Digital cytology: A short review of technical and methodological approaches and applications

Study on the Transformation Process of Thyroid Fine-Needle Aspiration Liquid-Based Cytology to Whole-Slide Image

OBJECTIVE

Analyse and summarise the reasons for failure in the digital acquisition of thyroid liquid-based cytology (LBC) slides and the technical challenges, and explore methods to obtain reliable and reproducible whole digital slide images for clinical thyroid cytology.

METHOD

Use the glass slide scanning imaging system to acquire whole-slide image (WSI) of thyroid LBC in sdpc format through different. Statistical analysis was conducted on the different acquisition methods, the quality of the glass slides, clinical and pathological characteristics of the case, TBSRTC grading and the quality of WSI.

RESULTS

The WSI obtained by different scanning methods showed a high level of consistency in quality (W = 0.325, p < 0.001), especially between fully automatic scanning with different focus densities (W = 0.9, p < 0.001). A total of 2114 images were obtained through different methods of multi-layer fusion and multi-point focusing scanning, with scan success rates of 100.0%, 100.0%, 100.0% and 23.6%, respectively. The correlation between the quality of thyroid LBC glass slides and the image quality of thyroid LBC WSI was statistically significant (p < 0.001). The correlation between TBSRTC grading and the quality of thyroid LBC digital WSI was statistically significant (p < 0.001).

CONCLUSIONS

Although the quality of glass slides has a significant impact, the success rate and image quality of malignant tumour scanning are both high. Overall, the risk of missed diagnosis of malignant tumours is low. In the future, we also need to improve the performance and algorithm of the scanner in cases of sparse cells.

Revolutionizing Cytology and Cytopathology with Natural Language Processing and Chatbot Technologies: A Narrative Review on Current Trends and Future Directions

The application of chatbots and Natural Language Processing (NLP) in cytology and cytopathology is an emerging field, which is currently characterized by a limited but growing body of research. Here, a narrative review has been proposed utilizing a standardized checklist and quality control procedure for including scientific papers. This narrative review explores the early developments and potential future impact of these technologies in medical diagnostics. The current literature, comprising 11 studies (after excluding comments, letters, and editorials) suggests that chatbots and NLP offer significant opportunities to enhance diagnostic accuracy, streamline clinical workflows, and improve patient engagement. By automating the extraction and classification of medical information, these technologies can reduce human error and increase precision. They also promise to make patient information more accessible and facilitate complex decision-making processes, thereby fostering greater patient involvement in healthcare. Despite these promising prospects, several challenges need to be addressed for the full potential of these technologies to be realized. These include the need for data standardization, mitigation of biases in Artificial Intelligence (AI) systems, and comprehensive clinical validation. Furthermore, ethical, privacy, and legal considerations must be navigated carefully to ensure responsible AI deployment. Compared to the more established fields of histology, histopathology, and especially radiology, the integration of digital tools in cytology and cytopathology is still in its infancy. Building on the advancements in related fields, especially radiology's experience with digital integration, where these technologies already offer promising solutions in mentoring, second opinions, and education, we can leverage this knowledge to further develop chatbots and natural language processing in cytology and cytopathology. Overall, this review underscores the transformative potential of these technologies while outlining the critical areas for future research and development.

AI in Cytopathology: A Narrative Umbrella Review on Innovations, Challenges, and Future Directions

The integration of artificial intelligence (AI) in cytopathology is an emerging field with transformative potential, aiming to enhance diagnostic precision and operational efficiency. This umbrella review seeks to identify prevailing themes, opportunities, challenges, and recommendations related to AI in cytopathology. Utilizing a standardized checklist and quality control procedures, this review examines recent advancements and future implications of AI technologies in this domain. Twenty-one review studies were selected through a systematic process. AI has demonstrated promise in automating and refining diagnostic processes, potentially reducing errors and improving patient outcomes. However, several critical challenges need to be addressed to realize the benefits of AI fully. This review underscores the necessity for rigorous validation, ongoing empirical data on diagnostic accuracy, standardized protocols, and effective integration with existing clinical workflows. Ethical issues, including data privacy and algorithmic bias, must be managed to ensure responsible AI applications. Additionally, high costs and substantial training requirements present barriers to widespread AI adoption. Future directions highlight the importance of applying successful integration strategies from histopathology and radiology to cytopathology. Continuous research is needed to improve model interpretability, validation, and standardization. Developing effective strategies for incorporating AI into clinical practice and establishing comprehensive ethical and regulatory frameworks will be crucial for overcoming these challenges. In conclusion, while AI holds significant promise for advancing cytopathology, its full potential can only be achieved by addressing challenges related to validation, cost, and ethics. This review provides an overview of current advancements, identifies ongoing challenges, and offers a roadmap for the successful integration of AI into diagnostic cytopathology, informed by insights from related fields.

Diagnostic Agreement in Whole Slide Digital Images of Cervical Cytology and Cell Blocks

BACKGROUND

Cell blocks (CB) are an established technique in non-gynecological cytology, but experience in gynecological cytology is still relatively limited. In this study, we compared the diagnostic agreement between liquid-based cytology (LBC) and CB using whole slide digital imaging (WSI).

METHODS

WSI of 82 specimens (41 LBC and 41 CB) were evaluated independently by three observers.

RESULTS

The overall agreement was 0.463 for all 82 cases including both LBC and CB, 0.439 for LBC alone, and 0.484 for CB alone. Agreement was highest for HSIL, NILM, cancers and lower for borderline (ASC-US, ASC-H). There was no significant difference in agreement between LBC and CB.

CONCLUSIONS

WSI of CB can be used for the diagnosis of cervical lesions and may be particularly useful in cases with high atypical cellularity or large hyperchromatic cell groups.

Deep learning pipeline for automated cell profiling from cyclic imaging

Cyclic fluorescence microscopy enables multiple targets to be detected simultaneously. This, in turn, has deepened our understanding of tissue composition, cell-to-cell interactions, and cell signaling. Unfortunately, analysis of these datasets can be time-prohibitive due to the sheer volume of data. In this paper, we present CycloNET, a computational pipeline tailored for analyzing raw fluorescent images obtained through cyclic immunofluorescence. The automated pipeline pre-processes raw image files, quickly corrects for translation errors between imaging cycles, and leverages a pre-trained neural network to segment individual cells and generate single-cell molecular profiles. We applied CycloNET to a dataset of 22 human samples from head and neck squamous cell carcinoma patients and trained a neural network to segment immune cells. CycloNET efficiently processed a large-scale dataset (17 fields of view per cycle and 13 staining cycles per specimen) in 10 min, delivering insights at the single-cell resolution and facilitating the identification of rare immune cell clusters. We expect that this rapid pipeline will serve as a powerful tool to understand complex biological systems at the cellular level, with the potential to facilitate breakthroughs in areas such as developmental biology, disease pathology, and personalized medicine.

Viability of Whole-Slide Imaging for Intraoperative Touch Imprint Cytological Diagnosis of Axillary Sentinel Lymph Nodes in Breast Cancer Patients

BACKGROUND

Whole-slide imaging (WSI) is a promising tool in pathology. However, the use of WSI in cytopathology has lagged behind that in histology. We aimed to evaluate the utility of WSI for the intraoperative touch imprint cytological diagnosis of axillary sentinel lymph nodes (SLNs) in breast cancer patients.

METHODS

Glass slides from touch imprint cytology of 480 axillary SLNs were scanned using two different WSI scanners. The intra- and interobserver concordance, accuracy, possible reasons for misdiagnosis, scanning time, and review time for three cytopathologists were compared between WSI and light microscopy (LM).

RESULTS

A total of 4320 diagnoses were obtained. There was substantial to strong intraobserver concordance when comparing reads among paired LM slides and WSI digital slides (κ coefficient ranged from 0.63 to 0.88, and concordance rates ranged from 94.58% to 98.33%). Substantial to strong interobserver agreement was also observed among the three cytopathologists (κ coefficient ranged from 0.67 to 0.85, and concordance rates ranged from 95.42% to 97.92%). The accuracy of LM was slightly higher (average of 98.06%) than that of WSI (averages of 96.81% and 97.78%). The majority of misdiagnoses were false negative diagnoses due to the following top three causes: few cancer cells, confusing cancer cells with histiocytes, and confusing cancer cells with lymphocytes.

CONCLUSIONS

This study is the first to address the feasibility of WSI in touch imprint cytology. The use of WSI for intraoperative touch imprint cytological diagnosis of SLNs is a practical option when experienced staff are not available on-site.

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

OBJECTIVE

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

METHODS

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

STATISTICAL ANALYSIS USED

Agreement calculated using the Kappa index.

RESULTS

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

CONCLUSIONS

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

Evaluating Urine Cytology Slide Digitization Efficiency: A Comparative Study Using an Artificial Intelligence-Based Heuristic Scanning Simulation and Multiple Z-Plane Scanning

INTRODUCTION

Digitizing cytology slides presents challenges because of their three-dimensional features and uneven cell distribution. While multi-Z-plane scan is a prevalent solution, its adoption in clinical digital cytopathology is hindered by prolonged scanning times, increased image file sizes, and the requirement for cytopathologists to review multiple Z-plane images.

METHODS

This study presents heuristic scan as a novel solution, using an artificial intelligence (AI)-based approach specifically designed for cytology slide scanning as an alternative to the multi-Z-plane scan. Both the 21 Z-plane scan and the heuristic scan simulation methods were used on 52 urine cytology slides from three distinct cytopreparations (Cytospin, ThinPrep, and BD CytoRich™ [SurePath]), generating whole-slide images (WSIs) via the Leica Aperio AT2 digital scanner. The AI algorithm inferred the WSI from 21 Z-planes to quantitate the total number of suspicious for high-grade urothelial carcinoma or more severe cells (SHGUC+) cells. The heuristic scan simulation calculated the total number of SHGUC+ cells from the 21 Z-plane scan data. Performance metrics including SHGUC+ cell coverage rates (calculated by dividing the number of SHGUC+ cells identified in multiple Z-planes or heuristic scan simulation by the total SHGUC+ cells in the 21 Z-planes for each WSI), scanning time, and file size were analyzed to compare the performance of each scanning method. The heuristic scan's metrics were linearly estimated from the 21 Z-plane scan data. Additionally, AI-aided interpretations of WSIs with scant SHGUC+ cells followed The Paris System guidelines and were compared with original diagnoses.

RESULTS

The heuristic scan achieved median SHGUC+ cell coverage rates similar to 5 Z-plane scans across three cytopreparations (0.78-0.91 vs. 0.75-0.88, p = 0.451-0.578). Notably, it substantially reduced both scanning time (137.2-635.0 s vs. 332.6-1,278.8 s, p < 0.05) and image file size (0.51-2.10 GB vs. 1.16-3.10 GB, p < 0.05). Importantly, the heuristic scan yielded higher rates of accurate AI-aided interpretations compared to the single Z-plane scan (62.5% vs. 37.5%).

CONCLUSION

We demonstrated that the heuristic scan offers a cost-effective alternative to the conventional multi-Z-plane scan in digital cytopathology. It achieves comparable SHGUC+ cell capture rates while reducing both scanning time and image file size, promising to aid digital urine cytology interpretations with a higher accuracy rate compared to the conventional single (optimal) plane scan. Further studies are needed to assess the integration of this new technology into compatible digital scanners for practical cytology slide scanning.

E-learning module for cytopathology education based on virtual microscopy

INTRODUCTION

In cytopathology education, Virtual Microscopy e-learning modules (VM-eLM) have achieved remarkable results in the improvement and personalization of learning. However, it remains to be determined whether these modules can significantly contribute to improving the accuracy of cytological diagnosis. The aim of this work was to create a VM-eLM for gynecologic cytopathology education designed to improve screening and interpretation skills in two groups of cytologists: experienced and nonexperienced.

MATERIALS AND METHODS

The module was designed in Moodle with both Whole Slide Images and Static Images taken from Papanicolaou smears that were diagnosed as: negative for intraepithelial lesion, low-grade squamous intraepithelial lesion, high-grade squamous intraepithelial lesion, squamous cell carcinoma, or adenocarcinoma. We assessed the effectiveness of the module using 1) clinical quality indicators to measure skill development and 2) a user survey.

RESULTS

After training, participants significantly improved their cytological screening skills, decreasing their false negative diagnosis by 78% in the non-experienced group and eliminating them entirely in the experienced group. Nonexperienced participants also significantly increased their recognition of low-grade squamous intraepithelial lesion and high-grade squamous intraepithelial lesion by 31% and 50%, respectively. Participants positively evaluated the module, highlighting its novelty, the possibility to train remotely, the immediate feedback and the quality of the Whole Slide Images.

CONCLUSIONS

We designed, implemented and tested a VM-eLM for Gynecologic Cytopathology Education that improved cytological screening skills for both non-experienced and experienced cytologists, also increasing the diagnostic accuracy of preinvasive lesions by less experienced cytologists. The module was positively evaluated by participants, who perceived an improvement in their interpretive skills.

Intra-nucleus mosaic pattern (InMop) and whole-cell Haralick combined-descriptor for identifying and characterizing acute leukemia blasts on single cell peripheral blood images

Acute leukemia is usually diagnosed when a test of peripheral blood shows at least 20% of abnormal immature cells (blasts), a figure even lower in case of recurrent cytogenetic abnormalities. Blast identification is crucial for white blood cell (WBC) counting, which depends on both identifying the cell type and characterizing the cellular morphology, processes susceptible of inter- and intraobserver variability. The present work introduces an image combined-descriptor to detect blasts and determine their probable lineage. This strategy uses an intra-nucleus mosaic pattern (InMop) descriptor that captures subtle nuclei differences within WBCs, and Haralick's statistics which quantify the local structure of both nucleus and cytoplasm. The InMop captures WBC inner-nucleus structure by applying a multiscale Shearlet decomposition over a repetitive pattern (mosaic) of automatically-segmented nuclei. As a complement, Haralick's statistics characterize the local structure of the whole cell from an intensity co-occurrence matrix representation. Both InMoP and Haralick-based descriptors are calculated using the b-channel from Lab color-space. The combined-descriptor is assessed by differentiating blasts from nonleukemic cells with support vector machine (SVM) classifiers and different transformation kernels, in two public and independent databases. The first database-D1 (n = 260) is composed of healthy and acute lymphoid leukemia (ALL) single cell images, and second database-D2 contains acute myeloid leukemia (AML) blasts (n = 3294) and nonblast (n = 15,071) cell images. In a first experiment, blasts versus nonblast differentiation is performed by training with a subset of D2 (n = 6588) and testing in D1 (n = 260), obtaining a training AUC of 0.991 ± 0.002 and AUC = 0.782 for the independent validation. A second experiment automatically differentiates AML blasts (260 images from D2) from ALL blasts (260 images from D1), with an AUC of 0.93. In a third experiment, state-of-the-art strategies, VGG16 and RESNEXT convolutional neural networks (CNN), separate blast from nonblast cells in both databases. The VGG16 showed an AUC of 0.673 and the RESNEXT of 0.75. Reported metrics for all the experiments are area under the ROC curve (AUC), accuracy and F1-score.

Delphi expert consensus for whole slide imaging in thyroid cytopathology

OBJECTIVE

Despite an increase in thyroid fine needle aspiration (FNA) and advances in whole slide imaging (WSI) adoption, digital pathology is still considered inadequate for primary diagnosis of these cases. Herein, we aim to validate the utility of WSI in thyroid FNAs employing the Delphi method strategy.

METHODS

A panel of experts from seven reference cytology centres was recruited. The study consisted of two consecutive rounds: (1) an open-ended, free-response questionnaire generating a list of survey items; and (2) a consensus analysis of 80 selected shared WSIs from 80 cases by six investigators answering six morphological questions utilising a 1 to 5 Likert scale.

RESULTS

High consensus was achieved for all parameters, with an overall average score of 4.27. The broad majority of items (84%) were ranked either 4 or 5 by each physician. Two badly scanned cases were responsible for more than half of the low-ranked (≤2) values (57%). Good to excellent (≥3) diagnostic confidence was reached in more than 95.2% of cases. For most cases (78%) WSI assessment was not limited by technical issues linked to the image acquisition process.

CONCLUSION

This systematic Delphi study indicates broad consensus among participating physicians on the application of DP to thyroid cytopathology, supporting expert opinion that WSI is reliable and safe for primary diagnostic purposes.

Diagnostic proficiency test using digital cytopathology and comparative assessment of whole slide images of cytologic samples for quality assurance program in Korea

BACKGROUND

The Korean Society for Cytopathology introduced a digital proficiency test (PT) in 2021. However, many doubtful opinions remain on whether digitally scanned images can satisfactorily present subtle differences in the nuclear features and chromatin patterns of cytological samples.

METHODS

We prepared 30 whole-slide images (WSIs) from the conventional PT archive by a selection process for digital PT. Digital and conventional PT were performed in parallel for volunteer institutes, and the results were compared using feedback. To assess the quality of cytological assessment WSIs, 12 slides were collected and scanned using five different scanners, with four cytopathologists evaluating image quality through a questionnaire.

RESULTS

Among the 215 institutes, 108 and 107 participated in glass and digital PT, respectively. No significant difference was noted in category C (major discordance), although the number of discordant cases was slightly higher in the digital PT group. Leica, 3DHistech Pannoramic 250 Flash, and Hamamatsu NanoZoomer 360 systems showed comparable results in terms of image quality, feature presentation, and error rates for most cytological samples. Overall satisfaction was observed with the general convenience and image quality of digital PT.

CONCLUSIONS

As three-dimensional clusters are common and nuclear/chromatin features are critical for cytological interpretation, careful selection of scanners and optimal conditions are mandatory for the successful establishment of digital quality assurance programs in cytology.

Real-Time Evaluation of Thyroid Cytology Using New Digital Microscopy Allows for Sample Adequacy Assessment, Morphological Classification, and Supports Molecular Analysis

Thyroid cytological examination, a key tool in preoperative thyroid nodule evaluation, is specific and accurate; some drawbacks are due to inadequate or indeterminate cytological reports and there is a need for an innovative approach overcoming the limits of traditional cytological diagnostics. Fluorescence laser confocal microscopes (FCM) is a new optical technique for allowing immediate digital imaging of fresh unfixed tissues and real-time assessment of sample adequacy and diagnostic evaluation for small biopsies and cytological samples. Currently, there are no data about the use of FCMs in the field of thyroid nodular pathology. The aims of this study were to test FCM technology for evaluating the adequacy of FNA samples at the time of the procedure and to assess the level of concordance between FCM cytological evaluations, paired conventional cytology, and final surgical histology. The secondary aim was to define the integrity of nucleic acids after FCM evaluation through NGS molecular analysis. Sample adequacy was correctly stated. Comparing FCM evaluation with the final histology, all cases resulting in malignant or suspicious for malignancy at FCM, were confirmed to be carcinomas (PPV 100%). In conclusion, we describe a successful application of FCM in thyroid preoperative cytological evaluation, with advantages in immediate adequacy assessment and diagnostic information, while preserving cellular specimens for permanent morphology and molecular analysis, thus improving timely and accurate patient management.

Validation of cytopathology specimens for digital pathology

INTRODUCTION

Digital cytopathology is being progressively implemented in centres worldwide, but impediments such as the three-dimensionality of specimens and the size of scanned images have prevented its use from becoming widespread. This study aimed to validate the use of digital whole slide image scanning of cytopathology samples for routine sign-out.

METHODS

Specimens were scanned using the Leica Aperio GT 450 System. The following sample types were used: liquid-based cytology, direct conventional smears from fine needle aspirates and cytospins. Cases were validated by the same pathologist who originally rendered the conventional diagnosis, with a washout of at least 3 months. Final digital diagnoses were compared to the original analogical diagnoses, and cases were considered concordant up to a one-degree difference between the original and digital diagnoses. Reasons for the unsuccessful scanning of slides were also noted. The technical procedures followed the College of American Pathologists' guidelines for digital pathology validation.

RESULTS

A total of 730 slides from 383 cases (337 female, 51 male; median age 42) were successfully scanned. These cases consisted of the following sample types: 81 (21.1%) conventional smears, 240 (62.7%) liquid-based cytology samples and 62 (16.2%) cytospins. There were only five discordant cases, with a 98.7% agreement between original and digital diagnoses using the difference rate of up to one degree. Seventy-seven slides (10.5%) had to be rescanned due to technical problems. The main reasons for unsuccessful scanning were paucicellular samples (44; 57.1%), the thickness of the smears (18; 23.4%) and issues with the coverslip (15; 19.5%).

CONCLUSION

Cytological specimens can be successfully scanned and used for digital pathology, with excellent agreement with the original diagnoses.

Development of Automatic Portable Pathology Scanner and Its Evaluation for Clinical Practice

Digital pathological scanners transform traditional glass slides into whole slide images (WSIs), which significantly improve the efficiency of pathological diagnosis and promote the development of digital pathology. However, the huge economic burden limits the spread and application of general WSI scanners in relatively remote and backward regions. In this paper, we develop an automatic portable cytopathology scanner based on mobile internet, Landing-Smart, to avert the above problems. Landing-Smart is a tiny device with a size of 208 mm × 107 mm × 104 mm and a weight of 1.8 kg, which integrates four main components including a smartphone, a glass slide carrier, an electric controller, and an optical imaging unit. By leveraging a simple optical imaging unit to substitute the sophisticated but complex conventional light microscope, the cost of Landing-Smart is less than $3000, much cheaper than general WSI scanners. On the one hand, Landing-Smart utilizes the built-in camera of the smartphone to acquire field of views (FoVs) in the section one by one. On the other hand, it uploads the images to the cloud server in real time via mobile internet, where the image processing and stitching method is implemented to generate the WSI of the cytological sample. The practical assessment of 209 cervical cytological specimens has demonstrated that Landing-Smart is comparable to general digital scanners in cytopathology diagnosis. Landing-Smart provides an effective tool for preliminary cytological screening in underdeveloped areas.

Digital cytology: Current status and future prospects

AIMS

In this paper, we reviewed the basic principle and the currentstatus of digital cytopathology.

MATERIALS AND METHODS

We reviewed the published papers on digitalcytology and analysed its future prospects.

RESULTS

Virtualcytology using digital platform is being increasingly used to render diagnosisrather than conventional glass slide microscopy. Whole slide imaging (WSI)offers the prospect of true virtual microscopy and in the near future, may evenreplace glass slides in routine practice. It may be pivotal in diagnosing andtraining pathology graduates faster and more accurately.

CONCLUSION

The digital cytopathology is a promising field and may have great impact indiagnosis, research and teaching.

Assessing the quality of cytopathology whole slide imaging for education from archived cases

INTRODUCTION

Many institutions have cytopathology case archives for education. Unfortunately, these slides deteriorate over time and have limited accessibility. Whole slide imaging (WSI) can overcome these limitations. However, suboptimal image quality and scanning effort are barriers.

MATERIALS AND METHODS

We selected 123 slides from cytopathology study sets for WSI scanning at 400x magnification without z-stacking. The Ventana DP 200 scanner and Virtuoso software were used. Slides were scanned in 2 rounds: the first round of slides was prepared for scanning with light cleaning, and the second round was performed only on slides that had unacceptable WSI quality after thorough cleaning. Slides were assessed with a 4-tier grading system created by the authors. Time to scan each slide was recorded.

RESULTS

Within the first round, 96 of 123 (78%) slides scanned were determined to be of acceptable quality. After the second round of scanning, in total, 118 of 123 (95.9%) slides were determined to be of acceptable quality. The average time needed to scan each slide was 213 seconds.

CONCLUSIONS

The majority of slides scanned were of acceptable quality in the first round of scanning. After cleaning and rescanning, nearly every slide investigated was of acceptable quality. The primary objective is to provide other institutions that may be considering a similar project a benchmark so that they know what to expect in terms of slide scan success rate and the amount of time needed to digitize slides for educational archiving. This pilot study demonstrates the feasibility of using WSI for cytology education cases.

Evaluating the role of Z-stack to improve the morphologic evaluation of urine cytology whole slide images for high-grade urothelial carcinoma: Results and review of a pilot study

BACKGROUND

Whole slide imaging (WSI) adoption has been slower in cytopathology due, in part, to challenges in multifocal plane scanning on 3-dimensional cell clusters. ThinPrep and other liquid-based preparations may alleviate the issue by reducing clusters in a concentrated area. This study investigates the use of Z-stacked images for diagnostic assessment and the experience of evaluating urine ThinPrep WSI.

METHODS

Thirty ThinPrep urine cases of high-grade urothelial carcinoma (n = 22) and cases of negative for high-grade urothelial carcinoma (n = 8) were included. Slides were scanned at 40× magnification without Z-stack and with Z-stack at 3 layers, 1 μm each. Six cytopathologists and 1 cytotechnologist evaluated the cases in 2 rounds with a 2-week wash-out period in a blinded manner. A Cohen's Kappa (CK) calculated concordance rates. A survey after each round evaluated participant experience.

RESULTS

CK with the original report ranged from 0.606 to 1.0 (P < .05) without Z-stack and 0.533 to 1.0 (P < .05) with Z-stack both indicating substantial-to-perfect concordance. For both rounds, interobserver CK was moderate-to-perfect (0.417-1.0, P < .05). Intraobserver CK was 0.697-1.0 (P < 0.05), indicating substantial to perfect concordance. The average scan time and file size for slides without Z-stack and with Z-stack are 6.27 minute/0.827 GB and 14.06 minute/2.650 GB, respectively. Surveys demonstrated a range in comfort and use with slightly more favorable opinions for Z-stacked cases.

CONCLUSIONS

Z-stack images provide minimal diagnostic benefit for urine ThinPrep WSI. In addition, Z-stacked urine WSI does not justify the prolonged scan times and larger storage needs compared to those without Z-stack.

DigiPatICS: Digital Pathology Transformation of the Catalan Health Institute Network of 8 Hospitals—Planification, Implementation, and Preliminary Results

Complete digital pathology transformation for primary histopathological diagnosis is a challenging yet rewarding endeavor. Its advantages are clear with more efficient workflows, but there are many technical and functional difficulties to be faced. The Catalan Health Institute (ICS) has started its DigiPatICS project, aiming to deploy digital pathology in an integrative, holistic, and comprehensive way within a network of 8 hospitals, over 168 pathologists, and over 1 million slides each year. We describe the bidding process and the careful planning that was required, followed by swift implementation in stages. The purpose of the DigiPatICS project is to increase patient safety and quality of care, improving diagnosis and the efficiency of processes in the pathological anatomy departments of the ICS through process improvement, digital pathology, and artificial intelligence tools.

Impact of mobile devices on cancer diagnosis in cytology

BACKGROUND

Digital pathology has widened pathologists' opportunities to examine both surgical and cytological samples. Recently, portable mobile devices like tablets and smartphones have been tested for application with digital technologies including static, dynamic, and more recently whole slide imaging. This study aimed to review the published literature on the impact of mobile devices on cancer diagnoses in cytology. This analysis focused on their diagnostic potential, technical details, critical issues and pitfalls, and economical aspects.

METHODS

A systematic search was carried out in the electronic databases Embase and PubMed. Studies dealing with the application of mobile devices for diagnosing cancer on cytological specimens were included. The quality of studies was assessed with the QUADAS-2 tool. The main themes addressed were the comparison of manual examination with light microscopy and the use of mobile tools for primary diagnosis. The technical features of different models of smartphones and tablets, software, and adapters were also studied in terms of feasibility and costs-analysis.

RESULTS

Of 2458 retrieved articles, 18 were included. Concordance with light microscopy was good and diagnostic performance comparable with an expert pathologist's diagnosis. The mobile devices studied differed, sometimes significantly, in terms of speed and cost. The utility was improved by employing specifically designed adapters. Image acquisition and transmission represent the main critical points in almost all studies.

CONCLUSION

The use of mobile devices demonstrated promising results regarding the digital evaluation of cytological samples. Widespread adoption even in underserved areas is anticipated following validation studies, technology improvements, and reduction in the costs.

Digital/Computational Technology for Molecular Cytology Testing: A Short Technical Note with Literature Review

This short article describes the method of digital cytopathology using Z-stack scanning with or without extended focusing. This technology is suitable to observe such thick clusters as adenocarcinoma on cytologic specimens. Artificial intelligence (AI) has been applied to histological images, but its application on cytologic images is still limited. This article describes our attempt to apply AI technology to cytologic digital images. For molecular analysis, cytologic materials, such as smear, LBC, and cell blocks, have been successfully used for targeted single gene detection and multiplex gene analysis with next-generation sequencing. As a future perspective, the system can be connected to full automation by combining digital cytopathology with AI application to detect target cancer cells and to perform molecular analysis. The literature review is updated according to the subjects.

Image Analysis Using Machine Learning for Automated Detection of Hemoglobin H Inclusions in Blood Smears - A Method for Morphologic Detection of Rare Cells

Background

Morphologic rare cell detection is a laborious, operator-dependent process which has the potential to be improved by the use of image analysis using artificial intelligence. Detection of rare hemoglobin H (HbH) inclusions in red cells in the peripheral blood is a common screening method for alpha-thalassemia. This study aims to develop a convolutional neural network-based algorithm for the detection of HbH inclusions.

Methods

Digital images of HbH-positive and HbH-negative blood smears were used to train and test the software. The software performance was tested on images obtained at various magnifications and on different scanning platforms. Another model was developed for total red cell counting and was used to confirm HbH cell frequency in alpha-thalassemia trait. The threshold minimum red cells to image for analysis was determined by Poisson modeling and validated on image sets.

Results

The sensitivity and specificity of the software for HbH+ cells on images obtained at ×100, ×60, and ×40 objectives were close to 91% and 99%, respectively. When an AI-aided diagnostic model was tested on a pilot of 40 whole slide images (WSIs), good inter-rater reliability and high sensitivity and specificity of slide-level classification were obtained. Using the lowest frequency of HbH+ cells (1 in 100,000) observed in our study, we estimated that a minimum of 2.4 × 10⁶ red cells would need to be analyzed to reduce misclassification at the slide level. The minimum required smear size was validated on 78 image sets which confirmed its validity.

Conclusions

WSI image analysis can be utilized effectively for morphologic rare cell detection. The software can be further developed on WISs and evaluated in future clinical validation studies comparing AI-aided diagnosis with the routine diagnostic method.

Experience Reviewing Digital Pap Tests using a Gallery of Images

Introduction:

Hologic is developing a digital cytology platform. An educational website was launched for users to review these digitized Pap test cases. The aim of this study was to analyze data captured from this website.

Materials and Methods:

ThinPrep^® Pap test slides were scanned at ×40 using a volumetric (14 focal plane) technique. Website cases consisted of an image gallery and whole slide image (WSI). Over a 13 month period data were recorded including diagnoses, time participants spent online, and number of clicks on the gallery and WSI.

Results:

51,289 cases were reviewed by 918 reviewers. Cytotechnologists spent less time (M [Median] = 65.0 s) than pathologists (M = 82.2 s) reviewing cases (P < 0.001). Longer times were associated with incorrect diagnoses and cases with organisms. Cytotechnologists matched the reference diagnoses in 85% of cases compared to pathologists who matched in 79.8%. While in 62% of cases reviewers only examined the gallery, they attained the correct diagnosis 92.7% of the time. Pathologists made more clicks on the gallery and WSI than cytotechnologists (P < 0.001). Diagnostic accuracy decreased with increasing clicks.

Conclusions:

Website participation provided feedback about how cytologists interact with a digital platform when reviewing cases. These data suggest that digital Pap test review when comprised of an image gallery displaying diagnostically relevant objects is quick and easy to interpret. The high diagnostic concordance of digital Pap tests with reference diagnoses can be attributed to high image quality with volumetric scanning, image gallery format, and ability for users to freely navigate the entire digital slide.

The histopathological diagnosis of atypical meningioma: glass slide versus whole slide imaging for grading assessment

Limited studies on whole slide imaging (WSI) in surgical neuropathology reported a perceived limitation in the recognition of mitoses. This study analyzed and compared the inter- and intra-observer concordance for atypical meningioma, using glass slides and WSI. Two neuropathologists and two residents assessed the histopathological features of 35 meningiomas-originally diagnosed as atypical-in a representative glass slide and corresponding WSI. For each histological parameter and final diagnosis, we calculated the inter- and intra-observer concordance in the two viewing modes and the predictive accuracy on recurrence. The concordance rates for atypical meningioma on glass slides and on WSI were 54% and 60% among four observers and 63% and 74% between two neuropathologists. The inter-observer agreement was higher using WSI than with glass slides for all parameters, with the exception of high mitotic index. For all histological features, we found median intra-observer concordance of ≥ 79% and similar predictive accuracy for recurrence between the two viewing modes. The higher concordance for atypical meningioma using WSI than with glass slides and the similar predictive accuracy for recurrence in the two modalities suggest that atypical meningioma may be safely diagnosed using WSI.

Challenges Developing Deep Learning Algorithms in Cytology

BACKGROUND

The incorporation of digital pathology into routine pathology practice is becoming more widespread. Definite advantages exist with respect to the implementation of artificial intelligence (AI) and deep learning in pathology, including cytopathology. However, there are also unique challenges in this regard.

SUMMARY

This review discusses cytology-specific challenges, including the need to implement digital cytology prior to AI; the large file sizes and increased acquisition times for whole slide images in cytology; the routine use of multiple stains, such as Papanicolaou and Romanowsky stains; the lack of high-quality annotated datasets on which to train algorithms; and the considerable computer resources required, in terms of both computer infrastructure and skilled personnel, for computing and storage of data. Global concerns regarding AI that are certainly applicable to cytology include the need for model validation and continued quality assurance, ethical issues such as the use of patient data in developing algorithms, the need to develop regulatory frameworks regarding what type of data can be utilized and ensuring cybersecurity during data collection and storage, and algorithm development. Key Messages: While AI will likely play a role in cytology practice in the future, applying this technology to cytology poses a unique set of challenges. A broad understanding of digital pathology and algorithm development is desirable to guide the development of algorithms, as well as the need to be cognizant of potential pitfalls to avoid when incorporating the technology in practice.

Non-human papilloma virus associated adenocarcinomas of the cervix uteri. Cytologic features and diagnostic agreement using whole slide digital cytology imaging

BACKGROUND

Non-Human Papilloma Virus associated adenocarcinomas (NHPVAs) are uncommon tumors of the cervix uteri which often show a deceptive morphology. Therefore, their diagnostic assessment may be challenging. Slide digital cytology imaging may be an useful tool to improve cytological diagnostic accuracy. However, this novel technology has not been applied to NHPVAs associated cytologies yet.

METHODS

The study included 31 whole slide digital cytology cases from 10 women with a proven histological diagnosis of NHPVA. As a control group, three further digital slides, from two women with a histological diagnosis of squamous intraepithelial lesion (SIL), were included. The digitally scanned cytological slides were revised to assess the concordance rate among three observers and to find out the most relevant NHPVA cytological criteria.

RESULTS

Overall diagnostic agreement between observers was 67.60% (K = 0.50; P < 0.0001). At the consensus diagnosis 34 cases were re-classified as at least suspicious for glandular lesion (n = 24), SIL (n = 2) and negative (n = 8). The most relevant cytologic features for atypical glandular cells or adenocarcinoma at consensus were evident nucleoli, nuclear overlapping and atypical enlarged nuclei.

CONCLUSIONS

The diagnosis of NHPVA in digital cytology is feasible using criteria which are also used in conventional microscopy. Our study shows a moderate agreement for the cytological diagnosis of NHPVAs using whole slide digital cytology approach. These results are discussed taking into account the most relevant differential diagnostic issues.

Current state of whole slide imaging use in cytopathology: Pros and pitfalls

Whole slide imaging (WSI) allows generation of large whole slide images and their navigation with zoom in and out like a true virtual microscope. It has become widely used in surgical pathology for many purposes, such as education and training, research activity, teleconsultation, and primary diagnosis. However, in cytopathology, the use of WSI has been lagging behind histology, mainly due to the cytological specimen's characteristics, as groups of cells of different thickness are distributed throughout the slide. To allow the same focusing capability of light microscope, slides have to be scanned at multiple focal planes, at the cost of longer scan times and larger file size. These are the main technical pitfalls of WSI for cytopathology, partly overcome by solutions like liquid-based preparations. Validation studies for the use in primary diagnosis are less numerous and more heterogeneous than in surgical pathology. WSI has been proved effective for training students and successfully used in proficiency testing, allowing the creation of digital cytology atlases. Longer scan times are also a barrier for use in rapid on-site evaluation, but WSI retains its advantages of easy sharing of images for consultation, multiple simultaneous viewing in different locations, the possibility of unlimited annotations and easy integration with medical records. Moreover, digital slides set the laboratory free from reliance on a physical glass slide, with no more concern of fading of stain or slide breakage. Costs are still a problem for small institutions, but WSI can also represent the beginning of a more efficient way of working.

The Landscape of Digital Pathology in Transplantation: From the Beginning to the Virtual E-Slide

Background

Digital pathology has progressed over the last two decades, with many clinical and nonclinical applications. Transplantation pathology is a highly specialized field in which the majority of practicing pathologists do not have sufficient expertise to handle critical needs. In this context, digital pathology has proven to be useful as it allows for timely access to expert second-opinion teleconsultation. The aim of this study was to review the experience of the application of digital pathology to the field of transplantation.

Methods

Papers on this topic were retrieved using PubMed as a search engine. Inclusion criteria were the presence of transplantation setting and the use of any type of digital image with or without the use of image analysis tools; the search was restricted to English language papers published in the 25 years until December 31, 2018.

Results

Literature regarding digital transplant pathology is mostly about the digital interpretation of posttransplant biopsies (75 vs. 19), with 15/75 (20%) articles focusing on agreement/reproducibility. Several papers concentrated on the correlation between biopsy features assessed by digital image analysis (DIA) and clinical outcome (45/75, 60%). Whole-slide imaging (WSI) only appeared in recent publications, starting from 2011 (13/75, 17.3%). Papers dealing with preimplantation biopsy are less numerous, the majority (13/19, 68.4%) of which focus on diagnostic agreement between digital microscopy and light microscopy (LM), with WSI technology being used in only a small quota of papers (4/19, 21.1%).

Conclusions

Overall, published studies show good concordance between digital microscopy and LM modalities for diagnosis. DIA has the potential to increase diagnostic reproducibility and facilitate the identification and quantification of histological parameters. Thus, with advancing technology such as faster scanning times, better image resolution, and novel image algorithms, it is likely that WSI will eventually replace LM.

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.

Digital pathology: semper ad meliora

This review is an evidence-based summary of digital pathology: past, present and future. It discusses digital surgical pathology and the cytopathology digitisation challenge as well as the performance of digital histopathology and cytopathology as a diagnostic tool, particularly in contrast to user perceptions. Time and cost efficiency of digital pathology, learning curves, education and quality assurance, with the importance of validation of systems, is emphasised. The review concludes with a discussion of digital pathology as a source of 'big data' and where this might lead pathologists in the digital pathology future.