Implementation of Whole Slide Imaging for Clinical Purposes: Issues to Consider From the Perspective of Early Adopters

AI drives the assessment of lung cancer microenvironment composition

Purpose

The abundance and distribution of tumor-infiltrating lymphocytes (TILs) as well as that of other components of the tumor microenvironment is of particular importance for predicting response to immunotherapy in lung cancer (LC). We describe here a pilot study employing artificial intelligence (AI) in the assessment of TILs and other cell populations, intending to reduce the inter- or intra-observer variability that commonly characterizes this evaluation.

Design

We developed a machine learning-based classifier to detect tumor, immune, and stromal cells on hematoxylin and eosin-stained sections, using the open-source framework QuPath. We evaluated the quantity of the aforementioned three cell populations among 37 LC whole slide images regions of interest, comparing the assessments made by five pathologists, both before and after using graphical predictions made by AI, for a total of 1110 quantitative measurements.

Results

Our findings indicate noteworthy variations in score distribution among pathologists and between individual pathologists and AI. The AI-guided pathologist's evaluations resulted in reduction of significant discrepancies across pathologists: three comparisons showed a loss of significance (p > 0.05), whereas other four showed a reduction in significance (p > 0.01).

Conclusions

We show that employing a machine learning approach in cell population quantification reduces inter- and intra-observer variability, improving reproducibility and facilitating its use in further validation studies.

Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives

Both pathology and cytopathology still rely on recognizing microscopical morphologic features, and image analysis plays a crucial role, enabling the identification, categorization, and characterization of different tissue types, cell populations, and disease states within microscopic images. Historically, manual methods have been the primary approach, relying on expert knowledge and experience of pathologists to interpret microscopic tissue samples. Early image analysis methods were often constrained by computational power and the complexity of biological samples. The advent of computers and digital imaging technologies challenged the exclusivity of human eye vision and brain computational skills, transforming the diagnostic process in these fields. The increasing digitization of pathological images has led to the application of more objective and efficient computer-aided analysis techniques. Significant advancements were brought about by the integration of digital pathology, machine learning, and advanced imaging technologies. The continuous progress in machine learning and the increasing availability of digital pathology data offer exciting opportunities for the future. Furthermore, artificial intelligence has revolutionized this field, enabling predictive models that assist in diagnostic decision making. The future of pathology and cytopathology is predicted to be marked by advancements in computer-aided image analysis. The future of image analysis is promising, and the increasing availability of digital pathology data will invariably lead to enhanced diagnostic accuracy and improved prognostic predictions that shape personalized treatment strategies, ultimately leading to better patient outcomes.

Multinational, Multicenter Evaluation of Prostate Cancer Tissue in Sub-Saharan Africa: Challenges and Opportunities

PURPOSE

Prostate cancer disproportionately affects men of African descent, yet their representation in tissue-based studies is limited. This multinational, multicenter pilot study aims to establish the groundwork for collaborative research on prostate cancer in sub-Saharan Africa.

METHODS

The Men of African Descent and Carcinoma of the Prostate network formed a pathologist working group representing eight institutions in five African countries. Formalin-fixed paraffin-embedded prostate tissue specimens were collected from Senegal, Nigeria, and Ghana. Histology slides were produced and digitally scanned. A central genitourinary pathologist (P.L.) and eight African general pathologists reviewed anonymized digital whole-slide images for International Society of Urological Pathology grade groups and other pathologic parameters. Discrepancies were re-evaluated, and consensus grading was assigned. A virtual training seminar on prostate cancer grading was followed by a second assessment on a subcohort of the same tissue set.

RESULTS

Of 134 tissue blocks, 133 had evaluable tissue; 13 lacked cancer evidence, and four were of insufficient quality. Post-training, interobserver agreement for grade groups improved to 56%, with a median Cohen's quadratic weighted kappa of 0.83 (mean, 0.74), compared with an initial 46% agreement and a quadratic weighted kappa of 0.77. Interobserver agreement between African pathologist groups was 40%, with a quadratic weighted kappa of 0.66 (95% CI, 0.51 to 0.76). African pathologists tended to overgrade (36%) more frequently than undergrade (18%) compared with the reference genitourinary pathologist. Interobserver variability tended to worsen with a decrease in tissue quality.

CONCLUSION

Tissue-based studies on prostate cancer in men of African descent are essential for a better understanding of this common disease. Standardized tissue handling protocols are crucial to ensure good tissue quality and data. The use of digital slide imaging can enhance collaboration among pathologists in multinational, multicenter studies.

Digital pathology implementation in cancer diagnostics: towards informed decision-making

Digital pathology (DP) has become a part of the cancer healthcare system, creating additional value for cancer patients. DP implementation in clinical practice provides plenty of benefits but also harbors hidden ethical challenges affecting physician-patient relationships. This paper addresses the ethical obligation to transform the physician-patient relationship for informed and responsible decision-making when using artificial intelligence (AI)-based tools for cancer diagnostics. DP application allows to improve the performance of the Human-AI Team shifting focus from AI challenges towards the Augmented Human Intelligence (AHI) benefits. AHI enhances analytical sensitivity and empowers pathologists to deliver accurate diagnoses and assess predictive biomarkers for further personalized treatment of cancer patients. At the same time, patients' right to know about using AI tools, their accuracy, strengths and limitations, measures for privacy protection, acceptance of privacy concerns and legal protection defines the duty of physicians to provide the relevant information about AHI-based solutions to patients and the community for building transparency, understanding and trust, respecting patients' autonomy and empowering informed decision-making in oncology.

Invisible for a few but essential for many: the role of Histotechnologists in the establishment of digital pathology

Digital pathology (DP) is indisputably the future for histopathology laboratories. The process of digital implementation requires deep workflow reorganisation which involves an interdisciplinary team. This transformation may have the greatest impact on the Histotechnologist (HTL) profession. Our review of the literature has clearly revealed that the role of HTLs in the establishment of DP is being unnoticed and guidance is limited. This article aims to bring HTLs from behind-the-scenes into the spotlight. Our objective is to provide them guidance and practical recommendations to successfully contribute to the implementation of a new digital workflow. Furthermore, it also intends to contribute for improvement of study programs, ensuring the role of HTL in DP is addressed as part of graduate and post-graduate education. In our review, we report on the differences encountered between workflow schemes and the limitations observed in this process. The authors propose a digital workflow to achieve its limitless potential, focusing on the HTL's role. This article explores the novel responsibilities of HTLs during specimen gross dissection, embedding, microtomy, staining, digital scanning, and whole slide image quality control. Furthermore, we highlight the benefits and challenges that DP implementation might bring the HTLs career. HTLs have an important role in the digital workflow: the responsibility of achieving the perfect glass slide.

Quantitative multiplexed imaging technologies for single-cell analysis to assess predictive markers for immunotherapy in thoracic immuno-oncology: promises and challenges

The past decade has witnessed a revolution in cancer treatment by the shift from conventional drugs (chemotherapies) towards targeted molecular therapies and immune-based therapies, in particular the immune-checkpoint inhibitors (ICIs). These immunotherapies selectively release the host immune system against the tumour and have shown unprecedented durable remission for patients with cancers that were thought incurable such as advanced non-small cell lung cancer (aNSCLC). The prediction of therapy response is based since the first anti-PD-1/PD-L1 molecules FDA and EMA approvals on the level of PD-L1 tumour cells expression evaluated by immunohistochemistry, and recently more or less on tumour mutation burden in the USA. However, not all aNSCLC patients benefit from immunotherapy equally, since only around 30% of them received ICIs and among them 30% have an initial response to these treatments. Conversely, a few aNSCLC patients could have an efficacy ICIs response despite low PD-L1 tumour cells expression. In this context, there is an urgent need to look for additional robust predictive markers for ICIs efficacy in thoracic oncology. Understanding of the mechanisms that enable cancer cells to adapt to and eventually overcome therapy and identifying such mechanisms can help circumvent resistance and improve treatment. However, more than a unique universal marker, the evaluation of several molecules in the tumour at the same time, particularly by using multiplex immunostaining is a promising open room to optimise the selection of patients who benefit from ICIs. Therefore, urgent further efforts are needed to optimise to individualise immunotherapy based on both patient-specific and tumour-specific characteristics. This review aims to rethink the role of multiplex immunostaining in immuno-thoracic oncology, with the current advantages and limitations in the near-daily practice use.

Impact of the transition to digital pathology in a clinical setting on histopathologists in training: experiences and perceived challenges within a UK training region

AIMS

With increasing utility of digital pathology (DP), it is important to consider the experiences of histopathologists in training, particularly in view of the varied access to DP across a training region and the consequent need to remain competent in reporting on glass slides (GS), which is also relevant for the Fellowship of the Royal College of Pathologists part 2 examination. Understanding the impact of DP on training is limited but could aid development of guidance to support the transition. We sought to investigate the perceptions of histopathologists in training around the introduction of DP for clinical diagnosis within a training region, and the potential training benefits and challenges.

METHODS

An anonymous online survey was circulated to 24 histopathologists in training within a UK training region, including a hospital which has been fully digitised since summer 2020.

RESULTS

19 of 24 histopathologists in training responded (79%). The results indicate that DP offers many benefits to training, including ease of access to cases to enhance individual learning and teaching in general. Utilisation of DP for diagnosis appears variable; almost half of the (10 of 19) respondents with DP experience using it only for ancillary purposes such as measurements, reporting varying levels of confidence in using DP clinically. For those yet to undergo the transition, there was a perceived anxiety regarding digital reporting despite experience with DP in other contexts.

CONCLUSIONS

The survey evidences the need for provision of training and support for histopathologists in training during the transition to DP, and for consideration of their need to maintain competence and confidence with GS reporting.

Modelling digital health data: The ExaMode ontology for computational pathology

Computational pathology can significantly benefit from ontologies to standardize the employed nomenclature and help with knowledge extraction processes for high-quality annotated image datasets. The end goal is to reach a shared model for digital pathology to overcome data variability and integration problems. Indeed, data annotation in such a specific domain is still an unsolved challenge and datasets cannot be steadily reused in diverse contexts due to heterogeneity issues of the adopted labels, multilingualism, and different clinical practices.

Material and methods

This paper presents the ExaMode ontology, modeling the histopathology process by considering 3 key cancer diseases (colon, cervical, and lung tumors) and celiac disease. The ExaMode ontology has been designed bottom-up in an iterative fashion with continuous feedback and validation from pathologists and clinicians. The ontology is organized into 5 semantic areas that defines an ontological template to model any disease of interest in histopathology.

Results

The ExaMode ontology is currently being used as a common semantic layer in: (i) an entity linking tool for the automatic annotation of medical records; (ii) a web-based collaborative annotation tool for histopathology text reports; and (iii) a software platform for building holistic solutions integrating multimodal histopathology data.

Discussion

The ontology ExaMode is a key means to store data in a graph database according to the RDF data model. The creation of an RDF dataset can help develop more accurate algorithms for image analysis, especially in the field of digital pathology. This approach allows for seamless data integration and a unified query access point, from which we can extract relevant clinical insights about the considered diseases using SPARQL queries.

Association of NK Cells with the Severity of Fibrosis in Patients with Chronic Hepatitis C

Some NK cell subpopulations may be involved in the modulation of fibrogenesis in the liver. The aim of the study was to evaluate the relationship between the number and phenotype of NK cell subsets in peripheral blood (PB) and total NK cell percentage, population density and the degree of liver fibrosis of patients infected with hepatitis C virus (HCV+). The study group consisted of 56 HCV+ patients, divided into two subgroups: patients with mild or moderate fibrosis and patients with advanced liver fibrosis or cirrhosis (F ≥ 3 in METAVIR classification). The preparations were stained with H-E and AZAN staining. NK cells were targeted with anti-CD56 antibody and identified automatically in situ using the DakoVision system. Assessment of different NK cell subsets in PB was performed with the flow cytometry technique. In the PB of HCV+ patients with advanced liver fibrosis, there was a lower proportion of CD62L+; CD62L+/CD94++; CD27+; CD127+/CD27+ and CXCR3+/CD27+ NK subsets, as compared to patients with mild/moderate liver fibrosis. The results also showed no association between total PB NK cell level and total intrahepatic NK cell population density between patients with mild/moderate fibrosis and with advanced liver fibrosis. However, positive correlations between the PB levels of CD94+ and CD62L+ NK cell subsets and the intrahepatic total NK cell percentage and population density in the liver, irrespectively to the extent of fibrosis, were observed. Additionally, positive correlation was found between the PB CXCR3+/CD94+ NK cell percentages and intrahepatic NK cell percentages in patients with advanced hepatic fibrosis. Lower blood availability of specific NK subsets in patients with chronic type C hepatitis might be a cause of progression of liver fibrosis via insufficient control over hepatic stellate cells.

Development of Automatic Portable Pathology Scanner and Its Evaluation for Clinical Practice

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

Validation of Remote Digital Pathology based diagnostic reporting of Frozen Sections from home

Background

Despite the promising applications of whole-slide imaging (WSI) for frozen section (FS) diagnosis, its adoption for remote reporting is limited.

Objective

To assess the feasibility and performance of home-based remote digital consultation for FS diagnosis.

Material & Method

Cases accessioned beyond regular working hours (5 pm-10 pm) were reported simultaneously using optical microscopy (OM) and WSI. Validation of WSI for FS diagnosis from a remote site, i.e. home, was performed by 5 pathologists. Cases were scanned using a portable scanner (Grundium Ocus®40) and previewed on consumer-grade computer devices through a web-based browser (http://grundium.net). Clinical data and diagnostic reports were shared through a google spreadsheet. The diagnostic concordance, inter- and intra-observer agreement for FS diagnosis by WSI versus OM, and turnaround time (TAT), were recorded.

Results

The overall diagnostic accuracy for OM and WSI (from home) was 98.2% (range 97%-100%) and 97.6% (range 95%-99%), respectively, when compared with the reference standard. Almost perfect inter-observer (k = 0.993) and intra-observer (k = 0.987) agreement for WSI was observed by 4 pathologists. Pathologists used consumer-grade laptops/desktops with an average screen size of 14.58 inches (range = 12.3-17.7 inches) and a network speed of 64 megabits per second (range: 10-90 Mbps). The mean diagnostic assessment time per case for OM and WSI was 1:48 min and 5:54 min, respectively. Mean TAT of 27.27 min per case was observed using WSI from home. Seamless connectivity was observed in approximately 75% of cases.

Conclusion

This study validates the role of WSI for remote FS diagnosis for its safe and efficient adoption in clinical use.

Decline of case reports in pathology and their renewal in the digital age: an analysis of publication trends over four decades

AIMS

We investigated the trend in case reports (CRs) publication in a sample of pathology journals. Furthermore, we proposed an alternative publishing route through new digital communication platforms, represented by the 'social media case report'.

METHODS

28 pathology journals were selected from SCImago database and searched in PubMed to identify the number of published CRs. Four reference decades (1981-2020) were selected. The 5-year impact factor (IF) was retrieved from the Academic Accelerator database.

RESULTS

CRs increased during the first three decades (6752, 8698 and 11148, respectively; mean values: 355, 27.3%; 334, 26.4%; 398, 28.8%) as the number of CR-publishing journals (19, 26 and 28, respectively). In the last decade, CRs significantly decreased (9341; mean 334, 23.6%) without variation in the number of CR-publishing journals (28). Half of the journals reduced CRs (from -1.1% to -37.9%; mean decreasing percentage -14.7%), especially if active since the first decade (11/14, 79%); the other half increased CRs (from +0.5% to +34.2%; mean increasing percentage +11.8%), with 8/14 (57%) starting publishing in the first decade. The 5-year IF ranged from 0.504 to 5.722. Most of the journals with IF ≥2 (10/14, 71%) reduced the CRs number, while 71% of journals with IF <2 increased CRs publication (especially journals with IF <1, +15.1%).

CONCLUSIONS

CRs publication decreased during the last decade, especially for journals which are older or have higher IF. Social media CRs may represent a valid alternative and by using standardised templates to enter all relevant data may be organised in digital databases and/or transformed in traditional CRs.

Next Generation Digital Pathology: Emerging Trends and Measurement Challenges for Molecular Pathology

Digital pathology is revolutionising the analysis of histological features and is becoming more and more widespread in both the clinic and research. Molecular pathology extends the tissue morphology information provided by conventional histopathology by providing spatially resolved molecular information to complement the structural information provided by histopathology. The multidimensional nature of the molecular data poses significant challenge for data processing, mining, and analysis. One of the key challenges faced by new and existing pathology practitioners is how to choose the most suitable molecular pathology technique for a given diagnosis. By providing a comparison of different methods, this narrative review aims to introduce the field of molecular pathology, providing a high-level overview of many different methods. Since each pixel of an image contains a wealth of molecular information, data processing in molecular pathology is more complex. The key data processing steps and variables, and their effect on the data, are also discussed.

RFID analysis of the complexity of cellular pathology workflow—An opportunity for digital pathology

Digital pathology (DP) offers potential for time efficiency gains over an analog workflow however, to date, evidence supporting this claim is relatively lacking. Studies available concentrate on specific workflow points such as diagnostic reporting time, rather than overall efficiencies in slide logistics that might be expected. This is in part a result of the complexity and variation in analog working, and the challenge therefore in capturing this. We have utilized RFID technology to conduct a novel study capturing the movement of diagnostic cases within the analog pathway in a large teaching hospital setting, thus providing benchmark data for potential efficiency gains with DP. This technology overcomes the need to manually record data items and has facilitated the capture of both the physical journey of a case and the time associated with relevant components of the analog pathway predicted to be redundant in the digital setting. RFID tracking of 1,173 surgical pathology cases and over 30 staff in an analog cellular pathology workflow illustrates the complexity of the physical movement of slides within the department, which impacts on case traceability within the system. Detailed analysis of over 400 case journeys highlights redundant periods created by batching of slides at workflow points, including potentially 2–3 h for a case to become available for reporting after release from the lab, and variable lag-times prior to collection for reporting, and provides an illustration of patterns of lab and pathologist working within the analog setting. This study supports the challenge in evidencing efficiency gains to be anticipated with DP in the context of the variation and complexity of the analog pathway, but also evidences the efficiency gains that may be expected through a greater understanding of patterns of working and movement of cases. Such data may benefit other departments building a business case for DP.

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.

Validation of whole slide imaging for frozen section diagnosis of lymph node metastasis: A retrospective study from a tertiary care hospital in Thailand

BACKGROUND

The use of whole slide imaging (WSI) for frozen section (FS) diagnosis is helpful, particularly in the context of pathologist shortages. However, there is minimal data on such usage in resource-limited settings. This study aims to validate the use of WSI for FS diagnosis of lymph node metastasis using a low-cost virtual microscope scanner with consumer-grade laptops at a tertiary care hospital in Thailand.

METHODS

FS slides were retrieved for which the clinical query was to evaluate lymph node metastasis. They were digitized by a virtual microscope scanner (MoticEasyScan, Hong Kong) using up to 40× optical magnification. Three observers with different pathology experience levels diagnosed each slide, reviewing glass slides (GS) followed by digital slides (DS) after two weeks of a wash out period. WSI and GS diagnoses were compared. The time used for scanning and diagnosis of each slide was recorded.

RESULTS

295 FS slides were retrieved and digitized. The first-time successful scanning rate was 93.6 %. The mean scanning time was 2 min per slide. Both intraobserver agreement and interobserver agreement of WSI and GS diagnoses were high (Cohen's K; kappa value >0.84). The time used for DS diagnosis decreased as the observer's experience with WSI increased.

CONCLUSIONS

Despite varying pathological experiences, observers using WSI provided accurate FS diagnoses of lymph node metastasis. The time required for DS diagnoses decreased with additional observer's experience with WSI. Therefore, a WSI system containing low-cost scanners and consumer-grade laptops could be used for FS services in hospital laboratories lacking pathologists.

Digital Pathology and Artificial Intelligence Applications in Pathology

Digital pathology is revolutionizing pathology. The introduction of digital pathology made it possible to comprehensively change the pathology diagnosis workflow, apply and develop pathological artificial intelligence (AI) models, generate pathological big data, and perform telepathology. AI algorithms, including machine learning and deep learning, are used for the detection, segmentation, registration, processing, and classification of digitized pathological images. Pathological AI algorithms can be helpfully utilized for diagnostic screening, morphometric analysis of biomarkers, the discovery of new meanings of prognosis and therapeutic response in pathological images, and improvement of diagnostic efficiency. In order to develop a successful pathological AI model, it is necessary to consider the selection of a suitable type of image for a subject, utilization of big data repositories, the setting of an effective annotation strategy, image standardization, and color normalization. This review will elaborate on the advantages and perspectives of digital pathology, AI-based approaches, the applications in pathology, and considerations and challenges in the development of pathological AI models.

Digital Pathology Implementation in Private Practice: Specific Challenges and Opportunities

Digital pathology (DP) is being deployed in many pathology laboratories, but most reported experiences refer to public health facilities. In this paper, we report our experience in DP transition at a high-volume private laboratory, addressing the main challenges in DP implementation in a private practice setting and how to overcome these issues. We started our implementation in 2020 and we are currently scanning 100% of our histology cases. Pre-existing sample tracking infrastructure facilitated this process. We are currently using two high-capacity scanners (Aperio GT450DX) to digitize all histology slides at 40×. Aperio eSlide Manager WebViewer viewing software is bidirectionally linked with the laboratory information system. Scanning error rate, during the test phase, was 2.1% (errors detected by the scanners) and 3.5% (manual quality control). Pre-scanning phase optimizations and vendor feedback and collaboration were crucial to improve WSI quality and are ongoing processes. Regarding pathologists' validation, we followed the Royal College of Pathologists recommendations for DP implementation (adapted to our practice). Although private sector implementation of DP is not without its challenges, it will ultimately benefit from DP safety and quality-associated features. Furthermore, DP deployment lays the foundation for artificial intelligence tools integration, which will ultimately contribute to improving patient care.

Digital Dermatopathology and Its Application to Mohs Micrographic Surgery

Digital pathology is being gradually adopted in hospitals due to technological advances. We propose that digital pathology can be used in Mohs micrographic surgery (Mohs surgery) to precisely check residual tumor cells in frozen tumor margin tissues. This would aid surgeons and pathologists in accurately recording tumor margins and give patients the benefit of shorter operation time.

Digital Pathology Transformation in a Supraregional Germ Cell Tumour Network

Background: In this article we share our experience of creating a digital pathology (DP) supraregional germ cell tumour service, including full digitisation of the central laboratory. Methods: DP infrastructure (Philips) was deployed across our hospital network to allow full central digitisation with partial digitisation of two peripheral sites in the supraregional testis germ cell tumour network. We used a survey-based approach to capture the quantitative and qualitative experiences of the multidisciplinary teams involved. Results: The deployment enabled case sharing for the purposes of diagnostic reporting, second opinion, and supraregional review. DP was seen as a positive step forward for the departments involved, and for the wider germ cell tumour network, and was completed without significant issues. Whilst there were challenges, the transition to DP was regarded as worthwhile, and examples of benefits to patients are already recognised. Conclusion: Pathology networks, including highly specialised services, such as in this study, are ideally suited to be digitised. We highlight many of the benefits but also the challenges that must be overcome for such clinical transformation. Overall, from the survey, the change was seen as universally positive for our service and highlights the importance of engagement of the whole team to achieve success.

Best Practice Recommendations for the Implementation of a Digital Pathology Workflow in the Anatomic Pathology Laboratory by the European Society of Digital and Integrative Pathology (ESDIP)

The interest in implementing digital pathology (DP) workflows to obtain whole slide image (WSI) files for diagnostic purposes has increased in the last few years. The increasing performance of technical components and the Food and Drug Administration (FDA) approval of systems for primary diagnosis led to increased interest in applying DP workflows. However, despite this revolutionary transition, real world data suggest that a fully digital approach to the histological workflow has been implemented in only a minority of pathology laboratories. The objective of this study is to facilitate the implementation of DP workflows in pathology laboratories, helping those involved in this process of transformation to identify: (a) the scope and the boundaries of the DP transformation; (b) how to introduce automation to reduce errors; (c) how to introduce appropriate quality control to guarantee the safety of the process and (d) the hardware and software needed to implement DP systems inside the pathology laboratory. The European Society of Digital and Integrative Pathology (ESDIP) provided consensus-based recommendations developed through discussion among members of the Scientific Committee. The recommendations are thus based on the expertise of the panel members and on the agreement obtained after virtual meetings. Prior to publication, the recommendations were reviewed by members of the ESDIP Board. The recommendations comprehensively cover every step of the implementation of the digital workflow in the anatomic pathology department, emphasizing the importance of interoperability, automation and tracking of the entire process before the introduction of a scanning facility. Compared to the available national and international guidelines, the present document represents a practical, handy reference for the correct implementation of the digital workflow in Europe.

Validation of a Portable Whole-Slide Imaging System for Frozen Section Diagnosis

Background

Frozen section (FS) diagnosis is one of the promising applications of digital pathology (DP). However, the implementation of an appropriate and economically viable DP solution for FS in routine practice is challenging. The objective of this study was to establish the non-inferiority of whole-slide imaging (WSI) versus optical microscopy (OM) for FS diagnosis using a low cost and portable DP system.

Materials and Methods

A validation study to investigate the technical performance and diagnostic accuracy of WSI versus OM for FS diagnosis was performed using 60 FS cases[120 slides i.e, 60 hematoxylin and eosin (H & E) and 60 toluidine blue (TOLB)]. The diagnostic concordance, inter- and intra-observer agreement for FS diagnosis by WSI versus OM were recorded.

Results

The first time successful scanning rate was 89.1% (107/120). Mean scanning time per slide for H and E and TOLB slide was 1:47 min (range; 0:22-3: 21 min) and 1:46 min (range; 0:21-3: 20 min), respectively. Mean storage space per slide for H and E and TOLB slide was 0.83 GB (range: 0.12-1.73 GB) and 0.71 GB (range: 0.11-1.66 GB), respectively. Considering major discrepancies, the overall diagnostic concordance for OM and WSI, when compared with the reference standard, was 95.42% and 95.83%, respectively. There was almost perfect intra as well as inter-observer agreement (k ≥ 0.8) among 4 pathologists between WSI and OM for FS diagnosis. Mean turnaround time (TAT) of 14:58 min was observed using WSI for FS diagnosis, which was within the College of American Pathologists recommended range for FS reporting. The image quality was average to best quality in most of the cases.

Conclusion

WSI was noninferior to OM for FS diagnosis across various specimen types. This portable WSI system can be safely adopted for routine FS diagnosis and provides an economically viable alternative to high-end scanners.

Perspectives on emerging technologies, personalised medicine, and clinical research for cancer control in Latin America and the Caribbean

Challenges of health systems in Latin America and the Caribbean include accessibility, inequity, segmentation, and poverty. These challenges are similar in different countries of the region and transcend national borders. The increasing digital transformation of health care holds promise of more precise interventions, improved health outcomes, increased efficiency, and ultimately reduced health-care costs. In Latin America and the Caribbean, the adoption of digital health tools is in early stages and the quality of cancer registries, electronic health records, and structured databases are problematic. Cancer research and innovation in the region are limited due to inadequate academic resources and translational research is almost fully dependent on public funding. Regulatory complexity and extended timelines jeopardise the potential improvement in participation in international studies. Emerging technologies, artificial intelligence, big data, and cancer research represent an opportunity to address the health-care challenges in Latin America and the Caribbean collectively, by optimising national capacities, sharing and comparing best practices, and transferring scientific and technical capabilities.

Automated Quantitative Image Evaluation of Antigen Retrieval Methods for 17 Antibodies in Placentation and Implantation Diagnostic and Research

Immunostaining in clinical routine and research highly depends on standardized staining methods and quantitative image analyses. We qualitatively and quantitatively compared antigen retrieval methods (no pretreatment, pretreatment with pepsin, and heat-induced pretreatment with pH 6 or pH 9) for 17 antibodies relevant for placenta and implantation diagnostics and research. Using our newly established, comprehensive automated quantitative image analysis approach, fluorescent signal intensities were evaluated. Automated quantitative image analysis found that 9 out of 17 antibodies needed antigen retrieval to show positive staining. Heat induction proved to be the most efficient form of antigen retrieval. Eight markers stained positive after pepsin digestion, with β-hCG and vWF showing enhanced staining intensities. To avoid the misinterpretation of quantitative image data, the qualitative aspect should always be considered. Results from native placental tissue were compared with sections of a placental invasion model based on thermo-sensitive scaffolds. Immunostaining on placentas in vitro leads to new insights into fetal development and maternal pathophysiological pathways, as pregnant women are justifiably excluded from clinical studies. Thus, there is a clear need for the assessment of reliable immunofluorescent staining and pretreatment methods. Our evaluation offers a powerful tool for antibody and pretreatment selection in placental research providing objective and precise results.

Comparative Assessment of Digital Pathology Systems for Primary Diagnosis

Background:

Despite increasing interest in whole-slide imaging (WSI) over optical microscopy (OM), limited information on comparative assessment of various digital pathology systems (DPSs) is available.

Materials and Methods:

A comprehensive evaluation was undertaken to investigate the technical performance–assessment and diagnostic accuracy of four DPSs with an objective to establish the noninferiority of WSI over OM and find out the best possible DPS for clinical workflow.

Results:

A total of 2376 digital images, 15,775 image reads (OM - 3171 + WSI - 12,404), and 6100 diagnostic reads (OM - 1245, WSI - 4855) were generated across four DPSs (coded as DPS: 1, 2, 3, and 4) using a total 240 cases (604 slides). Onsite technical evaluation revealed successful scan rate: DPS3 < DPS2 < DPS4 < DPS1; mean scanning time: DPS4 < DPS1 < DPS2 < DPS3; and average storage space: DPS3 < DPS2 < DPS1 < DPS4. Overall diagnostic accuracy, when compared with the reference standard for OM and WSI, was 95.44% (including 2.48% minor and 2.08% major discordances) and 93.32% (including 4.28% minor and 2.4% major discordances), respectively. The difference between the clinically significant discordances by WSI versus OM was 0.32%. Major discordances were observed mostly using DPS4 and least in DPS1; however, the difference was statistically insignificant. Almost perfect (κ ≥ 0.8)/substantial (κ = 0.6–0.8) inter/intra-observer agreement between WSI and OM was observed for all specimen types, except cytology. Overall image quality was best for DPS1 followed by DPS4. Mean digital artifact rate was 6.8% (163/2376 digital images) and maximum artifacts were noted in DPS2 (n = 77) followed by DPS3 (n = 36). Most pathologists preferred viewing software of DPS1 and DPS2.

Conclusion:

WSI was noninferior to OM for all specimen types, except for cytology. Each DPS has its own pros and cons; however, DPS1 closely emulated the real-world clinical environment. This evaluation is intended to provide a roadmap to pathologists for the selection of the appropriate DPSs while adopting WSI.

Integrated digital pathology at scale: A solution for clinical diagnostics and cancer research at a large academic medical center

OBJECTIVE

Broad adoption of digital pathology (DP) is still lacking, and examples for DP connecting diagnostic, research, and educational use cases are missing. We blueprint a holistic DP solution at a large academic medical center ubiquitously integrated into clinical workflows; researchapplications including molecular, genetic, and tissue databases; and educational processes.

MATERIALS AND METHODS

We built a vendor-agnostic, integrated viewer for reviewing, annotating, sharing, and quality assurance of digital slides in a clinical or research context. It is the first homegrown viewer cleared by New York State provisional approval in 2020 for primary diagnosis and remote sign-out during the COVID-19 (coronavirus disease 2019) pandemic. We further introduce an interconnected Honest Broker for BioInformatics Technology (HoBBIT) to systematically compile and share large-scale DP research datasets including anonymized images, redacted pathology reports, and clinical data of patients with consent.

RESULTS

The solution has been operationally used over 3 years by 926 pathologists and researchers evaluating 288 903 digital slides. A total of 51% of these were reviewed within 1 month after scanning. Seamless integration of the viewer into 4 hospital systems clearly increases the adoption of DP. HoBBIT directly impacts the translation of knowledge in pathology into effective new health measures, including artificial intelligence-driven detection models for prostate cancer, basal cell carcinoma, and breast cancer metastases, developed and validated on thousands of cases.

CONCLUSIONS

We highlight major challenges and lessons learned when going digital to provide orientation for other pathologists. Building interconnected solutions will not only increase adoption of DP, but also facilitate next-generation computational pathology at scale for enhanced cancer research.

Quantitative Image Analysis for Tissue Biomarker Use: A White Paper From the Digital Pathology Association

Tissue biomarkers have been of increasing utility for scientific research, diagnosing disease, and treatment response prediction. There has been a steady shift away from qualitative assessment toward providing more quantitative scores for these biomarkers. The application of quantitative image analysis has thus become an indispensable tool for in-depth tissue biomarker interrogation in these contexts. This white paper reviews current technologies being employed for quantitative image analysis, their application and pitfalls, regulatory framework demands, and guidelines established for promoting their safe adoption in clinical practice.

Continuing Undergraduate Pathology Medical Education in the Coronavirus Disease 2019 (COVID-19) Global Pandemic: The Johns Hopkins Virtual Surgical Pathology Clinical Elective

CONTEXT.—

In the early months of the response to the coronavirus disease 2019 (COVID-19) pandemic, the Johns Hopkins University School of Medicine (JHUSOM) (Baltimore, Maryland) leadership reached out to faculty to develop and implement virtual clinical clerkships after all in-person medical student clinical experiences were suspended.

OBJECTIVE.—

To develop and implement a digital slide-based virtual surgical pathology (VSP) clinical elective to meet the demand for meaningful and robust virtual clinical electives in response to the temporary suspension of in-person clinical rotations at JHUSOM.

DESIGN.—

The VSP elective was modeled after the in-person surgical pathology elective to include virtual previewing and sign-out with standardized cases supplemented by synchronous and asynchronous pathology educational content.

RESULTS.—

Validation of existing Web communications technology and slide-scanning systems was performed by feasibility testing. Curriculum development included drafting of course objectives and syllabus, Blackboard course site design, electronic-lecture creation, communications with JHUSOM leadership, scheduling, and slide curation. Subjectively, the weekly schedule averaged 35 to 40 hours of asynchronous, synchronous, and independent content, approximately 10 to 11 hours of which were synchronous. As of February 2021, VSP has hosted 35 JHUSOM and 8 non-JHUSOM students, who have provided positive subjective and objective course feedback.

CONCLUSIONS.—

The Johns Hopkins VSP elective provided meaningful clinical experience to 43 students in a time of immense online education need. Added benefits of implementing VSP included increased medical student exposure to pathology as a medical specialty and demonstration of how digital slides have the potential to improve standardization of the pathology clerkship curriculum.

Diagnostic digital pathology implementation: Learning from the digital health experience

Digital Pathology (also referred to as Telepathology and Whole Slide Imaging) is the process of producing high resolution digital images from tissue sections on glass slides. These glass slides are normally examined under a microscope by a pathologist as part of the diagnostic process. The emergence of digital pathology now means that digital images are stored on secure servers and can be viewed on computer monitors; enabling pathologists to work remotely and to collaborate with other colleagues when second opinions are needed. The implementation of digital pathology into clinical practice has many potential benefits. Although this has been long recognised, its adoption as a diagnostic tool remains low and pathologists’ projections about its future deployment are cautious. Notable early digital pathology adopters have led the way. The challenge now is to scale-up digital pathology beyond the relatively few large networks and centres of excellence. Many other areas of healthcare have accumulated experience about optimising approaches to digital health/healthcare technology deployment and sustainability. This has been done in a multi-disciplinary context and has applied theoretical/conceptual frameworks. Thus far there has been little use of similar frameworks in the planning of digital pathology deployment in clinical practice. In this essay, I will explore the scope of digital pathology implementation approaches that have been deployed in clinical practice and examine what can be learned from the wider healthcare experience of adopting, scaling-up and sustaining innovative healthcare solutions.

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.

Remote Reporting from Home for Primary Diagnosis in Surgical Pathology: A Tertiary Oncology Center Experience during the COVID-19 Pandemic

BACKGROUND

The COVID-19 pandemic accelerated the widespread adoption of digital pathology (DP) for primary diagnosis in surgical pathology. This paradigm shift is likely to influence how we function routinely in the postpandemic era. We present learnings from early adoption of DP for a live digital sign-out from home in a risk-mitigated environment.

MATERIALS AND METHODS

We aimed to validate DP for remote reporting from home in a real-time environment and evaluate the parameters influencing the efficiency of a digital workflow. Eighteen pathologists prospectively validated DP for remote use on 567 biopsy cases including 616 individual parts from 7 subspecialties over a duration from March 21, 2020, to June 30, 2020. The slides were digitized using Roche Ventana DP200 whole-slide scanner and reported from respective homes in a risk-mitigated environment.

RESULTS

Following re-review of glass slides, there was no major discordance and 1.2% (n = 7/567) minor discordance. The deferral rate was 4.5%. All pathologists reported from their respective homes from laptops with an average network speed of 20 megabits per second.

CONCLUSION

We successfully validated and adopted a digital workflow for remote reporting with available resources and were able to provide our patients, an undisrupted access to subspecialty expertise during these unprecedented times.

Challenges in the Development, Deployment, and Regulation of Artificial Intelligence in Anatomic Pathology

Significant advances in artificial intelligence (AI), deep learning, and other machine-learning approaches have been made in recent years, with applications found in almost every industry, including health care. AI has proved to be capable of completing a spectrum of mundane to complex medically oriented tasks previously performed only by boarded physicians, most recently assisting with the detection of cancers difficult to find on histopathology slides. Although computers will not replace pathologists any time soon, properly designed AI-based tools hold great potential for increasing workflow efficiency and diagnostic accuracy in the practice of pathology. Recent trends, such as data augmentation, crowdsourcing for generating annotated data sets, and unsupervised learning with molecular and/or clinical outcomes versus human diagnoses as a source of ground truth, are eliminating the direct role of pathologists in algorithm development. Proper integration of AI-based systems into anatomic-pathology practice will necessarily require fully digital imaging platforms, an overhaul of legacy information-technology infrastructures, modification of laboratory/pathologist workflows, appropriate reimbursement/cost-offsetting models, and ultimately, the active participation of pathologists to encourage buy-in and oversight. Regulations tailored to the nature and limitations of AI are currently in development and, when instituted, are expected to promote safe and effective use. This review addresses the challenges in AI development, deployment, and regulation to be overcome prior to its widespread adoption in anatomic pathology.

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.

Validation of a digital pathology system including remote review during the COVID-19 pandemic

Remote digital pathology allows healthcare systems to maintain pathology operations during public health emergencies. Existing Clinical Laboratory Improvement Amendments regulations require pathologists to electronically verify patient reports from a certified facility. During the 2019 pandemic of COVID-19 disease, caused by the SAR-CoV-2 virus, this requirement potentially exposes pathologists, their colleagues, and household members to the risk of becoming infected. Relaxation of government enforcement of this regulation allows pathologists to review and report pathology specimens from a remote, non-CLIA certified facility. The availability of digital pathology systems can facilitate remote microscopic diagnosis, although formal comprehensive (case-based) validation of remote digital diagnosis has not been reported. All glass slides representing routine clinical signout workload in surgical pathology subspecialties at Memorial Sloan Kettering Cancer Center were scanned on an Aperio GT450 at ×40 equivalent resolution (0.26 µm/pixel). Twelve pathologists from nine surgical pathology subspecialties remotely reviewed and reported complete pathology cases using a digital pathology system from a non-CLIA certified facility through a secure connection. Whole slide images were integrated to and launched within the laboratory information system to a custom vendor-agnostic, whole slide image viewer. Remote signouts utilized consumer-grade computers and monitors (monitor size, 13.3-42 in.; resolution, 1280 × 800-3840 × 2160 pixels) connecting to an institution clinical workstation via secure virtual private network. Pathologists subsequently reviewed all corresponding glass slides using a light microscope within the CLIA-certified department. Intraobserver concordance metrics included reporting elements of top-line diagnosis, margin status, lymphovascular and/or perineural invasion, pathology stage, and ancillary testing. The median whole slide image file size was 1.3 GB; scan time/slide averaged 90 s; and scanned tissue area averaged 612 mm². Signout sessions included a total of 108 cases, comprised of 254 individual parts and 1196 slides. Major diagnostic equivalency was 100% between digital and glass slide diagnoses; and overall concordance was 98.8% (251/254). This study reports validation of primary diagnostic review and reporting of complete pathology cases from a remote site during a public health emergency. Our experience shows high (100%) intraobserver digital to glass slide major diagnostic concordance when reporting from a remote site. This randomized, prospective study successfully validated remote use of a digital pathology system including operational feasibility supporting remote review and reporting of pathology specimens, and evaluation of remote access performance and usability for remote signout.

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.

Deep learning quantification of percent steatosis in donor liver biopsy frozen sections

Background

Pathologist evaluation of donor liver biopsies provides information for accepting or discarding potential donor livers. Due to the urgent nature of the decision process, this is regularly performed using frozen sectioning at the time of biopsy. The percent steatosis in a donor liver biopsy correlates with transplant outcome, however there is significant inter- and intra-observer variability in quantifying steatosis, compounded by frozen section artifact. We hypothesized that a deep learning model could identify and quantify steatosis in donor liver biopsies.

Methods

We developed a deep learning convolutional neural network that generates a steatosis probability map from an input whole slide image (WSI) of a hematoxylin and eosin-stained frozen section, and subsequently calculates the percent steatosis. Ninety-six WSI of frozen donor liver sections from our transplant pathology service were annotated for steatosis and used to train (n = 30 WSI) and test (n = 66 WSI) the deep learning model.

Findings

The model had good correlation and agreement with the annotation in both the training set (r of 0.88, intraclass correlation coefficient [ICC] of 0.88) and novel input test sets (r = 0.85 and ICC=0.85). These measurements were superior to the estimates of the on-service pathologist at the time of initial evaluation (r = 0.52 and ICC=0.52 for the training set, and r = 0.74 and ICC=0.72 for the test set).

Interpretation

Use of this deep learning algorithm could be incorporated into routine pathology workflows for fast, accurate, and reproducible donor liver evaluation.

Funding

Mid-America Transplant Society

Synthesis of diagnostic quality cancer pathology images by generative adversarial networks

Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images. However, there has been relatively little investigation of the utility of deep neural networks to synthesize medical images. In this study, we evaluated the efficacy of generative adversarial networks to synthesize high-resolution pathology images of 10 histological types of cancer, including five cancer types from The Cancer Genome Atlas and the five major histological subtypes of ovarian carcinoma. The quality of these images was assessed using a comprehensive survey of board-certified pathologists (n = 9) and pathology trainees (n = 6). Our results show that the real and synthetic images are classified by histotype with comparable accuracies and the synthetic images are visually indistinguishable from real images. Furthermore, we trained deep convolutional neural networks to diagnose the different cancer types and determined that the synthetic images perform as well as additional real images when used to supplement a small training set. These findings have important applications in proficiency testing of medical practitioners and quality assurance in clinical laboratories. Furthermore, training of computer-aided diagnostic systems can benefit from synthetic images where labeled datasets are limited (e.g. rare cancers). We have created a publicly available website where clinicians and researchers can attempt questions from the image survey (http://gan.aimlab.ca/). © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Current state of whole slide imaging use in cytopathology: Pros and pitfalls

Whole slide imaging (WSI) allows generation of large whole slide images and their navigation with zoom in and out like a true virtual microscope. It has become widely used in surgical pathology for many purposes, such as education and training, research activity, teleconsultation, and primary diagnosis. However, in cytopathology, the use of WSI has been lagging behind histology, mainly due to the cytological specimen's characteristics, as groups of cells of different thickness are distributed throughout the slide. To allow the same focusing capability of light microscope, slides have to be scanned at multiple focal planes, at the cost of longer scan times and larger file size. These are the main technical pitfalls of WSI for cytopathology, partly overcome by solutions like liquid-based preparations. Validation studies for the use in primary diagnosis are less numerous and more heterogeneous than in surgical pathology. WSI has been proved effective for training students and successfully used in proficiency testing, allowing the creation of digital cytology atlases. Longer scan times are also a barrier for use in rapid on-site evaluation, but WSI retains its advantages of easy sharing of images for consultation, multiple simultaneous viewing in different locations, the possibility of unlimited annotations and easy integration with medical records. Moreover, digital slides set the laboratory free from reliance on a physical glass slide, with no more concern of fading of stain or slide breakage. Costs are still a problem for small institutions, but WSI can also represent the beginning of a more efficient way of working.

The coming 15 years in gynaecological pathology: digitisation, artificial intelligence, and new technologies

Surgical pathology forms the cornerstone of modern oncological medicine, owing to the wealth of clinically relevant information that can be obtained from tissue morphology. Although several ancillary testing modalities have been added to surgical pathology, the way in which we view and interpret tissue morphology has remained largely unchanged since the inception of our profession. In this review, we discuss new technological advances that promise to transform the way in which we access tissue morphology and how we use it to guide patient care.

Establishment of a remote diagnostic histopathology service using whole slide imaging (digital pathology)

BACKGROUND

Whole slide imaging (WSI) has diverse applications in modern pathology practice, including providing histopathology services to remote locations.

MATERIALS AND METHODS

Utilising an existing contractual partnership with a Northern Ontario group of hospitals, the feasibility of using WSI for primary diagnostic services from Toronto was explored by the dedicated working group. All aspects explored from information technology (IT), laboratory information system (LIS) integration, scanning needs, laboratory workflow and pathologist needs and training, were taken into account in the developing the rationale and business case.

RESULTS

The financial outlay for a scanner was $CA180K (approximately £105.6 k) after discounts. There were no human resource requirements as staff were reorganised to cater for slide scanning. Additional IT/LIS costs were not incurred as existing connectivity was adapted to allow two site groups (gastrointestinal and skin) to pilot this study. Scanned slides were available for pathologist review 24-96 hours sooner than glass slides; there was a 2-day improvement for final authorised cases, and per annum savings were: $CA26 000 (£15.2 k) in courier costs, $CA60 000 (£35.2 k) travel and $CA45 000 (£26.4 k) in accommodation, meals and car rental expense.

CONCLUSION

WSI is a viable solution to provide timely, high-quality and cost efficient histopathology services to underserviced, remote areas.

Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring

Traditional histopathological allograft biopsy evaluation provides, within hours, diagnoses, prognostic information, and mechanistic insights into disease processes. However, proponents of an array of alternative monitoring platforms, broadly classified as "invasive" or "noninvasive" depending on whether allograft tissue is needed, question the value proposition of tissue histopathology. The authors explore the pros and cons of current analytical methods relative to the value of traditional and illustrate advancements of next-generation histopathological evaluation of tissue biopsies. We describe the continuing value of traditional histopathological tissue assessment and "next-generation pathology (NGP)," broadly defined as staining/labeling techniques coupled with digital imaging and automated image analysis. Noninvasive imaging and fluid (blood and urine) analyses promote low-risk, global organ assessment, and "molecular" data output, respectively; invasive alternatives promote objective, "mechanistic" insights by creating gene lists with variably increased/decreased expression compared with steady state/baseline. Proponents of alternative approaches contrast their preferred methods with traditional histopathology and: (1) fail to cite the main value of traditional and NGP-retention of spatial and inferred temporal context available for innumerable objective analyses and (2) belie an unfamiliarity with the impact of advances in imaging and software-guided analytics on emerging histopathology practices. Illustrative NGP examples demonstrate the value of multidimensional data that preserve tissue-based spatial and temporal contexts. We outline a path forward for clinical NGP implementation where "software-assisted sign-out" will enable pathologists to conduct objective analyses that can be incorporated into their final reports and improve patient care.

Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology

Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases.

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.

Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders

Histological analysis of hepatic tissue specimens is essential for evaluating the pathology of several liver disorders such as chronic liver diseases, hepatocellular carcinomas, liver steatosis, and infectious liver diseases. Manual examination of histological slides on the microscope is a classically used method to study these disorders. However, it is considered time-consuming, limited, and associated with intra- and inter-observer variability. Emerging technologies such as whole slide imaging (WSI), also termed virtual microscopy, have increasingly been used to improve the assessment of histological features with applications in both clinical and research laboratories. WSI enables the acquisition of the tissue morphology/pathology from glass slides and translates it into a digital form comparable to a conventional microscope, but with several advantages such as easy image accessibility and storage, portability, sharing, annotation, qualitative and quantitative image analysis, and use for educational purposes. WSI-generated images simultaneously provide high resolution and a wide field of observation that can cover the entire section, extending any single field of view. In this review, we summarize current knowledge on the application of WSI to histopathological analyses of liver disorders as well as to understand liver biology. We address how WSI may improve the assessment and quantification of multiple histological parameters in the liver, and help diagnose several hepatic conditions with important clinical implications. The WSI technical limitations are also discussed.

Whole slide imaging compared with light microscopy for primary diagnosis in surgical neuropathology: a validation study

BACKGROUND

Digital pathology practice is rapidly gaining popularity among practicing anatomic pathologists. Acceptance is higher among the newer generation of pathologists who are willing to adapt to this new diagnostic method due to the advantages offered by whole slide imaging (WSI) compared to traditional light microscopy (TLM). We performed this validation study because we plan to implement the WSI system for diagnostic services.

OBJECTIVES

Determine the feasibility of using digital pathology for diagnostic services by assessing the equivalency of WSI and TLM.

DESIGN

A laboratory-based cross-sectional study.

SETTING

Central laboratory at a tertiary health care center.

MATERIALS AND METHODS

Four practicing surgical pathologists participated in this study. Each pathologist blindly reviewed 60 surgical neuropathology cases with a minimum 8-week washout-period between the two diagnostic modalities (WSI vs. TLM). Intraobserver concordance rates between WSI and TLM diagnoses as compared to the original diagnosis were calculated.

MAIN OUTCOME MEASURES

Overall intraobserver concordance rates between each diagnostic method (WSI and TLM) and original diagnosis.

SAMPLE SIZE

60 in-house surgical neuropathology cases.

RESULTS

The overall intraobserver concordance rate between TLM and original diagnosis was 86.3% (range 76.7%-91.7%) versus 80.8% for WSI (range 68.3%-88.3%). These findings are suggestive of the superiority of TLM, but the Fleiss' Kappa statistic indicated that the two methods are equivalent, despite the low level of the K value.

CONCLUSION

WSI is not inferior to the light microscopy and is feasible for primary diagnosis in surgical neuropathology. However, to ensure the best results, only formally trained neuropathologists should handle the digital neuropathology service.

LIMITATIONS

Only one diagnostic slide per case rather than the whole set of slides, sample size was relatively small, and there was an insufficient number of participating neuropathologists.

CONFLICT OF INTEREST

None.

Machine Learning Methods for Computer-Aided Breast Cancer Diagnosis Using Histopathology: A Narrative Review

Histopathology is a method used for breast cancer diagnosis. Machine learning (ML) methods have achieved success for supervised learning tasks in the medical domain. In this article, we investigate the impact of ML for the diagnosis of breast cancer using histopathology images of conventional photomicroscopy. Cancer diagnosis is the identification of images as cancer or noncancer, and this involves image preprocessing, feature extraction, classification, and performance analysis. In this article, different approaches to perform these necessary steps are reviewed. We find that most ML research for breast cancer diagnosis has been focused on deep learning. Based on inferences from the recent research activities, we discuss how ML methods can benefit conventional microscopy-based breast cancer diagnosis. Finally, we discuss the research gaps of ML approaches for the implementation in a real pathology environment and propose future research guidelines.

The California Telepathology Service: UCLA's Experience in Deploying a Regional Digital Pathology Subspecialty Consultation Network

Background:

The need for extending pathology diagnostic expertise to more areas is now being met by the maturation of technology that can effectively deliver this level of care. The experience and lessons learned from our successfully deployed International Telepathology Service (ITS) to a hospital system in China guided us in starting a domestic telepathology network, the California Telepathology Service (CTS). Many of the lessons learned from the ITS project informed our decision-making for the CTS. New challenges were recognized and overcome, such as addressing the complexity and cost–benefit tradeoffs involved in setting up a digital consultation system that competes with an established conventional glass slide delivery system.

Methods:

The CTS is based on a hub-and-spoke telepathology network using Leica Biosystems whole-slide image scanners and the eSlide Manager (eSM Version 12.3.3.7055, Leica Biosystems) digital image management software solution. The service currently comprises six spoke sites (UC San Diego [UCSD], UC Irvine [UCI], UC Davis, Northridge Hospital Medical Center [NHMC], Olive View Medical Center [OVMC], and Children's Hospital Los Angeles) and one central hub site (UCLA Medical Center). So far, five sites have been validated for telepathology case consultations following established practice guidelines, and four sites (UCI, UCSD, NHMC, and OVMC) have activated the service.

Results:

For the active spoke sites, we reviewed the volume, turnaround time (TAT), and case types and evaluated for utility and value. From May 2017 to July 2018, a total of 165 cases were submitted. Of note, digital consultations were particularly advantageous for preliminary kidney biopsy diagnoses (avg TAT 0.7 day).

Conclusion:

For spoke sites, telepathology provided shortened TAT and significant financial savings over hiring faculty with expertise to support a potentially low-volume service. For the hub site, the value includes exposure to educationally valuable cases, additional caseload volume to support specialized services, and improved communication with referring facilities over traditional carrier mail. The creation of a hub-and-spoke telepathology network is an expensive undertaking, and careful consideration needs to be given to support the needs of the clinical services, acquisition and effective deployment of the appropriate equipment, network requirements, and laboratory workflows to ensure a successful and cost-effective system.

Annotations, Ontologies, and Whole Slide Images - Development of an Annotated Ontology-Driven Whole Slide Image Library of Normal and Abnormal Human Tissue

Objective:

Digital pathology is today a widely used technology, and the digitalization of microscopic slides into whole slide images (WSIs) allows the use of machine learning algorithms as a tool in the diagnostic process. In recent years, “deep learning” algorithms for image analysis have been applied to digital pathology with great success. The training of these algorithms requires a large volume of high-quality images and image annotations. These large image collections are a potent source of information, and to use and share the information, standardization of the content through a consistent terminology is essential. The aim of this project was to develop a pilot dataset of exhaustive annotated WSI of normal and abnormal human tissue and link the annotations to appropriate ontological information.

Materials and Methods:

Several biomedical ontologies and controlled vocabularies were investigated with the aim of selecting the most suitable ontology for this project. The selection criteria required an ontology that covered anatomical locations, histological subcompartments, histopathologic diagnoses, histopathologic terms, and generic terms such as normal, abnormal, and artifact. WSIs of normal and abnormal tissue from 50 colon resections and 69 skin excisions, diagnosed 2015-2016 at the Department of Clinical Pathology in Linköping, were randomly collected. These images were manually and exhaustively annotated at the level of major subcompartments, including normal or abnormal findings and artifacts.

Results:

Systemized nomenclature of medicine clinical terms (SNOMED CT) was chosen, and the annotations were linked to its codes and terms. Two hundred WSI were collected and annotated, resulting in 17,497 annotations, covering a total area of 302.19 cm², equivalent to 107,7 gigapixels. Ninety-five unique SNOMED CT codes were used. The time taken to annotate a WSI varied from 45 s to over 360 min, a total time of approximately 360 h.

Conclusion:

This work resulted in a dataset of 200 exhaustive annotated WSIs of normal and abnormal tissue from the colon and skin, and it has informed plans to build a comprehensive library of annotated WSIs. SNOMED CT was found to be the best ontology for annotation labeling. This project also demonstrates the need for future development of annotation tools in order to make the annotation process more efficient.