Whole Slide Imaging for Analytical Anatomic Pathology and Telepathology: Practical Applications Today, Promises, and Perils

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.

Digital image analysis and assisted reading of the HER2 score display reduced concordance: pitfalls in the categorisation of HER2-low breast cancer

AIMS

Digital image analysis (DIA) is used increasingly as an assisting tool to evaluate biomarkers, including human epidermal growth factor receptor 2 (HER2) in invasive breast cancer (BC). DIA can assist pathologists in HER2 evaluation by presenting quantitative information about the HER2 staining in APP assisted reading (AR). Concurrently, the HER2-low category (HER2-1+/2+ without HER2 gene amplification) has gained prominence due to newly developed antibody-drug conjugates. However, major inter- and intraobserver variability have been observed for the entity. The present quality assurance study investigated the concordance between DIA and AR in clinical use, especially concerning the HER2-low category.

METHODS AND RESULTS

HER2 immunohistochemistry (IHC) in 761 tumours from 727 patients was evaluated in tissue microarray (TMA) cores by DIA (Visiopharm HER2-CONNECT) and AR. Overall concordance between HER2-scores were 73% (n = 552, weighted-κ: 0.66), and 88% (n = 669, weighted-κ: 0.70), when combining HER2-0/1+. A total of 205 scores were discordant by one category, while four were discordant by two categories. A heterogeneous HER2 pattern was relatively common in the discordant cases and a pitfall in the categorisation of HER2-low BC. AR more commonly reassigned a lower HER2 score (from HER2-1+ to HER2-0) within the HER2-low subgroup (n = 624) compared with DIA.

CONCLUSION

DIA and AR display moderate agreement with heterogeneous and aberrant staining, representing a source of discordance and a pitfall in the evaluation of HER2.

Risk factors of positive resection margin differ in pancreaticoduodenectomy and distal pancreatosplenectomy for pancreatic ductal adenocarcinoma undergoing upfront surgery

OBJECTIVE

Positive resection margin indicates worse prognosis. The present study identified the independent risk factors of R1 resection in pancreaticoduodenectomy (PD) and distal pancreatosplenectomy (DP) for patients with pancreatic ductal adenocarcinoma (PDAC).

METHOD

Consecutive patients who were operated from 1st December 2017 to 30th December 2018 were analyzed retrospectively. A standardized pathological examination with digital whole-mount slide images (DWMSIs) was utilized for evaluation of resection margin status. R1 was defined as microscopic tumor infiltration within 1 mm to the resection margin. The potential risk factors of R1 resection for PD and DP were analyzed separately by univariate and multivariate logistic regression analyses.

RESULTS

For the 192 patients who underwent PD, and the 87 patients who underwent DP, the R1 resection rates were 31.8% and 35.6%, respectively. Univariate analysis on risk factors of R1 resection for PD were tumor location, lymphovascular invasion, N staging, and TNM staging; while those for DP were T staging and TNM staging. Multivariate logistic regression analysis showed the location of tumor in the neck and uncinate process, and N1/2 staging were independent risk factors of R1 resection for PD; while those for DP were T3 staging.

CONCLUSIONS

The clarification of the risk factors of R1 resection might clearly make surgeons take reasonable decisions on surgical strategies for different surgical procedures in patients with PDAC, so as to obtain the first attempt of R0 resection.

High throughput whole-slide scanning to enable large-scale data repository building

Digital pathology and artificial intelligence (AI) rely on digitization of patient material as a necessary first step. AI development benefits from large sample sizes and diverse cohorts, and therefore efforts to digitize glass slides must meet these needs in an efficient and cost-effective manner. Technical innovation in whole-slide imaging has enabled high throughput slide scanning through the coordinated increase in scanner capacity, speed, and automation. Combining these hardware innovations with automated informatics approaches has enabled more efficient workflows and the opportunity to provide higher quality imaging data using fewer personnel. Here we review several practical considerations for deploying high throughput scanning and we present strategies to increase efficiency with a focus on quality. Finally, we review remaining challenges and issue a call to vendors to innovate in the areas of automation and quality control in order to make high throughput scanning realizable to laboratories with limited resources. This article is protected by copyright. All rights reserved.

Cancer detection from stained biopsies using high-speed spectral imaging

The escalating demand for diagnosing pathological biopsies requires the procedures to be expedited and automated. The existing imaging systems for measuring biopsies only measure color, and even though a lot of effort is invested in deep learning analysis, there are still serious challenges regarding the performance and validity of the data for the intended medical setting. We developed a system that rapidly acquires spectral images from biopsies, followed by spectral classification algorithms. The spectral information is remarkably more informative than the color information, and leads to very high accuracy in identifying cancer cells, as tested on tens of cancer cases. This was improved even more by using artificial intelligence algorithms that required a rather small training set, indicating the high level of information that exists in the spectral images. The most important spectral differences are observed in the nucleus and they are related to aneuploidy in tumor cells. Rapid spectral imaging measurement therefore can bridge the gap in the machine-aided diagnostics of whole biopsies, thus improving patient care, and expediting the treatment procedure.

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

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

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.

Artificial intelligence and algorithmic computational pathology: an introduction with renal allograft examples

Whole slide imaging, which is an important technique in the field of digital pathology, has recently been the subject of increased interest and avenues for utilisation, and with more widespread whole slide image (WSI) utilisation, there will also be increased interest in and implementation of image analysis (IA) techniques. IA includes artificial intelligence (AI) and targeted or hypothesis-driven algorithms. In the overall pathology field, the number of citations related to these topics has increased in recent years. Renal pathology is one anatomical pathology subspecialty that has utilised WSIs and IA algorithms; it can be argued that renal transplant pathology could be particularly suited for whole slide imaging and IA, as renal transplant pathology is frequently classified by use of the semiquantitative Banff classification of renal allograft pathology. Hypothesis-driven/targeted algorithms have been used in the past for the assessment of a variety of features in the kidney (e.g. interstitial fibrosis, tubular atrophy, inflammation); in recent years, the amount of research has particularly increased in the area of AI/machine learning for the identification of glomeruli, for histological segmentation, and for other applications. Deep learning is the form of machine learning that is most often used for such AI approaches to the 'big data' of pathology WSIs, and deep learning methods such as artificial neural networks (ANNs)/convolutional neural networks (CNNs) are utilised. Unsupervised and supervised AI algorithms can be employed to accomplish image or semantic classification. In this review, AI and other IA algorithms applied to WSIs are discussed, and examples from renal pathology are covered, with an emphasis on renal transplant pathology.

Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies

Hepatic steatosis droplet quantification with histology biopsies has high clinical significance for risk stratification and management of patients with fatty liver diseases and in the decision to use donor livers for transplantation. However, pathology reviewing processes, when conducted manually, are subject to a high inter- and intra-reader variability, due to the overwhelmingly large number and significantly varying appearance of steatosis instances. This process is challenging as there is a large number of overlapped steatosis droplets with either missing or weak boundaries. In this study, we propose a deep-learning-based region-boundary integrated network for precise steatosis quantification with whole slide liver histopathology images. The proposed model consists of two sequential steps: a region extraction and a boundary prediction module for foreground regions and steatosis boundary prediction, followed by an integrated prediction map generation. Missing steatosis boundaries are next recovered from the predicted map and assembled from adjacent image patches to generate results for the whole slide histopathology image. The resulting steatosis measures both at the pixel level and steatosis object-level present strong correlation with pathologist annotations, radiology readouts and clinical data. In addition, the segregated steatosis object count is shown as a promising alternative measure to the traditional metrics at the pixel level. These results suggest a high potential of artificial intelligence-assisted technology to enhance liver disease decision support using whole slide images.

Quantitative assessment of liver fibrosis by digital image analysis reveals correlation with qualitative clinical fibrosis staging in liver transplant patients

Technologies for digitizing tissues provide important quantitative data for liver histopathology investigation. We aimed to assess liver fibrosis degree with quantitative morphometric measurements of histopathological sections utilizing digital image analysis (DIA) and to further investigate if a correlation with histopathologic scoring (Scheuer staging) exists. A retrospective study of patients with at least two post-liver transplant biopsies having a Scheuer stage of ≤ 2 at baseline were gathered. Portal tract fibrotic percentage (%) and size (μm²) were measured by DIA, while clinical fibrosis score was measured by the Scheuer system. Correlations between DIA measurements and Scheuer scores were computed by Spearman correlation analysis. Differences between mean levels of fibrosis (score, size, and percentage) at baseline versus second visit were computed by Student’s t-test. P values < 0.05 were considered significant. Of 22 patients who met the study criteria, 54 biopsies were included for analysis. Average levels ±standard error [S.E.] of portal tract fibrotic percentage (%) and size (μm²) progressed from 46.5 ± 3.6% at baseline to 61.8 ± 3.8% at the second visit (P = 0.005 by Student’s t-test), and from 28,075 ± 3,232 μm² at base line to 67,146 ± 10,639 μm² at the second visit (P = 0.002 by Student’s t-test), respectively. Average levels of Scheuer fibrosis scores progressed from 0.55±0.19 at baseline to 1.14±0.26 at the second visit (P = 0.02 by Student’s t-test). Portal tract fibrotic percentage (%) and portal tract fibrotic size were directly correlated with clinical Scheuer fibrosis stage, with Spearman correlation coefficient and P value computed as r = 0.70, P < 0.0001 and r = 0.41, P = 0.002, respectively. Digital quantitative assessment of portal triad size and fibrosis percentage demonstrates a strong correlation with visually assessed histologic stage of liver fibrosis and complements the standard assessment for allograft monitoring, suggesting the utility of future WSI analysis.

Banff Digital Pathology Working Group: Going digital in transplant pathology

The Banff Digital Pathology Working Group (DPWG) was formed in the time leading up to and during the joint American Society for Histocompatibility and Immunogenetics/Banff Meeting, September 23-27, 2019, held in Pittsburgh, Pennsylvania. At the meeting, the 14th Banff Conference, presentations directly and peripherally related to the topic of "digital pathology" were presented; and discussions before, during, and after the meeting have resulted in a list of issues to address for the DPWG. Included are practice standardization, integrative approaches for study classification, scoring of histologic parameters (eg, interstitial fibrosis and tubular atrophy and inflammation), algorithm classification, and precision diagnosis (eg, molecular pathways and therapeutics). Since the meeting, a survey with international participation of mostly pathologists (81%) was conducted, showing that whole slide imaging is available at the majority of centers (71%) but that artificial intelligence (AI)/machine learning was only used in ≈12% of centers, with a wide variety of programs/algorithms employed. Digitalization is not just an end in itself. It also is a necessary precondition for AI and other approaches. Discussions at the meeting and the survey highlight the unmet need for a Banff DPWG and point the way toward future contributions that can be made.

Programmed death-ligand 1 expression profiling in thymic epithelial cell tumors: Clinicopathological features and quantitative digital image analyses

OBJECTIVES

Whether the extent of expression of programmed death-ligand 1 (PD-L1) is clinically significant remains uncertain, although immuno-oncological features have been studied in thymic epithelial cell tumors (TETs). We investigated the histological features of PD-L1 expression in TETs, and assessed PD-L1 expression using digital image analysis.

MATERIALS AND METHODS

Participants comprised 66 patients with TET who underwent surgical resection between 2001 and 2016. We calculated tumor cell-positive ratio as total proportion score (TPS) with immunohistochemistry using SP263 anti-PD-L1 monoclonal antibody. PD-L1 expression was also quantified using digital image analysis of whole-slide images. We evaluated the relationship between conventional visual TPS using optical microscopy (TPS-V) and TPS from digital image analysis (TPS-IA). We further classified all TETs into high or low PD-L1 expression groups and assessed the clinical significance of PD-L1 expression level using TPS-V and TPS-IA.

RESULTS

WHO histological types were Type A (n = 8), AB (n = 18), B1 (n = 5), B2 (n = 16), B3 (n = 6), metaplastic thymoma (n = 2), and thymic carcinoma (TC) (n = 11). Median TPS-Vs were 2%, 2%, 10 %, 65 %, 90 %, 1%, and 20 %, respectively. TPS-IAs correlated with TPS-Vs in TETs overall and in thymomas, but not in TCs. PD-L1 expression levels in TETs differed significantly among histological types. Whether TPS-V or TPS-IA were used, the PD-L1^high group included more cases of the more aggressive histological types. Recurrence-free survival (RFS) was shorter in the PD-L1^high group than in the PD-L1^low group in thymoma using TPS-IA, whereas RFS of the PD-L1^high group was shorter in all TETs using TPS-V.

CONCLUSION

PD-L1 expression levels depended on the histological type of TET. Extensive PD-L1 expression in TETs was associated with poor prognosis. Digital image analysis is feasible for evaluating PD-L1 expression in TETs and might offer clinically relevant features of thymomas.

Theranostic Gastrointestinal Endoscopy: Bringing Healing Light to the Lumen

Current conventional endoscopes have restricted the accuracy of treatment delivery and monitoring. Over the past decade, there have been major developments in nanotechnology and light triggered therapy, potentially allowing a better detection of challenging lesions and targeted treatment of malignancies in the gastrointestinal tract. Theranostics is a developing form of personalized medicine because it combines diagnosis and targeted treatment delivered in one step using advances in nanotechnology. This review describes the light-triggered therapies (including photodynamic, photothermal, and photoimmunotherapies), nanotechnological advances with nanopowder, nanostent, nanogels, and nanoparticles, enhancements brought to endoscopic ultrasound, in addition to experimental endoscopic techniques, combining both enhanced diagnoses and therapies, including a developed prototype of a “smart” multifunctional endoscope for localized colorectal cancer, near-infrared laser endoscope targeting the gastrointestinal stromal tumors, the concept of endocapsule for obscure gastrointestinal bleed, and a proof-of-concept therapeutic capsule using ultrasound-mediated targeted drug delivery. Hence, the following term has been proposed encompassing these technologies: “Theranostic gastrointestinal endoscopy.” Future efforts for integration of these technologies into clinical practice would be directed toward translational and clinical trials translating into a more personalized and interdisciplinary diagnosis and treatment, shorter procedural time, higher precision, higher cost-effectiveness, and less need for repetitive procedures.

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.

Video compression to support the expansion of whole-slide imaging into cytology

Digital screening and diagnosis from cytology slides can be aided by capturing multiple focal planes. However, using conventional methods, the large file sizes of high-resolution whole-slide images increase linearly with the number of focal planes acquired, leading to significant data storage and bandwidth requirements for the efficient storage and transfer of cytology virtual slides. We investigated whether a sequence of focal planes contained sufficient redundancy to efficiently compress virtual slides across focal planes by applying a commonly available video compression standard, high-efficiency video coding (HEVC). By developing an adaptive algorithm that applied compression to achieve a target image quality, we found that the compression ratio of HEVC exceeded that obtained using JPEG and JPEG2000 compression while maintaining a comparable level of image quality. These results suggest an alternative method for the efficient storage and transfer of whole-slide images that contain multiple focal planes, expanding the utility of this rapidly evolving imaging technology into cytology.

Concordance, intra- and inter-observer agreements between light microscopy and whole slide imaging for samples acquired by EUS in pancreatic solid lesions

BACKGROUND

No study has compared the performance of light microscopy (LM) and whole slide imaging (WSI) for endoscopic ultrasound (EUS) histological acquired tissue samples from pancreatic solid lesions (PSLs). We evaluated the concordance between LM and WSI and the inter- and intra-observer agreements among pathologists on PSLs EUS acquired samples.

METHODS

LM and WSI from 60 patients with PSLs were evaluated by five expert pathologists to define: diagnostic classification, presence of a core, number and percentage of lesional cells. Washout period between evaluations was 3 months. Time of the procedures was also assessed.

RESULTS

Forty-eight cell-block and 12 biopsy samples were evaluated. A high concordance between LM and WSI was found. Inter- and intra-observer agreements for diagnostic classification were substantial and complete, respectively. For all the other parameters, the inter-observer agreement was usually higher for LM. For the intra-observer, a substantial agreement was reached regarding the presence of tissue core and the number and the percentage of malignant cells. Median time for performing LM was significantly shorter than for WSI (p < 0.0001).

CONCLUSIONS

LM and WSI of cell-block and biopsy samples acquired by EUS in PSLs were highly concordant, with a substantial inter-observer and a complete intra-observer agreements regarding diagnostic classification.

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.

Digital versus light microscopy assessment of extraprostatic extension in radical prostatectomy samples

Focal or non-focal/extensive extraprostatic extension of prostate carcinoma is an important pathologic prognostic parameter to be reported after radical prostatectomy. Currently, there is no agreement on how to measure and what are the best cutoff points to be used in practice. We hypothesized that digital microscopy would potentially provide more objective measurements of extraprostatic extension, thus better defining its clinical significance. To further our knowledge on digital prostate pathology, we evaluated the status of extraprostatic extension in 107 consecutive laparoscopic radical prostatectomy samples, using digital and conventional light microscopy. Mean linear and radial measurements of extraprostatic extension by digital microscopy significantly correlated to pT status (p = 0.022 and p = 0.050, respectively) but only radial measurements correlated to biochemical recurrence (p = 0.042) and grade groups (p = 0.022). None of the measurements, whether conventional or digital, were associated with lymph node status. Receiving operating characteristic analysis showed a potential cutoff point to assess linear measurements by conventional (< vs. > 24.21 mm) or digital microscopy (< vs. > 15 mm) or by radial measurement (< vs. > 1.6 mm). Finally, we observed an association between the number of paraffin blocks bearing EPE with pT (p = 0.041) status (digital microscopy), and linear measurements by conventional (p = 0.044) or digital microscopy (p = 0.045) with lymph node status. Reporting EPE measurements by digital microscopy, both linear and radial, and the number of paraffin blocks with EPE, might provide additional prognostic features after radical prostatectomy.

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.

Dealing with Lack of Training Data for Convolutional Neural Networks: The Case of Digital Pathology

Thanks to their capability to learn generalizable descriptors directly from images, deep Convolutional Neural Networks (CNNs) seem the ideal solution to most pattern recognition problems. On the other hand, to learn the image representation, CNNs need huge sets of annotated samples that are unfeasible in many every-day scenarios. This is the case, for example, of Computer-Aided Diagnosis (CAD) systems for digital pathology, where additional challenges are posed by the high variability of the cancerous tissue characteristics. In our experiments, state-of-the-art CNNs trained from scratch on histological images were less accurate and less robust to variability than a traditional machine learning framework, highlighting all the issues of fully training deep networks with limited data from real patients. To solve this problem, we designed and compared three transfer learning frameworks, leveraging CNNs pre-trained on non-medical images. This approach obtained very high accuracy, requiring much less computational resource for the training. Our findings demonstrate that transfer learning is a solution to the automated classification of histological samples and solves the problem of designing accurate and computationally-efficient CAD systems with limited training data.

[The age of artificial intelligence in lung cancer pathology: Between hope, gloom and perspectives]

Histopathology is the fundamental tool of pathology used for more than a century to establish the final diagnosis of lung cancer. In addition, the phenotypic data contained in the histological images reflects the overall effect of molecular alterations on the behavior of cancer cells and provides a practical visual reading of the aggressiveness of the disease. However, the human evaluation of the histological images is sometimes subjective and may lack reproducibility. Therefore, computational analysis of histological imaging using so-called "artificial intelligence" (AI) approaches has recently received considerable attention to improve this diagnostic accuracy. Thus, computational analysis of lung cancer images has recently been evaluated for the optimization of histological or cytological classification, prognostic prediction or genomic profile of patients with lung cancer. This rapidly growing field constantly demonstrates great power in the field of computing medical imaging by producing highly accurate detection, segmentation or recognition tasks. However, there are still several challenges or issues to be addressed in order to successfully succeed the actual transfer into clinical routine. The objective of this review is to emphasize recent applications of AI in pulmonary cancer pathology, but also to clarify the advantages and limitations of this approach, as well as the perspectives to be implemented for a potential transfer into clinical routine.

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

Context.—

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

Objectives.—

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

Data Sources.—

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

Conclusions.—

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

Digital pathology in nephrology clinical trials, research, and pathology practice

PURPOSE OF REVIEW

In this review, we will discuss (i) how the recent advancements in digital technology and computational engineering are currently applied to nephropathology in the setting of clinical research, trials, and practice; (ii) the benefits of the new digital environment; (iii) how recognizing its challenges provides opportunities for transformation; and (iv) nephropathology in the upcoming era of kidney precision and predictive medicine.

RECENT FINDINGS

Recent studies highlighted how new standardized protocols facilitate the harmonization of digital pathology database infrastructure and morphologic, morphometric, and computer-aided quantitative analyses. Digital pathology enables robust protocols for clinical trials and research, with the potential to identify previously underused or unrecognized clinically useful parameters. The integration of digital pathology with molecular signatures is leading the way to establishing clinically relevant morpho-omic taxonomies of renal diseases.

SUMMARY

The introduction of digital pathology in clinical research and trials, and the progressive implementation of the modern software ecosystem, opens opportunities for the development of new predictive diagnostic paradigms and computer-aided algorithms, transforming the practice of renal disease into a modern computational science.

PanCancer insights from The Cancer Genome Atlas: the pathologist's perspective

The Cancer Genome Atlas (TCGA) represents one of several international consortia dedicated to performing comprehensive genomic and epigenomic analyses of selected tumour types to advance our understanding of disease and provide an open-access resource for worldwide cancer research. Thirty-three tumour types (selected by histology or tissue of origin, to include both common and rare diseases), comprising >11 000 specimens, were subjected to DNA sequencing, copy number and methylation analysis, and transcriptomic, proteomic and histological evaluation. Each cancer type was analysed individually to identify tissue-specific alterations, and make correlations across different molecular platforms. The final dataset was then normalized and combined for the PanCancer Initiative, which seeks to identify commonalities across different cancer types or cells of origin/lineage, or within anatomically or morphologically related groups. An important resource generated along with the rich molecular studies is an extensive digital pathology slide archive, composed of frozen section tissue directly related to the tissues analysed as part of TCGA, and representative formalin-fixed paraffin-embedded, haematoxylin and eosin (H&E)-stained diagnostic slides. These H&E image resources have primarily been used to verify diagnoses and histological subtypes with some limited extraction of standard pathological variables such as mitotic activity, grade, and lymphocytic infiltrates. Largely overlooked is the richness of these scanned images for more sophisticated feature extraction approaches coupled with machine learning, and ultimately correlation with molecular features and clinical endpoints. Here, we document initial attempts to exploit TCGA imaging archives, and describe some of the tools, and the rapidly evolving image analysis/feature extraction landscape. Our hope is to inform, and ultimately inspire and challenge, the pathology and cancer research communities to exploit these imaging resources so that the full potential of this integral platform of TCGA can be used to complement and enhance the insightful integrated analyses from the genomic and epigenomic platforms. Copyright © 2017 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Whole-Slide Imaging for Esophageal Adenocarcinoma

Whole-slide imaging revolutionizes the field of pathology especially in the areas of facilitation of research, long-term storages, exchange of information, and image analysis. In this process, a scanning equipment (scanner) scans the whole glass slide into a digital file. In research in esophageal adenocarcinoma or other cancers, whole-slide imaging could help in production of high-resolution images for studying and sharing of research information, assessment of tissue microarray slides as well as allowing digital image analysis of the tissue information such as level of staining (e.g., HER2) in a more efficient and objective manner. In this chapter, we will elaborate the concepts, advantages, barriers, and the operations of whole-slide imaging scanning.