Validation of Whole-Slide Imaging for Histolopathogical Diagnosis: Current State

The Role of Artificial Intelligence in Early Diagnosis and Molecular Classification of Head and Neck Skin Cancers: A Multidisciplinary Approach

Cancer remains a significant global health concern, with increasing genetic and metabolic irregularities linked to its onset. Among various forms of cancer, skin cancer, including squamous cell carcinoma, basal cell carcinoma, and melanoma, is on the rise worldwide, often triggered by ultraviolet (UV) radiation. The propensity of skin cancer to metastasize highlights the importance of early detection for successful treatment. This narrative review explores the evolving role of artificial intelligence (AI) in diagnosing head and neck skin cancers from both radiological and pathological perspectives. In the past two decades, AI has made remarkable progress in skin cancer research, driven by advances in computational capabilities, digitalization of medical images, and radiomics data. AI has shown significant promise in image-based diagnosis across various medical domains. In dermatology, AI has played a pivotal role in refining diagnostic and treatment strategies, including genomic risk assessment. This technology offers substantial potential to aid primary clinicians in improving patient outcomes. Studies have demonstrated AI's effectiveness in identifying skin lesions, categorizing them, and assessing their malignancy, contributing to earlier interventions and better prognosis. The rising incidence and mortality rates of skin cancer, coupled with the high cost of treatment, emphasize the need for early diagnosis. Further research and integration of AI into clinical practice are warranted to maximize its benefits in skin cancer diagnosis and treatment.

Validation of digital image slides for diagnosis in cervico-vaginal cytology

OBJECTIVE

To test the diagnostic concordance between microscopic (MI) and digital (DG) observation of cervico-vaginal (CV) cytology in a validation study of the technique.

METHODS

Five cytotechnologists (CT) reviewed 888 routine CV cytology cases from the Cervical Pathology Unit of our center over a 2-week period of time. The cases were first observed by MI and at the end of the day the cases were observed by DG.

STATISTICAL ANALYSIS USED

Agreement calculated using the Kappa index.

RESULTS

Most of the diagnoses corresponded to benign (64%) or inflammatory conditions (14%) and 24% corresponded to the intraepithelial lesion or malignancy (ILM) category. The overall kappa coefficient of concordance was strong (0.87). Among the different CTs it was almost perfect in two, strong in two and moderate in one. In 18 cases (10%) there were discrepancies between techniques in the category of ILM. In 10 (56%) cases there was an overdiagnosis in DG and in 8 (44%) an overdiagnosis in MI. Only in two cases, the diagnostic discrepancy exceeded one degree of difference between lesions, and they were ASCUS or AGUS for DG and CIN 2 for MI.

CONCLUSIONS

In this validation test in which routine cases during a two-week period have been used, observing the cases with both techniques on the same day, we have obtained a strong degree of concordance. The discordances obtained have not been considered relevant.

Utility of Whole Slide Imaging for Intraoperative Consultation: Experience of a Large Academic Center

CONTEXT.—

In the United States, review of digital whole slide images (WSIs) using specific systems is approved for primary diagnosis but has not been implemented for intraoperative consultation.

OBJECTIVE.—

To evaluate the safety of review of WSIs and compare the efficiency of review of WSIs and glass slides (GSs) for intraoperative consultation.

DESIGN.—

Ninety-one cases previously submitted for frozen section evaluation were randomly selected from 8 different anatomic pathology subspecialties. GSs from these cases were scanned on a Leica Aperio AT2 scanner at ×20 magnification (0.25 μm/pixel). The slides were deidentified, and a short relevant clinical history was provided for each slide. Nine board-certified general pathologists who do not routinely establish primary diagnoses using WSIs reviewed the WSIs using Leica Aperio ImageScope viewing software. After a washout period of 2-3 weeks, the pathologists reviewed the corresponding GSs using a light microscope (Olympus BX43). The pathologists recorded the diagnosis and time to reach the diagnosis. Intraobserver concordance, time to diagnosis, and specificity and sensitivity compared to the original diagnosis were evaluated.

RESULTS.—

The rate of intraobserver concordance between GS results and WSI results was 93.7%. Mean time to diagnosis was 1.25 minutes for GSs and 1.76 minutes for WSIs (P < .001). Specificity was 91% for GSs and 90% for WSIs; sensitivity was 92% for GSs and 92% for WSIs.

CONCLUSIONS.—

Time to diagnosis was longer with WSIs than with GSs, and scanning GSs and uploading the data to whole slide imaging systems takes time. However, review of WSIs appears to be a safe alternative to review of GSs. Use of WSIs allows reporting from a remote site during a public health emergency such as the COVID-19 pandemic and facilitates subspecialty histopathology services.

Artificial Intelligence in pathology: current applications, limitations, and future directions

PURPOSE

Given AI's recent success in computer vision applications, majority of pathologists anticipate that it will be able to assist them with a variety of digital pathology activities. Massive improvements in deep learning have enabled a synergy between Artificial Intelligence (AI) and deep learning, enabling image-based diagnosis against the backdrop of digital pathology. AI-based solutions are being developed to eliminate errors and save pathologists time.

AIMS

In this paper, we will discuss the components that went into the use of Artificial Intelligence in Pathology, its use in the medical profession, the obstacles and constraints that it encounters, and the future possibilities of AI in the medical field.

CONCLUSIONS

Based on these factors, we elaborate upon the use of AI in medical pathology and provide future recommendations for its successful implementation in this field.

Utility of pathologist panels for achieving consensus in NASH histologic scoring in clinical trials: Data from a phase 3 study

BACKGROUND

Liver histopathologic assessment is the accepted surrogate endpoint in NASH trials; however, the scoring of NASH Clinical Research Network (CRN) histologic parameters is limited by intraobserver and interobserver variability. We designed a consensus panel approach to minimize variability when using this scoring system. We assessed agreement between readers, estimated linear weighted kappas between 2 panels, compared them with published pairwise kappa estimates, and addressed how agreement or disagreement might impact the precision and validity of the surrogate efficacy endpoint in NASH trials.

METHODS

Two panels, each comprising 3 liver fellowship-trained pathologists who underwent NASH histology training, independently evaluated scanned whole slide images, scoring fibrosis, inflammation, hepatocyte ballooning, and steatosis from baseline and month 18 biopsies for 100 patients from the precirrhotic NASH study REGENERATE. The consensus score for each parameter was defined as agreement by ≥2 pathologists. If consensus was not reached, all 3 pathologists read the slide jointly to achieve a consensus score.

RESULTS

Between the 2 panels, the consensus was 97%-99% for steatosis, 91%-93% for fibrosis, 88%-92% for hepatocyte ballooning, and 84%-91% for inflammation. Linear weighted kappa scores between panels were similar to published NASH CRN values.

CONCLUSIONS

A panel of 3 trained pathologists independently scoring 4 NASH CRN histology parameters produced high consensus rates. Interpanel kappa values were comparable to NASH CRN metrics, supporting the accuracy and reproducibility of this method. The high concordance for fibrosis scoring was reassuring, as fibrosis is predictive of liver-specific outcomes and all-cause mortality.

Digitization of Pathology Labs: A Review of Lessons Learned

Pathology laboratories are increasingly using digital workflows. This has the potential of increasing laboratory efficiency, but the digitization process also involves major challenges. Several reports have been published describing the individual experiences of specific laboratories with the digitization process. However, a comprehensive overview of the lessons learned is still lacking. We provide an overview of the lessons learned for different aspects of the digitization process, including digital case management, digital slide reading, and computer-aided slide reading. We also cover metrics used for monitoring performance and pitfalls and corresponding values observed in practice. The overview is intended to help pathologists, information technology decision makers, and administrators to benefit from the experiences of others and to implement the digitization process in an optimal way to make their own laboratory future-proof.

Preparing Data for Artificial Intelligence in Pathology with Clinical-Grade Performance

The pathology is decisive for disease diagnosis but relies heavily on experienced pathologists. In recent years, there has been growing interest in the use of artificial intelligence in pathology (AIP) to enhance diagnostic accuracy and efficiency. However, the impressive performance of deep learning-based AIP in laboratory settings often proves challenging to replicate in clinical practice. As the data preparation is important for AIP, the paper has reviewed AIP-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. An in-depth analysis of data preparation methods is conducted, encompassing the acquisition of pathological tissue slides, data cleaning, screening, and subsequent digitization. Expert review, image annotation, dataset division for model training and validation are also discussed. Furthermore, we delve into the reasons behind the challenges in reproducing the high performance of AIP in clinical settings and present effective strategies to enhance AIP's clinical performance. The robustness of AIP depends on a randomized collection of representative disease slides, incorporating rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and sufficient data volume. Digital pathology is fundamental in clinical-grade AIP, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. The key to performance reproducibility lies in having representative data, an adequate amount of labeling, and ensuring consistency across multiple centers. Digital pathology for clinical diagnosis, data standardization and the technique of WSI-based weakly supervised learning will hopefully build clinical-grade AIP.

Deep learning to predict cervical lymph node metastasis from intraoperative frozen section of tumour in papillary thyroid carcinoma: a multicentre diagnostic study

Background

Lymph node metastasis (LNM) assessment in patients with papillary thyroid carcinoma (PTC) is of great value. This study aimed to develop a deep learning model applied to intraoperative frozen section for prediction of LNM in PTC patients.

Methods

We established a deep-learning model (ThyNet-LNM) with the multiple-instance learning framework to predict LNM using whole slide images (WSIs) from intraoperative frozen sections of PTC. Data for the development and validation of ThyNet-LNM were retrospectively derived from four hospitals from January 2018 to December 2021. The ThyNet-LNM was trained using 1987 WSIs from 1120 patients obtained at the First Affiliated Hospital of Sun Yat-sen University. The ThyNet-LNM was then validated in the independent internal test set (479 WSIs from 280 patients) as well as three external test sets (1335 WSIs from 692 patients). The performance of ThyNet-LNM was further compared with preoperative ultrasound and computed tomography (CT).

Findings

The area under the receiver operating characteristic curves (AUCs) of ThyNet-LNM were 0.80 (95% CI 0.74-0.84), 0.81 (95% CI 0.77-0.86), 0.76 (95% CI 0.68-0.83), and 0.81 (95% CI 0.75-0.85) in internal test set and three external test sets, respectively. The AUCs of ThyNet-LNM were significantly higher than those of ultrasound and CT or their combination in all four test sets (all P < 0.01). Of 397 clinically node-negative (cN0) patients, the rate of unnecessary lymph node dissection decreased from 56.4% to 14.9% by ThyNet-LNM.

Interpretation

The ThyNet-LNM showed promising efficacy as a potential novel method in evaluating intraoperative LNM status, providing real-time guidance for decision. Furthermore, this led to a reduction of unnecessary lymph node dissection in cN0 patients.

Funding

National Natural Science Foundation of China, Guangzhou Science and Technology Project, and Guangxi Medical High-level Key Talents Training "139" Program.

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.

Current status of machine learning in thyroid cytopathology

The implementation of Digital Pathology has allowed the development of computational Pathology. Digital image-based applications that have received FDA Breakthrough Device Designation have been primarily focused on tissue specimens. The development of Artificial Intelligence-assisted algorithms using Cytology digital images has been much more limited due to technical challenges and a lack of optimized scanners for Cytology specimens. Despite the challenges in scanning whole slide images of cytology specimens, there have been many studies evaluating CP to create decision-support tools in Cytopathology. Among different Cytology specimens, thyroid fine needle aspiration biopsy (FNAB) specimens have one of the greatest potentials to benefit from machine learning algorithms (MLA) derived from digital images. Several authors have evaluated different machine learning algorithms focused on thyroid cytology in the past few years. The results are promising. The algorithms have mostly shown increased accuracy in the diagnosis and classification of thyroid cytology specimens. They have brought new insights and demonstrated the potential for improving future cytopathology workflow efficiency and accuracy. However, many issues still need to be addressed to further build on and improve current MLA models and their applications. To optimally train and validate MLA for thyroid cytology specimens, larger datasets obtained from multiple institutions are needed. MLAs hold great potential in improving thyroid cancer diagnostic speed and accuracy that will lead to improvements in patient management.

An update on computational pathology tools for genitourinary pathology practice: A review paper from the Genitourinary Pathology Society (GUPS)

Machine learning has been leveraged for image analysis applications throughout a multitude of subspecialties. This position paper provides a perspective on the evolutionary trajectory of practical deep learning tools for genitourinary pathology through evaluating the most recent iterations of such algorithmic devices. Deep learning tools for genitourinary pathology demonstrate potential to enhance prognostic and predictive capacity for tumor assessment including grading, staging, and subtype identification, yet limitations in data availability, regulation, and standardization have stymied their implementation.

Application of Artificial Intelligence in Pathology: Trends and Challenges

Given the recent success of artificial intelligence (AI) in computer vision applications, many pathologists anticipate that AI will be able to assist them in a variety of digital pathology tasks. Simultaneously, tremendous advancements in deep learning have enabled a synergy with artificial intelligence (AI), allowing for image-based diagnosis on the background of digital pathology. There are efforts for developing AI-based tools to save pathologists time and eliminate errors. Here, we describe the elements in the development of computational pathology (CPATH), its applicability to AI development, and the challenges it faces, such as algorithm validation and interpretability, computing systems, reimbursement, ethics, and regulations. Furthermore, we present an overview of novel AI-based approaches that could be integrated into pathology laboratory workflows.

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

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

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

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

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.

Telepathology Practice in Cancer Diagnosis in Saint Jean de Dieu Hospital - Tanguieta, Benin

CONTEXT.—

Both the incidence of cancer and cancer-related mortality rates are high in sub-Saharan Africa, while resources for diagnosis and management are inadequate. In Benin, there is an extreme shortage of pathology services. Because of this shortage we built a histopathology laboratory equipped with an automated immunohistochemistry and a whole-slide imaging and telepathology system.

OBJECTIVE.—

To report our experience of telepathology practice in the improvement of cancer diagnosis.

DESIGN.—

The study was performed in our histopathology laboratory from January 1, 2016, to December 31, 2018. Resident laboratory technicians were trained in the preparation of microscopic and virtual slides by European pathologists. Virtual slides were stored on a Web-accessible server area for reading by 21 telepathologists in Benin and Europe. All patients with a histologic diagnosis of cancer were included in this study. Demographic data of patients, anatomic site of cancer, its histologic type, and its histologic grade were recorded.

RESULTS.—

We registered 399 patients diagnosed with cancer of 1593 patients whose surgical specimens had been analyzed. There were 349 adults including 160 males and 189 females, and 50 children (both sexes) with a mean age of 53.40 years, 46.92 years, and 9.72 years, respectively. Eighty-three of 211 females (39.34%) had infiltrating breast carcinoma, and 34 of 188 males (18.09%) had prostatic carcinoma. Infiltrating carcinoma of no special type represented 51 (91.07%) of all infiltrating breast carcinomas. Prostatic carcinoma and infiltrating breast carcinoma were of high grade in 13 of 23 males (56.52%) and 34 of 56 females (60.71%), respectively.

CONCLUSIONS.—

Telepathology is enabling a great improvement in cancer diagnosis in our hospital.

Digital Whole Slide Imaging Compared With Light Microscopy for Primary Diagnosis in Surgical Pathology

Context.—

The adoption of digital capture of pathology slides as whole slide images (WSI) for educational and research applications has proven utility.

Objective.—

To compare pathologists' primary diagnoses derived from WSI versus the standard microscope. Because WSIs differ in format and method of observation compared with the current standard glass slide microscopy, this study is critical to potential clinical adoption of digital pathology.

Design.—

The study enrolled a total of 2045 cases enriched for more difficult diagnostic categories and represented as 5849 slides were curated and provided for diagnosis by a team of 19 reading pathologists separately as WSI or as glass slides viewed by light microscope. Cases were reviewed by each pathologist in both modalities in randomized order with a minimum 31-day washout between modality reads for each case. Each diagnosis was compared with the original clinical reference diagnosis by an independent central adjudication review.

Results.—

The overall major discrepancy rates were 3.64% for WSI review and 3.20% for manual slide review diagnosis methods, a difference of 0.44% (95% CI, −0.15 to 1.03). The time to review a case averaged 5.20 minutes for WSI and 4.95 minutes for glass slides. There was no specific subset of diagnostic category that showed higher rates of modality-specific discrepancy, though some categories showed greater discrepancy than others in both modalities.

Conclusions.—

WSIs are noninferior to traditional glass slides for primary diagnosis in anatomic pathology.

Eleven grand challenges in single-cell data science

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

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.

New advance in breast cancer pathology and imaging

The improvement of knowledge concerning the pathology of breast cancer could provide the rationale for the development of new imaging diagnostic protocols. Indeed, as for the microcalcifications, new histopathological markers can be used as target for in vivo early detection of breast cancer lesions by using molecular imaging techniques such as positron emission tomography. Specifically, the mutual contribution of these medical specialties can ‘nourish’ the dream of a personalized medicine that takes into account the intrinsic variability of breast cancer. In this review, we report the main discoveries concerning breast cancer pathology highlighting the possible cooperation between the departments of anatomic pathology and imaging diagnostics.

Analysis and Comparison of Tissue-Marking Dye Detection via Light Microscopy, Telemicroscopy, and Virtual Microscopy

Objectives

To examine the fidelity of ink color identification using light microscopy (LM), telemicroscopy (TM), and virtual microscopy (VM).

Methods

Twenty H&E-stained frozen section slides, prepared after tissue inking with five stain combinations, were assessed by three pathologists using LM, TM, and VM. TM was performed using Mikroscan D2 slide scanner/LiveQ software with various objectives. VM was performed using Mikroscan D2 scanner/Qumulus software, specimens digitized at20×.

Results

Sensitivity/specificity by LM was 100%/100% for all colors. TM showed high overall specificity but poor sensitivity, particularly red (54%). VM showed high specificity for all colors except black (69%) and, consequently, poor sensitivity for all colors except black (96%).

Conclusions

TMD identification via telepathology showed loss of sensitivity/specificity vs LM and highlighted the need for caution when interpreting TMDs with digital technologies and the need for validation protocols.

Interactive Digital Microscopy at the Center for a Cross-Continent Undergraduate Pathology Course in Mozambique

Background

Recent medical education trends encourage the use of teaching strategies that emphasize student centeredness and self-learning. In this context, the use of new educative technologies is stimulated at the Faculty of Medicine of Eduardo Mondlane University (FMUEM) in Mozambique. The Faculty of Medicine of University of Porto (FMUP) and FMUEM have a long-lasting record of collaborative work. Within this framework, both institutions embarked in a partnership, aimed to develop a blended learning course of pathology for undergraduates, shared between the two faculties and incorporating interactive digital microscopy as a central learning tool.

Methods

A core team of faculty members from both institutions identified the existing resources and previous experiences in the two faculties. The Moodle course for students from the University of Porto was the basis to implement the current project. The objective was to develop educational modules of mutual interest, designed for e-learning, followed by a voluntary student's survey conducted in FMUEM to get their perception about the process.

Results

We selected contents from the pathology curricula of FMUP and FMUEM that were of mutual interest. We next identified and produced new contents for the shared curricula. The implementation involved joint collaboration and training to prepare the new contents, together with building quizzes for self-evaluation. All the practical sessions were based on the use of interactive digital microscopy. The students have reacted enthusiastically to the incorporation of the online component that increased their performance and motivation for pathology learning. For the students in Porto, the major acquisition was the access to slides from infectious diseases as well as autopsy videos.

Conclusions

Our study indicates that students benefited from high-quality educational contents, with emphasis on digital microscopy, in a platform generated in a win-win situation for FMUP and FMUEM.

Twenty Years of Digital Pathology: An Overview of the Road Travelled, What is on the Horizon, and the Emergence of Vendor-Neutral Archives

Almost 20 years have passed since the commercial introduction of whole-slide imaging (WSI) scanners. During this time, the creation of various WSI devices with the ability to digitize an entire glass slide has transformed the field of pathology. Parallel advances in computational technology and storage have permitted rapid processing of large-scale WSI datasets. This article provides an overview of important past and present efforts related to WSI. An account of how the virtual microscope evolved from the need to visualize and manage satellite data for earth science applications is provided. The article also discusses important milestones beginning from the first WSI scanner designed by Bacus to the Food and Drug Administration approval of the first digital pathology system for primary diagnosis in surgical pathology. As pathology laboratories commit to going fully digitalize, the need has emerged to include WSIs into an enterprise-level vendor-neutral archive (VNA). The different types of VNAs available are reviewed as well as how best to implement them and how pathology can benefit from participating in this effort. Differences between traditional image algorithms that extract pixel-, object-, and semantic-level features versus deep learning methods are highlighted. The need for large-scale data management, analysis, and visualization in computational pathology is also addressed.

Evaluation of photoshop based image analysis in cytologic diagnosis of pleural fluid in comparison with conventional modalities

BACKGROUND

The aim of this study is to investigate and compare the results of digital image analysis in pleural effusion cytology samples with conventional modalities.

MATERIALS AND METHODS

In this cross-sectional study, 53 pleural fluid cytology smears from Qaem hospital pathology department, located in Mashhad, Iran were investigated. Prior to digital analysis, all specimens were evaluated by two pathologists and categorized into three groups as: benign, suspicious, and malignant. Using an Olympus microscope and Olympus DP3 digital camera, digital images from cytology slides were captured. Appropriate images (n = 130) were separately imported to Adobe Photoshop CS5 and parameters including area and perimeter, circularity, Gray Value mean, integrated density, and nucleus to cytoplasm area ratio were analyzed.

RESULTS

Gray Value mean, nucleus to cytoplasm area ratio, and circularity showed the best sensitivity and specificity rates as well as significant differences between all groups. Also, nucleus area and perimeter showed a significant relation between suspicious and malignant groups with benign group. Whereas, there was no such difference between suspicious and malignant groups.

CONCLUSION

We concluded that digital image analysis is welcomed in the field of research on pleural fluid smears as it can provide quantitative data to apply various comparisons and reduce interobserver variation which could assist pathologists to achieve a more accurate diagnosis.

Development of Whole Slide Imaging on Smartphones and Evaluation With ThinPrep Cytology Test Samples: Follow-Up Study

Background

The smartphone-based whole slide imaging (WSI) system represents a low-cost and effective alternative to automatic scanners for telepathology. In a previous study, the development of one such solution, named scalable whole slide imaging (sWSI), was presented and analyzed. A clinical evaluation of its iOS version with 100 frozen section samples verified the diagnosis-readiness of the produced virtual slides.

Objective

The first aim of this study was to delve into the quantifying issues encountered in the development of an Android version. It should also provide insights into future high-resolution real-time feedback medical imaging apps on Android and invoke the awareness of smartphone manufacturers for collaboration. The second aim of this study was to further verify the clinical value of sWSI with cytology samples. This type is different from the frozen section samples in that they require finer detail on the cellular level.

Methods

During sWSI development on Android, it was discovered that many models do not support uncompressed camera pixel data with sufficient resolution and full field of view. The proportion of models supporting the optimal format was estimated in a test on 200 mainstream Android models. Other factors, including slower processing speed and camera preview freezing, also led to inferior performance of sWSI on Android compared with the iOS version. The processing speed was mostly determined by the central processing unit frequency in theory, and the relationship was investigated in the 200-model simulation experiment with physical devices. The camera preview freezing was caused by the lag between triggering photo capture and resuming preview. In the clinical evaluation, 100 ThinPrep cytology test samples covering 6 diseases were scanned with sWSI and compared against the ground truth of optical microscopy.

Results

Among the tested Android models, only 3.0% (6/200) provided an optimal data format, meeting all criteria of quality and efficiency. The image-processing speed demonstrated a positive relationship with the central processing unit frequency but to a smaller degree than expected and was highly model-dependent. The virtual slides produced by sWSI on Android and iOS of ThinPrep cytology test samples achieved similar high quality. Using optical microscopy as the ground truth, pathologists made a correct diagnosis on 87.5% (175/200) of the cases with sWSI virtual slides. Depending on the sWSI version and the pathologist in charge, the kappa value varied between .70 and .82. All participating pathologists considered the quality of the sWSI virtual slides in the experiment to be adequate for routine usage.

Conclusions

Limited by hardware and operating system support, the performance of sWSI on mainstream Android smartphones did not fully match the iOS version. However, in practice, this difference was not significant, and both were adequate for digitizing most of the sample types for telepathology consultation.

Validation of Digital Microscopy Compared With Light Microscopy for the Diagnosis of Canine Cutaneous Tumors

Integration of new technologies, such as digital microscopy, into a highly standardized laboratory routine requires the validation of its performance in terms of reliability, specificity, and sensitivity. However, a validation study of digital microscopy is currently lacking in veterinary pathology. The aim of the current study was to validate the usability of digital microscopy in terms of diagnostic accuracy, speed, and confidence for diagnosing and differentiating common canine cutaneous tumor types and to compare it to classical light microscopy. Therefore, 80 histologic sections including 17 different skin tumor types were examined twice as glass slides and twice as digital whole-slide images by 6 pathologists with different levels of experience at 4 time points. Comparison of both methods found digital microscopy to be noninferior for differentiating individual tumor types within the category epithelial and mesenchymal tumors, but diagnostic concordance was slightly lower for differentiating individual round cell tumor types by digital microscopy. In addition, digital microscopy was associated with significantly shorter diagnostic time, but diagnostic confidence was lower and technical quality was considered inferior for whole-slide images compared with glass slides. Of note, diagnostic performance for whole-slide images scanned at 200× magnification was noninferior in diagnostic performance for slides scanned at 400×. In conclusion, digital microscopy differs only minimally from light microscopy in few aspects of diagnostic performance and overall appears adequate for the diagnosis of individual canine cutaneous tumors with minor limitations for differentiating individual round cell tumor types and grading of mast cell tumors.

Conventional Microscopical versus Digital Whole-Slide Imaging-Based Diagnosis of Thin-Layer Cervical Specimens: A Validation Study

BACKGROUND

Whole-slide imaging (WSI) has been implemented in many areas of pathology, but primary diagnostics of cytological specimens are lagging behind. One of the objectives of viewing scanned whole-slide images from histological or cytological specimens is remote exchange of knowledge and expertise of professionals to increase diagnostic accuracy. We compared the scoring results of our team obtained in double readings of two different data sets: conventional light microscopy (CLM) versus CLM and CLM versus WSI. We hypothesized that WSI is noninferior to CLM for primary diagnostics of thin-layer cervical slides.

MATERIALS AND METHODS

First, we determined the concordance rate at different thresholds of the participating cytotechnicians by double reading with CLM of 500 thin-layer cervical slides (Cohort 1). Next, CLM was compared with WSI examination of another 505 thin-layer cervical slides (Cohort 2) scanned at ×20 in single focus plane. Finally, all major discordant cases of Cohort 1 were evaluated by an external expert in the field of gynecological cytology and of Cohort 2 in the weekly case meetings.

RESULTS

The overall concordance rate of Cohort 1 (CLM vs. CLM) was 97.8% (95% confidence interval [CI]: 96.0%-98.7%) and of Cohort 2 was 95.3% (95% CI: 93.0%-96.9%).

CONCLUSION

Concordance rates of WSI versus CLM were comparable with those of CLM versus CLM. We have made a step forward paving the road to implementation of WSI also in routine diagnostic cytology.

Validation of whole-slide imaging in the primary diagnosis of liver biopsies in a University Hospital

BACKGROUND

Experience in the use of whole slide imaging (WSI) for primary diagnosis is limited and there are no comprehensive reports evaluating this technology in liver biopsy specimens.

AIMS

To determine the accuracy of interpretation of WSI compared with conventional light microscopy (CLM) in the diagnosis of needle liver biopsies.

METHODS

Two experienced liver pathologists blindly analyzed 176 consecutive biopsies from the Pathology Department at the Hospital Clinic of Barcelona. One of the observers performed the initial evaluation with CLM, and the second evaluation with WSI, whereas the second observer performed the first evaluation with WSI and the second with CLM. All slides were digitized in a Ventana iScan HT at 400× and evaluated with the Virtuoso viewer (Roche diagnostics). We used kappa statistics (κ) for two observations.

RESULTS

Intra-observer agreement between WSI and CLM evaluations was almost perfect (96.6%, κ=0.9; 95% confidence interval [95% CI]: 0.9-1 for observer 1, and 90.3%, κ=0.9; 95%CI: 0.8-0.9 for observer 2). Both native and transplantation biopsies showed an almost perfect concordance in the diagnosis.

CONCLUSION

Diagnosis of needle liver biopsy specimens using WSI is accurate. This technology can reliably be introduced in routine diagnosis.

RecutClub.com: An Open Source, Whole Slide Image-based Pathology Education System

BACKGROUND

Our institution's pathology unknown conferences provide educational cases for our residents. However, the cases have not been previously available digitally, have not been collated for postconference review, and were not accessible to a wider audience. Our objective was to create an inexpensive whole slide image (WSI) education suite to address these limitations and improve the education of pathology trainees.

MATERIALS AND METHODS

We surveyed residents regarding their preference between four unique WSI systems. We then scanned weekly unknown conference cases and study set cases and uploaded them to our custom built WSI viewer located at RecutClub.com. We measured site utilization and conference participation.

RESULTS

Residents preferred our OpenLayers WSI implementation to Ventana Virtuoso, Google Maps API, and OpenSlide. Over 16 months, we uploaded 1366 cases from 77 conferences and ten study sets, occupying 793.5 GB of cloud storage. Based on resident evaluations, the interface was easy to use and demonstrated minimal latency. Residents are able to review cases from home and from their mobile devices. Worldwide, 955 unique IP addresses from 52 countries have viewed cases in our site.

CONCLUSIONS

We implemented a low-cost, publicly available repository of WSI slides for resident education. Our trainees are very satisfied with the freedom to preview either the glass slides or WSI and review the WSI postconference. Both local users and worldwide users actively and repeatedly view cases in our study set.

Acquisition, Visualization and Potential Applications of 3D Data in Anatomic Pathology

Although human anatomy and histology are naturally three-dimensional (3D), commonly used diagnostic and educational tools are technologically restricted to providing two-dimensional representations (e.g. gross photography and glass slides). This limitation may be overcome by employing techniques to acquire and display 3D data, which refers to the digital information used to describe a 3D object mathematically. There are several established and experimental strategies to capture macroscopic and microscopic 3D data. In addition, recent hardware and software innovations have propelled the visualization of 3D models, including virtual and augmented reality. Accompanying these advances are novel clinical and non-clinical applications of 3D data in pathology. Medical education and research stand to benefit a great deal from utilizing 3D data as it can change our understanding of complex anatomical and histological structures. Although these technologies are yet to be adopted in routine surgical pathology, forensic pathology has embraced 3D scanning and model reconstruction. In this review, we intend to provide a general overview of the technologies and emerging applications involved with 3D data.