Digital imaging in pathology--current applications and challenges

Removing non-nuclei information from histopathological images: A preprocessing step towards improving nuclei segmentation methods

Disease interpretation by computer-aided diagnosis systems in digital pathology depends on reliable detection and segmentation of nuclei in hematoxylin and eosin (HE) images. These 2 tasks are challenging since appearance of both cell nuclei and background structures are very variable. This paper presents a method to improve nuclei detection and segmentation in HE images by removing tiles that only contain background information. The method divides each image into smaller patches and uses their projection to the noiselet space to capture different spatial features from non-nuclei background and nuclei structures. The noiselet features are clustered by a K-means algorithm and the resultant partition, defined by the cluster centroids, is herein named the noiselet code-book. A part of an image, a tile, is divided into patches and represented by the histogram of occurrences of the projected patches in the noiselet code-book. Finally, with these histograms, a classifier learns to differentiate between nuclei and non-nuclei tiles. By applying a conventional watershed-marked method to detect and segment nuclei, evaluation consisted in comparing pure watershed method against denoising-plus-watershed in an open database with 8 different types of tissues. The averaged F-score of nuclei detection improved from 0.830 to 0.86 and the dice score after segmentation increased from 0.701 to 0.723.

The role of telepathology in diagnosis of pre-malignant and malignant cervical lesions: Implementation at a tertiary hospital in Northern Tanzania

Introduction

Adequate and timely access to pathology services is a key to scale up cancer control, however, there is an extremely shortage of pathologists in Tanzania. Telepathology (scanned images microscopy) has the potential to increase access to pathology services and it is increasingly being employed for primary diagnosis and consultation services. However, the experience with the use of telepathology in Tanzania is limited. We aimed to investigate the feasibility of using scanned images for primary diagnosis of pre-malignant and malignant cervical lesions by assessing its equivalency to conventional (glass slide) microscopy in Tanzania.

Methods

In this laboratory-based study, assessment of hematoxylin and eosin stained glass slides of 175 cervical biopsies were initially performed conventionally by three pathologists independently. The slides were scanned at x 40 and one to three months later, the scanned images were reviewed by the pathologists in blinded fashion. The agreement between initial and review diagnoses across participating pathologists was described and measured using Cohen’s kappa coefficient (κ).

Results

The overall concordance of diagnoses established on conventional microscopy compared to scanned images across three pathologists was 87.7%; κ = 0.54; CI (0.49–0.57).The overall agreement of diagnoses established by local pathologist on conventional microscopy compared to scanned images was 87.4%; κ = 0.73; CI (0.65–0.79). The concordance of diagnoses established by senior pathologist compared to local pathologist on conventional microscopy and scanned images was 96% and 97.7% respectively. The inter-observer agreement (κ) value were 0.93, CI (0.87–1.00) and 0.94, CI (0.88–1.00) for conventional microscopy and scanned images respectively.

Conclusions

All κ coefficients expressed good intra- and inter-observer agreement, suggesting that telepathology is sufficiently accurate for primary diagnosis in surgical pathology. The discrepancies in interpretation of pre-malignant lesions highlights the importance of p16 immunohistochemistry in definitive diagnosis in these lesions. Sustainability factors including hardware and internet connectivity are essential components to be considered before telepathology may be deemed suitable for widely use in Tanzania.

Deep Learning-Based Diagnosis Method of Emergency Colorectal Pathology

One of the most common malignant tumors of the digestive tract is emergency colorectal cancer. In recent years, both morbidity and mortality rates, particularly in our country, are getting higher and higher. At present, diagnosis of colorectal cancer, specifically in the emergency department of a hospital, is based on the doctor's pathological diagnosis, and it is heavily dependent on the doctor's clinical experience. The doctor's workload is heavy, and misdiagnosis events occur from time to time. Therefore, computer-aided diagnosis technology is desperately needed for colorectal pathological images to assist pathologists in reducing their workload, improve the efficiency of diagnosis, and eliminate misdiagnosis. To address these issues, a gland segmentation of emergency colorectal pathology images and diagnosis of benign and malignant pathology is presented in this paper. Initially, a multifeatured auxiliary diagnosis is designed to enable diagnosis of benign and malignant diagnosis of emergency colorectal pathology. The proposed algorithm constructs an SVM-enabled pathological diagnosis model which is based on contour, color, and texture features. Additionally, their combination is used for pathological benign and malignant pathological diagnosis of two types of data sets D1 (original pathological image dataset) and D2 (dataset that has undergone glandular segmentation) diagnosis. Experimental results show that the proposed pathological diagnosis model has higher diagnostic accuracy on D2. Among these datasets, SVM based on the multifeature fusion of contour and texture achieved the highest diagnostic accuracy rate, i.e., 83.75%, which confirms that traditional image processing methods have limitations. Diagnosing benign and malignant colorectal pathology in an emergency is more difficult and must be treated on a priority basis. Finally, an emergency colorectal pathology diagnosis method, which is based on deep convolutional neural networks such as CIFAR and VGG, is proposed. After configuring and training process of the two networks, trained CIFAR and VGG network models are applied to the diagnosis of both datasets, i.e., D1 and D2, respectively.

Deep learning segmentation of glomeruli on kidney donor frozen sections

Purpose: Recent advances in computational image analysis offer the opportunity to develop automatic quantification of histologic parameters as aid tools for practicing pathologists. We aim to develop deep learning (DL) models to quantify nonsclerotic and sclerotic glomeruli on frozen sections from donor kidney biopsies. Approach: A total of 258 whole slide images (WSI) from cadaveric donor kidney biopsies performed at our institution ( n = 123 ) and at external institutions ( n = 135 ) were used in this study. WSIs from our institution were divided at the patient level into training and validation datasets (ratio: 0.8:0.2), and external WSIs were used as an independent testing dataset. Nonsclerotic ( n = 22767 ) and sclerotic ( n = 1366 ) glomeruli were manually annotated by study pathologists on all WSIs. A nine-layer convolutional neural network based on the common U-Net architecture was developed and tested for the segmentation of nonsclerotic and sclerotic glomeruli. DL-derived, manual segmentation, and reported glomerular count (standard of care) were compared. Results: The average Dice similarity coefficient testing was 0.90 and 0.83. And the F 1 , recall, and precision scores were 0.93, 0.96, and 0.90, and 0.87, 0.93, and 0.81, for nonsclerotic and sclerotic glomeruli, respectively. DL-derived and manual segmentation-derived glomerular counts were comparable, but statistically different from reported glomerular count. Conclusions: DL segmentation is a feasible and robust approach for automatic quantification of glomeruli. We represent the first step toward new protocols for the evaluation of donor kidney biopsies.

Automated 3D scoring of fluorescence in situ hybridization (FISH) using a confocal whole slide imaging scanner

Fluorescence in situ hybridization (FISH) is a technique to visualize specific DNA/RNA sequences within the cell nuclei and provide the presence, location and structural integrity of genes on chromosomes. A confocal Whole Slide Imaging (WSI) scanner technology has superior depth resolution compared to wide-field fluorescence imaging. Confocal WSI has the ability to perform serial optical sections with specimen imaging, which is critical for 3D tissue reconstruction for volumetric spatial analysis. The standard clinical manual scoring for FISH is labor-intensive, time-consuming and subjective. Application of multi-gene FISH analysis alongside 3D imaging, significantly increase the level of complexity required for an accurate 3D analysis. Therefore, the purpose of this study is to establish automated 3D FISH scoring for z-stack images from confocal WSI scanner. The algorithm and the application we developed, SHIMARIS PAFQ, successfully employs 3D calculations for clear individual cell nuclei segmentation, gene signals detection and distribution of break-apart probes signal patterns, including standard break-apart, and variant patterns due to truncation, and deletion, etc. The analysis was accurate and precise when compared with ground truth clinical manual counting and scoring reported in ten lymphoma and solid tumors cases. The algorithm and the application we developed, SHIMARIS PAFQ, is objective and more efficient than the conventional procedure. It enables the automated counting of more nuclei, precisely detecting additional abnormal signal variations in nuclei patterns and analyzes gigabyte multi-layer stacking imaging data of tissue samples from patients. Currently, we are developing a deep learning algorithm for automated tumor area detection to be integrated with SHIMARIS PAFQ.

Histologic grading of breast carcinoma: a multi-institution study of interobserver variation using virtual microscopy

Breast carcinoma grading is an important prognostic feature recently incorporated into the AJCC Cancer Staging Manual. There is increased interest in applying virtual microscopy (VM) using digital whole slide imaging (WSI) more broadly. Little is known regarding concordance in grading using VM and how such variability might affect AJCC prognostic staging (PS). We evaluated interobserver variability amongst a multi-institutional group of breast pathologists using digital WSI and how discrepancies in grading would affect PS. A digitally scanned slide from 143 invasive carcinomas was independently reviewed by 6 pathologists and assigned grades based on established criteria for tubule formation (TF), nuclear pleomorphism (NP), and mitotic count (MC). Statistical analysis was performed. Interobserver agreement for grade was moderate (κ = 0.497). Agreement was fair (κ = 0.375), moderate (κ = 0.491), and good (κ = 0.705) for grades 2, 3, and 1, respectively. Observer pair concordance ranged from fair to good (κ = 0.354-0.684) Perfect agreement was observed in 43 cases (30%). Interobserver agreement for the individual components was best for TF (κ = 0.503) and worst for MC (κ = 0.281). Seventeen of 86 (19.8%) discrepant cases would have resulted in changes in PS and discrepancies most frequently resulted in a PS change from IA to IB (n = 9). For two of these nine cases, Oncotype DX results would have led to a PS of 1A regardless of grade. Using VM, a multi-institutional cohort of pathologists showed moderate concordance for breast cancer grading, similar to studies using light microscopy. Agreement was the best at the extremes of grade and for evaluation of TF. Whether the higher variability noted for MC is a consequence of VM grading warrants further investigation. Discordance in grading infrequently leads to clinically meaningful changes in the prognostic stage.

Quantitative neurotoxicology: Potential role of artificial intelligence/deep learning approach

Neurotoxicity studies are important in the preclinical stages of drug development process, because exposure to certain compounds that may enter the brain across a permeable blood brain barrier damages neurons and other supporting cells such as astrocytes. This could, in turn, lead to various neurological disorders such as Parkinson's or Huntington's disease as well as various dementias. Toxicity assessment is often done by pathologists after these exposures by qualitatively or semiquantitatively grading the severity of neurotoxicity in histopathology slides. Quantification of the extent of neurotoxicity supports qualitative histopathological analysis and provides a better understanding of the global extent of brain damage. Stereological techniques such as the utilization of an optical fractionator provide an unbiased quantification of the neuronal damage; however, the process is time-consuming. Advent of whole slide imaging (WSI) introduced digital image analysis which made quantification of neurotoxicity automated, faster and with reduced bias, making statistical comparisons possible. Although automated to a certain level, simple digital image analysis requires manual efforts of experts which is time-consuming and limits analysis of large datasets. Digital image analysis coupled with a deep learning artificial intelligence model provides a good alternative solution to time-consuming stereological and simple digital analysis. Deep learning models could be trained to identify damaged or dead neurons in an automated fashion. This review has focused on and discusses studies demonstrating the role of deep learning in segmentation of brain regions, toxicity detection and quantification of degenerated neurons as well as the estimation of area/volume of degeneration.

Digital pathology and computational image analysis in nephropathology

The emergence of digital pathology - an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis - is changing the pathology ecosystem. In particular, by virtue of our new-found ability to generate and curate digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital era, with the establishment of consortia and digital pathology repositories for the collection, analysis and integration of pathology data with other domains. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. The application of these novel tools are placing pathology centre stage in the process of defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases.

Breast cancer histologic grading using digital microscopy: concordance and outcome association

Aims

Virtual microscopy utilising digital whole slide imaging (WSI) is increasingly used in breast pathology. Histologic grade is one of the strongest prognostic factors in breast cancer (BC). This study aims at investigating the agreement between BC grading using traditional light microscopy (LM) and digital WSI with consideration of reproducibility and impact on outcome prediction.

Methods

A large (n=1675) well-characterised cohort of BC originally graded by LM was re-graded using WSI. Two separate virtual-based grading sessions (V1 and V2) were performed with a 3-month washout period. Outcome was assessed using BC-specific and distant metastasis-free survival.

Results

The concordance between LM grading and WSI was strong (LM/WSI Cramer’s V: V1=0.576, and V2=0.579). The agreement regarding grade components was as follows: tubule formation=0.538, pleomorphism=0.422 and mitosis=0.514. Greatest discordance was observed between adjacent grades, whereas high/low grade discordance was uncommon (1.5%). The intraobserver agreement for the two WSI sessions was substantial for grade (V1/V2 Cramer’s V=0.676; kappa=0.648) and grade components (Cramer’s V T=0.628, p=0.573 and M=0.580). Grading using both platforms showed strong association with outcome (all p values <0.001). Although mitotic scores assessed using both platforms were strongly associated with outcome, WSI tends to underestimate mitotic counts.

Conclusions

Virtual microscopy is a reliable and reproducible method for assessing BC histologic grade. Regardless of the observer or assessment platform, histologic grade is a significant predictor of outcome. Continuing advances in imaging technology could potentially provide improved performance of WSI BC grading and in particular mitotic count assessment.

Daily Biopsy Diagnosis in Surgical Pathology: Concordance Between Light Microscopy and Whole-Slide Imaging in Real-Life Conditions

OBJECTIVES

The current challenge for the various digital whole-slide imaging (WSI) systems is to be definitively validated for diagnostic purposes. We designed a concordance study between glass slide and digital slide diagnosis in real-life conditions, coupled with an ergonomic study.

METHODS

Three senior pathologists evaluated, first in glass slides and then in digital slides, 119 biopsy cases, including 749 slides, with 332 H&E saffron stains and 417 additional techniques, mainly immunohistochemistry.

RESULTS

All digital slides, including specially stained slides, were interpretable. Concordance between glass slides and digital slides was observed in 87.4% of cases. Minor discordances were observed in 12 (10.1%) cases and major discordances, with therapeutic impact, in three (2.5%), including one related to WSI. The satisfaction of participants was high and increased with time.

CONCLUSIONS

Our study confirms the feasibility and accuracy of WSI diagnosis, even for cases having multiple samples and requiring special staining techniques, such as immunohistochemistry and in situ hybridization.

Novel histopathologic feature identified through image analysis augments stage II colorectal cancer clinical reporting

A number of candidate histopathologic factors show promise in identifying stage II colorectal cancer (CRC) patients at a high risk of disease-specific death, however they can suffer from low reproducibility and none have replaced classical pathologic staging. We developed an image analysis algorithm which standardized the quantification of specific histopathologic features and exported a multi-parametric feature-set captured without bias. The image analysis algorithm was executed across a training set (n = 50) and the resultant big data was distilled through decision tree modelling to identify the most informative parameters to sub-categorize stage II CRC patients. The most significant, and novel, parameter identified was the ‘sum area of poorly differentiated clusters’ (AreaPDC). This feature was validated across a second cohort of stage II CRC patients (n = 134) (HR = 4; 95% CI, 1.5– 11). Finally, the AreaPDC was integrated with the significant features within the clinical pathology report, pT stage and differentiation, into a novel prognostic index (HR = 7.5; 95% CI, 3–18.5) which improved upon current clinical staging (HR = 4.26; 95% CI, 1.7– 10.3). The identification of poorly differentiated clusters as being highly significant in disease progression presents evidence to suggest that these features could be the source of novel targets to decrease the risk of disease specific death.

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.

Updates in ophthalmic pathology

Ophthalmic pathology has a long history and rich heritage in the field of ophthalmology. This review article highlights updates in ophthalmic pathology that have developed significantly through the years because of the efforts of committed individuals and the confluence of technology such as molecular biology and digital pathology. This is an exciting period in the history of ocular pathology, with cutting-edge techniques paving the way for new developments in diagnostics, therapeutics, and research. Collaborations between ocular oncologists and pathologists allow for improved and comprehensive patient care. Ophthalmic pathology continues to be a relevant specialty that is important in the understanding and clinical management of ocular disease, education of eye care providers, and overall advancement of the field.

Embracing an integromic approach to tissue biomarker research in cancer: Perspectives and lessons learned

Modern approaches to biomedical research and diagnostics targeted towards precision medicine are generating 'big data' across a range of high-throughput experimental and analytical platforms. Integrative analysis of this rich clinical, pathological, molecular and imaging data represents one of the greatest bottlenecks in biomarker discovery research in cancer and other diseases. Following on from the publication of our successful framework for multimodal data amalgamation and integrative analysis, Pathology Integromics in Cancer (PICan), this article will explore the essential elements of assembling an integromics framework from a more detailed perspective. PICan, built around a relational database storing curated multimodal data, is the research tool sitting at the heart of our interdisciplinary efforts to streamline biomarker discovery and validation. While recognizing that every institution has a unique set of priorities and challenges, we will use our experiences with PICan as a case study and starting point, rationalizing the design choices we made within the context of our local infrastructure and specific needs, but also highlighting alternative approaches that may better suit other programmes of research and discovery. Along the way, we stress that integromics is not just a set of tools, but rather a cohesive paradigm for how modern bioinformatics can be enhanced. Successful implementation of an integromics framework is a collaborative team effort that is built with an eye to the future and greatly accelerates the processes of biomarker discovery, validation and translation into clinical practice.

Computer-aided prognosis on breast cancer with hematoxylin and eosin histopathology images: A review

With the advance of digital pathology, image analysis has begun to show its advantages in information analysis of hematoxylin and eosin histopathology images. Generally, histological features in hematoxylin and eosin images are measured to evaluate tumor grade and prognosis for breast cancer. This review summarized recent works in image analysis of hematoxylin and eosin histopathology images for breast cancer prognosis. First, prognostic factors for breast cancer based on hematoxylin and eosin histopathology images were summarized. Then, usual procedures of image analysis for breast cancer prognosis were systematically reviewed, including image acquisition, image preprocessing, image detection and segmentation, and feature extraction. Finally, the prognostic value of image features and image feature–based prognostic models was evaluated. Moreover, we discussed the issues of current analysis, and some directions for future research.

International Clinical Guidelines for the Adoption of Digital Pathology: A Review of Technical Aspects

Digital slides, also called whole-slide images, are being evaluated to replace conventional microscopy, and several guidelines have been published. This paper reviews technical specifications of digital pathology systems that have been included in the guidelines and position papers from the Canadian Association of Pathologists, the College of American Pathologists, the American Telemedicine Association, the Digital Pathology Association, the Food and Drug Administration, the Centers for Medicare and Medicaid Services, the Centers for Disease Control and Prevention, the Society of Toxicologic Pathology, the European Commission, the Spanish Society of Anatomic Pathology, The Royal College of Pathologists and The Royal College of Pathologists of Australasia. In conclusion, most technical aspects are well covered by these guidelines, although they offer limited information regarding image quality and compression, and file formats.

Development of a database system and image viewer to assist in the correlation of histopathologic features and digital image analysis with clinical and molecular genetic information

Pathologists are required to integrate data from multiple sources when making a diagnosis. Furthermore, whole slide imaging (WSI) and next generation sequencing will escalate data size and complexity. Development of well-designed databases that can allow efficient navigation between multiple data types is necessary for both clinical and research purposes. We developed and evaluated an interactive, web-based database that integrates clinical, histologic, immunohistochemical and genetic information to aid in pathologic diagnosis and interpretation with nine lung adenocarcinoma cases. To minimize sectioning artifacts, representative blocks were serially sectioned using automated tissue sectioning (Kurabo Industries, Osaka Japan) and selected slides were stained by multiple techniques, (hematoxylin and eosin [H&E], immunohistochemistry [IHC] or fluorescence in situ hybridization [FISH]). Slides were digitized by WSI scanners. An interactive relational database was designed based on a list of proposed fields covering a variety of clinical, pathologic and molecular parameters. By focusing on the three main tasks of 1.) efficient management of textual information, 2.) effective viewing of all varieties of stained whole slide images (WSI), and 3.) assistance in evaluating WSI with computer-aided diagnosis, this database prototype shows great promise for multi-modality research and diagnosis.

Working toward consensus among professionals in the identification of classical cervical cytomorphological characteristics in whole slide images

Introduction:

Cervical cancer is one of the most common causes of death in women worldwide.^[1] The introduction of cervical cytology in screening programs is an effective way for early detection and treatment of cervical precancerous lesions. Conventional screening of cervical cytology slides is still considered the current “gold standard” for the assessment of proficiency in becoming a cytotechnician, but diagnosis using digital whole slide images (WSI) may offer many advantages.

Materials and Methods:

In this study, we have used a selection of WSI from thin-layer specimens of the most common cervical infections and (pre) neoplastic lesions, and hypothesized that weekly WSI based case-meetings would help to obtain optimal acceptance of the new digital workflow in daily pathology practice. A questionnaire, before and after the test period was used to study the effect of our approach.

Results:

The participants clearly had to go through a learning curve to get accustomed to viewing WSI. In the beginning, there was a little self-confidence in recognizing classical cervical cytomorphological features in the WSI, and there were complaints about the speed of viewing and insufficient Z-resolution for cell groups. Adjusting the Z-stack settings resulted in better three-dimensional information due to better focusing options. Weekly meetings appeared to be instrumental in the implementation process, as participants had to select and present WSI from thematic cases themselves, and thereby, got used to viewing WSI. Some WSI were replaced by better ones until a final set of 45 representatives WSI remained. Eventually, the consensus was reached among all participants that cytomorphological features in WSI from thin-layers cervical specimens could comparably be appreciated in WSI as by conventional microscopy. The selection of 45 WSI was now used to create a digital WSI based reference atlas to support further studies.

Conclusion:

We have obtained consensus between professionals that WSI from cervical cytology can be used to identify cytomorphological features, necessary for diagnosis. In addition, we observed that active participation of professionals had a positive effect on the overall acceptance of WSI and was important in the change management.

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.

Morphology in the Digital Age: Integrating High-Resolution Description of Structural Alterations With Phenotypes and Genotypes

Conventional light microscopy has been used to characterize and classify renal diseases, evaluate histopathology in studies and trials, and educate renal pathologists and nephrologists. The advent of digital pathology, in which a glass slide can be scanned to create whole slide images (WSIs) for viewing and manipulating on a computer monitor, provides real and potential advantages compared with conventional light microscopy. Software tools such as annotation, morphometry, and image analysis can be applied to WSIs for studies or educational purposes, and the digital images are available globally to clinicians, pathologists, and investigators. New ways of assessing renal pathology with observational data collection may allow better morphologic correlations and integration with molecular and genetic signatures, refinements of classification schema, and understanding of disease pathogenesis. In multicenter studies, WSIs, which require additional quality assurance steps, provide efficiency by reducing slide shipping and consensus conference costs, and they allow slide viewing anytime and anywhere. Although validation studies for the routine diagnostic use of digital pathology still are needed, this is a powerful tool currently available for translational research, clinical trials, and education in renal pathology.

Validation of whole slide imaging in the primary diagnosis of gynaecological pathology in a University Hospital

AIMS

Experience in the use of whole slide imaging (WSI) for primary diagnosis in pathology is very limited. We aimed to determine the accuracy of interpretation of WSI compared with conventional light microscopy (CLM) in the diagnosis of routine gynaecological biopsies.

METHODS

All gynaecological specimens (n=452) received over a 2-month period at the Department of Pathology of the Hospital Clinic of Barcelona were analysed blindly by two gynaecological pathologists, one using CLM and the other WSI. All slides were digitised in a Ventana iScan HT (Roche diagnostics) at 200×. All discrepant diagnoses were reviewed, and a final consensus diagnosis was established. The results were evaluated by weighted κ statistics for two observers.

RESULTS

The level of interobserver agreement between WSI and CLM evaluations was almost perfect (κ value: 0.914; 95% CI 0.879 to 0.949) and increased during the study period: κ value 0.890; 95% CI 0.835 to 0.945 in the first period and 0.941; 95%; CI 0.899 to 0.983 in the second period. Major discrepancies (differences in clinical management or prognosis) were observed in 9 cases (2.0%). All discrepancies consisted of small lesions (8 high grade squamous intraepithelial lesions of the uterine cervix, one lymph node micrometastasis of an ovarian carcinoma) underdiagnosed or missed in the WSI or the CLM evaluation. Discrepancies with no or minor clinical relevance were identified in 3.8% of the biopsies. No discrepancy was related to the poor quality of the WSI image.

CONCLUSIONS

Diagnosis of gynaecological specimens by WSI is accurate and may be introduced into routine diagnosis.

A nuclear circularity-based classifier for diagnostic distinction of desmoplastic from spindle cell melanoma in digitized histological images

Context:

Distinction of spindle cell melanoma (SM) and desmoplastic melanoma (DM) is clinically important due to differences in metastatic rate and prognosis; however, histological distinction is not always straightforward. During a routine review of cases, we noted differences in nuclear circularity between SM and DM.

Aim:

The primary aim in our study was to determine whether these differences in nuclear circularity, when assessed using a basic ImageJ-based threshold extraction, can serve as a diagnostic classifier to distinguish DM from SM.

Settings and Design:

Our retrospective analysis of an established patient cohort (SM n = 9, DM n = 9) was employed to determine discriminatory power.

Subjects and Methods:

Regions of interest (total n = 108; 6 images per case) were selected from scanned H and E-stained histological sections, and nuclear circularity was extracted and quantified by computational image analysis using open source tools (plugins for ImageJ).

Statistical Analysis:

Using analysis of variance, t-tests, and Fisher's exact tests, we compared extracted quantitative shape measures; statistical significance was defined as P < 0.05.

Results:

Classifying circularity values into four shape categories (spindled, elongated, oval, round) demonstrated significant differences in the spindled and round categories. Paradoxically, DM contained more spindled nuclei than SM (P = 0.011) and SM contained more round nuclei than DM (P = 0.026). Performance assessment using a combined shape-classification of the round and spindled fractions showed 88.9% accuracy and a Youden index of 0.77.

Conclusions:

Spindle cell melanoma and DM differ significantly in their nuclear morphology with respect to fractions of round and spindled nuclei. Our study demonstrates that quantifying nuclear circularity can be used as an adjunct diagnostic tool for distinction of DM and SM.

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.

Molecular pathology via IR and Raman spectral imaging

During the last 15 years, vibrational spectroscopic methods have been developed that can be viewed as molecular pathology methods that depend on sampling the entire genome, proteome and metabolome of cells and tissues, rather than probing for the presence of selected markers. First, this review introduces the background and fundamentals of the spectroscopies underlying the new methodologies, namely infrared and Raman spectroscopy. Then, results are presented in the context of spectral histopathology of tissues for detection of metastases in lymph nodes, squamous cell carcinoma, adenocarcinomas, brain tumors and brain metastases. Results from spectral cytopathology of cells are discussed for screening of oral and cervical mucosa, and circulating tumor cells. It is concluded that infrared and Raman spectroscopy can complement histopathology and reveal information that is available in classical methods only by costly and time-consuming steps such as immunohistochemistry, polymerase chain reaction or gene arrays. Due to the inherent sensitivity toward changes in the bio-molecular composition of different cell and tissue types, vibrational spectroscopy can even provide information that is in some cases superior to that of any one of the conventional techniques.

Screening and dotting virtual slides: A new challenge for cytotechnologists

Digital images are increasingly being used in cytopathology. Whole-slide imaging (WSI) is a digital imaging modality that uses computerized technology to scan and convert entire cytology glass slides into digital images that can be viewed on a digital display using the image viewer software. Digital image acquisition of cytology glass slides has improved significantly over the years due to the use of liquid-based preparations and advances in WSI scanning technology such as automatic multipoint pre-scan focus technology or z-stack scanning technology. Screening cytotechnologists are responsible for every cell that is present on an imaged slide. One of the challenges users have to overcome is to establish a technique to review systematically the entire imaged slide and to dot selected abnormal or significant findings. The scope of this article is to review the current user interface technology available for virtual slide navigation when screening digital slides in cytology. WSI scanner vendors provide tools, built into the image viewer software that allow for a more systematic navigation of the virtual slides, such as auto-panning, keyboard-controlled slide navigation and track map. Annotation tools can improve communication between the screener and the final reviewer or can be used for education. The tracking functionality allows recording of the WSI navigation process and provides a mechanism for confirmation of slide coverage by the screening cytotechnologist as well as a useful tool for quality assurance. As the WSI technology matures, additional features and tools to support navigation of a cytology virtual slide are anticipated.

Implementation of Telecytology in Georgia for Quality Assurance Programs

The field of eHealth is rapidly evolving. The new models and protocols of application of info-communication technologies for healthcare purposes are developed. Despite of obvious advantages and benefits practical application of eHealth and its possibilities in everyday practice is slow. Much progress has been made around the world in the field of digital imaging and virtual slides. But in Georgia telecytology is still in evolving stages. It revolves around static telecytology. It has been revealed, that the application of easy available and adaptable technology together with the improvement of the infrastructure conditions is the essential basis for telecytology. This is a very useful and applicable tool for consulting on difficult cases and implementation of quality assurance programs in the field of cytology. Telecytology has significantly increased knowledge exchange and thereby ensured a better medical service. The chapter aimed description of practical application of telecytology under conditions of Georgia as well as presentation of telecytology usage for implementation of quality assurance programs in the field of cytology.