Current State of the Regulatory Trajectory for Whole Slide Imaging Devices in the USA

Pathologists light level preferences using the microscope-study to guide digital pathology display use

Background

Currently, there is a paucity of guidelines relating to displays used for digital pathology making procurement decisions, and optimal display configuration, challenging.Experience suggests pathologists have personal preferences for brightness when using a conventional microscope which we hypothesized could be used as a predictor for display setup.

Methods

We conducted an online survey across six NHS hospitals, totalling 108 practicing pathologists, to capture brightness adjustment habits on both microscopes and displays.A convenience subsample of respondents was then invited to take part in a practical task to determine microscope brightness and display luminance preferences in the normal working environment. A novel adaptation for a lightmeter was developed to directly measure the light output from the microscope eyepiece.

Results

The survey (response rate 59% n=64) indicates 81% of respondents adjust the brightness on their microscope. In comparison, only 11% report adjusting their digital display. Display adjustments were more likely to be for visual comfort and ambient light compensation rather than for tissue factors, common for microscope adjustments. Part of this discrepancy relates to lack of knowledge of how to adjust displays and lack of guidance on whether this is safe; But, 66% felt that the ability to adjust the light on the display was important.Twenty consultants took part in the practical brightness assessment. Light preferences on the microscope showed no correlation with display preferences, except where a pathologist has a markedly brighter microscope light preference. All of the preferences in this cohort were for a display luminance of <500 cd/m2, with 90% preferring 350 cd/m2 or less. There was no correlation between these preferences and the ambient lighting in the room.

Conclusions

We conclude that microscope preferences can only be used to predict display luminance requirements where the microscope is being used at very high brightness levels. A display capable of a brightness of 500 cd/m2 should be suitable for almost all pathologists with 300 cd/m2 suitable for the majority. Although display luminance is not frequently changed by users, the ability to do so was felt to be important by the majority of respondents.Further work needs to be undertaken to establish the relationship between diagnostic performance, luminance preferences, and ambient lighting levels.

Computational pathology: A survey review and the way forward

Computational Pathology (CPath) is an interdisciplinary science that augments developments of computational approaches to analyze and model medical histopathology images. The main objective for CPath is to develop infrastructure and workflows of digital diagnostics as an assistive CAD system for clinical pathology, facilitating transformational changes in the diagnosis and treatment of cancer that are mainly address by CPath tools. With evergrowing developments in deep learning and computer vision algorithms, and the ease of the data flow from digital pathology, currently CPath is witnessing a paradigm shift. Despite the sheer volume of engineering and scientific works being introduced for cancer image analysis, there is still a considerable gap of adopting and integrating these algorithms in clinical practice. This raises a significant question regarding the direction and trends that are undertaken in CPath. In this article we provide a comprehensive review of more than 800 papers to address the challenges faced in problem design all-the-way to the application and implementation viewpoints. We have catalogued each paper into a model-card by examining the key works and challenges faced to layout the current landscape in CPath. We hope this helps the community to locate relevant works and facilitate understanding of the field's future directions. In a nutshell, we oversee the CPath developments in cycle of stages which are required to be cohesively linked together to address the challenges associated with such multidisciplinary science. We overview this cycle from different perspectives of data-centric, model-centric, and application-centric problems. We finally sketch remaining challenges and provide directions for future technical developments and clinical integration of CPath. For updated information on this survey review paper and accessing to the original model cards repository, please refer to GitHub. Updated version of this draft can also be found from arXiv.

Expanding role and scope of artificial intelligence in the field of gastrointestinal pathology

Digital pathology (DP) and its subsidiaries including artificial intelligence (AI) are rapidly making inroads into the area of diagnostic anatomic pathology (AP) including gastrointestinal (GI) pathology. It is poised to revolutionize the field of diagnostic AP. Historically, AP has been slow to adopt digital technology, but this is changing rapidly, with many centers worldwide transitioning to DP. Coupled with advanced techniques of AI such as deep learning and machine learning, DP is likely to transform histopathology from a subjective field to an objective, efficient, and transparent discipline. AI is increasingly integrated into GI pathology, offering numerous advancements and improvements in overall diagnostic accuracy, efficiency, and patient care. Specifically, AI in GI pathology enhances diagnostic accuracy, streamlines workflows, provides predictive insights, integrates multimodal data, supports research, and aids in education and training, ultimately improving patient care and outcomes. This review summarized the latest developments in the role and scope of AI in AP with a focus on GI pathology. The main aim was to provide updates and create awareness among the pathology community.

Implementation of Artificial Intelligence in Personalized Prognostic Assessment of Lung Cancer: A Narrative Review

Artificial Intelligence (AI) has revolutionized the management of non-small-cell lung cancer (NSCLC) by enhancing different aspects, including staging, prognosis assessment, treatment prediction, response evaluation, recurrence/prognosis prediction, and personalized prognostic assessment. AI algorithms may accurately classify NSCLC stages using machine learning techniques and deep imaging data analysis. This could potentially improve precision and efficiency in staging, facilitating personalized treatment decisions. Furthermore, there are data suggesting the potential application of AI-based models in predicting prognosis in terms of survival rates and disease progression by integrating clinical, imaging and molecular data. In the present narrative review, we will analyze the preliminary studies reporting on how AI algorithms could predict responses to various treatment modalities, such as surgery, radiotherapy, chemotherapy, targeted therapy, and immunotherapy. There is robust evidence suggesting that AI also plays a crucial role in predicting the likelihood of tumor recurrence after surgery and the pattern of failure, which has significant implications for tailoring adjuvant treatments. The successful implementation of AI in personalized prognostic assessment requires the integration of different data sources, including clinical, molecular, and imaging data. Machine learning (ML) and deep learning (DL) techniques enable AI models to analyze these data and generate personalized prognostic predictions, allowing for a precise and individualized approach to patient care. However, challenges relating to data quality, interpretability, and the ability of AI models to generalize need to be addressed. Collaboration among clinicians, data scientists, and regulators is critical for the responsible implementation of AI and for maximizing its benefits in providing a more personalized prognostic assessment. Continued research, validation, and collaboration are essential to fully exploit the potential of AI in NSCLC management and improve patient outcomes. Herein, we have summarized the state of the art of applications of AI in lung cancer for predicting staging, prognosis, and pattern of recurrence after treatment in order to provide to the readers a large comprehensive overview of this challenging issue.

Whole Slide Imaging Technology and Its Applications: Current and Emerging Perspectives

Background. 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. It utilizes virtual microscopy wherein glass slides are converted into digital slides and are viewed by pathologists by automated image analysis. Its impact on pathology workflow, reproducibility, dissemination of educational material, expansion of service to underprivileged areas, and institutional collaboration exemplifies a significant innovative movement. The recent US Food and Drug Administration approval to WSI for its use in primary surgical pathology diagnosis has opened opportunities for wider application of this technology in routine practice. Main Text. The ongoing technological advances in digital scanners, image visualization methods, and the integration of artificial intelligence-derived algorithms with these systems provide avenues to exploit its applications. Its benefits are innumerable such as ease of access through the internet, avoidance of physical storage space, and no risk of deterioration of staining quality or breakage of slides to name a few. Although the benefits of WSI to pathology practices are many, the complexities of implementation remain an obstacle to widespread adoption. Some barriers including the high cost, technical glitches, and most importantly professional hesitation to adopt a new technology have hindered its use in routine pathology. Conclusions. In this review, we summarize the technical aspects of WSI, its applications in diagnostic pathology, training, and research along with future perspectives. It also highlights improved understanding of the current challenges to implementation, as well as the benefits and successes of the technology. WSI provides a golden opportunity for pathologists to guide its evolution, standardization, and implementation to better acquaint them with the key aspects of this technology and its judicial use. Also, implementation of routine digital pathology is an extra step requiring resources which (currently) does not usually result increased efficiency or payment.

Whole-Slide Imaging (WSI) Versus Traditional Microscopy (TM) Through Evaluation of Parameters in Oral Histopathology: A Pilot Study

Background

histopathology plays a pivotal role in clinical diagnosis, research, and medical education. In recent years, whole slide imaging (wsi) has emerged as a potential alternative to traditional microscopy for pathological examination. This study aims to provide a comprehensive comparison of wsi and traditional microscopy(tm) in various aspects of histopathology practice.

Materials and Methods

In this study, total of 30 cases comprising of oral premalignant and malignant cases which were diagnostically challenging was considered from the archives of the institute for validation. The slides were scanned with slide scanner and were evaluated by histopathologists. The comparative parameters which were noted were diagnostic discordances, number of fields observed to reach the diagnosis and time taken.

Results

The mean time taken by the pathologists to reach the diagnosis was significantly less in whole slide imaging technique. The average number of fields observed was higher by using wsi that too in a lesser time compared to tm, the results were found to be statistically significant with p=0.001.however the diagnostic disparity were seen to be maximum for verrucous lesions both in wsi and tm.

Conclusion

wsi has facilitated the specialty with rapid mode of diagnosis in a more efficient and error less manner. It has also aided in case banking as well as research possibilities. Hence with the advent of telepathology it is very much necessary to get trained with wsi as early as possible so that the professionals can render correct diagnosis.

Comparing Accuracy of Helicobacter pylori Identification Using Traditional Hematoxylin and Eosin-Stained Glass Slides With Digital Whole Slide Imaging

With advancements in the field of digital pathology, there has been a growing need to compare the diagnostic abilities of pathologists using digitized whole slide images against those when using traditional hematoxylin and eosin (H&E)-stained glass slides for primary diagnosis. One of the most common specimens received in pathology practices is an endoscopic gastric biopsy with a request to rule out Helicobacter pylori (H. pylori) infection. The current standard of care is the identification of the organisms on H&E-stained slides. Immunohistochemical or histochemical stains are used selectively. However, due to their small size (2-4 μm in length by 0.5-1 μm in width), visualization of the organisms can present a diagnostic challenge. The goal of the study was to compare the ability of pathologists to identify H. pylori on H&E slides using a digital platform against the gold standard of H&E glass slides using routine light microscopy. Diagnostic accuracy rates using glass slides vs digital slides were 81% vs 72% (P = .0142) based on H&E slides alone. When H. pylori immunohistochemical slides were provided, the diagnostic accuracy was significantly improved to comparable rates (96% glass vs 99% digital, P = 0.2199). Furthermore, differences in practice settings (academic/subspecialized vs community/general) and the duration of sign-out experience did not significantly impact the accuracy of detecting H. pylori on digital slides. We concluded that digital whole slide images, although amenable in different practice settings and teaching environments, does present some shortcomings in accuracy and precision, especially in certain circumstances and thus is not yet fully capable of completely replacing glass slide review for identification of H. pylori. We specifically recommend reviewing glass slides and/or performing ancillary stains, especially when there is a discrepancy between the degree of inflammation and the presence of microorganisms on digital images.

Complete digital pathology transition: A large multi-center experience

INTRODUCTION

Transitioning from glass slide pathology to digital pathology for primary diagnostics requires an appropriate laboratory information system, an image management system, and slide scanners; it also reinforces the need for sophisticated pathology informatics including synoptic reporting. Previous reports have discussed the transition itself and relevant considerations for it, but not the selection criteria and considerations for the infrastructure.

OBJECTIVE

To describe the process used to evaluate slide scanners, image management systems, and synoptic reporting systems for a large multisite institution.

METHODS

Six network hospitals evaluated six slide scanners, three image management systems, and three synoptic reporting systems. Scanners were evaluated based on the quality of image, speed, ease of operation, and special capabilities (including z-stacking, fluorescence and others). Image management and synoptic reporting systems were evaluated for their ease of use and capacity.

RESULTS

Among the scanners evaluated, the Leica GT450 produced the highest quality images, while the 3DHistech Pannoramic provided fluorescence and superior z-stacking. The newest generation of scanners, released relatively recently, performed better than slightly older scanners from major manufacturers Although the Olympus VS200 was not fully vetted due to not meeting all inclusion criteria, it is discussed herein due to its exceptional versatility. For Image Management Software, the authors believe that Sectra is, at the time of writing the best developed option, but this could change in the very near future as other systems improve their capabilities. All synoptic reporting systems performed impressively.

CONCLUSIONS

Specifics regarding quality and abilities of different components will change rapidly with time, but large pathology practices considering such a transition should be aware of the issues discussed and evaluate the most current generation to arrive at appropriate conclusions.

Intraoperative assessment of axillary sentinel lymph nodes by telepathology

PURPOSE

Although axillary dissection is no longer indicated for many breast cancer patients with 1-2 positive axillary sentinel lymph nodes (ASLN), intraoperative ASLN assessment is still performed in many institutions for patients undergoing mastectomy or neoadjuvant therapy. With recent advancements in digital pathology, pathologists increasingly evaluate ASLN via remote telepathology. We aimed to compare the performance characteristics of remote telepathology and conventional on-site intraoperative ASLN assessment.

METHODS

Data from ASLN evaluation for breast cancer patients performed at two sites between April 2021 and October 2022 was collated. Remote telepathology consultation was conducted via the Aperio eSlideManager system.

RESULTS

A total of 385 patients were identified during the study period (83 telepathology, 302 on-site evaluations). Although not statistically significant (P = 0.20), the overall discrepancy rate between intraoperative and final diagnoses was slightly higher at 9.6% (8/83) for telepathology compared with 5.3% (16/302) for on-site assessment. Further comparison of performance characteristics of ASLN assessment between telepathology and conventional on-site evaluation revealed no statistically significant differences between deferral rates, discrepancy rates, interpretive or sampling errors, major or minor disagreements, false negative or false positive results as well as clinical impact and turn-around time (P ≥ 0.12).

CONCLUSION

ASLN assessment via telepathology is not significantly different from conventional on-site evaluation, although it shows a slightly higher overall discrepancy rate between intraoperative and final diagnoses (9.6% vs. 5.3%). Further studies are warranted to ensure accuracy of ASLN assessment via telepathology.

Digital pathology systems enabling quality patient care

Pathology laboratories are undergoing digital transformations, adopting innovative technologies to enhance patient care. Digital pathology systems impact clinical, education, and research use cases where pathologists use digital technologies to perform tasks in lieu of using glass slides and a microscope. Pathology professional societies have established clinical validation guidelines, and the US Food and Drug Administration have also authorized digital pathology systems for primary diagnosis, including image analysis and machine learning systems. Whole slide images, or digital slides, can be viewed and navigated similar to glass slides on a microscope. These modern tools not only enable pathologists to practice their routine clinical activities, but can potentially enable digital computational discovery. Assimilation of whole slide images in pathology clinical workflow can further empower machine learning systems to support computer assisted diagnostics. The potential enrichment these systems can provide is unprecedented in the field of pathology. With appropriate integration, these clinical decision support systems will allow pathologists to increase the delivery of quality patient care. This review describes the digital pathology transformation process, applicable clinical use cases, incorporation of image analysis and machine learning systems in the clinical workflow, as well as future technologies that may further disrupt pathology modalities to deliver quality patient care.

Delphi expert consensus for whole slide imaging in thyroid cytopathology

OBJECTIVE

Despite an increase in thyroid fine needle aspiration (FNA) and advances in whole slide imaging (WSI) adoption, digital pathology is still considered inadequate for primary diagnosis of these cases. Herein, we aim to validate the utility of WSI in thyroid FNAs employing the Delphi method strategy.

METHODS

A panel of experts from seven reference cytology centres was recruited. The study consisted of two consecutive rounds: (1) an open-ended, free-response questionnaire generating a list of survey items; and (2) a consensus analysis of 80 selected shared WSIs from 80 cases by six investigators answering six morphological questions utilising a 1 to 5 Likert scale.

RESULTS

High consensus was achieved for all parameters, with an overall average score of 4.27. The broad majority of items (84%) were ranked either 4 or 5 by each physician. Two badly scanned cases were responsible for more than half of the low-ranked (≤2) values (57%). Good to excellent (≥3) diagnostic confidence was reached in more than 95.2% of cases. For most cases (78%) WSI assessment was not limited by technical issues linked to the image acquisition process.

CONCLUSION

This systematic Delphi study indicates broad consensus among participating physicians on the application of DP to thyroid cytopathology, supporting expert opinion that WSI is reliable and safe for primary diagnostic purposes.

Results of the European Society of Toxicologic Pathology Survey on the Use of Artificial Intelligence in Toxicologic Pathology

The European Society of Toxicologic Pathology (ESTP) initiated a survey through its Pathology 2.0 workstream in partnership with sister professional societies in Europe and North America to generate a snapshot of artificial intelligence (AI) usage in the field of toxicologic pathology. In addition to demographic information, some general questions explored AI relative to (1) the current status of adoption across organizations; (2) technical and methodological aspects; (3) perceived business value and finally; and (4) roadblocks and perspectives. AI has become increasingly established in toxicologic pathology with most pathologists being supportive of its development despite some areas of uncertainty. A salient feature consisted of the variability of AI awareness and adoption among the responders, as the spectrum extended from pathologists having developed familiarity and technical skills in AI, to colleagues who had no interest in AI as a tool in toxicologic pathology. Despite a general enthusiasm for these techniques, the overall understanding and trust in AI algorithms as well as their added value in toxicologic pathology were generally low, suggesting room for the need for increased awareness and education. This survey will serve as a basis to evaluate the evolution of AI penetration and acceptance in this domain.

Spatial- and Fourier-domain ptychography for high-throughput bio-imaging

First envisioned for determining crystalline structures, ptychography has become a useful imaging tool for microscopists. However, ptychography remains underused by biomedical researchers due to its limited resolution and throughput in the visible light regime. Recent developments of spatial- and Fourier-domain ptychography have successfully addressed these issues and now offer the potential for high-resolution, high-throughput optical imaging with minimal hardware modifications to existing microscopy setups, often providing an excellent trade-off between resolution and field of view inherent to conventional imaging systems, giving biomedical researchers the best of both worlds. Here, we provide extensive information to enable the implementation of ptychography by biomedical researchers in the visible light regime. We first discuss the intrinsic connections between spatial-domain coded ptychography and Fourier ptychography. A step-by-step guide then provides the user instructions for developing both systems with practical examples. In the spatial-domain implementation, we explain how a large-scale, high-performance blood-cell lens can be made at negligible expense. In the Fourier-domain implementation, we explain how adding a low-cost light source to a regular microscope can improve the resolution beyond the limit of the objective lens. The turnkey operation of these setups is suitable for use by professional research laboratories, as well as citizen scientists. Users with basic experience in optics and programming can build the setups within a week. The do-it-yourself nature of the setups also allows these procedures to be implemented in laboratory courses related to Fourier optics, biomedical instrumentation, digital image processing, robotics and capstone projects.

Optical ptychography for biomedical imaging: recent progress and future directions [Invited]

Ptychography is an enabling microscopy technique for both fundamental and applied sciences. In the past decade, it has become an indispensable imaging tool in most X-ray synchrotrons and national laboratories worldwide. However, ptychography's limited resolution and throughput in the visible light regime have prevented its wide adoption in biomedical research. Recent developments in this technique have resolved these issues and offer turnkey solutions for high-throughput optical imaging with minimum hardware modifications. The demonstrated imaging throughput is now greater than that of a high-end whole slide scanner. In this review, we discuss the basic principle of ptychography and summarize the main milestones of its development. Different ptychographic implementations are categorized into four groups based on their lensless/lens-based configurations and coded-illumination/coded-detection operations. We also highlight the related biomedical applications, including digital pathology, drug screening, urinalysis, blood analysis, cytometric analysis, rare cell screening, cell culture monitoring, cell and tissue imaging in 2D and 3D, polarimetric analysis, among others. Ptychography for high-throughput optical imaging, currently in its early stages, will continue to improve in performance and expand in its applications. We conclude this review article by pointing out several directions for its future development.

Application of Digital Imaging and Artificial Intelligence to Pathology of the Placenta

Digital imaging, including the use of artificial intelligence, has been increasingly applied to investigate the placenta and its related pathology. However, there has been no comprehensive review of this body of work to date. The aim of this study was to therefore review the literature regarding digital pathology of the placenta. A systematic literature search was conducted in several electronic databases. Studies involving the application of digital imaging and artificial intelligence techniques to human placental samples were retrieved and analyzed. Relevant articles were categorized by digital image technique and their relevance to studying normal and diseased placenta. Of 2008 retrieved articles, 279 were included. Digital imaging research related to the placenta was often coupled with immunohistochemistry, confocal microscopy, 3D reconstruction, and/or deep learning algorithms. By significantly increasing pathologists' ability to recognize potentially prognostic relevant features and by lessening inter-observer variability, published data overall indicate that the application of digital pathology to placental and perinatal diseases, along with clinical and radiology correlation, has great potential to improve fetal and maternal health care including the selection of targeted therapy in high-risk pregnancy.

Cultivating Clinical Clarity through Computer Vision: A Current Perspective on Whole Slide Imaging and Artificial Intelligence

Diagnostic devices, methodological approaches, and traditional constructs of clinical pathology practice, cultivated throughout centuries, have transformed radically in the wake of explosive technological growth and other, e.g., environmental, catalysts of change. Ushered into the fray of modern laboratory medicine are digital imaging devices and machine-learning (ML) software fashioned to mitigate challenges, e.g., practitioner shortage while preparing clinicians for emerging interconnectivity of environments and diagnostic information in the era of big data. As computer vision shapes new constructs for the modern world and intertwines with clinical medicine, cultivating clarity of our new terrain through examining the trajectory and current scope of computational pathology and its pertinence to clinical practice is vital. Through review of numerous studies, we find developmental efforts for ML migrating from research to standardized clinical frameworks while overcoming obstacles that have formerly curtailed adoption of these tools, e.g., generalizability, data availability, and user-friendly accessibility. Groundbreaking validatory efforts have facilitated the clinical deployment of ML tools demonstrating the capacity to effectively aid in distinguishing tumor subtype and grade, classify early vs. advanced cancer stages, and assist in quality control and primary diagnosis applications. Case studies have demonstrated the benefits of streamlined, digitized workflows for practitioners alleviated by decreased burdens.

Quantized spiral-phase-modulation based deep learning for real-time defocusing distance prediction

Whole slide imaging (WSI) has become an essential tool in pathological diagnosis, owing to its convenience on remote and collaborative review. However, how to bring the sample at the optimal position in the axial direction and image without defocusing artefacts is still a challenge, as traditional methods are either not universal or time-consuming. Until recently, deep learning has been shown to be effective in the autofocusing task in predicting defocusing distance. Here, we apply quantized spiral phase modulation on the Fourier domain of the captured images before feeding them into a light-weight neural network. It can significantly reduce the average predicting error to be lower than any previous work on an open dataset. Also, the high predicting speed strongly supports it can be applied on an edge device for real-time tasks with limited computational source and memory footprint.

High-throughput digital pathology via a handheld, multiplexed, and AI-powered ptychographic whole slide scanner

The recent advent of whole slide imaging (WSI) systems has moved digital pathology closer to diagnostic applications and clinical practices. Integrating WSI with machine learning promises the growth of this field in upcoming years. Here we report the design and implementation of a handheld, colour-multiplexed, and AI-powered ptychographic whole slide scanner for digital pathology applications. This handheld scanner is built using low-cost and off-the-shelf components, including red, green, and blue laser diodes for sample illumination, a modified stage for programmable sample positioning, and a synchronized image sensor pair for data acquisition. We smear a monolayer of goat blood cells on the main sensor for high-resolution lensless coded ptychographic imaging. The synchronized secondary sensor acts as a non-contact encoder for precisely tracking the absolute object position for ptychographic reconstruction. For WSI, we introduce a new phase-contrast-based focus metric for post-acquisition autofocusing of both stained and unstained specimens. We show that the scanner can resolve the 388-nm linewidth on the resolution target and acquire gigapixel images with a 14 mm × 11 mm area in ∼70 seconds. The imaging performance is validated with regular stained pathology slides, unstained thyroid smears, and malaria-infected blood smears. The deep neural network developed in this study further enables high-throughput cytometric analysis using the recovered complex amplitude. The reported do-it-yourself scanner offers a portable solution to transform the high-end WSI system into one that can be made widely available at a low cost. The capability of high-throughput quantitative phase imaging may also find applications in rapid on-site evaluations.

Learning to autofocus in whole slide imaging via physics-guided deep cascade networks

Whole slide imaging (WSI), is an essential technology for digital pathology, the performance of which is primarily affected by the autofocusing process. Conventional autofocusing methods either are time-consuming or require additional hardware and thus are not compatible with the current WSI systems. In this paper, we propose an effective learning-based method for autofocusing in WSI, which can realize accurate autofocusing at high speed as well as without any optical hardware modifications. Our method is inspired by an observation that sample images captured by WSI have distinctive characteristics with respect to positive / negative defocus offsets, due to the asymmetry effect of optical aberrations. Based on this physical knowledge, we develop novel deep cascade networks to enhance autofocusing quality. Specifically, to handle the effect of optical aberrations, a binary classification network is tailored to distinguish sample images with positive / negative defocus. As such, samples within the same category share similar characteristics. It facilitates the followed refocusing network, which is designed to learn the mapping between the defocus image and defocus distance. Experimental results demonstrate that our method achieves superior autofocusing performance to other related methods.

Application of Artificial Intelligence in Lung Cancer

Lung cancer is the leading cause of malignancy-related mortality worldwide due to its heterogeneous features and diagnosis at a late stage. Artificial intelligence (AI) is good at handling a large volume of computational and repeated labor work and is suitable for assisting doctors in analyzing image-dominant diseases like lung cancer. Scientists have shown long-standing efforts to apply AI in lung cancer screening via CXR and chest CT since the 1960s. Several grand challenges were held to find the best AI model. Currently, the FDA have approved several AI programs in CXR and chest CT reading, which enables AI systems to take part in lung cancer detection. Following the success of AI application in the radiology field, AI was applied to digitalized whole slide imaging (WSI) annotation. Integrating with more information, like demographics and clinical data, the AI systems could play a role in decision-making by classifying EGFR mutations and PD-L1 expression. AI systems also help clinicians to estimate the patient's prognosis by predicting drug response, the tumor recurrence rate after surgery, radiotherapy response, and side effects. Though there are still some obstacles, deploying AI systems in the clinical workflow is vital for the foreseeable future.

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.

Contemporary Whole Slide Imaging Devices and Their Applications within the Modern Pathology Department: A Selected Hardware Review

Digital pathology (DP) has disrupted the practice of traditional pathology, including applications in education, research, and clinical practice. Contemporary whole slide imaging (WSI) devices include technological advances that help address some of the challenges facing modern pathology, such as increasing workloads with fewer subspecialized pathologists, expanding integrated delivery networks with global reach, and greater customization when working up cases for precision medicine. This review focuses on integral hardware components of 43 market available and soon-to-be released digital WSI devices utilized throughout the world. Components such as objective lens type and magnification, scanning camera, illumination, and slide capacity were evaluated with respect to scan time, throughput, accuracy of scanning, and image quality. This analysis of assorted modern WSI devices offers essential, valuable information for successfully selecting and implementing a digital WSI solution for any given pathology practice.

Deep learning-enabled whole slide imaging (DeepWSI): oil-immersion quality using dry objectives, longer depth of field, higher system throughput, and better functionality

Whole slide imaging (WSI) has moved the traditional manual slide inspection process to the era of digital pathology. A typical WSI system translates the sample to different positions and captures images using a high numerical aperture (NA) objective lens. Performing oil-immersion microscopy is a major obstacle for WSI as it requires careful liquid handling during the scanning process. Switching between dry objective and oil-immersion lens is often impossible as it disrupts the acquisition process. For a high-NA objective lens, the sub-micron depth of field also poses a challenge to acquiring in-focus images of samples with uneven topography. Additionally, it implies a small field of view for each tile, thus limiting the system throughput and resulting in a long acquisition time. Here we report a deep learning-enabled WSI platform, termed DeepWSI, to substantially improve the system performance and imaging throughput. With this platform, we show that images captured with a regular dry objective lens can be transformed into images comparable to that of a 1.4-NA oil immersion lens. Blurred images with defocus distance from -5 µm to +5 µm can be virtually refocused to the in-focus plane post measurement. We demonstrate an equivalent data throughput of >2 gigapixels per second, the highest among existing WSI systems. Using the same deep neural network, we also report a high-resolution virtual staining strategy and demonstrate it for Fourier ptychographic WSI. The DeepWSI platform may provide a turnkey solution for developing high-performance diagnostic tools for digital pathology.

Colorimetrical uncertainty estimation for the performance assessment of whole slide imaging scanners

Purpose: Whole slide imaging (WSI) scanners produce tissue slide images with a large field of view and a high resolution for pathologists to use in diagnoses. Color performance tests of these color imaging devices are necessary and can use stained tissue slides if the color truth is established using a hyperspectral imaging microscopy system (HIMS). The purpose of this study was to estimate the reproducibility uncertainty of CIELAB coordinates for a reference tissue slide measured by both the HIMS and a WSI scanner. Approach: We compared the color performances of the WSI scanner to those of the reference established by the HIMS using the International Commission on Illumination (Commission Internationale de l'Éclairage, or CIE) 1976 Δ E a b * color difference with the just noticeable color difference (JNCD, Δ E a b * ≤ 2 ), and the results from the overlap of the CIELAB coordinates' uncertainty within the error bar, with a coverage factor k = 2 . The reported uncertainty results from measurements and image registration uncertainties. Results: For the blank area common to the HIMS and the WSI average images, the color agreement was higher using the JNCD condition versus the CIELAB uncertainty overlap criterion (82% and 20% of the pixels in the images, respectively). This difference is explained by the fact that numerous pixels have CIELAB coordinates near one another but corresponding to CIELAB uncertainty values small enough not to overlap. In the colored area of the images, the JNCD condition was met for 0.19% of the pixels in the images, compared with 4.3% for the CIELAB uncertainty overlap criterion. Conclusions: The distribution of uncertainties on the CIELAB coordinates was broader for the HIMS compared with the WSI scanner. The WSI scanner had a systemic error in the color reproduction, which pointed to a potential inadequate color calibration of this device.

The utility of digital pathology in improving the diagnostic skills of pathology trainees in commonly encountered pigmented cutaneous lesions during the COVID-19 pandemic: A single academic institution experience

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

Tissue Multiplex Analyte Detection in Anatomic Pathology - Pathways to Clinical Implementation

Background: Multiplex tissue analysis has revolutionized our understanding of the tumor microenvironment (TME) with implications for biomarker development and diagnostic testing. Multiplex labeling is used for specific clinical situations, but there remain barriers to expanded use in anatomic pathology practice.

Methods: We review immunohistochemistry (IHC) and related assays used to localize molecules in tissues, with reference to United States regulatory and practice landscapes. We review multiplex methods and strategies used in clinical diagnosis and in research, particularly in immuno-oncology. Within the framework of assay design and testing phases, we examine the suitability of multiplex immunofluorescence (mIF) for clinical diagnostic workflows, considering its advantages and challenges to implementation.

Results: Multiplex labeling is poised to radically transform pathologic diagnosis because it can answer questions about tissue-level biology and single-cell phenotypes that cannot be addressed with traditional IHC biomarker panels. Widespread implementation will require improved detection chemistry, illustrated by InSituPlex technology (Ultivue, Inc., Cambridge, MA) that allows coregistration of hematoxylin and eosin (H&E) and mIF images, greater standardization and interoperability of workflow and data pipelines to facilitate consistent interpretation by pathologists, and integration of multichannel images into digital pathology whole slide imaging (WSI) systems, including interpretation aided by artificial intelligence (AI). Adoption will also be facilitated by evidence that justifies incorporation into clinical practice, an ability to navigate regulatory pathways, and adequate health care budgets and reimbursement. We expand the brightfield WSI system “pixel pathway” concept to multiplex workflows, suggesting that adoption might be accelerated by data standardization centered on cell phenotypes defined by coexpression of multiple molecules.

Conclusion: Multiplex labeling has the potential to complement next generation sequencing in cancer diagnosis by allowing pathologists to visualize and understand every cell in a tissue biopsy slide. Until mIF reagents, digital pathology systems including fluorescence scanners, and data pipelines are standardized, we propose that diagnostic labs will play a crucial role in driving adoption of multiplex tissue diagnostics by using retrospective data from tissue collections as a foundation for laboratory-developed test (LDT) implementation and use in prospective trials as companion diagnostics (CDx).

Rapid Validation of Whole-Slide Imaging for Primary Histopathology Diagnosis

OBJECTIVES

The ongoing global severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitates adaptations in the practice of surgical pathology at scale. Primary diagnosis by whole-slide imaging (WSI) is a key component that would aid departments in providing uninterrupted histopathology diagnosis and maintaining revenue streams from disruption. We sought to perform rapid validation of the use of WSI in primary diagnosis meeting recommendations of the College of American Pathologists guidelines.

METHODS

Glass slides from clinically reported cases from 5 participating pathologists with a preset washout period were digitally scanned and reviewed in settings identical to typical reporting. Cases were classified as concordant or with minor or major disagreement with the original diagnosis. Randomized subsampling was performed, and mean concordance rates were calculated.

RESULTS

In total, 171 cases were included and distributed equally among participants. For the group as a whole, the mean concordance rate in sampled cases (n = 90) was 83.6% counting all discrepancies and 94.6% counting only major disagreements. The mean pathologist concordance rate in sampled cases (n = 18) ranged from 90.49% to 97%.

CONCLUSIONS

We describe a novel double-blinded method for rapid validation of WSI for primary diagnosis. Our findings highlight the occurrence of a range of diagnostic reproducibility when deploying digital methods.

Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association

We believe the switch to a digital pathology (DP) workflow is imminent and it is essential to understand the economic implications of conversion. Many aspects of the adoption of DP will be disruptive and have a direct financial impact, both in short term costs, such as investment in equipment and personnel, and long term revenue potential, such as improved productivity and novel tests. The focus of this whitepaper is to educate pathologists, laboratorians and other stakeholders about the business and monetary considerations of converting to a digital pathology workflow. The components of a DP business plan will be thoroughly summarized, and guidance will be provided on how to build a case for adoption and implementation as well as a roadmap for transitioning from an analog to a digital pathology workflow in various laboratory settings. It is important to clarify that this publication is not intended to list prices although some financials will be mentioned as examples. The authors encourage readers who are evaluating conversion to a DP workflow to use this paper as a foundational guide for conducting a thorough and complete assessment while incorporating in current market pricing. Contributors to this paper analyzed peer-reviewed literature and data collected from various institutions, some of which are mentioned. Digital pathology will change the way we practice through facilitating patient access to expert pathology services and enabling image analysis tools and assays to aid in diagnosis, prognosis, risk stratification and therapeutic selection. Together, they will result in the delivery of valuable information from which to make better decisions and improve the health of patients.

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

With advancements in whole slide imaging technology and improved understanding of the features of pathologist workstations required for digital slide evaluation, many institutions are investigating broad digital pathology adoption. The benefits of digital pathology evaluation include remote access to study or diagnostic case materials and integration of analysis and reporting tools. Diagnosis based on whole slide images is established in human medical pathology, and the use of digital pathology in toxicologic pathology is increasing. However, there has not been broad adoption in toxicologic pathology, particularly in the context of regulatory studies, due to lack of precedence. To address this topic, as well as practical aspects, the European Society of Toxicologic Pathology coordinated an expert international workshop to assess current applications and challenges and outline a set of minimal requirements needed to gain future regulatory acceptance for the use of digital toxicologic pathology workflows in research and development, so that toxicologic pathologists can benefit from digital slide technology.

Autofocusing technologies for whole slide imaging and automated microscopy

Whole slide imaging (WSI) has moved digital pathology closer to diagnostic practice in recent years. Due to the inherent tissue topography variability, accurate autofocusing remains a critical challenge for WSI and automated microscopy systems. The traditional focus map surveying method is limited in its ability to acquire a high degree of focus points while still maintaining high throughput. Real-time approaches decouple image acquisition from focusing, thus allowing for rapid scanning while maintaining continuous accurate focus. This work reviews the traditional focus map approach and discusses the choice of focus measure for focal plane determination. It also discusses various real-time autofocusing approaches including reflective-based triangulation, confocal pinhole detection, low-coherence interferometry, tilted sensor approach, independent dual sensor scanning, beam splitter array, phase detection, dual-LED illumination and deep-learning approaches. The technical concepts, merits and limitations of these methods are explained and compared to those of a traditional WSI system. This review may provide new insights for the development of high-throughput automated microscopy imaging systems that can be made broadly available and utilizable without loss of capacity.

Digital Pathology: Advantages, Limitations and Emerging Perspectives

Digital pathology is on the verge of becoming a mainstream option for routine diagnostics. Faster whole slide image scanning has paved the way for this development, but implementation on a large scale is challenging on technical, logistical, and financial levels. Comparative studies have published reassuring data on safety and feasibility, but implementation experiences highlight the need for training and the knowledge of pitfalls. Up to half of the pathologists are reluctant to sign out reports on only digital slides and are concerned about reporting without the tool that has represented their profession since its beginning. Guidelines by international pathology organizations aim to safeguard histology in the digital realm, from image acquisition over the setup of work-stations to long-term image archiving, but must be considered a starting point only. Cost-efficiency analyses and occupational health issues need to be addressed comprehensively. Image analysis is blended into the traditional work-flow, and the approval of artificial intelligence for routine diagnostics starts to challenge human evaluation as the gold standard. Here we discuss experiences from past digital pathology implementations, future possibilities through the addition of artificial intelligence, technical and occupational health challenges, and possible changes to the pathologist's profession.

Pathology Trainees' Experience and Attitudes on Use of Digital Whole Slide Images

Digital whole slide images are Food and Drug Administration approved for clinical diagnostic use in pathology; however, integration is nascent. Trainees from 9 pathology training programs completed an online survey to ascertain attitudes toward and experiences with whole slide images for pathological interpretations. Respondents (n = 76) reported attending 63 unique medical schools (45 United States, 18 international). While 63% reported medical school exposure to whole slide images, most reported ≤ 5 hours. Those who began training more recently were more likely to report at least some exposure to digital whole slide image training in medical school compared to those who began training earlier: 75% of respondents beginning training in 2017 or 2018 reported exposure to whole slide images compared to 54% for trainees beginning earlier. Trainees exposed to whole slide images in medical school were more likely to agree they were comfortable using whole slide images for interpretation compared to those not exposed (29% vs 12%; P = .06). Most trainees agreed that accurate diagnoses can be made using whole slide images for primary diagnosis (92%; 95% CI: 86-98) and that whole slide images are useful for obtaining second opinions (93%; 95% CI: 88-99). Trainees reporting whole slide image experience during training, compared to those with no experience, were more likely to agree they would use whole slide images in 5 years for primary diagnosis (64% vs 50%; P = .3) and second opinions (86% vs 76%; P = .4). In conclusion, although exposure to whole slide images in medical school has increased, overall exposure is limited. Positive attitudes toward future whole slide image diagnostic use were associated with exposure to this technology during medical training. Curricular integration may promote adoption.

Display Characteristics and Their Impact on Digital Pathology: A Current Review of Pathologists' Future "Microscope"

Digital displays (monitors) are an indispensable component of a pathologists’ daily workflow, from writing reports, viewing whole-slide images, or browsing the Internet. Due to a paucity of literature and experience surrounding display use and standardization in pathology, the Food and Drug Administration's (FDA) has currently restricted FDA-cleared whole-slide imaging systems to a specific model of display for each system, which at this time consists of only medical-grade (MG) displays. Further, given that a pathologists’ display will essentially become their new surrogate “microscope,” it becomes exceedingly important that all pathologists have a basic understanding of fundamental display properties and their functional consequences. This review seeks to: (a) define and summarize the current and emerging display technology, terminology, features, and regulation as they pertain to pathologists and review the current literature on the impact of different display types (e.g. MG vs. consumer off the shelf vs. professional grade) on pathologists’ diagnostic performance and (b) discuss the impact of the recent digital pathology device componentization and the coronavirus disease 2019 public emergency on the pixel pathway and display use for remote digital pathology. Display technology has changed dramatically over the past 20 years and continues to change at a rapid rate. There is a paucity of published studies to date that investigate how display type affects pathologist performance, with more research necessary in order to develop standards and minimum specifications for displays in digital pathology. Given the complexity of modern displays, pathologists must become better informed regarding display technology if they wish to have more choice over their future “microscopes.”

A Regulatory Science Initiative to Harmonize and Standardize Digital Pathology and Machine Learning Processes to Speed up Clinical Innovation to Patients

Unlocking the full potential of pathology data by gaining computational access to histological pixel data and metadata (digital pathology) is one of the key promises of computational pathology. Despite scientific progress and several regulatory approvals for primary diagnosis using whole-slide imaging, true clinical adoption at scale is slower than anticipated. In the U.S., advances in digital pathology are often siloed pursuits by individual stakeholders, and to our knowledge, there has not been a systematic approach to advance the field through a regulatory science initiative. The Alliance for Digital Pathology (the Alliance) is a recently established, volunteer, collaborative, regulatory science initiative to standardize digital pathology processes to speed up innovation to patients. The purpose is: (1) to account for the patient perspective by including patient advocacy; (2) to investigate and develop methods and tools for the evaluation of effectiveness, safety, and quality to specify risks and benefits in the precompetitive phase; (3) to help strategize the sequence of clinically meaningful deliverables; (4) to encourage and streamline the development of ground-truth data sets for machine learning model development and validation; and (5) to clarify regulatory pathways by investigating relevant regulatory science questions. The Alliance accepts participation from all stakeholders, and we solicit clinically relevant proposals that will benefit the field at large. The initiative will dissolve once a clinical, interoperable, modularized, integrated solution (from tissue acquisition to diagnostic algorithm) has been implemented. In times of rapidly evolving discoveries, scientific input from subject-matter experts is one essential element to inform regulatory guidance and decision-making. The Alliance aims to establish and promote synergistic regulatory science efforts that will leverage diverse inputs to move digital pathology forward and ultimately improve patient care.

Computational imaging modalities for multi-focal whole-slide imaging systems

Whole-slide imaging systems can generate full-color image data of tissue slides efficiently, which are needed for digital pathology applications. This paper focuses on a scanner architecture that is based on a multi-line image sensor that is tilted with respect to the optical axis, such that every line of the sensor scans the tissue slide at a different focus level. This scanner platform is designed for imaging with continuous autofocus and inherent color registration at a throughput of the order of 400 MPx/s. Here, single-scan multi-focal whole-slide imaging, enabled by this platform, is explored. In particular, two computational imaging modalities based on multi-focal image data are studied. First, 3D imaging of thick absorption stained slides (∼60µm) is demonstrated in combination with deconvolution to ameliorate the inherently weak contrast in thick-tissue imaging. Second, quantitative phase tomography is demonstrated on unstained tissue slides and on fluorescently stained slides, revealing morphological features complementary to features made visible with conventional absorption or fluorescence stains. For both computational approaches simplified algorithms are proposed, targeted for straightforward parallel processing implementation at ∼GPx/s throughputs.

Advances in tissue-based imaging: impact on oncology research and clinical practice

INTRODUCTION

Tissue-based imaging has emerged as a critical tool in translational cancer research and is rapidly gaining traction within a clinical context. Significant progress has been made in the digital pathology arena, particularly in respect of brightfield and fluorescent imaging. Critically, the cellular context of molecular alterations occurring at DNA, RNA, or protein level within tumor tissue is now being more fully appreciated. Moreover, the emergence of novel multi-marker imaging approaches can now provide unprecedented insights into the tumor microenvironment, including the potential interplay between various cell types.

AREAS COVERED

This review summarizes the recent developments within the field of tissue-based imaging, centering on the application of these approaches in oncology research and clinical practice.

EXPERT OPINION

Significant advances have been made in digital pathology during the last 10 years. These include the use of quantitative image analysis algorithms, predictive artificial intelligence (AI) on large datasets of H&E images, and quantification of fluorescence multiplexed tissue imaging data. We believe that new methodologies that can integrate AI-derived histologic data with omic data, together with other forms of imaging data (such as radiologic image data), will enhance our ability to deliver better diagnostics and treatment decisions to the cancer patient.

Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis

IMPORTANCE

Histopathological diagnoses of tumors from tissue biopsy after hematoxylin and eosin (H&E) dye staining is the criterion standard for oncological care, but H&E staining requires trained operators, dyes and reagents, and precious tissue samples that cannot be reused.

OBJECTIVES

To use deep learning algorithms to develop models that perform accurate computational H&E staining of native nonstained prostate core biopsy images and to develop methods for interpretation of H&E staining deep learning models and analysis of computationally stained images by computer vision and clinical approaches.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study used hundreds of thousands of native nonstained RGB (red, green, and blue channel) whole slide image (WSI) patches of prostate core tissue biopsies obtained from excess tissue material from prostate core biopsies performed in the course of routine clinical care between January 7, 2014, and January 7, 2017, at Brigham and Women's Hospital, Boston, Massachusetts. Biopsies were registered with their H&E-stained versions. Conditional generative adversarial neural networks (cGANs) that automate conversion of native nonstained RGB WSI to computational H&E-stained images were then trained. Deidentified whole slide images of prostate core biopsy and medical record data were transferred to Massachusetts Institute of Technology, Cambridge, for computational research. Results were shared with physicians for clinical evaluations. Data were analyzed from July 2018 to February 2019.

MAIN OUTCOMES AND MEASURES

Methods for detailed computer vision image analytics, visualization of trained cGAN model outputs, and clinical evaluation of virtually stained images were developed. The main outcome was interpretable deep learning models and computational H&E-stained images that achieved high performance in these metrics.

RESULTS

Among 38 patients who provided samples, single core biopsy images were extracted from each whole slide, resulting in 102 individual nonstained and H&E dye-stained image pairs that were compared with matched computationally stained and unstained images. Calculations showed high similarities between computationally and H&E dye-stained images, with a mean (SD) structural similarity index (SSIM) of 0.902 (0.026), Pearson correlation coefficient (PCC) of 0.962 (0.096), and peak signal to noise ratio (PSNR) of 22.821 (1.232) dB. A second cGAN performed accurate computational destaining of H&E-stained images back to their native nonstained form, with a mean (SD) SSIM of 0.900 (0.030), PCC of 0.963 (0.011), and PSNR of 25.646 (1.943) dB compared with native nonstained images. A single blind prospective study computed approximately 95% pixel-by-pixel overlap among prostate tumor annotations provided by 5 board certified pathologists on computationally stained images, compared with those on H&E dye-stained images. This study also used the first visualization and explanation of neural network kernel activation maps during H&E staining and destaining of RGB images by cGANs. High similarities between kernel activation maps of computationally and H&E-stained images (mean-squared errors <0.0005) provide additional mathematical and mechanistic validation of the staining system.

CONCLUSIONS AND RELEVANCE

These findings suggest that computational H&E staining of native unlabeled RGB images of prostate core biopsy could reproduce Gleason grade tumor signatures that were easily assessed and validated by clinicians. Methods for benchmarking, visualization, and clinical validation of deep learning models and virtually H&E-stained images communicated in this study have wide applications in clinical informatics and oncology research. Clinical researchers may use these systems for early indications of possible abnormalities in native nonstained tissue biopsies prior to histopathological workflows.

Histopathologist features predictive of diagnostic concordance at expert level among a large international sample of pathologists diagnosing Barrett's dysplasia using digital pathology

OBJECTIVE

Guidelines mandate expert pathology review of Barrett's oesophagus (BO) biopsies that reveal dysplasia, but there are no evidence-based standards to corroborate expert reviewer status. We investigated BO concordance rates and pathologist features predictive of diagnostic discordance.

DESIGN

Pathologists (n=51) from over 20 countries assessed 55 digitised BO biopsies from across the diagnostic spectrum, before and after viewing matched p53 labelling. Extensive demographic and clinical experience data were obtained via online questionnaire. Reference diagnoses were obtained from a review panel (n=4) of experienced Barrett's pathologists.

RESULTS

We recorded over 6000 case diagnoses with matched demographic data. Of 2805 H&E diagnoses, we found excellent concordance (>70%) for non-dysplastic BO and high-grade dysplasia, and intermediate concordance for low-grade dysplasia (42%) and indefinite for dysplasia (23%). Major diagnostic errors were found in 248 diagnoses (8.8%), which reduced to 232 (8.3%) after viewing p53 labelled slides. Demographic variables correlating with diagnostic proficiency were analysed in multivariate analysis, which revealed that at least 5 years of professional experience was protective against major diagnostic error for H&E slide review (OR 0.48, 95% CI 0.31 to 0.74). Working in a non-teaching hospital was associated with increased odds of major diagnostic error (OR 1.76, 95% CI 1.15 to 2.69); however, this was neutralised when pathologists viewed p53 labelled slides. Notably, neither case volume nor self-identifying as an expert predicted diagnostic proficiency. Extrapolating our data to real-world case prevalence suggests that 92.3% of major diagnostic errors are due to overinterpreting non-dysplastic BO.

CONCLUSION

Our data provide evidence-based criteria for diagnostic proficiency in Barrett's histopathology.

Current status of pathological image analysis technology in pharmaceutical companies: a questionnaire survey of the Japan Pharmaceutical Manufacturers Association

The Japan Pharmaceutical Manufacturers Association (JPMA) has instituted a task force (TF) for the "development of image analysis technology for histopathological changes" as part of the collaboration for realizing cutting-edge drug development since 2016. In recent years, there has been progress in the digital pathology technology; however, few applications in nonclinical drug development studies have been observed. Therefore, TF performed a questionnaire survey to investigate the current status, needs, possibility, and development of image analysis. The subjects were 35 member companies of the JPMA. The questionnaire was set to assess the efficacy and/or safety of researchers engaged in pathological evaluations for each company. The questions focused on the experiences, implementation, and issues regarding histopathological examinations; the need for image analysis software; and future views. Valid responses were obtained from 26 companies. Most companies assumed that the beneficial aspect of image analysis is to gain objectivity and persuasiveness; however, challenges in the analysis conditions with regard to accuracy and without subjectivity persist. Additionally, there seems to be a need for image analysis software with advanced digital pathology technology, with most companies believing that, in the future, pathological evaluations will be partly performed by computers. In conclusion, in this questionnaire survey, TF extracted the current status of image analysis in nonclinical studies performed by pharmaceutical companies and collected opinions on future prospects regarding the development of image analysis software with advanced digital pathology technology.

Whole slide imaging system using deep learning-based automated focusing

The auto focusing system, which involves moving a microscope stage along a vertical axis to find an optimal focus position, is the chief component of an automated digital microscope. Current automated focusing algorithms, especially those deployed in cost effective microscopy systems, often cannot match the efficiency of a skilled human operator in keeping a sample in focus. This work presents an auto focusing system that utilises the recent advances in machine learning, namely deep convolutional neural networks (CNN). It improves upon prior work in this domain. The results of the focusing algorithm are demonstrated on an open data set. We describe the practical implementation of this method on a low cost digital microscope to create a whole slide imaging system (WSI). Results of a clinical study using this WSI system are presented. The study demonstrates the efficacy of this system in a practical scenario.

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.

Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice

The use of artificial intelligence will transform clinical practice over the next decade and the early impact of this will likely be the integration of image analysis and machine learning into routine histopathology. In the UK and around the world, a digital revolution is transforming the reporting practice of diagnostic histopathology and this has sparked a proliferation of image analysis software tools. While this is an exciting development that could discover novel predictive clinical information and potentially address international pathology workforce shortages, there is a clear need for a robust and evidence-based framework in which to develop these new tools in a collaborative manner that meets regulatory approval. With these issues in mind, the NCRI Cellular Molecular Pathology (CM-Path) initiative and the British In Vitro Diagnostics Association (BIVDA) have set out a roadmap to help academia, industry, and clinicians develop new software tools to the point of approved clinical use. © 2019 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Feasibility of using the Omnyx digital pathology system for cytology practice

INTRODUCTION

Whole slide imaging systems have focused mostly on surgical pathologic evaluation. However, for pathology laboratories to become fully digital, cytology slides will also need to be digitized, managed, viewed, and analyzed. Our aim was to determine the feasibility of using the Omnyx whole slide imaging system for various purposes in cytology.

MATERIALS AND METHODS

Our institution implemented the Omnyx digital pathology system, which was tested for feasibility and not implemented for clinical use in cytology. Glass slides (n = 18), scanned using various whole slide scanners, were uploaded into the Omnyx system. The system was evaluated for its feasibility with cytology case management, digital slide navigation and annotation, telecytology, and cytologic-histologic correlation.

RESULTS

The Omnyx software was able to manage cases similar to a laboratory information system. Users were able to electronically distribute, search, and sort the clinical cases. A graphic dashboard approach and virtual slide tray is available for end users to evaluate cases. The ability to create custom folders and drag-and-drop images into these folders fulfilled clinical, quality assurance, education, and research needs. Innovative tools for digital slide navigation and annotation (eg, auto-pan, adding text to annotation, hiding annotations, image coregistration) offered innovative methods to work with slides. Omnyx also provided a mechanism for sharing images with others to perform teleconsultation.

CONCLUSIONS

We have demonstrated the feasibility of using the Omnyx whole slide imaging system for various purposes in cytology practice. The application supported, not only case management, but also the ability to perform cytologic-histologic correlation and telecytology. The viewer offered many features that improved digital slide navigation and annotation.

Whole Slide Imaging for High-Throughput Sensing Antibiotic Resistance at Single-Bacterium Level and Its Application to Rapid Antibiotic Susceptibility Testing

Since conventional culture-based antibiotic susceptibility testing (AST) methods are too time-consuming (typically 24–72 h), rapid AST is urgently needed for preventing the increasing emergence and spread of antibiotic resistant infections. Although several phenotypic antibiotic resistance sensing modalities are able to reduce the AST time to a few hours or less, concerning the biological heterogeneity, their accuracy or limit of detection are limited by low throughput. Here, we present a rapid AST method based on whole slide imaging (WSI)-enabled high-throughput sensing antibiotic resistance at single-bacterium level. The time for determining the minimum inhibitory concentration (MIC) was theoretically shortest, which ensures that the growth of each individual cell present in a large population is inhibited. As a demonstration, our technique was able to sense the growth of at least several thousand bacteria at single-cell level. Reliable MIC of Enterobacter cloacae against gentamicin was obtained within 1 h, while the gold standard broth dilution method required at least 16 h for the same result. In addition, the application of our method prevails over other imaging-based AST approaches in allowing rapid and accurate determination of antibiotic susceptibility for phenotypically heterogeneous samples, in which the number of antibiotic resistant cells was negligible compared to that of the susceptible cells. Hence, our method shows great promise for both rapid AST determination and point-of-care testing of complex clinical bacteria isolates.

Rapid and robust whole slide imaging based on LED-array illumination and color-multiplexed single-shot autofocusing

Background

Digital pathology is experiencing an exponential period of growth catalyzed by advancements in imaging hardware and progresses in machine learning. The use of whole slide imaging (WSI) for digital pathology has recently been cleared for primary diagnosis in the US. The demand for using frozen section procedure for rapid identification of cancerous tissue during surgery is another driving force for the development of WSI. A conventional WSI system scans the tissue slide to different positions and acquires the digital images. In a typical implementation, a focus map is created prior to the scanning process, leading to significant overhead time and a necessity for high positional accuracy of the mechanical system. The resulting cost of WSI system is often prohibitive for frozen section procedure during surgery.

Methods

We report a novel WSI scheme based on a programmable LED array for sample illumination. In between two regular brightfield image acquisitions, we acquire one additional image by turning on a red and a green LED for color multiplexed illumination. We then identify the translational shift of the red- and green-channel images by maximizing the image mutual information or cross-correlation. The resulting translational shift is used for dynamic focus correction in the scanning process. Since we track the differential focus during adjacent acquisitions, there is no positional repeatability requirement in our scheme.

Results

We demonstrate a prototype WSI platform with a mean focusing error of ~0.3 microns. Different from previous implementations, this prototype platform requires no focus map surveying, no secondary camera or additional optics, and allows for continuous sample motion in the focus tracking process.

Conclusions

A programmable LED array can be used for color-multiplexed single-shot autofocusing in WSI. The reported scheme may enable the development of cost-effective WSI platforms without positional repeatability requirement. It may also provide a turnkey solution for other high-content microscopy applications.