Digital pathology: exploring its applications in diagnostic surgical pathology practice

Computational pathology: A survey review and the way forward

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

A Multiscale Connected UNet for the Segmentation of Lung Cancer Cells in Pathology Sections Stained Using Rapid On-Site Cytopathological Evaluation

Lung cancer is an increasingly serious health problem worldwide, and early detection and diagnosis are crucial for successful treatment. With the development of artificial intelligence and the growth of data volume, machine learning techniques can play a significant role in improving the accuracy of early detection in lung cancer. This study proposes a deep learning-based segmentation algorithm for rapid on-site cytopathological evaluation (ROSE) to enhance the diagnostic efficiency of endobronchial ultrasound-guided transbronchial needle aspiration biopsy (EBUS-TBNA) during surgery. By utilizing the CUNet3+ network model, cell clusters, including cancer cell clusters, can be accurately segmented in ROSE-stained pathological sections. The model demonstrated high accuracy, with an F1-score of 0.9604, recall of 0.9609, precision of 0.9654, and accuracy of 0.9834 on the internal testing data set. It also achieved an area under the receiver-operating characteristic curve of 0.9972 for cancer identification. The proposed algorithm saved time for on-site diagnosis, improved EBUS-TBNA efficiency, and outperformed classical segmentation algorithms in accurately identifying lung cancer cell clusters in ROSE-stained images. It effectively reduced over-segmentation, decreased network parameters, and enhanced computational efficiency, making it suitable for real-time patient evaluation during surgical procedures.

Computerised assessment-a novel approach for calculation of percentage of hypomineralized lesion on incisors and its correlation with aesthetic concern

Introduction

Molar-incisor hypomineralization (MIH) is a localized, qualitative, demarcated enamel defect that affects first permanent molars (FPMs) and/or permanent incisors. The aim of present study was to introduce a novel computerised assessment process to detect and quantify the percentage opacity associated with MIH affected maxillary central incisors.

Methodology

Children (8-16 years) enrolled in the primary study having mild (white/cream or yellow/brown) MIH lesion on fully erupted maxillary permanent central incisor. 50 standardised images of MIH lesions were captured in an artificially lit room with fixed parameters and were anonymized and securely stored. Images were analysed by AI-driven computerised software and generates output classifications via a sophisticated algorithm crafted using a meticulously annotated image dataset as reference through supervised machine learning (SML). For the validation of computerised assessment of MIH lesions, the percentage of demarked opacity was calculated using ADOBE PHOTOSHOP CS7.

Results

The percentage of MIH lesion was calculated through histogram plotting with the maxima ranging from 7.29 % to 71.21 % with the mean value of 34.51 %. The validation score ranged from 10.29 % to 67.27 % with the mean value of 35.32 %. The difference between the two was statistically not significant. Out of 50 patients; 11 patients had 1-30 % of surface affected with MIH and 2 had aesthetic concern; 24 had 30-60 % of surface affected and 13 had aesthetic concern; 15 had >60 % of surface affected and 12 had aesthetic concerns.

Conclusions

The proposed approach exhibit sufficient quality to be integrated into a dental software addressing practical challenges encountered in daily clinical settings.

BoNuS: Boundary Mining for Nuclei Segmentation With Partial Point Labels

Nuclei segmentation is a fundamental prerequisite in the digital pathology workflow. The development of automated methods for nuclei segmentation enables quantitative analysis of the wide existence and large variances in nuclei morphometry in histopathology images. However, manual annotation of tens of thousands of nuclei is tedious and time-consuming, which requires significant amount of human effort and domain-specific expertise. To alleviate this problem, in this paper, we propose a weakly-supervised nuclei segmentation method that only requires partial point labels of nuclei. Specifically, we propose a novel boundary mining framework for nuclei segmentation, named BoNuS, which simultaneously learns nuclei interior and boundary information from the point labels. To achieve this goal, we propose a novel boundary mining loss, which guides the model to learn the boundary information by exploring the pairwise pixel affinity in a multiple-instance learning manner. Then, we consider a more challenging problem, i.e., partial point label, where we propose a nuclei detection module with curriculum learning to detect the missing nuclei with prior morphological knowledge. The proposed method is validated on three public datasets, MoNuSeg, CPM, and CoNIC datasets. Experimental results demonstrate the superior performance of our method to the state-of-the-art weakly-supervised nuclei segmentation methods. Code: https://github.com/hust-linyi/bonus.

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.

Machine Learning Model with Texture Analysis for Automatic Classification of Histopathological Images of Ocular Adnexal Mucosa-associated Lymphoid Tissue Lymphoma of Two Different Origins

PURPOSE

The purpose of this study was to develop artificial intelligence algorithms that can distinguish between orbital and conjunctival mucosa-associated lymphoid tissue (MALT) lymphomas in pathological images.

METHODS

Tissue blocks with residual MALT lymphoma and data from histological and flow cytometric studies and molecular genetic analyses such as gene rearrangement were procured for 129 patients treated between April 2008 and April 2020. We collected pathological hematoxylin and eosin-stained (HE) images of lymphoma from these patients and cropped 10 different image patches at a resolution of 2048 × 2048 from pathological images from each patient. A total of 990 images from 99 patients were used to create and evaluate machine-learning models. Each image patch of three different magnification rates at ×4, ×20, and ×40 underwent texture analysis to extract features, and then seven different machine-learning algorithms were applied to the results to create models. Cross-validation on a patient-by-patient basis was used to create and evaluate models, and then 300 images from the remaining 30 cases were used to evaluate the average accuracy rate.

RESULTS

Ten-fold cross-validation using the support vector machine with linear kernel algorithm was identified as the best algorithm for discriminating between conjunctival mucosa-associated lymphoid tissue and orbital MALT lymphomas, with an average accuracy rate under cross-validation of 85%. There were ×20 magnification HE images that were more accurate in distinguishing orbital and conjunctival MALT lymphomas among ×4, ×20, and ×40.

CONCLUSION

Artificial intelligence algorithms can successfully distinguish HE images between orbital and conjunctival MALT lymphomas.

Deep learning in precision medicine and focus on glioma

Deep learning (DL) has been successfully applied to different fields for a range of tasks. In medicine, DL methods have been also used to improve the efficiency of disease diagnosis. In this review, we first summarize the history of the development of artificial intelligence models, demonstrate the features of the subtypes of machine learning and different DL networks, and then explore their application in the different fields of precision medicine, such as cardiology, gastroenterology, ophthalmology, dermatology, and oncology. By digging more information and extracting multilevel features from medical data, we found that DL helps doctors assess diseases automatically and monitor patients' physical health. In gliomas, research regarding application prospect of DL was mainly shown through magnetic resonance imaging and then by pathological slides. However, multi-omics data, such as whole exome sequence, RNA sequence, proteomics, and epigenomics, have not been covered thus far. In general, the quality and quantity of DL datasets still need further improvements, and more fruitful multi-omics characteristics will bring more comprehensive and accurate diagnosis in precision medicine and glioma.

The experience of introducing telepathology in Mongolia

Background

Anatomical pathology care services play an essential role in cancer diagnosis through histological analysis, effective treatment of patients, and determination of prognosis. Therefore, quality control is necessary for the diagnosis of pathology. Based on this need, telepathology technology is rapidly developing in the world. This study aimed to share the experience of implementing telepathology case consultation between Mongolian and Japanese expert pathologists.

Methods

The study included 173 cases that required telepathology consultation, which was complicated and doubtful in diagnosis, submitted by Mongolian pathologists between May 2019 and April 2022. The scanned digital slides were transmitted with the help of the LOOKREC cloud-based system, and the expert pathologists of Hiroshima University Hospital, Japan, browsed the images through the data on the internet and their advice and made a mutual diagnosis.

Results

During the study period, 173 cases were consulted. Out of 58.4% of all cases, consultation reports were released in 2022. The majority of the cases in 2020 had a mean standard deviation turn-around time of 4.2±6.2 days. The most cases were from the lung and mediastinum were 29.4%, followed by head and neck at 12.6%, the bone at 11.9%, lymph nodes at 8.4%, GIT at 7.7%, soft tissues at 6.3%, etc. Comparing the sample submission of biopsy and cytology was significantly higher in the under 10 years of an experienced group than over 10 years of an experienced group (p<.005). The diagnostic agreement between submitter Mongolian pathologists and expert Japanese pathologists was 82.7%, and disagreement was 17.3% of all cases, with a sensitivity of 67.3% and specificity of 85.5%.

Conclusions

Telepathology could save many lost opportunities and play an essential role in developing quality control and surgical pathology in Mongolia. This digital technology and the appropriate strategy and policy of the government could accelerate the overall pathology field development.

CroReLU: Cross-Crossing Space-Based Visual Activation Function for Lung Cancer Pathology Image Recognition

Lung cancer is one of the most common malignant tumors in human beings. It is highly fatal, as its early symptoms are not obvious. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the final diagnosis of many diseases. Therefore, pathology diagnosis is known as the gold standard for disease diagnosis. However, the complexity of the information contained in pathology images and the increase in the number of patients far outpace the number of pathologists, especially for the treatment of lung cancer in less developed countries. To address this problem, we propose a plug-and-play visual activation function (AF), CroReLU, based on a priori knowledge of pathology, which makes it possible to use deep learning models for precision medicine. To the best of our knowledge, this work is the first to optimize deep learning models for pathology image diagnosis from the perspective of AFs. By adopting a unique crossover window design for the activation layer of the neural network, CroReLU is equipped with the ability to model spatial information and capture histological morphological features of lung cancer such as papillary, micropapillary, and tubular alveoli. To test the effectiveness of this design, 776 lung cancer pathology images were collected as experimental data. When CroReLU was inserted into the SeNet network (SeNet_CroReLU), the diagnostic accuracy reached 98.33%, which was significantly better than that of common neural network models at this stage. The generalization ability of the proposed method was validated on the LC25000 dataset with completely different data distribution and recognition tasks in the face of practical clinical needs. The experimental results show that CroReLU has the ability to recognize inter- and intra-class differences in cancer pathology images, and that the recognition accuracy exceeds the extant research work on the complex design of network layers.

Integrating artificial intelligence in pathology: a qualitative interview study of users' experiences and expectations

Recent progress in the development of artificial intelligence (AI) has sparked enthusiasm for its potential use in pathology. As pathology labs are currently starting to shift their focus towards AI implementation, a better understanding how AI tools can be optimally aligned with the medical and social context of pathology daily practice is urgently needed. Strikingly, studies often fail to mention the ways in which AI tools should be integrated in the decision-making processes of pathologists, nor do they address how this can be achieved in an ethically sound way. Moreover, the perspectives of pathologists and other professionals within pathology concerning the integration of AI within pathology remains an underreported topic. This article aims to fill this gap in the literature and presents the first in-depth interview study in which professionals' perspectives on the possibilities, conditions and prerequisites of AI integration in pathology are explicated. The results of this study have led to the formulation of three concrete recommendations to support AI integration, namely: (1) foster a pragmatic attitude toward AI development, (2) provide task-sensitive information and training to health care professionals working in pathology departments and (3) take time to reflect upon users' changing roles and responsibilities.

L’intelligence artificielle pourrait-elle intervenir dans l’aide au diagnostic des cancers du sein ? – L’exemple de HER2

HER2 is an important prognostic and predictive biomarker in breast cancer. Its detection makes it possible to define which patients will benefit from a targeted treatment. While assessment of HER2 status by immunohistochemistry in positive vs negative categories is well implemented and reproducible, the introduction of a new "HER2-low" category could raise some concerns about its scoring and reproducibility. We herein described the current HER2 testing methods and the application of innovative machine learning techniques to improve these determinations, as well as the main challenges and opportunities related to the implementation of digital pathology in the up-and-coming AI era.

Digital Pathology Workflow Implementation at IPATIMUP

The advantages of the digital methodology are well known. In this paper, we provide a detailed description of the process for the digital transformation of the pathology laboratory at IPATIMUP, the major modifications that operate throughout the processing pipeline, and the advantages of its implementation. The model of digital workflow implementation at IPATIMUP demonstrates that careful planning and adoption of simple measures related to time, space, and sample management can be adopted by any pathology laboratory to achieve higher quality and easy digital transformation.

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.

Homology-Based Image Processing for Automatic Classification of Histopathological Images of Lung Tissue

Simple Summary

The purpose of this study was to develop a computer-aided diagnosis (CAD) system for automatic classification of histopathological images of lung tissues. Homology-based image processing (HI) was proposed for CAD. For developing and validating CAD with HI, two datasets of histopathological images of lung tissues were used. The private dataset consists of 94 histopathological images that were obtained for the following five categories: normal, emphysema, atypical adenomatous hyperplasia, lepidic pattern of adenocarcinoma, and invasive adenocarcinoma. The public dataset consists of 15,000 histopathological images that were obtained for the following three categories: lung adenocarcinoma, lung squamous cell carcinoma, and benign lung tissue. For the two datasets, our results show that HI was more useful than conventional texture analysis for the CAD system.

Abstract

The purpose of this study was to develop a computer-aided diagnosis (CAD) system for automatic classification of histopathological images of lung tissues. Two datasets (private and public datasets) were obtained and used for developing and validating CAD. The private dataset consists of 94 histopathological images that were obtained for the following five categories: normal, emphysema, atypical adenomatous hyperplasia, lepidic pattern of adenocarcinoma, and invasive adenocarcinoma. The public dataset consists of 15,000 histopathological images that were obtained for the following three categories: lung adenocarcinoma, lung squamous cell carcinoma, and benign lung tissue. These images were automatically classified using machine learning and two types of image feature extraction: conventional texture analysis (TA) and homology-based image processing (HI). Multiscale analysis was used in the image feature extraction, after which automatic classification was performed using the image features and eight machine learning algorithms. The multicategory accuracy of our CAD system was evaluated in the two datasets. In both the public and private datasets, the CAD system with HI was better than that with TA. It was possible to build an accurate CAD system for lung tissues. HI was more useful for the CAD systems than TA.

Histo-genomics: digital pathology at the forefront of precision medicine

The toughest challenge OMICs face is that they provide extremely high molecular resolution but poor spatial information. Understanding the cellular/histological context of the overwhelming genetic data is critical for a full understanding of the clinical behavior of a malignant tumor. Digital pathology can add an extra layer of information to help visualize in a spatial and microenvironmental context the molecular information of cancer. Thus, histo-genomics provide a unique chance for data integration. In the era of a precision medicine, a four-dimensional (4D) (temporal/spatial) analysis of cancer aided by digital pathology can be a critical step to understand the evolution/progression of different cancers and consequently tailor individual treatment plans. For instance, the integration of molecular biomarkers expression into a three-dimensional (3D) image of a digitally scanned tumor can offer a better understanding of its subtype, behavior, host immune response and prognosis. Using advanced digital image analysis, a larger spectrum of parameters can be analyzed as potential predictors of clinical behavior. Correlation between morphological features and host immune response can be also performed with therapeutic implications. Radio-histomics, or the interface of radiological images and histology is another emerging exciting field which encompasses the integration of radiological imaging with digital pathological images, genomics, and clinical data to portray a more holistic approach to understating and treating disease. These advances in digital slide scanning are not without technical challenges, which will be addressed carefully in this review with quick peek at its future.

Automated oral squamous cell carcinoma identification using shape, texture and color features of whole image strips

Despite profound knowledge of the incidence of oral cancers and a large body of research beyond it, it continues to beat diagnosis and treatment management. Post physical observation by clinicians, a biopsy is a gold standard for accurate detection of any abnormalities. Towards the application of artificial intelligence as an aid to diagnosis, automated cell nuclei segmentation is the most essential step for the recognition of the cancer cells. In this study, we have extracted the shape, texture and color features from the histopathological images collected indigenously from regional hospitals. A dataset of 42 whole slide slices was used to automatically segment and generate a cell level dataset of 720 nuclei. Next, different classifiers were applied for classification purposes. 99.4 % accuracy using Decision Tree Classifier, 100 % accuracy using both SVM and Logistic regression and 100 % accuracy using SVM, Logistic regression and Linear Discriminant were acquired for shape, textural and color features respectively. The in-depth analysis showed SVM and Linear Discriminant classifier gave the best result for texture and color features respectively. The achieved result can be effectively converted to software as an assistant diagnostic tool.

Artificial Intelligence in Lung Cancer Pathology Image Analysis

OBJECTIVE

Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection.

MATERIALS AND METHODS

In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer.

RESULTS

We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis.

DISCUSSION AND CONCLUSION

With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

Pathology Image Analysis Using Segmentation Deep Learning Algorithms

With the rapid development of image scanning techniques and visualization software, whole slide imaging (WSI) is becoming a routine diagnostic method. Accelerating clinical diagnosis from pathology images and automating image analysis efficiently and accurately remain significant challenges. Recently, deep learning algorithms have shown great promise in pathology image analysis, such as in tumor region identification, metastasis detection, and patient prognosis. Many machine learning algorithms, including convolutional neural networks, have been proposed to automatically segment pathology images. Among these algorithms, segmentation deep learning algorithms such as fully convolutional networks stand out for their accuracy, computational efficiency, and generalizability. Thus, deep learning-based pathology image segmentation has become an important tool in WSI analysis. In this review, the pathology image segmentation process using deep learning algorithms is described in detail. The goals are to provide quick guidance for implementing deep learning into pathology image analysis and to provide some potential ways of further improving segmentation performance. Although there have been previous reviews on using machine learning methods in digital pathology image analysis, this is the first in-depth review of the applications of deep learning algorithms for segmentation in WSI analysis.

Digital Image Analyses on Whole-Lung Slides in Mouse Models of Acute Pneumonia

Descriptive histopathology of mouse models of pneumonia is essential in assessing the outcome of infections, molecular manipulations, or therapies in the context of whole lungs. Quantitative comparisons between experimental groups, however, have been limited to laborious stereology or ill-defined scoring systems that depend on the subjectivity of a more or less experienced observer. Here, we introduce self-learning digital image analyses that allow us to transform optical information from whole mouse lung sections into statistically testable data. A pattern-recognition-based software and a nuclear count algorithm were adopted to quantify user-defined pathologies from whole slide scans of lungs infected with Streptococcus pneumoniae or influenza A virus compared with PBS-challenged lungs. The readout parameters "relative area affected" and "nuclear counts per area" are proposed as relevant criteria for the quantification of lesions from hematoxylin and eosin-stained sections, also allowing for the generation of a heat map of, for example, immune cell infiltrates with anatomical assignments across entire lung sections. Moreover, when combined with immunohistochemical labeling of marker proteins, both approaches are useful for the identification and counting of, for example, immune cell populations, as validated here by direct comparisons with flow cytometry data. The solutions can easily and flexibly be adjusted to specificities of different models or pathogens. Automated digital analyses of whole mouse lung sections may set a new standard for the user-defined, high-throughput comparative quantification of histological and immunohistochemical images. Still, our algorithms established here are only a start, and need to be tested in additional studies and other applications in the future.

A Different Approach for Digital Pathology: Lexicon-semantic Analysis of Histopathological Reports for the Assessment of their Quality

An analysis of the costs related to the processes involved in a pathological analysis of a biopsy justifies the traditional view of digital pathology. However, this traditional conception has left aside another important aspect of this process, the writing of pathological reports. The efficiency and effectiveness of this subprocess has been raised in recent years as a challenge in the field of digital pathology. This work explores in this aspect offering a system of lexical-semantic analysis to determine the usefulness of pathological reports. It is a tool that assists the pathologist in the drafting of a useful report and establishes the bases for the management of the veracity of information in the automatic generation of pathological reports.

Constant Quest for Quality: Digital Cytopathology

Background:

Special consideration should be given when creating and selecting cytopathology specimens for digitization to maximize quality. Advances in scanning and viewing technology can also improve whole-slide imaging (WSI) output quality.

Methods:

Accumulated laboratory experience with digitization of glass cytopathology slides was collected.

Results:

This paper describes characteristics of a cytopathology glass slide that can reduce quality on resulting WSI. Important points in the glass cytopathology slide selection process, preparation, scanning, and WSI-editing process that will maximize the quality of the resulting acquired digital image are covered. The paper outlines scanning solutions which have potential to predict issues with a glass cytopathology slide before image acquisition, allowing for adjustment of the scanning approach. WSI viewing solutions that better simulate the traditional microscope experience are also discussed.

Conclusion:

In addition to taking advantage of technical advances, practical steps can taken to maximize quality of cytopathology WSI.

Emerging paradigm of virtual-microscopy for histopathology diagnosis: survey of US and Canadian oral pathology trainees

Objectives/Aims:

The application of virtual microscopy (VM) to research, pre-doctoral medical and dental educational training, and diagnostic surgical and anatomic pathology is well-documented but its application to the field of oral and maxillofacial pathology has not been explored. This is the first study to evaluate the enthusiasm and readiness of US-/Canada-based oral and maxillofacial pathology (OMFP) residents toward employing VM use over conventional microscopy (CM) for diagnostic purposes.

Materials and Methods:

All 46 current US-/Canada-based OMFP residents were invited to participate in an anonymous electronic survey via ‘Survey Monkey’ in 2015. The survey comprised sixteen multiple choice questions and two ‘free text’ questions.

Results:

14% of respondents of the 22 (48%) respondents who completed the survey indicated a willingness to substitute CM with VM in <5 years, and 33% within 10 years. 52% reported they would never substitute CM with VM. Approximately 10 and 57% of respondents thought VM will become an acceptable sole diagnostic tool in most centers within 5 and 10 years, respectively. These findings are irrespective of the fact that overall, 90% of respondents reported being familiar with VM use.

Discussion:

VM technology is unlikely to substitute CM in diagnostic oral and maxillofacial histopathology practice among future OMFP practitioners in the foreseeable future.

Digital image analysis in breast pathology-from image processing techniques to artificial intelligence

Breast cancer is the most common malignant disease in women worldwide. In recent decades, earlier diagnosis and better adjuvant therapy have substantially improved patient outcome. Diagnosis by histopathology has proven to be instrumental to guide breast cancer treatment, but new challenges have emerged as our increasing understanding of cancer over the years has revealed its complex nature. As patient demand for personalized breast cancer therapy grows, we face an urgent need for more precise biomarker assessment and more accurate histopathologic breast cancer diagnosis to make better therapy decisions. The digitization of pathology data has opened the door to faster, more reproducible, and more precise diagnoses through computerized image analysis. Software to assist diagnostic breast pathology through image processing techniques have been around for years. But recent breakthroughs in artificial intelligence (AI) promise to fundamentally change the way we detect and treat breast cancer in the near future. Machine learning, a subfield of AI that applies statistical methods to learn from data, has seen an explosion of interest in recent years because of its ability to recognize patterns in data with less need for human instruction. One technique in particular, known as deep learning, has produced groundbreaking results in many important problems including image classification and speech recognition. In this review, we will cover the use of AI and deep learning in diagnostic breast pathology, and other recent developments in digital image analysis.

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

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

Digital cytopathology

The advancements in the fields of technology and networking have revolutionized the world including the fields of medicine and dentistry. Telemedicine and its various branches provide a broad platform to medical professionals for consultations and investigations and can also act as a valuable educational aid. This review highlights the components, methods employed, clinical applications, advantages, disadvantages of telepathology and telecytology.

Image Montaging for Creating a Virtual Pathology Slide: An Innovative and Economical Tool to Obtain a Whole Slide Image

Background. Microscopes are omnipresent throughout the field of biological research. With microscopes one can see in detail what is going on at the cellular level in tissues. Though it is a ubiquitous tool, the limitation is that with high magnification there is a small field of view. It is often advantageous to see an entire sample at high magnification. Over the years technological advancements in optics have helped to provide solutions to this limitation of microscopes by creating the so-called dedicated "slide scanners" which can provide a "whole slide digital image." These scanners can provide seamless, large-field-of-view, high resolution image of entire tissue section. The only disadvantage of such complete slide imaging system is its outrageous cost, thereby hindering their practical use by most laboratories, especially in developing and low resource countries. Methods. In a quest for their substitute, we tried commonly used image editing software Adobe Photoshop along with a basic image capturing device attached to a trinocular microscope to create a digital pathology slide. Results. The seamless image created using Adobe Photoshop maintained its diagnostic quality. Conclusion. With time and effort photomicrographs obtained from a basic camera-microscope set up can be combined and merged in Adobe Photoshop to create a whole slide digital image of practically usable quality at a negligible cost.

Automated quantitative cytological analysis using portable microfluidic microscopy

In this article, a portable microfluidic microscopy based approach for automated cytological investigations is presented. Inexpensive optical and electronic components have been used to construct a simple microfluidic microscopy system. In contrast to the conventional slide-based methods, the presented method employs microfluidics to enable automated sample handling and image acquisition. The approach involves the use of simple in-suspension staining and automated image acquisition to enable quantitative cytological analysis of samples. The applicability of the presented approach to research in cellular biology is shown by performing an automated cell viability assessment on a given population of yeast cells. Further, the relevance of the presented approach to clinical diagnosis and prognosis has been demonstrated by performing detection and differential assessment of malaria infection in a given sample.

Nucleus Morphometry in Cultured Epithelial Cells Correlates with Phenotype

Phenotype of cultured ocular epithelial transplants has been shown to affect clinical success rates following transplantation to the cornea. The purpose of this study was to evaluate the relationship between cell nucleus morphometry and phenotype in three types of cultured epithelial cells. This study provides knowledge for the development of a non-invasive method of determining the phenotype of cultured epithelium before transplantation. Cultured human conjunctival epithelial cells (HCjE), human epidermal keratinocytes (HEK), and human retinal pigment epithelial cells (HRPE) were analyzed by quantitative immunofluorescence. Assessments of nucleus morphometry and nucleus-to-cytoplasm ratio (N/C ratio) were performed using ImageJ. Spearman's correlation coefficient was employed for statistical analysis. Levels of the proliferation marker PCNA in HCjE, HEK, and HRPE correlated positively with nuclear area. Nuclear area correlated significantly with levels of the undifferentiated cell marker ABCG2 in HCjE. Bmi1 levels, but not p63α levels, correlated significantly with nuclear area in HEK. The N/C ratio did not correlate significantly with any of the immunomarkers in HCjE (ABCG2, CK7, and PCNA) and HRPE (PCNA). In HEK, however, the N/C ratio was negatively correlated with levels of the undifferentiated cell marker CK14 and positively correlated with Bmi1 expression. The size of the nuclear area correlated positively with proliferation markers in all three epithelia. Morphometric indicators of phenotype in cultured epithelia can be identified using ImageJ. Conversely, the N/C ratio did not show a uniform relationship with phenotype in HCjE, HEK, or HRPE. N/C ratio therefore, may not be a useful morphometric marker for in vitro assessment of phenotype in these three epithelia.

"Slide less pathology": Fairy tale or reality?

Pathology practice is significantly advanced in various frontiers. Therefore, "slide less digital" pathology will not be a mere imagination in near future. Digitalization of histopathological slides (whole slide imaging [WSI]) is possible with the help of whole slide scanner. The WSI has a positive impact not only in routine practice but also in research field, medical education and bioindustry. Even if digital pathology has definitive advantages, its widespread use is not yet possible. As it is an upcoming technology in our field, this article is aimed to discussessential aspects of WSI.

A Framework for 3D Vessel Analysis using Whole Slide Images of Liver Tissue Sections

Three-dimensional (3D) high resolution microscopic images have high potential for improving the understanding of both normal and disease processes where structural changes or spatial relationship of disease features are significant. In this paper, we develop a complete framework applicable to 3D pathology analytical imaging, with an application to whole slide images of sequential liver slices for 3D vessel structure analysis. The analysis workflow consists of image registration, segmentation, vessel cross-section association, interpolation, and volumetric rendering. To identify biologically-meaningful correspondence across adjacent slides, we formulate a similarity function for four association cases. The optimal solution is then obtained by constrained Integer Programming. We quantitatively and qualitatively compare our vessel reconstruction results with human annotations. Validation results indicate a satisfactory concordance as measured both by region-based and distance-based metrics. These results demonstrate a promising 3D vessel analysis framework for whole slide images of liver tissue sections.

Enabling digital pathology in the diagnostic setting: navigating through the implementation journey in an academic medical centre

AIMS

As digital pathology (DP) and whole slide imaging (WSI) technology advance and mature, there is an increasing drive to incorporate DP into the diagnostic environment. However, integration of DP into the diagnostic laboratory is a non-trivial task and filled with unexpected challenges unlike standalone implementations. We share our journey of implementing DP in the diagnostic laboratory setting, highlighting seven key guiding principles that drive the progression through implementation into deployment and beyond.

METHODS

The DP implementation with laboratory information system integration was completed in 8 months, including validation of the solution for diagnostic use in accordance with College of American Pathologists guidelines. We also conducted prospective validation via paired delivery of glass slides and WSI to our pathologists postdeployment.

RESULTS

Common themes in our guiding principles included emphasis on workflow and being comprehensive in the approach, looking beyond pathologist user champions and expanding into an extended project team involving laboratory technicians, clerical/data room staff and archival staff. Concordance between glass slides and WSI ranged from 93% to 100% among various applications on validation. We also provided equal opportunities for every pathologist throughout the department to be competent and confident with DP through prospective validation, with overall concordance of 96% compared with glass slides, allowing appreciation of the advantages and limitations of WSI, hence enabling the use of DP as a useful diagnostic modality.

CONCLUSIONS

Smooth integration of DP into the diagnostic laboratory is possible with careful planning, discipline and a systematic approach adhering to our guiding principles.

Performance of residents using digital images versus glass slides on certification examination in anatomical pathology: a mixed methods pilot study

BACKGROUND

It is anticipated that many licensing examination centres for pathology will begin fully digitizing the certification examinations. The objective of our study was to test the feasibility of a fully digital examination and to assess the needs, concerns and expectations of pathology residents in moving from a glass slide-based examination to a fully digital examination.

METHODS

We conducted a mixed methods study that compared, after randomization, the performance of senior residents (postgraduate years 4 and 5) in 7 accredited anatomical pathology training programs across Canada on a pathology examination using either glass slides or digital whole-slide scanned images of the slides. The pilot examination was followed by a post-test survey. In addition, pathology residents from all levels of training were invited to participate in an online survey.

RESULTS

A total of 100 residents participated in the pilot examination; 49 were given glass slides instead of digital images. We found no significant difference in examination results between the 2 groups of residents (estimated marginal mean 8.23/12, 95% confidence interval [CI] 7.72-8.87, for glass slides; 7.84/12, 95% CI 7.28-8.41, for digital slides). In the post-test survey, most of the respondents expressed concerns with the digital examination, including slowly functioning software, blurring and poor detail of images, particularly nuclear features. All of the respondents of the general survey (n = 179) agreed that additional training was required if the examination were to become fully digital.

INTERPRETATION

Although the performance of residents completing pathology examinations with glass slides was comparable to that of residents using digital images, our study showed that residents were not comfortable with the digital technology, especially given their current level of exposure to it. Additional training may be needed before implementing a fully digital examination, with consideration for a gradual transition.

Free digital image analysis software helps to resolve equivocal scores in HER2 immunohistochemistry

Evaluation of human epidermal growth factor receptor 2 (HER2) immunohistochemistry (IHC) is subject to interobserver variation and lack of reproducibility. Digital image analysis (DIA) has been shown to improve the consistency and accuracy of the evaluation and its use is encouraged in current testing guidelines. We studied whether digital image analysis using a free software application (ImmunoMembrane) can assist in interpreting HER2 IHC in equivocal 2+ cases. We also compared digital photomicrographs with whole-slide images (WSI) as material for ImmunoMembrane DIA. We stained 750 surgical resection specimens of invasive breast cancers immunohistochemically for HER2 and analysed staining with ImmunoMembrane. The ImmunoMembrane DIA scores were compared with the originally responsible pathologists' visual scores, a researcher's visual scores and in situ hybridisation (ISH) results. The originally responsible pathologists reported 9.1 % positive 3+ IHC scores, for the researcher this was 8.4 % and for ImmunoMembrane 9.5 %. Equivocal 2+ scores were 34 % for the pathologists, 43.7 % for the researcher and 10.1 % for ImmunoMembrane. Negative 0/1+ scores were 57.6 % for the pathologists, 46.8 % for the researcher and 80.8 % for ImmunoMembrane. There were six false positive cases, which were classified as 3+ by ImmunoMembrane and negative by ISH. Six cases were false negative defined as 0/1+ by IHC and positive by ISH. ImmunoMembrane DIA using digital photomicrographs and WSI showed almost perfect agreement. In conclusion, digital image analysis by ImmunoMembrane can help to resolve a majority of equivocal 2+ cases in HER2 IHC, which reduces the need for ISH testing.

Digitized whole slides for breast pathology interpretation: current practices and perceptions

Digital whole slide imaging (WSI) is an emerging technology for pathology interpretation; however, little is known about pathologists' practice patterns or perceptions regarding WSI. A national sample (N = 252) of pathologists from New Hampshire, Vermont, Washington, Oregon, Arizona, Alaska, Maine, and Minnesota were surveyed in this cross-sectional study (2011-2013). The survey included questions on pathologists' experience, WSI practice patterns, and perceptions using a six-point Likert scale. Agreement was summarized with descriptive statistics to characterize pathologists' use and perceptions of WSI. The majority of participating pathologists were males (63%) between 40 and 59 years of age (70%) and not affiliated with an academic medical center (72%). Experience with WSI was reported by 49%. Types of use reported included CME/board exams/teaching (28%), tumor board/clinical conference (22%), archival purposes (6%), consultative diagnosis (4%), research (4%), and other uses (12%). Most respondents (79%) agreed that accurate diagnoses can be made with this technology, and that WSI is useful for obtaining a second opinion (88%). However, 78% of pathologists agreed that digital slides are too slow for routine clinical interpretation. Fifty-nine percent agreed that the benefits of WSI outweigh concerns. The respondents were equally split as to whether they would like to adopt WSI (51%) or not (49%). About half of pathologists reported experience with the WSI technology, largely for CME, licensure/board exams, and teaching. Positive perceptions regarding WSI slightly outweigh negative perceptions. Understanding practice patterns with WSI as dissemination advances may facilitate concordance of perceptions with adoption of the technology.

Whole slide imaging: uses and limitations for surgical pathology and teaching

Advances in computer and software technology and in the quality of images produced by digital cameras together with development of robotic devices that can take glass histology slides from a cassette holding many slides and place them in a conventional microscope for electronic scanning have facilitated the development of whole slide imaging (WSI) systems during the past decade. Anatomic pathologists now have opportunities to test the utility of WSI systems for diagnostic, teaching and research purposes and to determine their limitations. Uses include rendering primary diagnoses from scanned hematoxylin and eosin stained tissues on slides, reviewing frozen section or routine slides from remote locations for interpretation or consultation. Also, WSI can replace physical storage of glass slides with digital images, storing images of slides from outside institutions, presenting slides at clinical or research conferences, teaching residents and medical students, and storing fluorescence images without fading or quenching of the fluorescence signal. Limitations include the high costs of the scanners, maintenance contracts and IT support, storage of digital files and pathologists' lack of familiarity with the technology. Costs are falling as more devices and systems are sold and cloud storage costs drop. Pathologist familiarity with the technology will grow as more institutions purchase WSI systems. The technology holds great promise for the future of anatomic pathology.

Validating whole slide digital morphometric analysis as a microscopy tool

Whole slide imaging (WSI) can be used to quantify multiple responses within tissue sections during histological analysis. Feature Analysis on Consecutive Tissue Sections (FACTS®) allows the investigator to perform digital morphometric analysis (DMA) within specified regions of interest (ROI) across multiple serial sections at faster rates when compared with manual morphometry methods. Using FACTS® in conjunction with WSI is a powerful analysis tool, which allows DMA to target specific ROI across multiple tissue sections stained for different biomarkers. DMA may serve as an appropriate alternative to classic, manual, histologic morphometric measures, which have historically relied on the selection of high-powered fields of views and manual scoring (e.g., a gold standard). In the current study, existing preserved samples were used to determine if DMA would provide similar results to manual counting methods. Rodent hearts (n=14, left ventricles) were stained with Masson's trichrome, and reacted for cluster of differentiation 68 (CD-68). This study found no statistical significant difference between a classic, manual method and the use of digital algorithms to perform the similar counts (p=0.38). DMA offers researchers the ability to accurately evaluate morphological characteristics in a reproducible fashion without investigator bias and with higher throughput.

A nationwide telepathology consultation and quality control program in China: implementation and result analysis

Background

Telepathology may play an important role in pathology consultation and quality control for cancer diagnosis in China, as the country has the largest population of cancer patients worldwide. In 2011, the Pathology Quality Control Center of China and Ministry of Health developed and implemented a nationwide telepathology consultation and quality control program for cancer diagnosis in China. We here report the results of the two-year implementation and experiences.

Methods

the program built an Internet based telepathology platform to connect participating hospitals and expert consultants. The hardware and software used for the platform were validated in previous validation studies in China. The program had three regional centers consisting of Peking Union Medical College, Huasi Medical College of Sichuan and 2nd affiliated hospital of Zhejiang University. It also had 20 provincial consultation centers based in the provincial referral hospitals. 80 provincial or national pathologists served as expert consultants for the program, providing telepathology consultation for cancer diagnosis for more than 60 participating hospitals.

Results

from 2011 to July 2013, 16,247 pathology cases were submitted to the platform for consultation. Among them, 84% were due to diagnostic difficulty and 16% were due to request by patients. The preliminary diagnosis provided by submitting pathologists were in agreement with expert opinion in 59.8% of cases but was in disagreement with expert opinion in 24.2% of cases. 16.0% of cases were not provided with preliminary diagnosis. The distribution of pathology cases by system or organ were: digestive system, 17.3%; gynecologic system, 16.7%; head and neck, 15.7%; bone and soft tissue, 10.4%; lung and mediastinum, 8.6%; breast, 7.6%; urinary system, 7.5%; hematopathology, 6.4%; skin, 5.2%; neuropathology, 2.5% and cytopathology, 1.3%. Expert consultants also provided assessment of quality of slide preparation and staining, online lectures and guidance for pathology quality control.

Conclusion

our results of two years' implementation indicated that telepathology could solve the problem of uneven distribution of pathology resources and provide a solution for countrywide pathology quality control in China. Telepathology could play an important role in improving pathology diagnosis in China.

Evaluation environment for digital and analog pathology: a platform for validation studies

We present a platform for designing and executing studies that compare pathologists interpreting histopathology of whole slide images (WSIs) on a computer display to pathologists interpreting glass slides on an optical microscope. eeDAP is an evaluation environment for digital and analog pathology. The key element in eeDAP is the registration of the WSI to the glass slide. Registration is accomplished through computer control of the microscope stage and a camera mounted on the microscope that acquires real-time images of the microscope field of view (FOV). Registration allows for the evaluation of the same regions of interest (ROIs) in both domains. This can reduce or eliminate disagreements that arise from pathologists interpreting different areas and focuses on the comparison of image quality. We reduced the pathologist interpretation area from an entire glass slide (10 to [Formula: see text]) to small ROIs ([Formula: see text]). We also made possible the evaluation of individual cells. We summarize eeDAP's software and hardware and provide calculations and corresponding images of the microscope FOV and the ROIs extracted from the WSIs. The eeDAP software can be downloaded from the Google code website (project: eeDAP) as a MATLAB source or as a precompiled stand-alone license-free application.

Observer performance in the use of digital and optical microscopy for the interpretation of tissue-based biomarkers

BACKGROUND

We conducted a validation study of digital pathology for the quantitative assessment of tissue-based biomarkers with immunohistochemistry.

OBJECTIVE

To examine observer agreement as a function of viewing modality (digital versus optical microscopy), whole slide versus tissue microarray (TMA) review, biomarker type (HER2 incorporating membranous staining and Ki-67 with nuclear staining), and data type (continuous and categorical).

METHODS

Eight pathologists reviewed 50 breast cancer whole slides (25 stained with HER2 and 25 with Ki-67) and 2 TMAs (1 stained with HER2, 1 with Ki-67, each containing 97 cores), using digital and optical microscopy.

RESULTS

Results showed relatively high overall interobserver and intermodality agreement, with different patterns specific to biomarker type. For HER2, there was better interobserver agreement for optical compared to digital microscopy for whole slides as well as better interobserver and intermodality agreement for TMAs. For Ki-67, those patterns were not observed.

CONCLUSIONS

The differences in agreement patterns when examining different biomarkers and different scoring methods and reviewing whole slides compared to TMA stress the need for validation studies focused on specific pathology tasks to eliminate sources of variability that might dilute findings. The statistical uncertainty observed in our analyses calls for adequate sampling for each individual task rather than pooling cases.

Web-based oil immersion whole slide imaging increases efficiency and clinical team satisfaction in hematopathology tumor board

Background:

Whole slide imaging (WSI) is widely used for education and research, but is increasingly being used to streamline clinical workflow. We present our experience with regard to satisfaction and time utilization using oil immersion WSI for presentation of blood/marrow aspirate smears, core biopsies, and tissue sections in hematology/oncology tumor board/treatment planning conferences (TPC).

Methods:

Lymph nodes and bone marrow core biopsies were scanned at ×20 magnification and blood/marrow smears at 83X under oil immersion and uploaded to an online library with areas of interest to be displayed annotated digitally via web browser. Pathologist time required to prepare slides for scanning was compared to that required to prepare for microscope projection (MP). Time required to present cases during TPC was also compared. A 10-point evaluation survey was used to assess clinician satisfaction with each presentation method.

Results:

There was no significant difference in hematopathologist preparation time between WSI and MP. However, presentation time was significantly less for WSI compared to MP as selection and annotation of slides was done prior to TPC with WSI, enabling more efficient use of TPC presentation time. Survey results showed a significant increase in satisfaction by clinical attendees with regard to image quality, efficiency of presentation of pertinent findings, aid in clinical decision-making, and overall satisfaction regarding pathology presentation. A majority of respondents also noted decreased motion sickness with WSI.

Conclusions:

Whole slide imaging, particularly with the ability to use oil scanning, provides higher quality images compared to MP and significantly increases clinician satisfaction. WSI streamlines preparation for TPC by permitting prior slide selection, resulting in greater efficiency during TPC presentation.

Carnegie Mellon University bioimaging day 2014: Challenges and opportunities in digital pathology

Recent advances in digital imaging is impacting the practice of pathology. One of the key enabling technologies that is leading the way towards this transformation is the use of whole slide imaging (WSI) which allows glass slides to be converted into large image files that can be shared, stored, and analyzed rapidly. Many applications around this novel technology have evolved in the last decade including education, research and clinical applications. This publication highlights a collection of abstracts, each corresponding to a talk given at Carnegie Mellon University's (CMU) Bioimaging Day 2014 co-sponsored by the Biomedical Engineering and Lane Center for Computational Biology Departments at CMU. Topics related specifically to digital pathology are presented in this collection of abstracts. These include topics related to digital workflow implementation, imaging and artifacts, storage demands, and automated image analysis algorithms.

Can digital pathology result in cost savings? A financial projection for digital pathology implementation at a large integrated health care organization

Background:

Digital pathology offers potential improvements in workflow and interpretive accuracy. Although currently digital pathology is commonly used for research and education, its clinical use has been limited to niche applications such as frozen sections and remote second opinion consultations. This is mainly due to regulatory hurdles, but also to a dearth of data supporting a positive economic cost-benefit. Large scale adoption of digital pathology and the integration of digital slides into the routine anatomic/surgical pathology “slide less” clinical workflow will occur only if digital pathology will offer a quantifiable benefit, which could come in the form of more efficient and/or higher quality care.

Aim:

As a large academic-based health care organization expecting to adopt digital pathology for primary diagnosis upon its regulatory approval, our institution estimated potential operational cost savings offered by the implementation of an enterprise-wide digital pathology system (DPS).

Methods:

Projected cost savings were calculated for the first 5 years following implementation of a DPS based on operational data collected from the pathology department. Projected savings were based on two factors: (1) Productivity and lab consolidation savings; and (2) avoided treatment costs due to improvements in the accuracy of cancer diagnoses among nonsubspecialty pathologists. Detailed analyses of incremental treatment costs due to interpretive errors, resulting in either a false positive or false negative diagnosis, was performed for melanoma and breast cancer and extrapolated to 10 other common cancers.

Results:

When phased in over 5-years, total cost savings based on anticipated improvements in pathology productivity and histology lab consolidation were estimated at $12.4 million for an institution with 219,000 annual accessions. The main contributing factors to these savings were gains in pathologist clinical full-time equivalent capacity impacted by improved pathologist productivity and workload distribution. Expanding the current localized specialty sign-out model to an enterprise-wide shared general/subspecialist sign-out model could potentially reduce costs of incorrect treatment by $5.4 million. These calculations were based on annual over and under treatment costs for breast cancer and melanoma estimated to be approximately $26,000 and $11,000/case, respectively, and extrapolated to $21,500/case for other cancer types.

Conclusions:

The projected 5-year total cost savings for our large academic-based health care organization upon fully implementing a DPS was approximately $18 million. If the costs of digital pathology acquisition and implementation do not exceed this value, the return on investment becomes attractive to hospital administrators. Furthermore, improved patient outcome enabled by this technology strengthens the argument supporting adoption of an enterprise-wide DPS.

Validation of a novel robotic telepathology platform for neuropathology intraoperative touch preparations

Background:

Robotic telepathology (RT) allows a remote pathologist to control and view a glass slide over the internet. This technology has been demonstrated to be effective on several platforms, but we present the first report on the validation of RT using the iScan Coreo Au whole slide imaging scanner.

Methods:

One intraoperative touch preparation slide from each of 100 cases were examined twice (200 total cases) using glass slides and RT, with a 3 week washout period between viewings, on two different scanners at two remote sites. This included 75 consecutive neuropathology cases and 25 consecutive general surgical pathology cases. Interpretations were compared using intraobserver variability.

Results:

Of the 200 total cases, one failed on RT. There were 47 total interpretive variances. Most of these were the result of less specific interpretations or an inability to identify scant diagnostic material on RT. Nine interpretive variances had potentially significant clinical implications (4.5%). Using the final diagnosis as a basis for comparison to evaluate these nine cases, three RT interpretations and three glass slide interpretations were considered to be discrepant. In the other three cases, both modalities were discrepant. This distribution of discrepancies indicates that underlying case difficulty, not the RT technology, probably accounts for these major variances. For the subset of 68 neoplastic neuropathology cases, the unweighted kappa of agreement between glass slides and RT was 0.68 (good agreement). RT took 225 s on average versus only 71 s per glass slide.

Conclusions:

This validation demonstrates that RT using the iScan Coreo Au system is a reasonable method for supplying remote neuropathology expertise for the intraoperative interpretation of touch preparations, but is limited by the slowness of the robotics, crude focusing, and the challenge of determining where to examine the slide using small thumbnail images.

High agreement between whole slide imaging and optical microscopy for assessment of HER2 expression in breast cancer: whole slide imaging for the assessment of HER2 expression

Whole slide imaging (WSI) technology has been used for training, teaching, researching, and remote consultation. Few studies compared HER2 expression using optical microscopy (OM) and WSI evaluations in breast carcinomas. However, no consensus has been achieved comparing both assessments.

MATERIAL AND METHODS

Sections from tissue microarray containing 200 preselected invasive breast carcinomas were submitted to immunohistochemistry applying three anti-HER2 antibodies (HercepTest™, CB11, SP3) and in situ hybridization (DDISH). Slides were evaluated using OM and WSI (Pannoramic MIDI and Viewer, 3DHISTECH). Sensitivity and specificity were calculated comparing the anti-HER2 antibodies and DDISH.

RESULTS

WSI and OM HER2 evaluations agreement was considered good (SP3, k=0.80) to very good (CB11 and HercepTest™, k=0.81). WSI evaluation led to higher sensitivity (ranging from 100 of SP3 and HercepTest™ to 97 of CB11) and lower specificity (ranging from 86.4 of SP3 to 89.4 of HercepTest™) compared to OM evaluation (sensitivity ranged from 92.1 of CB11 to 98 of SP3 and specificity ranged from 95.2 of SP3 and HercepTest™ to 97.1 of CB11 and SP3).

CONCLUSION

High agreement was achieved between WSI and OM evaluations. All three antibodies were highly sensitive and specific using both evaluations. WSI can be considered a useful tool for HER2 immunohistochemical assessment.

Current usage and future trends in gross digital photography in Canada

BACKGROUND

The purpose of this study was to assess the current usage, utilization and future direction of digital photography of gross surgical specimens in pathology laboratories across Canada.

METHODS

An online survey consisting of 23 multiple choice and free-text questions regarding gross digital photography was sent out to via email to laboratory staff across Canada involved in gross dissection of surgical specimens.

RESULTS

Sixty surveys were returned with representation from most of the provinces. Results showed that gross digital photography is utilized at most institutions (90.0%) and the primary users of the technology are Pathologists (88.0%), Pathologists' Assistants (54.0%) and Pathology residents (50.0%). Most respondents felt that there is a definite need for routine digital imaging of gross surgical specimens in their practice (80.0%). The top two applications for gross digital photography are for documentation of interesting/ complex cases (98.0%) and for teaching purposes (84.0%). The main limitations identified by the survey group are storage space (42.5%) and security issues (40.0%). Respondents indicated that future applications of gross digital photography mostly include teaching (96.6%), presentation at tumour boards/ clinical rounds (89.8%), medico-legal documentation (72.9%) and usage for consultation purposes (69.5%).

CONCLUSIONS

The results of this survey indicate that pathology staff across Canada currently utilizes gross digital images for regular documentation and educational reasons. They also show that the technology will be needed for future applications in teaching, consultation and medico-legal purposes.

Digital pathology and image analysis augment biospecimen annotation and biobank quality assurance harmonization

Standardization of biorepository best practices will enhance the quality of translational biomedical research utilizing patient-derived biobank specimens. Harmonization of pathology quality assurance procedures for biobank accessions has lagged behind other avenues of biospecimen research and biobank development. Comprehension of the cellular content of biorepository specimens is important for discovery of tissue-specific clinically relevant biomarkers for diagnosis and treatment. While rapidly emerging technologies in molecular analyses and data mining create focus on appropriate measures for minimizing pre-analytic artifact-inducing variables, less attention gets paid to annotating the constituent makeup of biospecimens for more effective specimen selection by biobank clients. Both pre-analytic tissue processing and specimen composition influence acquisition of relevant macromolecules for downstream assays. Pathologist review of biorepository submissions, particularly tissues as part of quality assurance procedures, helps to ensure that the intended target cells are present and in sufficient quantity in accessioned specimens. This manual procedure can be tedious and subjective. Incorporating digital pathology into biobank quality assurance procedures, using automated pattern recognition morphometric image analysis to quantify tissue feature areas in digital whole slide images of tissue sections, can minimize variability and subjectivity associated with routine pathologic evaluations in biorepositories. Whole-slide images and pathologist-reviewed morphometric analyses can be provided to researchers to guide specimen selection. Harmonization of pathology quality assurance methods that minimize subjectivity and improve reproducibility among collections would facilitate research-relevant specimen selection by investigators and could facilitate information sharing in an integrated network approach to biobanking.

Validating whole slide imaging for diagnostic purposes in pathology: guideline from the College of American Pathologists Pathology and Laboratory Quality Center

CONTEXT

There is increasing interest in using whole slide imaging (WSI) for diagnostic purposes (primary and/or consultation). An important consideration is whether WSI can safely replace conventional light microscopy as the method by which pathologists review histologic sections, cytology slides, and/or hematology slides to render diagnoses. Validation of WSI is crucial to ensure that diagnostic performance based on digitized slides is at least equivalent to that of glass slides and light microscopy. Currently, there are no standard guidelines regarding validation of WSI for diagnostic use.

OBJECTIVE

To recommend validation requirements for WSI systems to be used for diagnostic purposes.

DESIGN

The College of American Pathologists Pathology and Laboratory Quality Center convened a nonvendor panel from North America with expertise in digital pathology to develop these validation recommendations. A literature review was performed in which 767 international publications that met search term requirements were identified. Studies outside the scope of this effort and those related solely to technical elements, education, and image analysis were excluded. A total of 27 publications were graded and underwent data extraction for evidence evaluation. Recommendations were derived from the strength of evidence determined from 23 of these published studies, open comment feedback, and expert panel consensus.

RESULTS

Twelve guideline statements were established to help pathology laboratories validate their own WSI systems intended for clinical use. Validation of the entire WSI system, involving pathologists trained to use the system, should be performed in a manner that emulates the laboratory's actual clinical environment. It is recommended that such a validation study include at least 60 routine cases per application, comparing intraobserver diagnostic concordance between digitized and glass slides viewed at least 2 weeks apart. It is important that the validation process confirm that all material present on a glass slide to be scanned is included in the digital image.

CONCLUSIONS

Validation should demonstrate that the WSI system under review produces acceptable digital slides for diagnostic interpretation. The intention of validating WSI systems is to permit the clinical use of this technology in a manner that does not compromise patient care.