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

The use of telepathology in veterinary medicine: a scoping review

Telepathology, as a subset of teleconsulting, is pathology interpretation performed at a distance. Telepathology is not a new phenomenon, but since ~2015, significant advances in information technology and telecommunications coupled with the pandemic have led to unprecedented sophistication, accessibility, and use of telepathology in human and veterinary medicine. Furthermore, telepathology can connect veterinary practices to distant laboratories and provide support for underserved animals and communities. Through our scoping review, we provide an overview of how telepathology is being used in veterinary medicine, identify gaps in the literature, and highlight future areas of research and service development. We searched MEDLINE, CAB Abstracts, and the gray literature, and included all relevant literature. Despite the widespread use of digital microscopy in large veterinary diagnostic laboratories, we identified a paucity of literature describing the use of telepathology in veterinary medicine, with a significant gap in studies addressing the validation of whole-slide imaging for primary diagnosis. Underutilization of telepathology to support postmortem examinations conducted in the field was also identified, which indicates a potential area for service development. The use of telepathology is increasing in veterinary medicine, and pathologists must keep pace with the changing technology, ensure the validation of innovative technologies, and identify novel uses to advance the profession.

Algorithm-assisted diagnosis of Hirschsprung's disease - evaluation of robustness and comparative image analysis on data from various labs and slide scanners

BACKGROUND

Differences in the preparation, staining and scanning of digital pathology slides create significant pre-analytic variability. Algorithm-assisted tools must be able to contend with this variability in order to be applicable in clinical practice. In a previous study, a decision support algorithm was developed to assist in the diagnosis of Hirschsprung's disease. In the current study, we tested the robustness of this algorithm while assessing for pre-analytic factors which may affect its performance.

METHODS

The decision support algorithm was used on digital pathology slides obtained from four different medical centers (A-D) and scanned by three different scanner models (by Philips, Hamamatsu and 3DHISTECH). A total of 192 cases and 1782 slides were used in this study. RGB histograms were constructed to compare images from the various medical centers and scanner models and highlight the differences in color and contrast.

RESULTS

The algorithm was able to correctly identify ganglion cells in 99.2% of cases, from all medical centers (All scanned by the Philips slide scanner) as well as 95.5% and 100% of the slides scanned by the 3DHISTECH and Hamamatsu brand slide scanners, respectively. The total error rate for center D was lower than the other medical centers (3.9% vs 7.1%, 10.8% and 6% for centers A-C, respectively), the vast majority of errors being false positives (3.45% vs 0.45% false negatives). The other medical centers showed a higher rate of false negatives in relation to false positives (6.81% vs 0.29%, 9.8% vs 1.2% and 5.37% vs 0.63% for centers A-C, respectively). The total error rates for the Philips, Hamamatsu and 3DHISTECH brand scanners were 3.9%, 3.2% and 9.8%, respectively. RGB histograms demonstrated significant differences in pixel value distribution between the four medical centers, as well as between the 3DHISTECH brand scanner when compared to the Philips and Hamamatsu brand scanners.

CONCLUSIONS

The results reported in this paper suggest that the algorithm-based decision support system has sufficient robustness to be applicable for clinical practice. In addition, the novel method used in its development - Hierarchial-Contexual Analysis (HCA) may be applicable to the development of algorithm-assisted tools in other diseases, for which available datasets are limited. Validation of any given algorithm-assisted support system should nonetheless include data from as many medical centers and scanner models as possible.

Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images

Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training, n = 245 whole-slide images (WSIs), validation (n = 35 WSIs), and test sets (n = 70 WSIs). Full annotations included the 7 tumor classes and 6 normal skin structures. The data set was used to train a convolutional neural network (CNN) for the automatic segmentation of tumor and nontumor classes. Subsequently, the detected tumor regions were classified patch-wise into 1 of the 7 tumor classes. A majority of patches-approach led to a tumor classification accuracy of the network on the slide-level of 95% (133/140 WSIs), with a patch-level precision of 85%. The same 140 WSIs were provided to 6 experienced pathologists for diagnosis, who achieved a similar slide-level accuracy of 98% (137/140 correct majority votes). Our results highlight the feasibility of artificial intelligence-based methods as a support tool in diagnostic oncologic pathology with future applications in other species and tumor types.

Quantitative Analysis of Inflammatory Uterine Lesions of Pregnant Gilts with Digital Image Analysis Following Experimental PRRSV-1 Infection

Reproductive disorders caused by porcine reproductive and respiratory syndrome virus-1 are not yet fully characterized. We report QuPath-based digital image analysis to count inflammatory cells in 141 routinely, and 35 CD163 immunohistochemically stained endometrial slides of vaccinated or unvaccinated pregnant gilts inoculated with a high or low virulent PRRSV-1 strain. To illustrate the superior statistical feasibility of the numerical data determined by digital cell counting, we defined the association between the number of these cells and endometrial, placental, and fetal features. There was strong concordance between the two manual scorers. Distributions of total cell counts and endometrial and placental qPCR results differed significantly between examiner1's endometritis grades. Total counts' distribution differed significantly between groups, except for the two unvaccinated. Higher vasculitis scores were associated with higher endometritis scores, and higher total cell counts were expected with high vasculitis/endometritis scores. Cell number thresholds of endometritis grades were determined. A significant correlation between fetal weights and total counts was shown in unvaccinated groups, and a significant positive correlation was found between these counts and endometrial qPCR results. We revealed significant negative correlations between CD163+ counts and qPCR results of the unvaccinated group infected with the highly virulent strain. Digital image analysis was efficiently applied to assess endometrial inflammation objectively.

Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset

Due to morphological similarities, the differentiation of histologic sections of cutaneous tumors into individual subtypes can be challenging. Recently, deep learning-based approaches have proven their potential for supporting pathologists in this regard. However, many of these supervised algorithms require a large amount of annotated data for robust development. We present a publicly available dataset of 350 whole slide images of seven different canine cutaneous tumors complemented by 12,424 polygon annotations for 13 histologic classes, including seven cutaneous tumor subtypes. In inter-rater experiments, we show a high consistency of the provided labels, especially for tumor annotations. We further validate the dataset by training a deep neural network for the task of tissue segmentation and tumor subtype classification. We achieve a class-averaged Jaccard coefficient of 0.7047, and 0.9044 for tumor in particular. For classification, we achieve a slide-level accuracy of 0.9857. Since canine cutaneous tumors possess various histologic homologies to human tumors the added value of this dataset is not limited to veterinary pathology but extends to more general fields of application.

Measurement(s)	canine cutaneous tissue
Technology Type(s)	bright-field microscopy • H&E slide staining • whole slide scanning
Sample Characteristic - Organism	Canis

Digital Cytology in Exotic Practice: Tips to Optimize Diagnosis

Digitization has enhanced the utility of cytology in private practice by allowing for rapid sample receipt and analysis, leading to better informed real-time patient care. Despite many advantages of digital cytology, understanding its limitations is required to avoid common pitfalls. A strong foundation in sample preparation and imaging techniques is also required to obtain high-quality diagnostic samples. By optimizing these factors, the benefits of digital cytology are maximized, allowing for the practice of high-quality point-of-care medicine that best addresses the needs of the patient and pet owner in a rapid time frame.

Evaluation of a Probability-Based Predictive Tool on Pathologist Agreement Using Urinary Bladder as a Pilot Tissue

Simple Summary

There is a common joke in pathology—put three pathologists in a room and you will obtain three different answers. This saying comes from the fact that pathology can be subjective; pathologists’ diagnoses can be influenced by many different biases, and pathologists are also influenced by the presence or absence of animal information and medical history. Compared to pathology, statistics is a much more objective field. This study aimed to develop a probability-based tool using statistics obtained by analyzing 338 histopathology slides of canine and feline urinary bladders, then see if the tool affected agreement between the test pathologists. Four pathologists diagnosed 25 canine and feline bladder slides and they conducted this three times: without animal and clinical information, then with this information, and finally using the probability tool. Results showed large differences in the pathologists’ interpretation of bladder slides, with kappa agreement values (low value for digital slide images, high value for glass slides) of 7–37% without any animal or clinical information, 23–37% with animal signalment and history, and 31–42% when our probability tool was used. This study provides a starting point for the use of probability-based tools in standardizing pathologist agreement in veterinary pathology.

Abstract

Inter-pathologist variation is widely recognized across human and veterinary pathology and is often compounded by missing animal or clinical information on pathology submission forms. Variation in pathologist threshold levels of resident inflammatory cells in the tissue of interest can further decrease inter-pathologist agreement. This study applied a predictive modeling tool to bladder histology slides that were assessed by four pathologists: first without animal and clinical information, then with this information, and finally using the predictive tool. All three assessments were performed twice, using digital whole-slide images (WSI) and then glass slides. Results showed marked variation in pathologists’ interpretation of bladder slides, with kappa agreement values of 7–37% without any animal or clinical information, 23–37% with animal signalment and history, and 31–42% when our predictive tool was applied, for digital WSI and glass slides. The concurrence of test pathologists to the reference diagnosis was 60% overall. This study provides a starting point for the use of predictive modeling in standardizing pathologist agreement in veterinary pathology. It also highlights the importance of high-quality whole-slide imaging to limit the effect of digitization on inter-pathologist agreement and the benefit of continued standardization of tissue assessment in veterinary pathology.

Computer-assisted mitotic count using a deep learning–based algorithm improves interobserver reproducibility and accuracy

The mitotic count (MC) is an important histological parameter for prognostication of malignant neoplasms. However, it has inter- and intraobserver discrepancies due to difficulties in selecting the region of interest (MC-ROI) and in identifying or classifying mitotic figures (MFs). Recent progress in the field of artificial intelligence has allowed the development of high-performance algorithms that may improve standardization of the MC. As algorithmic predictions are not flawless, computer-assisted review by pathologists may ensure reliability. In the present study, we compared partial (MC-ROI preselection) and full (additional visualization of MF candidates and display of algorithmic confidence values) computer-assisted MC analysis to the routine (unaided) MC analysis by 23 pathologists for whole-slide images of 50 canine cutaneous mast cell tumors (ccMCTs). Algorithmic predictions aimed to assist pathologists in detecting mitotic hotspot locations, reducing omission of MFs, and improving classification against imposters. The interobserver consistency for the MC significantly increased with computer assistance (interobserver correlation coefficient, ICC = 0.92) compared to the unaided approach (ICC = 0.70). Classification into prognostic stratifications had a higher accuracy with computer assistance. The algorithmically preselected hotspot MC-ROIs had a consistently higher MCs than the manually selected MC-ROIs. Compared to a ground truth (developed with immunohistochemistry for phosphohistone H3), pathologist performance in detecting individual MF was augmented when using computer assistance (F1-score of 0.68 increased to 0.79) with a reduction in false negatives by 38%. The results of this study demonstrate that computer assistance may lead to more reproducible and accurate MCs in ccMCTs.

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

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

Classification for avian malaria parasite Plasmodium gallinaceum blood stages by using deep convolutional neural networks

The infection of an avian malaria parasite (Plasmodium gallinaceum) in domestic chickens presents a major threat to the poultry industry because it causes economic loss in both the quality and quantity of meat and egg production. Computer-aided diagnosis has been developed to automatically identify avian malaria infections and classify the blood infection stage development. In this study, four types of deep convolutional neural networks, namely Darknet, Darknet19, Darknet19-448 and Densenet201 are used to classify P. gallinaceum blood stages. We randomly collected a dataset of 12,761 single-cell images consisting of three parasite stages from ten-infected blood films stained by Giemsa. All images were confirmed by three well-trained examiners. The study mainly compared several image classification models and used both qualitative and quantitative data for the evaluation of the proposed models. In the model-wise comparison, the four neural network models gave us high values with a mean average accuracy of at least 97%. The Darknet can reproduce a superior performance in the classification of the P. gallinaceum development stages across any other model architectures. Furthermore, the Darknet has the best performance in multiple class-wise classification, with average values of greater than 99% in accuracy, specificity, and sensitivity. It also has a low misclassification rate (< 1%) than the other three models. Therefore, the model is more suitable in the classification of P. gallinaceum blood stages. The findings could help us create a fast-screening method to help non-experts in field studies where there is a lack of specialized instruments for avian malaria diagnostics.

International Guidelines for Veterinary Tumor Pathology: A Call to Action

Standardization of tumor assessment lays the foundation for validation of grading systems, permits reproducibility of oncologic studies among investigators, and increases confidence in the significance of study results. Currently, there is minimal methodological standardization for assessing tumors in veterinary medicine, with few attempts to validate published protocols and grading schemes. The current article attempts to address these shortcomings by providing standard guidelines for tumor assessment parameters and protocols for evaluating specific tumor types. More detailed information is available in the Supplemental Files, the intention of which is 2-fold: publication as part of this commentary, but more importantly, these will be available as "living documents" on a website (www.vetcancerprotocols.org), which will be updated as new information is presented in the peer-reviewed literature. Our hope is that veterinary pathologists will agree that this initiative is needed, and will contribute to and utilize this information for routine diagnostic work and oncologic studies. Journal editors and reviewers can utilize checklists to ensure publications include sufficient detail and standardized methods of tumor assessment. To maintain the relevance of the guidelines and protocols, it is critical that the information is periodically updated and revised as new studies are published and validated with the intent of providing a repository of this information. Our hope is that this initiative (a continuation of efforts published in this journal in 2011) will facilitate collaboration and reproducibility between pathologists and institutions, increase case numbers, and strengthen clinical research findings, thus ensuring continued progress in veterinary oncologic pathology and improving patient care.

Histopathological Classification of Canine Cutaneous Round Cell Tumors Using Deep Learning: A Multi-Center Study

Canine cutaneous round cell tumors (RCT) represent one of the routine diagnostic challenges for veterinary pathologists. Computer-aided approaches are developed to overcome these restrictions and to increase accuracy and consistency of diagnosis. These systems are also of high benefit reducing errors when a large number of cases are screened daily. In this study we describe ARCTA (Automated Round Cell Tumors Assessment), a fully automated algorithm for cutaneous RCT classification and mast cell tumors grading in canine histopathological images. ARCTA employs a deep learning strategy and was developed on 416 RCT images and 213 mast cell tumors images. In the test set, our algorithm exhibited an excellent classification performance in both RCT classification (accuracy: 91.66%) and mast cell tumors grading (accuracy: 100%). Misdiagnoses were encountered for histiocytomas in the train set and for melanomas in the test set. For mast cell tumors the reduction of a grade was observed in the train set, but not in the test set. To the best of our knowledge, the proposed model is the first fully automated algorithm in histological images specifically developed for veterinary medicine. Being very fast (average computational time 2.63 s), this algorithm paves the way for an automated and effective evaluation of canine tumors.

Mitotic Figures-Normal, Atypical, and Imposters: A Guide to Identification

Counting mitotic figures (MF) in hematoxylin and eosin-stained histologic sections is an integral part of the diagnostic pathologist's tumor evaluation. The mitotic count (MC) is used alone or as part of a grading scheme for assessment of prognosis and clinical decisions. Determining MCs is subjective, somewhat laborious, and has interobserver variation. Proposals for standardizing this parameter in the veterinary field are limited to terminology (use of the term MC) and area (MC is counted in an area measuring 2.37 mm²). Digital imaging techniques are now commonplace and widely used among veterinary pathologists, and field of view area can be easily calculated with digital imaging software. In addition to standardizing the methods of counting MF, the morphologic characteristics of MF and distinguishing atypical mitotic figures (AMF) versus mitotic-like figures (MLF) need to be defined. This article provides morphologic criteria for MF identification and for distinguishing normal phases of MF from AMF and MLF. Pertinent features of digital microscopy and application of computational pathology (CPATH) methods are discussed. Correct identification of MF will improve MC consistency, reproducibility, and accuracy obtained from manual (glass slide or whole-slide imaging) and CPATH approaches.

Virtual Microscopy Is More Effective Than Conventional Microscopy for Teaching Cytology to Veterinary Students: A Randomized Controlled Trial

Virtual microscopy (VM) using scanned slides and imaging software is increasingly used in medical curricula alongside instruction in conventional microscopy (CM). Limited reports suggest that VM is useful in the veterinary education setting, and generally well-received by students. Whether students can apply knowledge gained through VM to practical use is unknown. Our objective was to determine whether instruction using VM, compared to CM, is a successful method of training veterinary students for the application of cytology in practice (i.e., using light microscopes). Seventy-one veterinary students from Colorado State University who attended a voluntary 3-hour cytology workshop were randomized to receive the same instruction with either VM (n = 35) or CM (n = 36). We compared these students to a control group (n = 22) of students who did not attend a workshop. All students took a post-workshop assessment involving the interpretation of four cases on glass slides with CM, designed to simulate the use of cytology in general practice. Students also took an 18-question survey related to the effectiveness of the workshop, providing their opinions on cytology instruction in the curriculum and their learning preference (VM or CM). The mean assessment score of the VM group (14.18 points) was significantly higher than the control group (11.33 points, p = .003), whereas the mean of the CM group (12.77 points) was not statistically significantly different from controls (p = .170). Not only is VM an effective method of teaching cytology to veterinary students that can be translated to a real-world case scenario, but it outperformed CM instruction in this study.

Computerized Calculation of Mitotic Count Distribution in Canine Cutaneous Mast Cell Tumor Sections: Mitotic Count Is Area Dependent

Mitotic count (MC) is an important element for grading canine cutaneous mast cell tumors (ccMCTs) and is determined in 10 consecutive high-power fields with the highest mitotic activity. However, there is variability in area selection between pathologists. In this study, the MC distribution and the effect of area selection on the MC were analyzed in ccMCTs. Two pathologists independently annotated all mitotic figures in whole-slide images of 28 ccMCTs (ground truth). Automated image analysis was used to examine the ground truth distribution of the MC throughout the tumor section area, which was compared with the manual MCs of 11 pathologists. Computerized analysis demonstrated high variability of the MC within different tumor areas. There were 6 MCTs with consistently low MCs (MC<7 in all tumor areas), 13 cases with mostly high MCs (MC ≥7 in ≥75% of 10 high-power field areas), and 9 borderline cases with variable MCs around 7, which is a cutoff value for ccMCT grading. There was inconsistency among pathologists in identifying the areas with the highest density of mitotic figures throughout the 3 ccMCT groups; only 51.9% of the counts were consistent with the highest 25% of the ground truth MC distribution. Regardless, there was substantial agreement between pathologists in detecting tumors with MC ≥7. Falsely low MCs below 7 mainly occurred in 4 of 9 borderline cases that had very few ground truth areas with MC ≥7. The findings of this study highlight the need to further standardize how to select the region of the tumor in which to determine the MC.

Impact of photographer experience and number of images on telecytology accuracy

BACKGROUND

Studies evaluating the potential impact of photographer experience or the number of images evaluated using the "store-and-forward" method of telecytology are not reported.

OBJECTIVES

This study aimed to determine the diagnostic sensitivity (Se) and specificity (Sp) of static telecytology when images were taken by experienced and inexperienced cytologists and when the number of images taken varied. Clinical agreement between the diagnoses was compared.

METHODS

Fifty archived cytology cases were randomly chosen. A board-certified clinical pathologist and a recent veterinary graduate took five images of each case. A third pathologist made a preliminary diagnosis after reviewing two images, and a final diagnosis after reviewing all images. The gold standard for comparison was the glass slide cytologic diagnosis.

RESULTS

Se and Sp were higher for the experienced cytologist and the evaluation of more images, but differences were not statistically significant. Clinical agreement between the image and glass slide diagnoses was significantly higher when images were taken by an experienced rather than inexperienced cytologist after the evaluation of two (P = .007) and five images (P = .008). The telecytology diagnoses agreed with the gold standard diagnoses more frequently after evaluation of five images rather than two when images were captured by both the experienced (P < .001) and inexperienced cytologist (P < .001).

CONCLUSIONS

There is more clinical agreement when the photographer has more cytology experience and when more images are provided for interpretation.

Agreement in Histological Assessment of Mitotic Activity Between Microscopy and Digital Whole Slide Images Informs Conversion for Clinical Diagnosis

Validating digital pathology as substitute for conventional microscopy in diagnosis remains a priority to assure effectiveness. Intermodality concordance studies typically focus on achieving the same diagnosis by digital display of whole slide images and conventional microscopy. Assessment of discrete histological features in whole slide images, such as mitotic figures, has not been thoroughly evaluated in diagnostic practice. To further gauge the interchangeability of conventional microscopy with digital display for primary diagnosis, 12 pathologists examined 113 canine naturally occurring mucosal melanomas exhibiting a wide range of mitotic activity. Design reflected diverse diagnostic settings and investigated independent location, interpretation, and enumeration of mitotic figures. Intermodality agreement was assessed employing conventional microscopy (CM40×), and whole slide image specimens scanned at 20× (WSI20×) and at 40× (WSI40×) objective magnifications. An aggregate 1647 mitotic figure count observations were available from conventional microscopy and whole slide images for comparison. The intraobserver concordance rate of paired observations was 0.785 to 0.801; interobserver rate was 0.784 to 0.794. Correlation coefficients between the 2 digital modes, and as compared to conventional microscopy, were similar and suggest noninferiority among modalities, including whole slide image acquired at lower 20× resolution. As mitotic figure counts serve for prognostic grading of several tumor types, including melanoma, 6 of 8 pathologists retrospectively predicted survival prognosis using whole slide images, compared to 9 of 10 by conventional microscopy, a first evaluation of whole slide image for mitotic figure prognostic grading. This study demonstrated agreement of replicate reads obtained across conventional microscopy and whole slide images. Hence, quantifying mitotic figures served as surrogate histological feature with which to further credential the interchangeability of whole slide images for primary diagnosis.

Evaluation of Region of Interest Digital Cytology Compared to Light Microscopy for Veterinary Medicine

Digital microscopy (DM) has been employed for primary diagnosis in human medicine and for research and teaching applications in veterinary medicine, but there are few veterinary DM validation studies. Region of interest (ROI) digital cytology is a subset of DM that uses image-stitching software to create a low-magnification image of a slide, then selected ROI at higher magnification, and stitches the images into a relatively small file of the embedded magnifications. This study evaluated the concordance of ROI-DM compared to traditional light microscopy (LM) between 2 blinded clinical pathologists. Sixty canine and feline cytology samples from a variety of anatomic sites, including 31 cases of malignant neoplasia, 15 cases of hyperplastic or benign neoplastic lesions, and 14 infectious/inflammatory lesions, were evaluated. Two separate nonblinded adjudicating clinical pathologists evaluated the reports and diagnoses and scored each paired case as fully concordant, partially concordant, or discordant. The average overall concordance (full and partial concordance) for both pathologists was 92%. Full concordance was significantly higher for malignant lesions than benign. For the 40 neoplastic lesions, ROI-DM and LM agreed on general category of tumor type in 78 of 80 cases (98%). ROI-DM cytology showed robust concordance with the current gold standard of LM cytology and is potentially a viable alternative to current LM cytology techniques.

Diagnostic Validation of a Whole-Slide Imaging Scanner in Cytological Samples: Diagnostic Accuracy and Comparison With Light Microscopy

Digital slides created by whole-slide imaging scanners can be evaluated by pathologists located in remote sites, but the process must be validated before this technology can be applied to routine cytological diagnosis. The aim of this study was to validate a whole-slide imaging scanner for cytological samples. Sixty cytological samples, whose diagnoses were confirmed by gold-standard examinations (histology or flow cytometry), were digitalized using a whole-slide imaging scanner. Digital slides and glass slides were examined by 3 observers with different levels of cytopathological expertise. No significant differences were noted between digital and glass slides in regard to the number of cases correctly diagnosed, or the sensitivity, specificity, or diagnostic accuracy, irrespective of the observers’ expertise. The agreements between the digital slides and the gold-standard examinations were moderate to substantial, while the agreements between the glass slides and the gold-standard examinations were substantial for all 3 observers. The intraobserver agreements between digital and glass slides were substantial to almost perfect. The interobserver agreements when evaluating digital slides were moderate between observers 1 and 2 and between observers 1 and 3 while they were substantial between observers 2 and 3. In conclusion, our study demonstrated that the digital slides produced by the whole-slide imaging scanner are adequate to diagnose cytological samples and are similar among clinical pathologists with differing levels of expertise.