Digital Pathology: Advantages, Limitations and Emerging Perspectives

Perspectives on Reducing Barriers to the Adoption of Digital and Computational Pathology Technology by Clinical Labs

Background/Objectives: Digital and computational pathology (DP/CP) tools have the potential to improve the efficiency and accuracy of the anatomic pathology workflow; however, current adoption among US hospital and reference labs remains low. Methods: To better understand the current utilization of DP/CP technology and barriers to widespread adoption, we conducted a survey among 63 anatomic pathologists and lab directors within the US health system. Results: The survey results indicated that current use cases for DP/CP involve streamlining traditional manual pathology and that labs would have substantial difficulty providing AI-guided image analysis if it were required by physicians today. Among potential catalysts for the broader adoption of DP/CP, pathologists identified clinical guidelines as a key resource for anatomic pathology, whose endorsement of DP/CP would be highly impactful for reducing current barriers. Conclusions: Expanded access to DP/CP may ultimately benefit all major stakeholders-patients, physicians, clinical laboratory professionals, care settings, and payers-and will therefore require collaboration across these groups.

Potential of Proliferative Markers in Pancreatic Cancer Management: A Systematic Review

Background and Aims

Pancreatic cancer is an aggressive malignancy with poor prognosis and limited treatment options. Chemotherapy remains a primary therapeutic approach, but patient responses vary significantly, emphasizing the need for reliable biomarkers. This review explores the potential role of proliferative markers, including Ki-67, PCNA, Cyclin D1, and PHH3, as predictive and prognostic indicators in pancreatic cancer management, aiming to enhance personalized treatment strategies.

Methods

We conducted a narrative review by searching Scopus, PubMed, and Google Scholar for studies focusing on Ki-67, PCNA, Cyclin D1, and PHH3 in relation to pancreatic cancer and chemotherapy. The literature was reviewed to evaluate the role of these markers in predicting chemotherapy response, tumor progression, and overall patient survival.

Results

The review highlights the clinical significance of these markers. Ki-67 and PCNA are associated with cell proliferation, while Cyclin D1 regulates cell cycle progression and PHH3 is linked to mitotic activity. High expression levels of these markers often correlate with increased tumor aggressiveness and poorer patient outcomes. Moreover, they show promise in predicting chemotherapy response, which can inform tailored therapeutic strategies. However, challenges remain, including standardization of detection methods and determination of optimal cutoff values.

Conclusion

Proliferative markers such as Ki-67, PCNA, Cyclin D1, and PHH3 hold potential as predictive and prognostic tools in pancreatic cancer management. Their integration into clinical practice could improve the accuracy of treatment decisions and enhance patient outcomes. Further research and validation are necessary to overcome existing challenges and optimize their application in personalized oncology.

Digital Pathology as an Innovative Tool for Improving Cancer Diagnosis and Treatment

For more than a century, the "gold" standard for diagnosing malignant neoplasms has been pathohistology. However, the continuous advancement of modern technologies is leading to a radical transformation of this field and the emergence of digital pathology. The main advantages of digital pathology include the convenience of the data storage and transfer, as well as the potential for automating diagnostic processes through the application of artificial intelligence technologies. Integrating digital pathology into clinical practice is expected to accelerate the analysis of histological samples, reduce the costs associated with such procedures, and enable the accumulation of large datasets for future scientific research. At the same time, the development of digital pathology faces certain challenges such as the need for technical upgrades in laboratories, ensuring data cybersecurity, and training qualified personnel.

An update on applications of digital pathology: primary diagnosis; telepathology, education and research

Digital Pathology or whole slide imaging (WSI) is a diagnostic evaluation technique that produces digital images of high quality from tissue fragments. These images are formed on glass slides and evaluated by pathologist with the aid of microscope. As the concept of digital pathology is introduced, these high quality images are digitized and produced on-screen whole slide images in the form of digital files. This has paved the way for pathologists to collaborate with other pathology professionals in case of any additional recommendations and also provides remote working opportunities. The application of digital pathology in clinical practice is glazed with several advantages and adopted by pathologists and researchers for clinical, educational and research purposes. Moreover, digital pathology system integration requires an intensive effort from multiple stakeholders. All pathology departments have different needs, case usage, and blueprints, even though the framework elements and variables for effective clinical integration can be applied to any institution aiming for digital transformation. This article reviews the background and developmental phases of digital pathology and its application in clinical services, educational and research activities.

Enhancing Cancer Screening and Early Diagnosis in India: Overcoming Challenges and Leveraging Emerging Technologies

This review addresses the significant challenges and technological developments in cancer screening and early diagnosis in the context of India's diverse and resource-constrained healthcare landscape. Selected cancers like breast, cervical, oral, lung, and colorectal cancers are focused on, and established screening methods such as clinical breast examination (CBE), mammography, visual inspection with acetic acid (VIA), HPV DNA testing, and oral visual inspection (OVI) are reviewed. These are cost-effective strategies that are proven to reduce mortality. However, they face systemic barriers, including low awareness, socio-cultural stigma, and discontinuous healthcare access. Emerging technologies in cancer screening like liquid biopsy (detecting circulating tumor DNA), artificial intelligence (AI)-driven imaging (enhancing radiological accuracy), next-generation sequencing (identifying genetic mutations), and methylation-based ctDNA analysis (epigenetic profiling) are considered to be transformative in cancer management. Digital pathology and telemedicine are also found to improve diagnostic precision and rural/remote outreach. However, high costs, technical complexity, and limited validation in Indian settings are the major challenges that hinder their widespread adoption. The review emphasizes the need for culturally tailored awareness campaigns, integration of screening with the already existing public health programs, and increased investments in indigenous research to address genetic and environmental risk factors. It specifically advocates for strengthening the primary healthcare infrastructure, training community health workers, and leveraging mobile screening units to bridge urban-rural disparities. A combination of scalable low-resource methods and strategic adoption of emerging technologies can help in mitigating India's growing cancer burden. This aligns with global targets to reduce premature non-communicable disease (NCD) mortality by 2030. This synthesis of evidence-based practices and innovative strategies offers a roadmap for policymakers and stakeholders to enhance equitable cancer care delivery nationwide.

The Expression of Programmed Cell Death Ligand-1 and its Relationship with Infiltration, Metastasis and Prognosis in Cervical Squamous Cell Carcinoma

Cervical cancer (CC) represents a major gynecologic health problem. Respecting the role of programmed cell death ligand-1 (PDL-1) in cancer prognosis, we investigated its relationship with cervical squamous cell carcinoma (CSCC) invasion, metastasis and prognosis. A total of 184 CSCC patients were retrospectively selected, with normal paracarcinoma tissues as the Control group. PDL-1 expression was assessed, and its relationship with CSCC prognosis and clinical value on predicting CSCC invasion/metastasis and poor prognosis were determined. PDL-1 was up-regulated in CSCC. CSCC patients at International Federation of Gynecology and Obstetrics stage II/III, and with lymph node metastasis (LNM), parauterine/vascular infiltration, and history of sexually transmitted diseases exhibited up-regulated PDL-1. The areas under the curve of PDL-1 on predicting the invasion and metastasis/poor prognosis of CSCC patients were 0.930 (95%Cl: 0.883-0.962)/0.935 (95%Cl: 0.886-0.967), with cut-off values of 23.27/24.86 (86.76%/80.95% sensitivity, 95.69%/92.68% specificity). The CSCC patients with highly-expressed PDL-1 showed increased cumulative incidence of poor prognosis. Additionally, occurence of vascular infiltration/LNM, and up-regulated PDL-1 were independent risk factors for poor prognosis in CSCC patients. Briefly, PDL-1 expression rised in CSCC. High PDL-1 expression might promote tumor infiltration and LNM, while close monitoring of its expression contributed to evaluating prognosis of CSCC patients.

Digital pathology and artificial intelligence in renal cell carcinoma focusing on feature extraction: a literature review

The integrated application of artificial intelligence (AI) and digital pathology (DP) technology has opened new avenues for advancements in oncology and molecular pathology. Consequently, studies in renal cell carcinoma (RCC) have emerged, highlighting potential in histological subtype classification, molecular aberration identification, and outcome prediction by extracting high-throughput features. However, reviews of these studies are still rare. To address this gap, we conducted a thorough literature review on DP and AI applications in RCC through database searches. Notably, we found that AI models based on deep learning achieved area under the curve (AUC) of over 0.93 in subtype classification, 0.89-0.96 in grading of clear cell RCC, 0.70-0,89 in molecular prediction, and over 0.78 in survival prediction. This review finally discussed the current state of researches and potential future directions.

Spatial transcriptome reveals histology-correlated immune signature learnt by deep learning attention mechanism on H&E-stained images for ovarian cancer prognosis

BACKGROUND

The ability to predict the prognosis of patients with ovarian cancer can greatly improve disease management. However, the knowledge on the mechanism of the prediction is limited. We sought to deconvolute the attention feature learnt by a deep learning convolutional neural networks trained with whole-slide images (WSIs) of hematoxylin-and-eosin (H&E)-stained tumor samples using spatial transcriptomic data.

METHODS

In this study, 773 WSIs of H&E-stained tumor sections from 335 patients with treatment naïve high-grade serous ovarian cancer who were included in The Cancer Genome Atlas (TCGA) Pan-Cancer study were used to train, and validate, and to test a ResNet101 CNN model modified with attention mechanism. WSIs from patients in an independent cohort were used to further evaluate the model.

RESULTS

The prognostic value of the predicted H&E-based survival scores from the trained model on patient survival was evaluated. The attention signals learnt by the model were then examined their correlation with immune signatures using spatial transcriptome. After validating the model with the testing datasets, pathway enrichment analysis showed that the H&E-based survival score significantly correlated with certain immune signatures and this was validated spatially using spatial transcriptome data generated from ovarian cancer FFPE samples by correlating the selected signature and attention signal.

CONCLUSIONS

In conclusion, attention mechanism might be useful to identify regions for their specific immune activities. This could guide future pathological study for the useful immunological features that are important in modulating the prognosis of ovarian cancer patients.

iForensic, multicentric validation of digital whole slide images (WSI) in forensic histopathology setting according to the College of American Pathologists guidelines

Pathology has benefited from the rapid progress of image-digitizing technology during the last decade. However, the application of digital whole slide images (WSI) in forensic pathology still needs to be improved. WSI validation is crucial to ensure diagnostic performance, at least equivalent to glass slides and light microscopy. The College of American Pathologists Pathology and Laboratory Quality Center recently updated internal digital pathology system validation recommendations. Following these guidelines, this pilot study aimed to validate the performance of a digital approach for forensic histopathological diagnosis. Six independent skilled forensic pathologists from different forensic medicine institutes evaluated 100 glass slides of forensic interest (80 stained with standard hematoxylin and eosin, 20 with special staining), including different organs and tissues, with light microscopy (Olympus BX51, Tokyo, Japan). Glass slides were scanned using the Aperio GT 450 DX Digital Slides Scanner (Leica Biosystems, Nussloch, Germany). After two wash-out weeks, forensic pathologists evaluated WSIs in front of a widescreen using computer devices with dedicated software (O3 viewer, O3 Enterprise, Zucchetti, Trieste, Italy). Side-by-side comparisons between diagnoses performed on tissue glass slides versus WSIs were above the threshold stated in the validation guidelines (mean concordance of 97.8%). CSUQ Version 3 questionnaire showed high satisfaction for all pathologists (mean result: 6.6/7). Our institutional digital forensic pathology system has been validated for practical casework application. This approach opens new scenarios in practical forensic casework investigations, such as sharing live histological ex-glass slides online, as well as educational and research perspectives, with improving impacts on the whole daily workflow.

Trends and Challenges of the Modern Pathology Laboratory for Biopharmaceutical Research Excellence

Pathology, a fundamental discipline that bridges basic scientific discovery to the clinic, is integral to successful drug development. Intrinsically multimodal and multidimensional, anatomic pathology continues to be empowered by advancements in molecular and digital technologies enabling the spatial tissue detection of biomolecules such as genes, transcripts, and proteins. Over the past two decades, breakthroughs in spatial molecular biology technologies and advancements in automation and digitization of laboratory processes have enabled the implementation of higher throughput assays and the generation of extensive molecular data sets from tissue sections in biopharmaceutical research and development research units. It is our goal to provide readers with some rationale, advice, and ideas to help establish a modern molecular pathology laboratory to meet the emerging needs of biopharmaceutical research. This manuscript provides (1) a high-level overview of the current state and future vision for excellence in research pathology practice and (2) shared perspectives on how to optimally leverage the expertise of discovery, toxicologic, and translational pathologists to provide effective spatial, molecular, and digital pathology data to support modern drug discovery. It captures insights from the experiences, challenges, and solutions from pathology laboratories of various biopharmaceutical organizations, including their approaches to troubleshooting and adopting new technologies.

Comparison of Digital Pathology and Light Microscopy Among Dermatology Residents: A Reappraisal Following Practice Changes

BACKGROUND

Following transition to digital pathology for primary diagnosis at our institution, dermatology residents have reduced exposure to light microscopy. This study compares resident competency with light microscopy versus digital pathology following practice changes.

METHODS

Twenty-one dermatology residents were administered a dermatopathology examination composed of 32 diagnoses evaluated using digital slides and 32 with light microscopy. Case difficulty was graded and balanced between modalities. Diagnostic accuracy was measured using the number of correct diagnoses for each modality. Participants were surveyed regarding their experience and preferences.

RESULTS

Diagnostic accuracy was higher with digital pathology than light microscopy (22/32 vs. 18/32, P < 0.001). Diagnostic accuracy with digital pathology increased with years of training, but accuracy with light microscopy did not. Residents with previous light microscopy experience achieved an average score of 19/32 on glass, as compared with 10/32 for those without experience ( P = 0.039). Digital pathology was preferred over light microscopy (18/21, 85.7%).

CONCLUSIONS

Trainees had better diagnostic proficiency with digital pathology and preferred this modality. Most practices at this time continue to use light microscopy. Therefore, we need to maintain proficiency in microscopy during training while concurrently preparing trainees for a digital future.

Advancements in pathology: Digital transformation, precision medicine, and beyond

Pathology, a cornerstone of medical diagnostics and research, is undergoing a revolutionary transformation fueled by digital technology, molecular biology advancements, and big data analytics. Digital pathology converts conventional glass slides into high-resolution digital images, enhancing collaboration and efficiency among pathologists worldwide. Integrating artificial intelligence (AI) and machine learning (ML) algorithms with digital pathology improves diagnostic accuracy, particularly in complex diseases like cancer. Molecular pathology, facilitated by next-generation sequencing (NGS), provides comprehensive genomic, transcriptomic, and proteomic insights into disease mechanisms, guiding personalized therapies. Immunohistochemistry (IHC) plays a pivotal role in biomarker discovery, refining disease classification and prognostication. Precision medicine integrates pathology's molecular findings with individual genetic, environmental, and lifestyle factors to customize treatment strategies, optimizing patient outcomes. Telepathology extends diagnostic services to underserved areas through remote digital pathology. Pathomics leverages big data analytics to extract meaningful insights from pathology images, advancing our understanding of disease pathology and therapeutic targets. Virtual autopsies employ non-invasive imaging technologies to revolutionize forensic pathology. These innovations promise earlier diagnoses, tailored treatments, and enhanced patient care. Collaboration across disciplines is essential to fully realize the transformative potential of these advancements in medical practice and research.

Polarised light scanner for digital pathology

Digital pathology is rapidly transforming diagnostic pathology by allowing remote work and integration of artificial intelligence solutions. Nevertheless, certain technical issues remain to be resolved. Notably, digital images captured by conventional scanners cannot be subjected to polarised light analysis [1]. We have therefore studied if images obtained using the Glissando POL Brightfield and Polarised Light Scanner are comparable to those obtained using conventional polarised light microscopy. Hematoxylin and eosin stained sections from 75 cases, including cases of amyloidosis, periprosthetic membranes, foreign body granulomas, gout, pseudogout, and breast tissues with calcium oxalate crystals were examined using both, a polarised light microscope and the Glissando POL scanner. Representative digital images were acquired for comparison. We here show that in all settings, the images obtained by conventional polarised light microscopy and using the Glissando POL scanner were comparable. We conclude that the Glissando POL scanner can be safely integrated into a digital pathology workflow.

Object-based feedback attention in convolutional neural networks improves tumour detection in digital pathology

Human visual attention allows prior knowledge or expectations to influence visual processing, allocating limited computational resources to only that part of the image that are likely to behaviourally important. Here, we present an image recognition system based on biological vision that guides attention to more informative locations within a larger parent image, using a sequence of saccade-like motions. We demonstrate that at the end of the saccade sequence the system has an improved classification ability compared to the convolutional neural network (CNN) that represents the feedforward part of the model. Feedback activations highlight salient image features supporting the explainability of the classification. Our attention model deviates substantially from more common feedforward attention mechanisms, which linearly reweight part of the input. This model uses several passes of feedforward and backward activation, which interact non-linearly. We apply our feedback architecture to histopathology patch images, demonstrating a 3.5% improvement in accuracy (p < 0.001) when retrospectively processing 59,057 9-class patches from 689 colorectal cancer WSIs. In the saccade implementation, overall agreement between expert-labelled patches and model prediction reached 93.23% for tumour tissue, surpassing inter-pathologist agreement. Our method is adaptable to other areas of science which rely on the analysis of extremely large-scale images.

Whole slide images as non-fungible tokens: A decentralized approach to secure, scalable data storage and access

Background

Distributed ledger technology (DLT) enables the creation of tamper-resistant, decentralized, and secure digital ledgers. A non-fungible token (NFT) represents a record on-chain associated with a digital or physical asset, such as a whole-slide image (WSI). The InterPlanetary File System (IPFS) represents an off-chain network, hypermedia, and file sharing peer-to-peer protocol for storing and sharing data in a distributed file system. Today, we need cheaper, more efficient, highly scalable, and transparent solutions for WSI data storage and access of medical records and medical imaging data.

Methods

WSIs were created from non-human tissues and H&E-stained sections were scanned on a Philips Ultrafast WSI scanner at 40× magnification objective lens (1 μm/pixel). TIFF images were stored on IPFS, while NFTs were minted on the Ethereum blockchain network in ERC-1155 standard. WSI-NFTs were stored on MetaMask and OpenSea was used to display the WSI-NFT collection. Filebase storage application programing interface (API) were used to create dedicated gateways and content delivery networks (CDN).

Results

A total of 10 WSI-NFTs were minted on the Ethereum blockchain network, found on our collection "Whole Slide Images as Non-fungible Tokens Project" on Open Sea: https://opensea.io/collection/untitled-collection-126765644. WSI TIFF files ranged in size from 1.6 to 2.2 GB and were stored on IPFS and pinned on 3 separate nodes. Under optimal conditions, and using a dedicated CDN, WSI reached retrieved at speeds of over 10 mb/s, however, download speeds and WSI retrieval times varied significantly depending on the file and gateway used. Overall, the public IPFS gateway resulted in variably poorer WSI download retrieval performance compared to gateways provided by Filebase storage API.

Conclusion

Whole-slide images, as the most complex and substantial data files in healthcare, demand innovative solutions. In this technical report, we identify pitfalls in IPFS, and demonstrate proof-of-concept using a 3-layer architecture for scalable, decentralized storage, and access. Optimized through dedicated gateways and CDNs, which can be effectively applied to all medical data and imaging modalities across the healthcare sector. DLT and off-chain network solutions present numerous opportunities for advancements in clinical care, education, and research. Such approaches uphold the principles of equitable healthcare data ownership, security, and democratization, and are poised to drive significant innovation.

The 1000 Mitoses Project: A Consensus-Based International Collaborative Study on Mitotic Figures Classification

Introduction. The identification of mitotic figures is essential for the diagnosis, grading, and classification of various different tumors. Despite its importance, there is a paucity of literature reporting the consistency in interpreting mitotic figures among pathologists. This study leverages publicly accessible datasets and social media to recruit an international group of pathologists to score an image database of more than 1000 mitotic figures collectively. Materials and Methods. Pathologists were instructed to randomly select a digital slide from The Cancer Genome Atlas (TCGA) datasets and annotate 10-20 mitotic figures within a 2 mm2 area. The first 1010 submitted mitotic figures were used to create an image dataset, with each figure transformed into an individual tile at 40x magnification. The dataset was redistributed to all pathologists to review and determine whether each tile constituted a mitotic figure. Results. Overall pathologists had a median agreement rate of 80.2% (range 42.0%-95.7%). Individual mitotic figure tiles had a median agreement rate of 87.1% and a fair inter-rater agreement across all tiles (kappa = 0.284). Mitotic figures in prometaphase had lower percentage agreement rates compared to other phases of mitosis. Conclusion. This dataset stands as the largest international consensus study for mitotic figures to date and can be utilized as a training set for future studies. The agreement range reflects a spectrum of criteria that pathologists use to decide what constitutes a mitotic figure, which may have potential implications in tumor diagnostics and clinical management.

Computational pathology in the identification of HER2-low breast cancer: Opportunities and challenges

For the past 2 decades, pathologists have been accustomed to reporting the HER2 status of breast cancer as either positive or negative, based on HER2 IHC. Today, however, there is a clinical imperative to employ a 3-tier approach to interpreting HER2 IHC that can also identify tumours categorised as HER2-low. Meeting this need for a finer degree of discrimination may be challenging, and in this article, we consider the potential for the integration of computational approaches to support pathologists in achieving accurate and reproducible HER2 IHC scoring as well as outlining some of the practicalities involved.

Understanding the financial aspects of digital pathology: A dynamic customizable return on investment calculator for informed decision-making

Background

The adoption of digital pathology has transformed the field of pathology, however, the economic impact and cost analysis of implementing digital pathology solutions remain a critical consideration for institutions to justify. Digital pathology implementation requires a thorough evaluation of associated costs and should identify and optimize resource allocation to facilitate informed decision-making. A dynamic cost calculator to estimate the financial implications of deploying digital pathology systems was needed to estimate the financial effects on transitioning to a digital workflow.

Methods

A systematic approach was used to comprehensively assess the various components involved in implementing and maintaining a digital pathology system. This consisted of: (1) identification of key cost categories associated with digital pathology implementation; (2) data collection and analysis of cost estimation; (3) cost categorization and quantification of direct and indirect costs associated with different use cases, allowing customization of each factor based on specific intended uses and market rates, industry standards, and regional variations; (4) opportunities for savings realized by digitization of glass slides and (5) integration of the cost calculator into a unified framework for a holistic view of the financial implications associated with digital pathology implementation. The online tool enables the user to test various scenarios specific to their institution and provides adjustable parameters to assure organization specific relatability.

Results

The Digital Pathology Association has developed a web-based calculator as a companion tool to provide an exhaustive list of the necessary concepts needed when assessing the financial implications of transitioning to a digital pathology system. The dynamic return on investment (ROI) calculator successfully integrated relevant cost and cost-saving components associated with digital pathology implementation and maintenance. Considerations include factors such as digital pathology infrastructure, clinical operations, staffing, hardware and software, information technology, archive and retrieval, medical-legal, and potential reimbursements. The ROI calculator developed for digital pathology workflows offers a comprehensive, customizable tool for institutions to assess their anticipated upfront and ongoing annual costs as they start or expand their digital pathology journey. It also offers cost-savings analysis based on specific user case volume, institutional geographic considerations, and actual costs. In addition, the calculator also serves as a tool to estimate number of required whole slide scanners, scanner throughput, and data storage (TB). This tool is intended to estimate the potential costs and cost savings resulting from the transition to digital pathology for business plan justifications and return on investment calculations.

Conclusions

The digital pathology online cost calculator provides a comprehensive and reliable means of estimating the financial implications associated with implementing and maintaining a digital pathology system. By considering various cost factors and allowing customization based on institution-specific variables, the calculator empowers pathology laboratories, healthcare institutions, and administrators to make informed decisions and optimize resource allocation when adopting or expanding digital pathology technologies. The ROI calculator will enable healthcare institutions to assess the financial feasibility and potential return on investment on adopting digital pathology, facilitating informed decision-making and resource allocation.

Eye tracking in digital pathology: A comprehensive literature review

Eye tracking has been used for decades in attempt to understand the cognitive processes of individuals. From memory access to problem-solving to decision-making, such insight has the potential to improve workflows and the education of students to become experts in relevant fields. Until recently, the traditional use of microscopes in pathology made eye tracking exceptionally difficult. However, the digital revolution of pathology from conventional microscopes to digital whole slide images allows for new research to be conducted and information to be learned with regards to pathologist visual search patterns and learning experiences. This has the promise to make pathology education more efficient and engaging, ultimately creating stronger and more proficient generations of pathologists to come. The goal of this review on eye tracking in pathology is to characterize and compare the visual search patterns of pathologists. The PubMed and Web of Science databases were searched using 'pathology' AND 'eye tracking' synonyms. A total of 22 relevant full-text articles published up to and including 2023 were identified and included in this review. Thematic analysis was conducted to organize each study into one or more of the 10 themes identified to characterize the visual search patterns of pathologists: (1) effect of experience, (2) fixations, (3) zooming, (4) panning, (5) saccades, (6) pupil diameter, (7) interpretation time, (8) strategies, (9) machine learning, and (10) education. Expert pathologists were found to have higher diagnostic accuracy, fewer fixations, and shorter interpretation times than pathologists with less experience. Further, literature on eye tracking in pathology indicates that there are several visual strategies for diagnostic interpretation of digital pathology images, but no evidence of a superior strategy exists. The educational implications of eye tracking in pathology have also been explored but the effect of teaching novices how to search as an expert remains unclear. In this article, the main challenges and prospects of eye tracking in pathology are briefly discussed along with their implications to the field.

AI drives the assessment of lung cancer microenvironment composition

Purpose

The abundance and distribution of tumor-infiltrating lymphocytes (TILs) as well as that of other components of the tumor microenvironment is of particular importance for predicting response to immunotherapy in lung cancer (LC). We describe here a pilot study employing artificial intelligence (AI) in the assessment of TILs and other cell populations, intending to reduce the inter- or intra-observer variability that commonly characterizes this evaluation.

Design

We developed a machine learning-based classifier to detect tumor, immune, and stromal cells on hematoxylin and eosin-stained sections, using the open-source framework QuPath. We evaluated the quantity of the aforementioned three cell populations among 37 LC whole slide images regions of interest, comparing the assessments made by five pathologists, both before and after using graphical predictions made by AI, for a total of 1110 quantitative measurements.

Results

Our findings indicate noteworthy variations in score distribution among pathologists and between individual pathologists and AI. The AI-guided pathologist's evaluations resulted in reduction of significant discrepancies across pathologists: three comparisons showed a loss of significance (p > 0.05), whereas other four showed a reduction in significance (p > 0.01).

Conclusions

We show that employing a machine learning approach in cell population quantification reduces inter- and intra-observer variability, improving reproducibility and facilitating its use in further validation studies.

Preparing Physicians of the Future: Incorporating Data Science into Medical Education

The recent excitement surrounding artificial intelligence (AI) in health care underscores the importance of physician engagement with new technologies. Future clinicians must develop a strong understanding of data science (DS) to further enhance patient care. However, DS remains largely absent from medical school curricula, even though it is recognized as vital by medical students and residents alike. Here, we evaluate the current DS landscape in medical education and illustrate its impact in medicine through examples in pathology classification and sepsis detection. We also explore reasons for the exclusion of DS and propose solutions to integrate it into existing medical education frameworks.

Computational pathology: A survey review and the way forward

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

Generating and evaluating synthetic data in digital pathology through diffusion models

Synthetic data is becoming a valuable tool for computational pathologists, aiding in tasks like data augmentation and addressing data scarcity and privacy. However, its use necessitates careful planning and evaluation to prevent the creation of clinically irrelevant artifacts.This manuscript introduces a comprehensive pipeline for generating and evaluating synthetic pathology data using a diffusion model. The pipeline features a multifaceted evaluation strategy with an integrated explainability procedure, addressing two key aspects of synthetic data use in the medical domain.The evaluation of the generated data employs an ensemble-like approach. The first step includes assessing the similarity between real and synthetic data using established metrics. The second step involves evaluating the usability of the generated images in deep learning models accompanied with explainable AI methods. The final step entails verifying their histopathological realism through questionnaires answered by professional pathologists. We show that each of these evaluation steps are necessary as they provide complementary information on the generated data's quality.The pipeline is demonstrated on the public GTEx dataset of 650 Whole Slide Images (WSIs), including five different tissues. An equal number of tiles from each tissue are generated and their reliability is assessed using the proposed evaluation pipeline, yielding promising results.In summary, the proposed workflow offers a comprehensive solution for generative AI in digital pathology, potentially aiding the community in their transition towards digitalization and data-driven modeling.

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.

Optimizing Re-staining Techniques for the Restoration of Faded Hematoxylin and Eosin-stained Histopathology Slides: A Comparative Study

Hematoxylin and eosin (H&E)-stained slides inevitably deteriorate over time, frequently becoming unreadable. Reutilizing these slides can reduce the need for additional serial sections, particularly when the target region is no longer available in the tissue block. This study aims to develop efficient protocols for recycling faded H&E-stained slides, providing benefits for future research on stored samples. Seventy-one faded slides, representing a variety of tissue types and pathologies, were randomly divided into two groups. Slides were de-stained and re-stained using the conventional procedure and a modified Tris and HCl procedure. Three observers independently assessed all slides based on predefined parameters. The stability of the re-stained slides was re-assessed in 6 months. The modified Tris and HCl method yielded significantly higher scores compared to the conventional method for crispness of staining, nuclear staining, cytoplasmic staining, and vibrancy of staining (p < 0.05), as well as greater durability, as evidenced by minimal score reduction 6 months after staining. Thus, incorporating a Tris and HCl step into the process effectively enhances and restores faded H&E slides, offering a valuable technique for revitalizing histology slides for future research and educational purposes.

Biomarkers in high grade serous ovarian cancer

High-grade serous ovarian cancer (HGSC) is the most common subtype of ovarian cancer. HGSC patients typically present with advanced disease, which is often resistant to chemotherapy and recurs despite initial responses to therapy, resulting in the poor prognosis associated with this disease. There is a need to utilise biomarkers to manage the various aspects of HGSC patient care. In this review we discuss the current state of biomarkers in HGSC, focusing on the various available immunohistochemical (IHC) and blood-based biomarkers, which have been examined for their diagnostic, prognostic and theranostic potential in HGSC. These include various routine clinical IHC biomarkers such as p53, WT1, keratins, PAX8, Ki67 and p16 and clinical blood-borne markers and algorithms such as CA125, HE4, ROMA, RMI, ROCA, and others. We also discuss various components of the liquid biopsy as well as a number of novel IHC biomarkers and non-routine blood-borne biomarkers, which have been examined in various ovarian cancer studies. We also discuss the future of ovarian cancer biomarker research and highlight some of the challenges currently facing the field.

Transforming Diagnostics: A Comprehensive Review of Advances in Digital Pathology

Digital pathology has emerged as a revolutionary field, transforming traditional diagnostic practices by integrating advanced imaging technologies, computational tools, and artificial intelligence (AI). Adopting digital slides over conventional glass slides enables high-resolution imaging, facilitating remote consultations, second opinions, and telepathology. The digitalization of pathology laboratories enhances workflow efficiency and allows for large-scale data storage, retrieval, and analysis, paving the way for developing robust diagnostic algorithms. One of the most transformative aspects of digital pathology is its synergy with AI and machine learning (ML). These technologies have enabled the automation of repetitive processes, including diseased feature detection, biomarker quantification, and tissue segmentation. This has decreased inter-observer variability and increased diagnostic accuracy. AI-driven algorithms are particularly beneficial in complex cases, assisting pathologists in detecting subtle patterns that might be missed through manual examination. Furthermore, digital pathology plays a critical role in personalized medicine by enabling the precise characterization of tumors, which leads to targeted therapy decisions. Integrating digital pathology with genomics and other omics data holds promise for a more holistic understanding of diseases, driving innovation in diagnostics and treatment. However, the transition to digital pathology is challenging. Issues such as data standardization, regulatory compliance, and the need for robust IT infrastructure must be addressed to realize its full potential. This review provides a detailed examination of these advances, their clinical applications, and the challenges faced in the widespread adoption of digital pathology. As the field continues to evolve, it is poised to play a pivotal role in shaping the future of diagnostics, offering new possibilities for improving patient outcomes. This comprehensive review explores the significant advances in digital pathology, highlighting its impact on diagnostics, research, and patient care.

MSGM: An Advanced Deep Multi-Size Guiding Matching Network for Whole Slide Histopathology Images Addressing Staining Variation and Low Visibility Challenges

Matching whole slide histopathology images to provide comprehensive information on homologous tissues is beneficial for cancer diagnosis. However, the challenge arises with the Giga-pixel whole slide images (WSIs) when aiming for high-accuracy matching. Learning-based methods are difficult to generalize well with large-size WSIs, necessitating the integration of traditional matching methods to enhance accuracy as the size increases. In this paper, we propose a multi-size guiding matching method applicable high-accuracy requirements. Specifically, we design learning multiscale texture to train deep descriptors, called TDescNet, that trains 64 × 64 × 256 and 256 × 256 × 128 size convolution layer as C64 and C256 descriptors to overcome staining variation and low visibility challenges. Furthermore, we develop the 3D-ring descriptor using sparse keypoints to support the description of large-size WSIs. Finally, we employ C64, C256, and 3D-ring descriptors to progressively guide refined local matching, utilizing geometric consistency to identify correct matching results. Experiments show that when matching WSIs of size 4096 × 4096 pixels, our average matching error is 123.48 μm and the success rate is 93.02 % in 43 cases. Notably, our method achieves an average improvement of 65.52 μm in matching accuracy compared to recent state-of-the-art methods, with enhancements ranging from 36.27 μm to 131.66 μm. Therefore, we achieve high-fidelity whole-slice image matching, and overcome staining variation and low visibility challenges, enabling assistance in comprehensive cancer diagnosis through matched WSIs.

Multidisciplinary Teams in Musculoskeletal Infection - From a Pathologist's Perspective

Multidisciplinary team (MDT) meetings have emerged as a promising approach for the treatment of cancer patients. These meetings involve a team of healthcare professionals from different disciplines working together to develop a holistic, patient-centered treatment. Although MDT meetings are well established in oncology, they play a minor role in other diseases. Recent evidence suggests that the implementation of MDT meetings can improve patient outcomes in musculoskeletal infections. The aim of this retrospective, observational study was to present the agenda of our multidisciplinary limb board including live microscopy with a special focus on the pathologist's role. The descriptive analysis of the limb board included 66 cases receiving live microscopy at the meeting and a total of 124 histopathological findings and 181 stainings. We could elucidate that pathologists seem to play an important role especially in clarifying the correct diagnosis. In 80.3 % of the findings, the pathologist specified the clinical diagnosis of the requesting physician leading to a consensus-based treatment plan for each patient. The implementation of MDT meetings including live microscopy in patients with musculoskeletal infections holds potential benefits, such as improved communication, scientific collaboration, and raising clinicians' awareness and understanding of histopathology findings. However, potential challenges, such as organizational effort and technical prerequisites should be considered.

Digital dermatopathology implementation: Experience at a multisite academic institution

BACKGROUND

Technology has revolutionized not only direct patient care but also diagnostic care processes. This study evaluates the transition from glass-slide microscopy to digital pathology (DP) at a multisite academic institution, using mixed methods to understand user perceptions of digitization and key productivity metrics of practice change.

METHODS

Participants included dermatopathologists, pathology reporting specialists, and clinicians. Electronic surveys and individual or group interviews included questions related to technology comfort, trust in DP, and rationale for DP adoption. Case volumes and turnaround times were abstracted from the electronic health record from Qtr 4 2020 to Qtr 1 2023 (inclusive). Data were analyzed descriptively, while interviews were analyzed using methods of content analysis.

RESULTS

Thirty-four staff completed surveys and 22 participated in an interview. Case volumes and diagnostic turnaround time did not differ across the institution during or after implementation timelines (p = 0.084; p = 0.133, respectively). 82.5% (28/34) of staff agreed that DP improved the sign-out experience, with accessibility, ergonomics, and annotation features described as key factors. Clinicians reported positive perspectives of DP impact on patient safety and interdisciplinary collaboration.

CONCLUSIONS

Our study demonstrates that DP has a high acceptance rate, does not adversely impact productivity, and may improve patient safety and care collaboration.

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

INTRODUCTION

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.

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

A Microscope Setup and Methodology for Capturing Hyperspectral and RGB Histopathological Imaging Databases

The digitization of pathology departments in hospitals around the world is now a reality. The current commercial solutions applied to digitize histopathological samples consist of a robotic microscope with an RGB-type camera attached to it. This technology is very limited in terms of information captured, as it only works with three spectral bands of the visible electromagnetic spectrum. Therefore, we present an automated system that combines RGB and hyperspectral technology. Throughout this work, the hardware of the system and its components are described along with the developed software and a working methodology to ensure the correct capture of histopathological samples. The software is integrated by the controller of the microscope, which features an autofocus functionality, whole slide scanning with a stitching algorithm, and hyperspectral scanning functionality. As a reference, the time to capture and process a complete sample with 20 regions of high biological interest using the proposed method is estimated at a maximum of 79 min, reducing the time required by a manual operator by at least three times. Both hardware and software can be easily adapted to other systems that might benefit from the advantages of hyperspectral technology.

EpidermaQuant: Unsupervised Detection and Quantification of Epidermal Differentiation Markers on H-DAB-Stained Images of Reconstructed Human Epidermis

The integrity of the reconstructed human epidermis generated in vitro can be assessed using histological analyses combined with immunohistochemical staining of keratinocyte differentiation markers. Technical differences during the preparation and capture of stained images may influence the outcome of computational methods. Due to the specific nature of the analyzed material, no annotated datasets or dedicated methods are publicly available. Using a dataset with 598 unannotated images showing cross-sections of in vitro reconstructed human epidermis stained with DAB-based immunohistochemistry reaction to visualize four different keratinocyte differentiation marker proteins (filaggrin, keratin 10, Ki67, HSPA2) and counterstained with hematoxylin, we developed an unsupervised method for the detection and quantification of immunohistochemical staining. The pipeline consists of the following steps: (i) color normalization; (ii) color deconvolution; (iii) morphological operations; (iv) automatic image rotation; and (v) clustering. The most effective combination of methods includes (i) Reinhard's normalization; (ii) Ruifrok and Johnston color-deconvolution method; (iii) proposed image-rotation method based on boundary distribution of image intensity; and (iv) k-means clustering. The results of the work should enhance the performance of quantitative analyses of protein markers in reconstructed human epidermis samples and enable the comparison of their spatial distribution between different experimental conditions.

Expanding role and scope of artificial intelligence in the field of gastrointestinal pathology

Digital pathology (DP) and its subsidiaries including artificial intelligence (AI) are rapidly making inroads into the area of diagnostic anatomic pathology (AP) including gastrointestinal (GI) pathology. It is poised to revolutionize the field of diagnostic AP. Historically, AP has been slow to adopt digital technology, but this is changing rapidly, with many centers worldwide transitioning to DP. Coupled with advanced techniques of AI such as deep learning and machine learning, DP is likely to transform histopathology from a subjective field to an objective, efficient, and transparent discipline. AI is increasingly integrated into GI pathology, offering numerous advancements and improvements in overall diagnostic accuracy, efficiency, and patient care. Specifically, AI in GI pathology enhances diagnostic accuracy, streamlines workflows, provides predictive insights, integrates multimodal data, supports research, and aids in education and training, ultimately improving patient care and outcomes. This review summarized the latest developments in the role and scope of AI in AP with a focus on GI pathology. The main aim was to provide updates and create awareness among the pathology community.

DLCNBC-SA: a model for assessing axillary lymph node metastasis status in early breast cancer patients

Background

Axillary lymph node (ALN) status is a crucial prognostic indicator for breast cancer metastasis, with manual interpretation of whole slide images (WSIs) being the current standard practice. However, this method is subjective and time-consuming. Recent advancements in deep learning-based methods for medical image analysis have shown promise in improving clinical diagnosis. This study aims to leverage these technological advancements to develop a deep learning model based on features extracted from primary tumor biopsies for preoperatively identifying ALN metastasis in early-stage breast cancer patients with negative nodes.

Methods

We present DLCNBC-SA, a deep learning-based network specifically tailored for core needle biopsy and clinical data feature extraction, which integrates a self-attention mechanism (CNBC-SA). The proposed model consists of a feature extractor based on convolutional neural network (CNN) and an improved self-attention mechanism module, which can preserve the independence of features in WSIs for analysis and enhancement to provide rich feature representation. To validate the performance of the proposed model, we conducted comparative experiments and ablation studies using publicly available datasets, and verification was performed through quantitative analysis.

Results

The comparative experiment illustrates the superior performance of the proposed model in the task of binary classification of ALNs, as compared to alternative methods. Our method achieved outstanding performance [area under the curve (AUC): 0.882] in this task, significantly surpassing the state-of-the-art (SOTA) method on the same dataset (AUC: 0.862). The ablation experiment reveals that incorporating RandomRotation data augmentation technology and utilizing Adadelta optimizer can effectively enhance the performance of the proposed model.

Conclusions

The experimental results demonstrate that the model proposed in this paper outperforms the SOTA model on the same dataset, thereby establishing its reliability as an assistant for pathologists in analyzing WSIs of breast cancer. Consequently, it significantly enhances both the efficiency and accuracy of doctors during the diagnostic process.

Transforming diagnostics: The implementation of digital pathology in clinical laboratories

Digital pathology (DP) has emerged as a cutting-edge technology that promises to revolutionise diagnostics in clinical laboratories. This perspective article explores the implementation planning and considerations of DP in a single multicentre institution in Canada, the University Health Network, discussing benefits, challenges, potential implications and considerations for future adopters. We examine the transition from traditional microscopy to digital slide scanning and its impact on pathology practice, patient care and medical research. Furthermore, we address the regulatory, infrastructure and change management considerations for successful integration into clinical laboratories. By highlighting the advantages and addressing concerns, we aim to shed light on the transformative potential of DP and its role in shaping the future of diagnostics.

Development and Assessment of Multiple Illumination Color Fourier Ptychographic Microscopy for High Throughput Sample Digitization

In this study, we proposed a multiplexed color illumination strategy to improve the data acquisition efficiency of Fourier ptychography microscopy (FPM). Instead of sequentially lighting up one single channel LED, our method turns on multiple white light LEDs for each image acquisition via a color camera. Thus, each raw image contains multiplexed spectral information. An FPM prototype was developed, which was equipped with a 4×/0.13 NA objective lens to achieve a spatial resolution equivalent to that of a 20×/0.4 NA objective lens. Both two- and four-LED illumination patterns were designed and applied during the experiments. A USAF 1951 resolution target was first imaged under these illumination conditions, based on which MTF curves were generated to assess the corresponding imaging performance. Next, H&E tissue samples and analyzable metaphase chromosome cells were used to evaluate the clinical utility of our strategy. The results show that the single and multiplexed (two- or four-LED) illumination results achieved comparable imaging performance on all the three channels of the MTF curves. Meanwhile, the reconstructed tissue or cell images successfully retain the definition of cell nuclei and cytoplasm and can better preserve the cell edges as compared to the results from the conventional microscopes. This study initially validates the feasibility of multiplexed color illumination for the future development of high-throughput FPM scanning systems.

1 Million Segmented Red Blood Cells With 240 K Classified in 9 Shapes and 47 K Patches of 25 Manual Blood Smears

Around 20% of complete blood count samples necessitate visual review using light microscopes or digital pathology scanners. There is currently no technological alternative to the visual examination of red blood cells (RBCs) morphology/shapes. True/non-artifact teardrop-shaped RBCs and schistocytes/fragmented RBCs are commonly associated with serious medical conditions that could be fatal, increased ovalocytes are associated with almost all types of anemias. 25 distinct blood smears, each from a different patient, were manually prepared, stained, and then sorted into four groups. Each group underwent imaging using different cameras integrated into light microscopes with 40X microscopic lenses resulting in total 47 K + field images/patches. Two hematologists processed cell-by-cell to provide one million + segmented RBCs with their XYWH coordinates and classified 240 K + RBCs into nine shapes. This dataset (Elsafty_RBCs_for_AI) enables the development/testing of deep learning-based (DL) automation of RBCs morphology/shapes examination, including specific normalization of blood smear stains (different from histopathology stains), detection/counting, segmentation, and classification. Two codes are provided (Elsafty_Codes_for_AI), one for semi-automated image processing and another for training/testing of a DL-based image classifier.

Possible benefits, challenges, pitfalls, and future perspective of using ChatGPT in pathology

The much-hyped artificial intelligence (AI) model called ChatGPT developed by Open AI can have great benefits for physicians, especially pathologists, by saving time so that they can use their time for more significant work. Generative AI is a special class of AI model, which uses patterns and structures learned from existing data and can create new data. Utilizing ChatGPT in Pathology offers a multitude of benefits, encompassing the summarization of patient records and its promising prospects in Digital Pathology, as well as its valuable contributions to education and research in this field. However, certain roadblocks need to be dealt like integrating ChatGPT with image analysis which will act as a revolution in the field of pathology by increasing diagnostic accuracy and precision. The challenges with the use of ChatGPT encompass biases from its training data, the need for ample input data, potential risks related to bias and transparency, and the potential adverse outcomes arising from inaccurate content generation. Generation of meaningful insights from the textual information which will be efficient in processing different types of image data, such as medical images, and pathology slides. Due consideration should be given to ethical and legal issues including bias.

Pathomics and single-cell analysis of papillary thyroid carcinoma reveal the pro-metastatic influence of cancer-associated fibroblasts

BACKGROUND

Papillary thyroid carcinoma (PTC) is globally prevalent and associated with an increased risk of lymph node metastasis (LNM). The role of cancer-associated fibroblasts (CAFs) in PTC remains unclear.

METHODS

We collected postoperative pathological hematoxylin-eosin (HE) slides from 984 included patients with PTC to analyze the density of CAF infiltration at the invasive front of the tumor using QuPath software. The relationship between CAF density and LNM was assessed. Single-cell RNA sequencing (scRNA-seq) data from GSE193581 and GSE184362 datasets were integrated to analyze CAF infiltration in PTC. A comprehensive suite of in vitro experiments, encompassing EdU labeling, wound scratch assays, Transwell assays, and flow cytometry, were conducted to elucidate the regulatory role of CD36+CAF in two PTC cell lines, TPC1 and K1.

RESULTS

A significant correlation was observed between high fibrosis density at the invasive front of the tumor and LNM. Analysis of scRNA-seq data revealed metastasis-associated myoCAFs with robust intercellular interactions. A diagnostic model based on metastasis-associated myoCAF genes was established and refined through deep learning methods. CD36 positive expression in CAFs can significantly promote the proliferation, migration, and invasion abilities of PTC cells, while inhibiting the apoptosis of PTC cells.

CONCLUSION

This study addresses the significant issue of LNM risk in PTC. Analysis of postoperative HE pathological slides from a substantial patient cohort reveals a notable association between high fibrosis density at the invasive front of the tumor and LNM. Integration of scRNA-seq data comprehensively analyzes CAF infiltration in PTC, identifying metastasis-associated myoCAFs with strong intercellular interactions. In vitro experimental results indicate that CD36 positive expression in CAFs plays a promoting role in the progression of PTC. Overall, these findings provide crucial insights into the function of CAF subset in PTC metastasis.

Congenital heart diseases (CHDs) and forensic investigations: Searching for the cause of death

Congenital Heart Diseases (CHDs) are a group of structural abnormalities or defects of the heart that are present at birth. CHDs could be connected to sudden death (SD), defined by the WHO (World Health Organization) as "death occurring within 24 h after the onset of the symptoms" in an apparently "healthy" subject. These conditions can range from relatively mild defects to severe, life-threatening anomalies. The prevalence of CHDs varies across populations, but they affect millions of individuals worldwide. This article aims to discuss the post-mortem investigation of death related to CHDs, exploring the forensic approach, current methodologies, challenges, and potential advancements in this challenging field. A further goal of this article is to provide a guide for understanding these complex diseases, highlighting the pivotal role of autopsy, histopathology, and genetic investigations in defining the cause of death, and providing evidence about the translational use of autopsy reports. Forensic investigations play a crucial role in understanding the complexities of CHDs and determining the cause of death accurately. Through collaboration between medical professionals and forensic experts, meticulous examinations, and analysis of evidence, valuable insights can be gained. These insights not only provide closure to the families affected but also contribute to the prevention of future tragedies.

A novel approach correlating pathologic complete response with digital pathology and radiomics in triple-negative breast cancer

This rapid communication highlights the correlations between digital pathology-whole slide imaging (WSI) and radiomics-magnetic resonance imaging (MRI) features in triple-negative breast cancer (TNBC) patients. The research collected 12 patients who had both core needle biopsy and MRI performed to evaluate pathologic complete response (pCR). The results showed that higher collagenous values in pathology data were correlated with more homogeneity, whereas higher tumor expression values in pathology data correlated with less homogeneity in the appearance of tumors on MRI by size zone non-uniformity normalized (SZNN). Higher myxoid values in pathology data are correlated with less similarity of gray-level non-uniformity (GLN) in tumor regions on MRIs, while higher immune values in WSIs correlated with the more joint distribution of smaller-size zones by small area low gray-level emphasis (SALGE) in the tumor regions on MRIs. Pathologic complete response (pCR) was associated with collagen, tumor, and myxoid expression in WSI and GLN and SZNN in radiomic features. The correlations of WSI and radiomic features may further our understanding of the TNBC tumoral microenvironment (TME) and could be used in the future to better tailor the use of neoadjuvant chemotherapy (NAC). This communication will focus on the post-NAC MRI features correlated with pCR and their association with WSI features from core needle biopsies.

Whole-Slide Imaging (WSI) Versus Traditional Microscopy (TM) Through Evaluation of Parameters in Oral Histopathology: A Pilot Study

Background

histopathology plays a pivotal role in clinical diagnosis, research, and medical education. In recent years, whole slide imaging (wsi) has emerged as a potential alternative to traditional microscopy for pathological examination. This study aims to provide a comprehensive comparison of wsi and traditional microscopy(tm) in various aspects of histopathology practice.

Materials and Methods

In this study, total of 30 cases comprising of oral premalignant and malignant cases which were diagnostically challenging was considered from the archives of the institute for validation. The slides were scanned with slide scanner and were evaluated by histopathologists. The comparative parameters which were noted were diagnostic discordances, number of fields observed to reach the diagnosis and time taken.

Results

The mean time taken by the pathologists to reach the diagnosis was significantly less in whole slide imaging technique. The average number of fields observed was higher by using wsi that too in a lesser time compared to tm, the results were found to be statistically significant with p=0.001.however the diagnostic disparity were seen to be maximum for verrucous lesions both in wsi and tm.

Conclusion

wsi has facilitated the specialty with rapid mode of diagnosis in a more efficient and error less manner. It has also aided in case banking as well as research possibilities. Hence with the advent of telepathology it is very much necessary to get trained with wsi as early as possible so that the professionals can render correct diagnosis.

Digital pathology for reporting histopathology samples, including cancer screening samples - definitive evidence from a multisite study

AIMS

To conduct a definitive multicentre comparison of digital pathology (DP) with light microscopy (LM) for reporting histopathology slides including breast and bowel cancer screening samples.

METHODS

A total of 2024 cases (608 breast, 607 GI, 609 skin, 200 renal) were studied, including 207 breast and 250 bowel cancer screening samples. Cases were examined by four pathologists (16 study pathologists across the four speciality groups), using both LM and DP, with the order randomly assigned and 6 weeks between viewings. Reports were compared for clinical management concordance (CMC), meaning identical diagnoses plus differences which do not affect patient management. Percentage CMCs were computed using logistic regression models with crossed random-effects terms for case and pathologist. The obtained percentage CMCs were referenced to 98.3% calculated from previous studies.

RESULTS

For all cases LM versus DP comparisons showed the CMC rates were 99.95% [95% confidence interval (CI) = 99.90-99.97] and 98.96 (95% CI = 98.42-99.32) for cancer screening samples. In speciality groups CMC for LM versus DP showed: breast 99.40% (99.06-99.62) overall and 96.27% (94.63-97.43) for cancer screening samples; [gastrointestinal (GI) = 99.96% (99.89-99.99)] overall and 99.93% (99.68-99.98) for bowel cancer screening samples; skin 99.99% (99.92-100.0); renal 99.99% (99.57-100.0). Analysis of clinically significant differences revealed discrepancies in areas where interobserver variability is known to be high, in reads performed with both modalities and without apparent trends to either.

CONCLUSIONS

Comparing LM and DP CMC, overall rates exceed the reference 98.3%, providing compelling evidence that pathologists provide equivalent results for both routine and cancer screening samples irrespective of the modality used.

[Digital transformation of a routine histopathology lab : Dos and don'ts!]

The implementation of digital histopathology in the laboratory marks a crucial milestone in the overall digital transformation of pathology. This shift offers a range of new possibilities, including access to extensive datasets for AI-assisted analyses, the flexibility of remote work and home office arrangements for specialists, and the expedited and simplified sharing of images and data for research, conferences, and tumor boards. However, the transition to a fully digital workflow involves significant technological and personnel-related efforts. It necessitates careful and adaptable change management to minimize disruptions, particularly in the personnel domain, and to prevent the loss of valuable potential from employees who may be resistant to change. This article consolidates our institute's experiences, highlighting technical and personnel-related challenges encountered during the transition to digital pathology. It also presents a comprehensive overview of potential difficulties at various interfaces when converting routine operations to a digital workflow.

Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application

In recent years, artificial intelligence (AI) has demonstrated exceptional performance in mitosis identification and quantification. However, the implementation of AI in clinical practice needs to be evaluated against the existing methods. This study is aimed at assessing the optimal method of using AI-based mitotic figure scoring in breast cancer (BC). We utilized whole slide images from a large cohort of BC with extended follow-up comprising a discovery (n = 1715) and a validation (n = 859) set (Nottingham cohort). The Cancer Genome Atlas of breast invasive carcinoma (TCGA-BRCA) cohort (n = 757) was used as an external test set. Employing automated mitosis detection, the mitotic count was assessed using 3 different methods, the mitotic count per tumor area (MCT; calculated by dividing the number of mitotic figures by the total tumor area), the mitotic index (MI; defined as the average number of mitotic figures per 1000 malignant cells), and the mitotic activity index (MAI; defined as the number of mitotic figures in 3 mm2 area within the mitotic hotspot). These automated metrics were evaluated and compared based on their correlation with the well-established visual scoring method of the Nottingham grading system and Ki67 score, clinicopathologic parameters, and patient outcomes. AI-based mitotic scores derived from the 3 methods (MCT, MI, and MAI) were significantly correlated with the clinicopathologic characteristics and patient survival (P < .001). However, the mitotic counts and the derived cutoffs varied significantly between the 3 methods. Only MAI and MCT were positively correlated with the gold standard visual scoring method used in Nottingham grading system (r = 0.8 and r = 0.7, respectively) and Ki67 scores (r = 0.69 and r = 0.55, respectively), and MAI was the only independent predictor of survival (P < .05) in multivariate Cox regression analysis. For clinical applications, the optimum method of scoring mitosis using AI needs to be considered. MAI can provide reliable and reproducible results and can accurately quantify mitotic figures in BC.

Fourier Ptychographic Microscopy 10 Years on: A Review

Fourier ptychographic microscopy (FPM) emerged as a prominent imaging technique in 2013, attracting significant interest due to its remarkable features such as precise phase retrieval, expansive field of view (FOV), and superior resolution. Over the past decade, FPM has become an essential tool in microscopy, with applications in metrology, scientific research, biomedicine, and inspection. This achievement arises from its ability to effectively address the persistent challenge of achieving a trade-off between FOV and resolution in imaging systems. It has a wide range of applications, including label-free imaging, drug screening, and digital pathology. In this comprehensive review, we present a concise overview of the fundamental principles of FPM and compare it with similar imaging techniques. In addition, we present a study on achieving colorization of restored photographs and enhancing the speed of FPM. Subsequently, we showcase several FPM applications utilizing the previously described technologies, with a specific focus on digital pathology, drug screening, and three-dimensional imaging. We thoroughly examine the benefits and challenges associated with integrating deep learning and FPM. To summarize, we express our own viewpoints on the technological progress of FPM and explore prospective avenues for its future developments.