sensitivity, and specificity.

DATA SOURCES

- This manuscript reviews the current literature from the past decades available for traditional subjective pathology scoring paradigms and known cognitive and visual traps relevant to these scoring paradigms.

CONCLUSIONS

- Awareness of the gold standard paradox is necessary when using traditional pathologist scores to analytically validate a tIA tool because image analysis is used specifically to overcome known sources of bias in visual assessment of tissue sections.

Quantitative assessment of pancreatic cancer precursor lesions in IHC-stained tissue with a tissue image analysis platform

Tissue image analysis (tIA) is emerging as a powerful tool for quantifying biomarker expression and distribution in complex diseases and tissues. Pancreatic ductal adenocarcinoma (PDAC) develops in a highly complex and heterogeneous tissue environment and, generally, has a very poor prognosis. Early detection of PDAC is confounded by limited knowledge of the pre-neoplastic disease stages and limited methods to quantitatively assess disease heterogeneity. We sought to develop a tIA approach to assess the most common PDAC precursor lesions, pancreatic intraepithelial neoplasia (PanIN), in tissues from Kras^LSL-G12D/+; Trp53^LSL-R172H/+; Pdx-Cre (KPC) mice, a validated model of PDAC development. tIA profiling of training regions of PanIN and tumor microenvironment (TME) cells was utilized to guide identification of PanIN/TME tissue compartment stratification criteria. A custom CellMap algorithm implementing these criteria was applied to whole-slide images of KPC mice pancreata sections to quantify p53 and Ki-67 biomarker staining in each tissue compartment as a proof-of-concept for the algorithm platform. The algorithm robustly identified a higher percentage of p53-positive cells in PanIN lesions relative to the TME, whereas no difference was observed for Ki-67. Ki-67 expression was also quantified in a human pancreatic tissue sample available to demonstrate the translatability of the CellMap algorithm to human samples. Together, our data demonstrated the utility of CellMap to enable objective and quantitative assessments, across entire tissue sections, of PDAC precursor lesions in preclinical and clinical models of this disease to support efforts leading to novel insights into disease progression, diagnostic markers, and potential therapeutic targets.