Digital Cytology Research Articles

Value and Key Concepts for Performing a Proof-of-Concept Study for Digital Cytology

Value and Key Concepts for Performing a Proof-of-Concept Study for Digital Cytology

Digital Cytology in Veterinary Education: A Comprehensive Survey of Its Application and Perception among Undergraduate and Postgraduate Students.

The COVID-19 pandemic accelerated technological changes in veterinary education, particularly in clinical pathology and anatomic pathology courses transitioning from traditional methods to digital pathology (DP). This study evaluates the personal effectiveness and satisfaction, as well as the advantages and disadvantages, of DP, in particular digital cytology (DC), as a teaching method among European veterinary students, both at the undergraduate and postgraduate level, who attended digital pathology courses during and before the pandemic. A further aim is to discuss the differences between the two student groups. A Google Form survey consisting of 11 multiple-choice questions was emailed to pathology teachers and distributed to their students. Results indicated that undergraduate students showed greater digital pathology training, favouring DC as the most effective learning modality. In contrast, postgraduate students reported less digital slide training, and their preference for learning cytology was split between DC alone and DC integrated with traditional microscopy. All students experienced whole slide imaging for learning cytology slides prevalently, and they stated that DC enhanced their learning experience. While DC demonstrates personal effectiveness and satisfaction as a teaching method, it is important to not replace pathology training with light microscopy completely, as almost a third of the students indicated.

The current state of digital cytology and artificial intelligence (AI): global survey results from the American Society of Cytopathology Digital Cytology Task Force

The integration of whole slide imaging (WSI) and artificial intelligence (AI) with digital cytology has been growing gradually. Therefore, there is a need to evaluate the current state of digital cytology. This study aimed to determine the current landscape of digital cytology via a survey conducted as part of the American Society of Cytopathology (ASC) Digital Cytology White Paper Task Force. A survey with 43 questions pertaining to the current practices and experiences of WSI and AI in both surgical pathology and cytology was created. The survey was sent to members of the ASC, the International Academy of Cytology (IAC), and the Papanicolaou Society of Cytopathology (PSC). Responses were recorded and analyzed. In total, 327 individuals participated in the survey, spanning a diverse array of practice settings, roles, and experiences around the globe. The majority of responses indicated there was routine scanning of surgical pathology slides (n = 134; 61%) with fewer respondents scanning cytology slides (n = 150; 46%). The primary challenge for surgical WSI is the need for faster scanning and cost minimization, whereas image quality is the top issue for cytology WSI. AI tools are not widely utilized, with only 16% of participants using AI for surgical pathology samples and 13% for cytology practice. Utilization of digital pathology is limited in cytology laboratories as compared to surgical pathology. However, as more laboratories are willing to implement digital cytology in the near future, the establishment of practical clinical guidelines is needed.

Changing digital and telecytology practices post COVID-19 comparing ASC survey results from 2016 to 2023

During the COVID-19 pandemic, the need for digital pathology tools became more urgent. However, there needs to be more knowledge of the use in cytology. We aimed to evaluate current digital cytology practices and attitudes and compare the results with a pre-COVID-19 American Society of Cytopathology (ASC) survey. Fourteen survey questions assessing current attitudes toward digital cytology were developed from a 2016 ASC Digital Pathology Survey. Ten new survey questions were also created to evaluate telecytology use. The survey was e-mailed to ASC members over 6 weeks in2023. A total of 123 individuals responded (116 in 2016). Attitudes toward digital cytology were unchanged; most participants stated digital cytology is beneficial (87% 2023 versus 90% 2016). The percentage of individuals using digital cytology was unchanged (56% in 2016 and 2023). However, telecytology for rapid onsite assessment (ROSE) is now considered the best application (55% 2023 versus 31% 2016). Forty-three institutions reported using digital and telecytology tools; 40% made implementations after 2020; most did not feel that COVID-19 affected digital cytology (56%). Telecytology for ROSE is the most common application now (78%) compared with education (30%) in 2016. Limitations for implementing digital imaging in cytology included inability to focus (38%) and expense (33%). General attitudes toward digital tools by the cytology community have essentially remained the same between 2016 and now. However, telecytology for ROSE is increasingly being used, which supports a need for validation and competency guidelines.

TIE-GANs: single-shot quantitative phase imaging using transport of intensity equation with integration of GANs.

Artificial intelligence (AI) has become a prominent technology in computational imaging over the past decade. The expeditious and label-free characteristics of quantitative phase imaging (QPI) render it a promising contender for AI investigation. Though interferometric methodologies exhibit potential efficacy, their implementation involves complex experimental platforms and computationally intensive reconstruction procedures. Hence, non-interferometric methods, such as transport of intensity equation (TIE), are preferred over interferometric methods. TIE method, despite its effectiveness, is tedious as it requires the acquisition of many images at varying defocus planes. The proposed methodology holds the ability to generate a phase image utilizing a single intensity image using generative adversarial networks (GANs). We present a method called TIE-GANs to overcome the multi-shot scheme of conventional TIE. The present investigation employs the TIE as a QPI methodology, which necessitates reduced experimental and computational efforts. TIE is being used for the dataset preparation as well. The proposed method captures images from different defocus planes for training. Our approach uses an image-to-image translation technique to produce phase maps and is based on GANs. The main contribution of this work is the introduction of GANs with TIE (TIE:GANs) that can give better phase reconstruction results with shorter computation times. This is the first time the GANs is proposed for TIE phase retrieval. The characterization of the system was carried out with microbeads of size and structural similarity index (SSIM) for microbeads was found to be 0.98. We demonstrated the application of the proposed method with oral cells, which yielded a maximum SSIM value of 0.95. The key characteristics include mean squared error and peak-signal-to-noise ratio values of 140 and 26.42dB for oral cells and 100 and 28.10dB for microbeads. The proposed methodology holds the ability to generate a phase image utilizing a single intensity image. Our method is feasible for digital cytology because of its reported high value of SSIM. Our approach can handle defocused images in such a way that it can take intensity image from any defocus plane within the provided range and able to generate phase map.

Ehmt2 inactivation in pancreatic epithelial cells shapes the transcriptional landscape and inflammation response of the whole pancreas.

Introduction: The Euchromatic Histone Methyl Transferase Protein 2 (EHMT2), also known as G9a, deposits transcriptionally repressive chromatin marks that play pivotal roles in the maturation and homeostasis of multiple organs. Recently, we have shown that Ehmt2 inactivation in the mouse pancreas alters growth and immune gene expression networks, antagonizing Kras-mediated pancreatic cancer initiation and promotion. Here, we elucidate the essential role of Ehmt2 in maintaining a transcriptional landscape that protects organs from inflammation. Methods: Comparative RNA-seq studies between normal postnatal and young adult pancreatic tissue from Ehmt2 conditional knockout animals (Ehmt2 fl/fl ) targeted to the exocrine pancreatic epithelial cells (Pdx1-Cre and P48 Cre/+ ), reveal alterations in gene expression networks in the whole organ related to injury-inflammation-repair, suggesting an increased predisposition to damage. Thus, we induced an inflammation repair response in the Ehmt2 fl/fl pancreas and used a data science-based approach to integrate RNA-seq-derived pathways and networks, deconvolution digital cytology, and spatial transcriptomics. We also analyzed the tissue response to damage at the morphological, biochemical, and molecular pathology levels. Results and discussion: The Ehmt2 fl/fl pancreas displays an enhanced injury-inflammation-repair response, offering insights into fundamental molecular and cellular mechanisms involved in this process. More importantly, these data show that conditional Ehmt2 inactivation in exocrine cells reprograms the local environment to recruit mesenchymal and immunological cells needed to mount an increased inflammatory response. Mechanistically, this response is an enhanced injury-inflammation-repair reaction with a small contribution of specific Ehmt2-regulated transcripts. Thus, this new knowledge extends the mechanisms underlying the role of the Ehmt2-mediated pathway in suppressing pancreatic cancer initiation and modulating inflammatory pancreatic diseases.

Assessing an Artificial Intelligence-driven Digital Cytology Tool for Efficient Diagnosis of Upper Tract Urothelial Carcinoma in Upper Urinary Tract Cytology

Assessing an Artificial Intelligence-driven Digital Cytology Tool for Efficient Diagnosis of Upper Tract Urothelial Carcinoma in Upper Urinary Tract Cytology

The Current State of Whole Slide Imaging and Artificial Intelligence in Cytology Practice: Global Survey Results from the American Society of Cytopathology Digital Cytology White Paper Task Force

The Current State of Whole Slide Imaging and Artificial Intelligence in Cytology Practice: Global Survey Results from the American Society of Cytopathology Digital Cytology White Paper Task Force

Integrating cytology into routine digital pathology workflow: a 5-year journey.

Despite recent advances in digital imaging, the adoption of digital cytology is challenging due to technical limitations. This study describes our 5-year institutional experience with the implementation of digital cytology. The routine cytology workflow included conventional two-step screening by cytotechnologists, followed by sign out by pathologists. We introduced sign out of cytologic cases using a microscopic digital imaging platform operated by cytotechnologists, which allowed for remote review of slides by cytopathologists via video streaming. We also provided cytologic correlation to support the virtual slide-based sign out of histopathological specimens and for a weekly pathology-radiology conference. In addition, positive cytology cases were archived for integration into the laboratory information system and for prospective computational pathology studies. We also summarized lessons learned over the years and outlined our vision for future developments. This unique experience may serve as a role model for other institutions.

Comparison of the Hologic Genius Digital Diagnostics System with the ThinPrep Imaging System-A retrospective assessment.

Digital cytology (DC) with artificial intelligence (AI) is a new approach. The authors compared DC with liquid-based cytology (LBC) using computer assistance (CAS) in a retrospective, noninterventional study. In total, 1994 ThinPrep LBC slides (Hologic), which were previously analyzed in 2020 using an imaging system with CAS in routine cotesting for cytology/human papillomavirus, were reviewed in a blinded mode using the Genius Digital Diagnostics System (Hologic). In 555 cases, a histology result was available. The slides were digitally scanned (volumetric scan) at 14 levels integrated into one. AI algorithms were used to present a gallery of six tiles each (containing objects of interest) in five categories. Six additional tile rows were available, from which the diagnoses were made. All cases with a mismatch between DC and imaging system results were reviewed by an additional cytopathologist. In 86.56% of cases, a complete match between both systems was observed using the same cytology categories. When also considering the histology results, the match was 90.37%. In addition, when a cytology follow-up and/or a retrospective review was applied, the match reached 97.34%. In only 0.65% of cases was a major discrepancy observed (two grades of cytology or a low-grade squamous intraepithelial lesion/high-grade squamous intraepithelial lesion [LSIL/HSIL] shift), and none were identified by DC. Significantly more cases of higher severity (atypical squamous cells cannot exclude high grade [ASC-H], high-grade squamous intraepithelial lesion [HSIL]) were identified with DC, and its negative predictive value was higher. The screening time was significantly shorter with DC. With the Genius system for DC, the sensitivity for HSIL+/ASC-H and the specificity for LSIL and HSIL were superior to LBC and CAS. Screening time was significantly lower.

Segmentation of cervical nuclei using convolutional neural network for conventional cytology

ABSTRACT Although implementing the Pap smear has drastically reduced the mortality rates from cervical cancer, false positives and negatives are related to the quality of the analysis and the cytopathologist experience. An alternative is the insertion of digital cytology in the quality monitoring to assist the screening. However, conventional cytology is still a major challenge, as it presents a lot of cellular overlap and several epithelial structures that make it difficult to implement computational methodologies. This article compares the performance of U-net and SegNet neural networks for nuclei segmentation in cervical images. Experiments were performed with different activation functions, batch sizes, and datasets, ISBI (synthetic images from liquid cytology) and CRIC Cervix-Seg (conventional cytology real images). The models achieved a Dice coefficient of 0.9783 for ISBI2015 and 0.9429 for CRIC Cervix-Seg. These results suggest a methodology capable of segmenting real images of cervical nuclei with quality, even in situations of overlap and artefacts, advancing efforts towards the automation of tasks as part of the cytopathological analysis in the laboratory work routine.

Digital cytology: Current status and future prospects.

In this paper, we reviewed the basic principle and the currentstatus of digital cytopathology. We reviewed the published papers on digitalcytology and analysed its future prospects. 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. The digital cytopathology is a promising field and may have great impact indiagnosis, research and teaching.

Digital Cytology.

Microscopic evaluation of cytologic specimens can provide rapid diagnostic information and aid in formulating diagnostic and treatment plans. The primary benefit of cytologic evaluation is the rapid collection, processing, and evaluation of samples. However, physical transport of glass slides and body fluids to a diagnostic laboratory takes time and can negatively affect patient management. Digital cytology allows specimens to be processed in the clinic and immediately sent to pathologists. With technology becoming more affordable, digital cytology is revolutionizing the field of clinical pathology and patient care.

Inter- and intraobserver agreement in whole-slide digital ThinPrep samples of low-grade squamous lesions of the cervix uteri with known high-risk HPV status: A multicentric international study.

High-risk human papilloma virus (HR HPV) testing and liquid-based cytology are used for primary cervical screening. Digital cytology, based on whole-slide scanned samples, is a promising technique for teaching and diagnostic purposes. The aim of our study was to evaluate the interobserver and intraobserver variation in low-grade squamous lesions, HR HPV status bias, and the use of whole-slide scanned digital cervical cytology slides. Fifteen expert cytopathologists evaluated 71 digitalized ThinPrep slides (31 atypical squamous cells of undetermined significance [ASC-US], 21 negative for intraepithelial lesion or malignancy, and 19 low-grade squamous intraepithelial lesion cases). HR HPV data were accessible only in the second round. In interobserver analysis, Kendall's coefficient of concordance was 0.52 in the first round and 0.58 in the second round. Fleiss' kappa values were 0.29 in the first round and 0.31 in the second round. In the ASC-US category, Fleiss kappa increased from 0.19 to 0.22 in the second round and the increase was even higher expressed by Kendall's coefficient: from 0.42 to 0.52. In intraobserver analysis, personal scores were higher in the second round. The interobserver and intraobserver variability in low-grade squamous lesions was within fair agreement values in the present study, in line with previous works. The comparison of two rounds showed that expert cytopathologists are generally unbiased by the knowledge of HR HPV data, but that being informed of the HR HPV status leads to a better agreement. Stain quality and back discomfort were highlighted as factors affecting digital cytopathology use.

Automated staining analysis in digital cytopathology and applications.

The progress of digital pathology in recent years has been an opportunity for the development of automated image analysis algorithms for quantitative measurements and computer aided diagnosis. With those new methods comes the need for high staining quality and reproducibility, as image analysis tools are typically more sensible to slight stain variations than trained pathologists. This article presents a method for the automated analysis of cytology slides stains specifically adapted to the challenges encountered in digital cytopathology. In particular, the variety of cell types in cytology slides, the 3D distribution of the cellular material, the presence of superposed cells and the need for independent analysis of sub-cellular compartments are addressed. The proposed method is applied to the quantification of staining variations for quality control, resulting from changes in the staining protocol such as reagent immersion time or a reagent change. Another demonstrated application is the selection of staining protocol parameters that maximize the visible details in nucleus. Finally the analysis pipeline is also used to compare different stain normalization algorithms on digital cytology slides. Code available at: https://gitlab.com/vitadx/articles/automated_staining_analysis.

Transcriptional Landscape Established by the Euchromatic Histone‐lysine N‐methyltransferase Pathway During Pancreas Ontogenesis and Pancreatitis

The Euchromatic Histone‐lysine N‐Methyltransferase 1/2 (EHMT1/2) complex, responsible for catalyzing mono‐ and di‐methylation of histone H3K9, has emerged as key regulator of embryogenesis and carcinogenesis. We have recently shown that EHMT2 inactivation antagonizes oncogenic KRAS‐mediated pancreatic cancer initiation and promotion by altering growth and immune gene expression networks. The current study unravels the transcriptional landscape regulated by EHMT2 during pancreatic development and acute pancreatitis. We performed conditional knockout of EHMT2 in the pancreas using the Pdx1‐Cre model, comparing perinatal (10‐day‐old) vs. young adult mice (4‐week‐old). Notably, despite carrying EHMT2 inactivation, both the whole animal and pancreas develop normally through adulthood. The transcriptional landscape resulting from EHMT2 inactivation, however, is different in perinatal mice from young adults, suggesting that this epigenomic regulator plays a role in modulating distinct gene networks during the development of this organ. Congruent with a role for EHMT2 in gene silencing, we show that its inactivation in both life stages primarily resulted in gene expression signatures dominated by upregulated transcripts. Notably, among these genes, we find pancreas‐specific clusters, including those encoding zymogen granules, during the transition to young adult, suggesting that EHMT2 facilitates acinar cell‐fate decisions. DEGs from young adults with EHMT2 loss displayed prominent activation of TP53 signaling‐related and DNA damage response gene networks that were not present in the perinatal cohort, demonstrating that cell cycle arrest accompanies this differentiation event. We also find de‐repression of genes that participate in early endodermal development, metabolism, and inflammation. Studies on acute pancreatitis, induced by repeated injections of the CCK‐agonist peptide, caerulein, show that EHMT2 inactivation results in more severe disease compared to that in controls, as measured by histological scoring of edema, inflammatory cell infiltrate, and necrosis. More importantly, and congruent with our histological assessment, EHMT2 inactivation in acinar epithelial cells regulates the expression of key inflammation‐associated genes that reprograms the non‐epithelial responses to pancreatitis. Digital cytology analysis demonstrated that the transcriptional landscape resulting from EHMT2 inactivation triggers a more aggressive inflammatory response for the pancreas. This data demonstrates that, although not critical to development of the organ, EHMT2 is involved in pancreas‐specific gene expression. Lastly, during pancreatitis, EHMT2 behaves as an important regulator of inflammation, extending our knowledge of epigenomic mechanisms underlying development, homeostasis, and diseases of the pancreas.

Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards.

This commentary aims to address the field of Artificial intelligence (AI) in Digital Pathology (DP) both in terms of the global situation and research perspectives. It has four polarities. First, it revisits the evolutions of digital pathology with particular care to the two fields of the digital cytology and the digital histology. Second, it illustrates the main fields in the employment of AI in DP. Third, it looks at the future directions of the research challenges from both a clinical and technological point of view. Fourth, it discusses the transversal problems among these challenges and implications and introduces the immediate work to implement.

Point-of-Care Digital Cytology With Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting

Cervical cancer is highly preventable but remains a common and deadly cancer in areas without screening programs. The creation of a diagnostic system to digitize Papanicolaou test samples and analyze them using a cloud-based deep learning system (DLS) may provide needed cervical cancer screening to resource-limited areas. To determine whether artificial intelligence-supported digital microscopy diagnostics can be implemented in a resource-limited setting and used for analysis of Papanicolaou tests. In this diagnostic study, cervical smears from 740 HIV-positive women aged between 18 and 64 years were collected between September 1, 2018, and September 30, 2019. The smears were digitized with a portable slide scanner, uploaded to a cloud server using mobile networks, and used to train and validate a DLS for the detection of atypical cervical cells. This single-center study was conducted at a local health care center in rural Kenya. Detection of squamous cell atypia in the digital samples by analysis with the DLS. The accuracy of the DLS in the detection of low- and high-grade squamous intraepithelial lesions in Papanicolaou test whole-slide images. Papanicolaou test results from 740 HIV-positive women (mean [SD] age, 41.8 [10.3] years) were collected. The DLS was trained using 350 whole-slide images and validated on 361 whole-slide images (average size, 100 387 × 47 560 pixels). For detection of cervical cellular atypia, sensitivities were 95.7% (95% CI, 85.5%-99.5%) and 100% (95% CI, 82.4%-100%), and specificities were 84.7% (95% CI, 80.2%-88.5%) and 78.4% (95% CI, 73.6%-82.4%), compared with the pathologist assessment of digital and physical slides, respectively. Areas under the receiver operating characteristic curve were 0.94 and 0.96, respectively. Negative predictive values were high (99%-100%), and accuracy was high, particularly for the detection of high-grade lesions. Interrater agreement was substantial compared with the pathologist assessment of digital slides (κ = 0.72) and fair compared with the assessment of glass slides (κ = 0.36). No samples that were classified as high grade by manual sample analysis had false-negative assessments by the DLS. In this study, digital microscopy with artificial intelligence was implemented at a rural clinic and used to detect atypical cervical smears with a high sensitivity compared with visual sample analysis.

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.

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

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. 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. 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. 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.