Whole Slide Imaging: Technology and Applications

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.

Concordance of whole slide imaging and conventional light microscopy for assessment of pathologic response following neoadjuvant therapy for lung cancer

Pathologic response is an endpoint in many ongoing clinical trials for neoadjuvant regimens, including immune checkpoint blockade and chemotherapy. Whole slide scanning of glass slides generates high resolution digital images and allows for remote review and potential measurement with image analysis tools, but concordance of pathologic response assessment on digital scans compared to glass slides has yet to be evaluated. Such a validation goes beyond previous concordance studies which focused on establishing surgical pathology diagnoses, as it requires quantitative assessment of tumor, necrosis, and regression. Further, as pathologic response assessment is being used as an endpoint, such concordance studies have regulatory implications. The purpose of this study was two fold: firstly, to determine the concordance between pathologic response assessed on glass slides and on digital scans; and secondly, to determine if pathologists benefited from using measurement tools when determining pathologic response. To that end, H&E-stained glass slides from 64 non-small cell lung carcinoma specimens were visually assessed for percent residual viable tumor (%RVT). The sensitivity and specificity for digital vs. glass reads of complete pathologic response (pCR, 0% RVT) and major pathologic response (MPR, ≤10% RVT) were all >95%. When %RVT was considered as a continuous variable, intraclass correlation coefficient of digital vs. glass reads was 0.94. The visual assessments of pathologic response were supported by pathologist annotations of residual tumor and tumor bed areas. In a separate subset of H&E-stained glass slides, several measurement approaches to quantifying %RVT were performed. Pathologist estimates strongly reflected measured %RVT. This study demonstrates the high level of concordance between glass slides evaluated using light microscopy and digital whole slide images for pathologic response assessments. Pathologists did not require measurement tools to generate robust %RVT values from slide annotations. These findings have broad implications for improving clinical workflows and multisite clinical trials.

Implementation of Digital Pathology and Artificial Intelligence in Routine Pathology Practice

The advent of affordable technology has significantly influenced the practice of digital pathology, leading to its growing adoption within the pathology community. This review article aimed to outline the latest developments in digital pathology, the cutting-edge advancements in artificial intelligence (AI) applications within this field, and the pertinent United States regulatory frameworks. The content is based on a thorough analysis of original research articles and official United States Federal guidelines. Findings from our review indicate that several Food and Drug Administration-approved digital scanners and image management systems are establishing a solid foundation for the seamless integration of advanced technologies into everyday pathology workflows, which may reduce device and operational costs in the future. AI is particularly transforming the way morphologic diagnoses are automated, notably in cancers like prostate and colorectal, within screening initiatives, albeit challenges such as data privacy issues and algorithmic biases remain. The regulatory environment, shaped by standards from the Food and Drug Administration, Centers for Medicare & Medicaid Services/Clinical Laboratory Improvement Amendments, and College of American Pathologists, is evolving to accommodate these innovations while ensuring safety and reliability. Centers for Medicare & Medicaid Services/Clinical Laboratory Improvement Amendments have issued policies to allow pathologists to review and render diagnoses using digital pathology remotely. Moreover, the introduction of new digital pathology Current Procedural Terminology codes designed to complement existing pathology Current Procedural Terminology codes is facilitating reimbursement processes. Overall, these advancements are heralding a new era in pathology that promises enhanced diagnostic precision and efficiency through digital and AI technologies, potentially improving patient care as well as bolstering educational and research activities.

Semi-automated analysis of HER2 immunohistochemistry in invasive breast carcinoma using whole slide images: utility for interpretation in clinical practice

Human epidermal growth factor receptor 2 (HER2) gene amplification and subsequent protein overexpression is a strong prognostic and predictive biomarker in invasive breast carcinoma (IBC). ASCO/CAP recommended tests for HER2 assessment include immunohistochemistry (IHC) and/or in situ hybridization (ISH). Accurate HER2 IHC scoring (0, 1+, 2+, 3+) is key for appropriate classification and treatment of IBC. HER2-targeted therapies, including anti-HER2 monoclonal antibodies and antibody drug conjugates (ADC), have revolutionized the treatment of HER2-positive IBC. Recently, ADC have also been approved for treatment of HER2-low (IHC 1+, IHC 2+/ISH-) advanced breast carcinoma, making a distinction between IHC 0 and 1+ crucial. In this focused study, 32 IBC with HER2 IHC scores from 0 to 3+ and HER2 FISH results formed a calibration dataset, and 77 IBC with HER2 IHC score 2+ and paired FISH results (27 amplified, 50 non-amplified) formed a validation dataset. H&E and HER2 IHC whole slide images (WSI) were scanned. Regions of interest were manually annotated and IHC scores generated by the software QuantCenter (MembraneQuant application) by 3DHISTECH Ltd. (Budapest, Hungary) and compared to the microscopic IHC score. H-scores [(3×%IHC3+) +(2×%IHC2+) +(1×%IHC1+)] were calculated for semi-automated (MembraneQuant) analysis. Concordance between microscopic IHC scoring and 3DHISTECH MembraneQuant semi-automated scoring in the calibration dataset showed a Kappa value of 0.77 (standard error 0.09). Microscopic IHC and MembraneQuant image analysis for the detection of HER2 amplification yielded a sensitivity of 100% for both and a specificity of 56% and 61%, respectively. In the validation set of IHC 2+ cases, only 13 of 77 cases (17%) had discordant results between microscopic and MembraneQuant images, and various artifacts limiting the interpretation of HER2 IHC, including cytoplasmic/granular staining and crush artifact were noted. Semi-automated analysis using WSI and microscopic evaluation yielded similar HER2 IHC scores, demonstrating the potential utility of this tool for interpretation in clinical practice and subsequent accurate treatment. In this study, it was shown that semi-automatic HER2 IHC interpretation provides an objective approach to a test known to be quite subjective.

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.

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.

Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis

Background

Pathomics has emerged as a promising biomarker that could facilitate personalized immunotherapy in lung cancer. It is essential to elucidate the global research trends and emerging prospects in this domain.

Methods

The annual distribution, journals, authors, countries, institutions, and keywords of articles published between 2018 and 2023 were visualized and analyzed using CiteSpace and other bibliometric tools.

Results

A total of 109 relevant articles or reviews were included, demonstrating an overall upward trend; The terms "deep learning", "tumor microenvironment", "biomarkers", "image analysis", "immunotherapy", and "survival prediction", etc. are hot keywords in this field.

Conclusion

In future research endeavors, advanced methodologies involving artificial intelligence and pathomics will be deployed for the digital analysis of tumor tissues and the tumor microenvironment in lung cancer patients, leveraging histopathological tissue sections. Through the integration of comprehensive multi-omics data, this strategy aims to enhance the depth of assessment, characterization, and understanding of the tumor microenvironment, thereby elucidating a broader spectrum of tumor features. Consequently, the development of a multimodal fusion model will ensue, enabling precise evaluation of personalized immunotherapy efficacy and prognosis for lung cancer patients, potentially establishing a pivotal frontier in this domain of investigation.

Artificial intelligence to unlock real-world evidence in clinical oncology: A primer on recent advances

PURPOSE

Real world evidence is crucial to understanding the diffusion of new oncologic therapies, monitoring cancer outcomes, and detecting unexpected toxicities. In practice, real world evidence is challenging to collect rapidly and comprehensively, often requiring expensive and time-consuming manual case-finding and annotation of clinical text. In this Review, we summarise recent developments in the use of artificial intelligence to collect and analyze real world evidence in oncology.

METHODS

We performed a narrative review of the major current trends and recent literature in artificial intelligence applications in oncology.

RESULTS

Artificial intelligence (AI) approaches are increasingly used to efficiently phenotype patients and tumors at large scale. These tools also may provide novel biological insights and improve risk prediction through multimodal integration of radiographic, pathological, and genomic datasets. Custom language processing pipelines and large language models hold great promise for clinical prediction and phenotyping.

CONCLUSIONS

Despite rapid advances, continued progress in computation, generalizability, interpretability, and reliability as well as prospective validation are needed to integrate AI approaches into routine clinical care and real-time monitoring of novel therapies.

A highly accurate and semi-automated method for quantifying spherical microplastics based on digital slide scanners and image processing

Microplastics (MPs), the emerging pollutants appeared in water environment, have grabbed significant attention from researchers. The quantitative method of spherical MPs is the premise and key for the study of MPs in laboratory researches. However, the manual counting is time-consuming, and the existing semi-automated analysis lacked of robustness. In this study, a highly accurate quantification method for spherical MPs, called VS120-MC was proposed. VS120-MC consisted of the digital slide scanner VS120 and the MPs image processing software, MPs-Counter. The full-area scanning photography was employed to fundamentally avoid the error caused by random or partition sampling modes. To accomplish high-performance batch recognition, the Weak-Circle Elimination Algorithm (WEA) and the Variable Coefficient Threshold (VCT) was developed. Finally, lower than 0.6% recognition error rate of simulated images with different aggregated indices was achieved by MPs-Counter with fast processing speed (about 2 s/image). The smallest size for VS120-MC to detect was 1 μm. And the applicability of VS120-MC in real water body was investigated. The measured value of 1 μm spherical MPs in ultra-pure water and two kinds of polluted water after digestion showed a good linear relationship with the Manual measurements (R2 = 0.982,0.987 and 0.978, respectively). For 10 μm spherical MPs, R2 reached 0.988 for ultra-pure water and 0.984 for both of the polluted water. MPs-Counter also showed robustness when using the same set of parameters processing the images with different conditions. Overall, VS120-MC eliminated the error caused by traditional photography and realized an accurate, efficient, stable image processing tool, providing a reliable alternative for the quantification of spherical MPs.

Whole Slide Imaging Technology and Its Applications: Current and Emerging Perspectives

Background. Whole slide imaging (WSI) represents a paradigm shift in pathology, serving as a necessary first step for a wide array of digital tools to enter the field. It utilizes virtual microscopy wherein glass slides are converted into digital slides and are viewed by pathologists by automated image analysis. Its impact on pathology workflow, reproducibility, dissemination of educational material, expansion of service to underprivileged areas, and institutional collaboration exemplifies a significant innovative movement. The recent US Food and Drug Administration approval to WSI for its use in primary surgical pathology diagnosis has opened opportunities for wider application of this technology in routine practice. Main Text. The ongoing technological advances in digital scanners, image visualization methods, and the integration of artificial intelligence-derived algorithms with these systems provide avenues to exploit its applications. Its benefits are innumerable such as ease of access through the internet, avoidance of physical storage space, and no risk of deterioration of staining quality or breakage of slides to name a few. Although the benefits of WSI to pathology practices are many, the complexities of implementation remain an obstacle to widespread adoption. Some barriers including the high cost, technical glitches, and most importantly professional hesitation to adopt a new technology have hindered its use in routine pathology. Conclusions. In this review, we summarize the technical aspects of WSI, its applications in diagnostic pathology, training, and research along with future perspectives. It also highlights improved understanding of the current challenges to implementation, as well as the benefits and successes of the technology. WSI provides a golden opportunity for pathologists to guide its evolution, standardization, and implementation to better acquaint them with the key aspects of this technology and its judicial use. Also, implementation of routine digital pathology is an extra step requiring resources which (currently) does not usually result increased efficiency or payment.

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

INTRODUCTION

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.

MATERIALS AND METHODS

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 in 2023.

RESULTS

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%).

CONCLUSIONS

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.

Optimizing Vision Transformers for Histopathology: Pretraining and Normalization in Breast Cancer Classification

This paper introduces a self-attention Vision Transformer model specifically developed for classifying breast cancer in histology images. We examine various training strategies and configurations, including pretraining, dimension resizing, data augmentation and color normalization strategies, patch overlap, and patch size configurations, in order to evaluate their impact on the effectiveness of the histology image classification. Additionally, we provide evidence for the increase in effectiveness gathered through geometric and color data augmentation techniques. We primarily utilize the BACH dataset to train and validate our methods and models, but we also test them on two additional datasets, BRACS and AIDPATH, to verify their generalization capabilities. Our model, developed from a transformer pretrained on ImageNet, achieves an accuracy rate of 0.91 on the BACH dataset, 0.74 on the BRACS dataset, and 0.92 on the AIDPATH dataset. Using a model based on the prostate small and prostate medium HistoEncoder models, we achieve accuracy rates of 0.89 and 0.86, respectively. Our results suggest that pretraining on large-scale general datasets like ImageNet is advantageous. We also show the potential benefits of using domain-specific pretraining datasets, such as extensive histopathological image collections as in HistoEncoder, though not yet with clear advantages.

The Information Technology (IT) Infrastructure of the Multicenter Archipelago of Ovarian Cancer Research Biobank: A Potential Blueprint for Other Biobanks

Objective: Biobanks play a crucial role in fundamental and translational research by storing valuable biomaterials and data for future analyses. However, the design of their information technology (IT) infrastructures is often customized to specific requirements, thereby lacking the ability to be used for biobanks comprising other (types of) diseases. This results in substantial costs, time, and efforts for each new biobank project. The Dutch multicenter Archipelago of Ovarian Cancer Research (AOCR) biobank has developed an innovative, reusable IT infrastructure capable of adaptation to various biobanks, thereby enabling cost-effective and efficient implementation and management of biobank IT systems. Methods and Results: The AOCR IT infrastructure incorporates preexisting biobank software, mainly managed by Health-RI. The web-based registration tool Ldot is used for secure storage and pseudonymization of patient data. Clinicopathological data are retrieved from the Netherlands Cancer Registry and the Dutch nationwide pathology databank (Palga), both established repositories, reducing administrative workload and ensuring high data quality. Metadata of collected biomaterials are stored in the OpenSpecimen system. For digital pathology research, a hematoxylin and eosin-stained slide from each patient's tumor is digitized and uploaded to Slide Score. Furthermore, adhering to the Findable, Accessible, Interoperable, and Reusable (FAIR) principles, genomic data derived from the AOCR samples are stored in cBioPortal. Conclusion: The IT infrastructure of the AOCR biobank represents a new standard for biobanks, offering flexibility to handle diverse diseases and types of biomaterials. This infrastructure bypasses the need for disease-specific, custom-built software, thereby being cost- and time-effective while ensuring data quality and legislative compliance. The adaptability of this infrastructure highlights its potential to serve as a blueprint for the development of IT infrastructures in both new and existing biobanks.

HSG-MGAF Net: Heterogeneous subgraph-guided multiscale graph attention fusion network for interpretable prediction of whole-slide image

BACKGROUND AND OBJECTIVE

Pathological whole slide image (WSI) prediction and region of interest (ROI) localization are important issues in computer-aided diagnosis and postoperative analysis in clinical applications. Existing computer-aided methods for predicting WSI are mainly based on multiple instance learning (MIL) and its variants. However, most of the methods are based on instance independence and identical distribution assumption and performed at a single scale, which not fully exploit the hierarchical multiscale heterogeneous information contained in WSI.

METHODS

Heterogeneous Subgraph-Guided Multiscale Graph Attention Fusion Network (HSG-MGAF Net) is proposed to build the topology of critical image patches at two scales for adaptive WSI prediction and lesion localization. The HSG-MGAF Net simulates the hierarchical heterogeneous information of WSI through graph and hypergraph at two scales, respectively. This framework not only fully exploits the low-order and potential high-order correlations of image patches at each scale, but also leverages the heterogeneous information of the two scales for adaptive WSI prediction.

RESULTS

We validate the superiority of the proposed method on the CAMELYON16 and the TCGA- NSCLC, and the results show that HSG-MGAF Net outperforms the state-of-the-art method on both datasets. The average ACC, AUC and F1 score of HSG-MGAF Net can reach 92.7 %/0.951/0.892 and 92.2 %/0.957/0.919, respectively. The obtained heatmaps can also localize the positive regions more accurately, which have great consistency with the pixel-level labels.

CONCLUSIONS

The results demonstrate that HSG-MGAF Net outperforms existing weakly supervised learning methods by introducing critical heterogeneous information between the two scales. This approach paves the way for further research on light weighted heterogeneous graph-based WSI prediction and ROI localization.

Artificial Intelligence-Based Treatment Decisions: A New Era for NSCLC

Non-small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality among women and men, in developed countries, despite the public health interventions including tobacco-free campaigns, screening and early detection methods, recent therapeutic advances, and ongoing intense research on novel antineoplastic modalities. Targeting oncogenic driver mutations and immune checkpoint inhibition has indeed revolutionized NSCLC treatment, yet there still remains the unmet need for robust and standardized predictive biomarkers to accurately inform clinical decisions. Artificial intelligence (AI) represents the computer-based science concerned with large datasets for complex problem-solving. Its concept has brought a paradigm shift in oncology considering its immense potential for improved diagnosis, treatment guidance, and prognosis. In this review, we present the current state of AI-driven applications on NSCLC management, with a particular focus on radiomics and pathomics, and critically discuss both the existing limitations and future directions in this field. The thoracic oncology community should not be discouraged by the likely long road of AI implementation into daily clinical practice, as its transformative impact on personalized treatment approaches is undeniable.

Image annotation and curation in radiology: an overview for machine learning practitioners

"Garbage in, garbage out" summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train and validate models should indeed be consistent, standardised, traceable, correctly annotated, and de-identified, considering local regulations. This narrative review presents a summary of the techniques that are used to ensure that all these requirements are fulfilled, with special emphasis on radiological imaging and freely available software solutions that can be directly employed by the interested researcher. Topics discussed include key imaging concepts, such as image resolution and pixel depth; file formats for medical image data storage; free software solutions for medical image processing; anonymisation and pseudonymisation to protect patient privacy, including compliance with regulations such as the Regulation (EU) 2016/679 "General Data Protection Regulation" (GDPR) and the 1996 United States Act of Congress "Health Insurance Portability and Accountability Act" (HIPAA); methods to eliminate patient-identifying features within images, like facial structures; free and commercial tools for image annotation; and techniques for data harmonisation and normalisation.Relevance statement This review provides an overview of the methods and tools that can be used to ensure high-quality data for machine learning and artificial intelligence applications in radiology.Key points• High-quality datasets are essential for reliable artificial intelligence algorithms in medical imaging.• Software tools like ImageJ and 3D Slicer aid in processing medical images for AI research.• Anonymisation techniques protect patient privacy during dataset preparation.• Machine learning models can accelerate image annotation, enhancing efficiency and accuracy.• Data curation ensures dataset integrity, compliance, and quality for artificial intelligence development.

Masked autoencoders with handcrafted feature predictions: Transformer for weakly supervised esophageal cancer classification

BACKGROUND AND OBJECTIVE

Esophageal cancer is a serious disease with a high prevalence in Eastern Asia. Histopathology tissue analysis stands as the gold standard in diagnosing esophageal cancer. In recent years, there has been a shift towards digitizing histopathological images into whole slide images (WSIs), progressively integrating them into cancer diagnostics. However, the gigapixel sizes of WSIs present significant storage and processing challenges, and they often lack localized annotations. To address this issue, multi-instance learning (MIL) has been introduced for WSI classification, utilizing weakly supervised learning for diagnosis analysis. By applying the principles of MIL to WSI analysis, it is possible to reduce the workload of pathologists by facilitating the generation of localized annotations. Nevertheless, the approach's effectiveness is hindered by the traditional simple aggregation operation and the domain shift resulting from the prevalent use of convolutional feature extractors pretrained on ImageNet.

METHODS

We propose a MIL-based framework for WSI analysis and cancer classification. Concurrently, we introduce employing self-supervised learning, which obviates the need for manual annotation and demonstrates versatility in various tasks, to pretrain feature extractors. This method enhances the extraction of representative features from esophageal WSI for MIL, ensuring more robust and accurate performance.

RESULTS

We build a comprehensive dataset of whole esophageal slide images and conduct extensive experiments utilizing this dataset. The performance on our dataset demonstrates the efficiency of our proposed MIL framework and the pretraining process, with our framework outperforming existing methods, achieving an accuracy of 93.07% and AUC (area under the curve) of 95.31%.

CONCLUSION

This work proposes an effective MIL method to classify WSI of esophageal cancer. The promising results indicate that our cancer classification framework holds great potential in promoting the automatic whole esophageal slide image analysis.

Correlation Between ImageJ and Conventional Manual Scoring Methods for Programmed Death-Ligand 1 Immuno-Histochemically Stained Sections

Background: One of the most frequently used methods for quantifying PD-L1 (programmed cell death-ligand 1) expression in tumor tissue is IHC (immunohistochemistry). This may predict the patient's response to anti-PD1/PD-L1 therapy in cancer. Methods: ImageJ software was used to score IHC-stained sections for PD-L1 and compare the results with the conventional manual method. Results: In diffuse large B cell lymphoma, no significant difference between the scores obtained by the conventional method and ImageJ scores obtained using the option "RGB" or "Brightness/Contrast." On the other hand, a significant difference was found between the conventional and HSB scoring methods. ImageJ faced some challenges in analyzing head and neck squamous cell carcinoma tissues because of tissue heterogenicity. A significant difference was found between the conventional and ImageJ scores using HSB or RGB but not with the "Brightness/Contrast" option. Scores obtained by ImageJ analysis after taking images using 20 × objective lens gave significantly higher readings compared to 40 × magnification. A significant difference between camera-captured images' scores and scanner whole slide images' scores was observed. Conclusion: ImageJ can be used to score homogeneous tissues. In the case of highly heterogeneous tissues, it is advised to use the conventional method rather than ImageJ scoring.

Deep pathomics: A new image-based tool for predicting response to treatment in stage III non-small cell lung cancer

Despite the advantages offered by personalized treatments, there is presently no way to predict response to chemoradiotherapy in patients with non-small cell lung cancer (NSCLC). In this exploratory study, we investigated the application of deep learning techniques to histological tissue slides (deep pathomics), with the aim of predicting the response to therapy in stage III NSCLC. We evaluated 35 digitalized tissue slides (biopsies or surgical specimens) obtained from patients with stage IIIA or IIIB NSCLC. Patients were classified as responders (12/35, 34.7%) or non-responders (23/35, 65.7%) based on the target volume reduction shown on weekly CT scans performed during chemoradiation treatment. Digital tissue slides were tested by five pre-trained convolutional neural networks (CNNs)-AlexNet, VGG, MobileNet, GoogLeNet, and ResNet-using a leave-two patient-out cross validation approach, and we evaluated the networks' performances. GoogLeNet was globally found to be the best CNN, correctly classifying 8/12 responders and 10/11 non-responders. Moreover, Deep-Pathomics was found to be highly specific (TNr: 90.1) and quite sensitive (TPr: 0.75). Our data showed that AI could surpass the capabilities of all presently available diagnostic systems, supplying additional information beyond that currently obtainable in clinical practice. The ability to predict a patient's response to treatment could guide the development of new and more effective therapeutic AI-based approaches and could therefore be considered an effective and innovative step forward in personalised medicine.

Building Automation Pipeline for Diagnostic Classification of Sporadic Odontogenic Keratocysts and Non-Keratocysts Using Whole-Slide Images

The microscopic diagnostic differentiation of odontogenic cysts from other cysts is intricate and may cause perplexity for both clinicians and pathologists. Of particular interest is the odontogenic keratocyst (OKC), a developmental cyst with unique histopathological and clinical characteristics. Nevertheless, what distinguishes this cyst is its aggressive nature and high tendency for recurrence. Clinicians encounter challenges in dealing with this frequently encountered jaw lesion, as there is no consensus on surgical treatment. Therefore, the accurate and early diagnosis of such cysts will benefit clinicians in terms of treatment management and spare subjects from the mental agony of suffering from aggressive OKCs, which impact their quality of life. The objective of this research is to develop an automated OKC diagnostic system that can function as a decision support tool for pathologists, whether they are working locally or remotely. This system will provide them with additional data and insights to enhance their decision-making abilities. This research aims to provide an automation pipeline to classify whole-slide images of OKCs and non-keratocysts (non-KCs: dentigerous and radicular cysts). OKC diagnosis and prognosis using the histopathological analysis of tissues using whole-slide images (WSIs) with a deep-learning approach is an emerging research area. WSIs have the unique advantage of magnifying tissues with high resolution without losing information. The contribution of this research is a novel, deep-learning-based, and efficient algorithm that reduces the trainable parameters and, in turn, the memory footprint. This is achieved using principal component analysis (PCA) and the ReliefF feature selection algorithm (ReliefF) in a convolutional neural network (CNN) named P-C-ReliefF. The proposed model reduces the trainable parameters compared to standard CNN, achieving 97% classification accuracy.

Digital pathology systems enabling quality patient care

Pathology laboratories are undergoing digital transformations, adopting innovative technologies to enhance patient care. Digital pathology systems impact clinical, education, and research use cases where pathologists use digital technologies to perform tasks in lieu of using glass slides and a microscope. Pathology professional societies have established clinical validation guidelines, and the US Food and Drug Administration have also authorized digital pathology systems for primary diagnosis, including image analysis and machine learning systems. Whole slide images, or digital slides, can be viewed and navigated similar to glass slides on a microscope. These modern tools not only enable pathologists to practice their routine clinical activities, but can potentially enable digital computational discovery. Assimilation of whole slide images in pathology clinical workflow can further empower machine learning systems to support computer assisted diagnostics. The potential enrichment these systems can provide is unprecedented in the field of pathology. With appropriate integration, these clinical decision support systems will allow pathologists to increase the delivery of quality patient care. This review describes the digital pathology transformation process, applicable clinical use cases, incorporation of image analysis and machine learning systems in the clinical workflow, as well as future technologies that may further disrupt pathology modalities to deliver quality patient care.

Quality Management System in Clinical Digital Pathology Operations at a Tertiary Cancer Center

Digital pathology workflows can improve pathology operations by allowing reliable and fast retrieval of digital images, digitally reviewing pathology slides, enabling remote work and telepathology, use of computer-aided tools, and sharing of digital images for research and educational purposes. The need for quality systems is a prerequisite for successful clinical-grade digital pathology adoption and patient safety. In this article, we describe the development of a structured digital pathology laboratory quality management system (QMS) for clinical digital pathology operations at Memorial Sloan Kettering Cancer Center (MSK). This digital pathology-specific QMS development stemmed from the gaps that were identified when MSK integrated digital pathology into its clinical practice. The digital scan team in conjunction with the Department of Pathology and Laboratory Medicine quality team developed a QMS tailored to the scanning operation to support departmental and institutional needs. As a first step, systemic mapping of the digital pathology operations identified the prescan, scan, and postscan processes; instrumentation; and staffing involved in the digital pathology operation. Next, gaps identified in quality control and quality assurance measures led to the development of standard operating procedures and training material for the different roles and workflows in the process. All digital pathology-related documents were subject to regulatory review and approval by departmental leadership. The quality essentials were developed into an extensive Digital Pathology Quality Essentials framework to specifically address the needs of the growing clinical use of digital pathology technologies. Using the unique digital experience gained at MSK, we present our recommendations for QMS for large-scale digital pathology operations in clinical settings.

Rapid Remote Online Evaluation in Endoscopic Diagnostics: An Analysis of Biopsy-Proven Respiratory Cytopathology

BACKGROUND

This prospective study assesses the use of rapid remote online cytological evaluation for diagnosing endoscopical achieved biopsies. It focuses on its effectiveness in identifying benign and malignant conditions using digital image processing.

METHODS

The study was conducted between April 2021 and September 2022 and involved analyses of 314 Rapid Remote Online Cytological Evaluations in total (154 imprint cytologies, 143 fine needle aspirations and 17 brush cytologies) performed on 239 patients at the LungenClinic Grosshansdorf. During on-site evaluation via telecytology, the time requirement was recorded and the findings were compared with the cyto-/histological and final diagnoses.

RESULTS

By means of rapid remote online evaluation, findings of 86 cytological benign, 190 malignant and 38 unclear diagnoses were recorded (Ø assessment time, 100 s; range, 11-370 s). In 27 of the 37 specimens with unclear diagnoses, the final findings were malignant tumours and only 6 were benign changes. The diagnosis of another 4 of these 37 findings remained unclear. Excluding these 37 specimens, rapid remote online evaluation achieved a sensitivity of 90.5% with a specificity of 98.5% and a correct classification rate of 92.4% with regard to the final diagnosis of all cases. As expected, an increase in the sensitivity rate for the cytological detection of malignant tumours (76.1% vs. 92.5%) was found especially in fine-needle aspirations.

CONCLUSIONS

Rapid remote online analysis allows the fast quantitative and qualitative evaluation of clinically obtained cytological specimens. With a correct classification rate of more than 93%, sampling deficiencies can be corrected promptly and diagnostic and therapeutic approaches can be derived.

Cost analysis of archives in the pathology laboratories: from safety to management

CONTEXT

Despite the reluctance to invest and the challenging estimation of necessary supporting costs, optimising the archives seems to be one of the hottest topics in the future management of the pathology laboratories. Historically, archives were only partially designed to securely store and organise tissue specimens, and tracking systems were often flawed, posing significant risks to patients' health and legal ramifications for pathologists.

OBJECTIVE

The current review explores the available data from the literature on archives' management in pathology, including comprehensive business plans, structure setup, outfit, inventories, ongoing conservation and functional charges.

DATA SOURCES

Electronic searches in PubMed-MEDLINE and Embase were made to extract pertinent articles from the literature. Works about the archiving process and storage were included and analysed to extract information. Prepublication servers were ignored. Italian Institutional Regional databases for public competitive bidding processes were queried too.

CONCLUSIONS

A new emergent feeling in the pathology laboratory is growing for archives management; the digital pathology era is a great opportunity to apply innovation to tracking systems and samples preservation. The main aim is a critical evaluation of the return of investment in developing automatic and tracked archiving processes for improving not only quality, efficacy and efficiency of the labs but also patients' healthcare.

Artificial intelligence feasibility in veterinary medicine: A systematic review

Background and Aim

In recent years, artificial intelligence (AI) has become increasingly necessary in the life sciences, particularly medicine and healthcare. This study aimed to systematically review the literature and critically analyze multiple databases on the use of AI in veterinary medicine to assess its challenges. We aim to foster an understanding of the effects that can be approached and applied for professional awareness.

Materials and Methods

This study used multiple electronic databases with information on applied AI in veterinary medicine based on the current guidelines outlined in PRISMA and Cochrane for systematic review. The electronic databases PubMed, Embase, Google Scholar, Cochrane Library, and Elsevier were thoroughly screened through March 22, 2023. The study design was carefully chosen to emphasize evidence quality and population heterogeneity.

Results

A total of 385 of the 883 citations initially obtained were thoroughly reviewed. There were four main areas that AI addressed; the first was diagnostic issues, the second was education, animal production, and epidemiology, the third was animal health and welfare, pathology, and microbiology, and the last was all other categories. The quality assessment of the included studies found that they varied in their relative quality and risk of bias. However, AI aftereffect-linked algorithms have raised criticism of their generated conclusions.

Conclusion

Quality assessment noted areas of AI outperformance, but there was criticism of its performance as well. It is recommended that the extent of AI in veterinary medicine should be increased, but it should not take over the profession. The concept of ambient clinical intelligence is adaptive, sensitive, and responsive to the digital environment and may be attractive to veterinary professionals as a means of lowering the fear of automating veterinary medicine. Future studies should focus on an AI model with flexible data input, which can be expanded by clinicians/users to maximize their interaction with good algorithms and reduce any errors generated by the process.

Deep Learning Neural Network-Guided Detection of Asbestos Bodies in Bronchoalveolar Lavage Samples

INTRODUCTION

Asbestos is a global occupational health hazard, and exposure to it by inhalation predisposes to interstitial as well as malignant pulmonary morbidity. Over time, asbestos fibers embedded in lung tissue can become coated with iron-rich proteins and mucopolysaccharides, after which they are called asbestos bodies (ABs) and can be detected in light microscopy (LM). Bronchoalveolar lavage, a cytological sample from the lower airways, is one of the methods for diagnosing lung asbestosis and related morbidity. Search for ABs in these samples is generally laborious and time-consuming. We describe a novel diagnostic method, which implements deep learning neural network technology for the detection of ABs in bronchoalveolar lavage samples (BALs).

METHODS

BALs with suspicion of asbestos exposure were scanned as whole slide images (WSIs) and uploaded to a cloud-based virtual microscopy platform with a neural network training interface. The images were used for training and testing a neural network model capable of recognizing ABs. To prioritize the model's sensitivity, we allowed it to also make false-positive suggestions. To test the model, we compared its performance to standard LM diagnostic data as well as the ground truth (GT) number of ABs, which we established by a thorough manual search of the WSIs.

RESULTS

We were able to reach overall sensitivity of 93.4% (95% CI: 90.3-95.7%) in the detection of ABs in comparison to their GT number. Compared to standard LM diagnostic data, our model showed equal to or higher sensitivity in most cases.

CONCLUSION

Our results indicate that deep learning neural network technology offers promising diagnostic tools for routine assessment of BALs. However, at this stage, a human expert is required to confirm the findings.

Overcoming the Interobserver Variability in Lung Adenocarcinoma Subtyping: A Clustering Approach to Establish a Ground Truth for Downstream Applications

CONTEXT.—

The accurate identification of different lung adenocarcinoma histologic subtypes is important for determining prognosis but can be challenging because of overlaps in the diagnostic features, leading to considerable interobserver variability.

OBJECTIVE.—

To provide an overview of the diagnostic agreement for lung adenocarcinoma subtypes among pathologists and to create a ground truth using the clustering approach for downstream computational applications.

DESIGN.—

Three sets of lung adenocarcinoma histologic images with different evaluation levels (small patches, areas with relatively uniform histology, and whole slide images) were reviewed by 17 international expert lung pathologists and 1 pathologist in training. Each image was classified into one or several lung adenocarcinoma subtypes.

RESULTS.—

Among the 4702 patches of the first set, 1742 (37%) had an overall consensus among all pathologists. The overall Fleiss κ score for the agreement of all subtypes was 0.58. Using cluster analysis, pathologists were hierarchically grouped into 2 clusters, with κ scores of 0.588 and 0.563 in clusters 1 and 2, respectively. Similar results were obtained for the second and third sets, with fair-to-moderate agreements. Patches from the first 2 sets that obtained the consensus of the 18 pathologists were retrieved to form consensus patches and were regarded as the ground truth of lung adenocarcinoma subtypes.

CONCLUSIONS.—

Our observations highlight discrepancies among experts when assessing lung adenocarcinoma subtypes. However, a subsequent number of consensus patches could be retrieved from each cluster, which can be used as ground truth for the downstream computational pathology applications, with minimal influence from interobserver variability.

Validation of cytopathology specimens for digital pathology

INTRODUCTION

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

METHODS

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

RESULTS

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

CONCLUSION

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

Digital pathology operations at a tertiary cancer center: Infrastructure requirements and operational cost

Whole slide imaging is revolutionizing the field of pathology and is currently being used for clinical, educational, and research initiatives by an increasing number of institutions. Pathology departments have distinct needs for digital pathology systems, yet the cost of digital workflows is cited as a major barrier for widespread adoption by many organizations. Memorial Sloan Kettering Cancer Center (MSK) is an early adopter of whole slide imaging with incremental investments in resources that started more than 15 years ago. This experience and the large-scale scan operations led to the identification of required framework components of digital pathology operations. The cost of these components for the 2021 digital pathology operations at MSK were studied and calculated to enable an understanding of the operation and benchmark the accompanying costs. This paper describes the unique infrastructure cost and the costs associated with the digital pathology clinical operation use cases in a large, tertiary cancer center. These calculations can serve as a blueprint for other institutions to provide the necessary concepts and offer insights towards the financial requirements for digital pathology adoption by other institutions.

Impact of a centralized archive for pathology laboratories on the health system

The pathology archive of any hospital is likely to contain tens of thousands of slides and formalin-fixed and paraffin-embedded (FFPE) blocks, with their number constantly increasing. As a result, serious space and management issues are created. There has always been a favorable location for the pathology laboratory to rapidly and efficiently collect specimens and to meet the different service requirements of clinicians and patients. However, archiving may be one of the most neglected issues in the planning of spaces and activities, so much so that many laboratories are currently in trouble and looking for space inside and outside their hospitals. Another crucial issue is related to the environmental conditions of the identified preservation place, which, based on their characteristics, probably provide suboptimal habitats in most cases. For FFPE blocks, controlled temperature (<27 °C) and humidity (>30% and <70%) are recommended, with control systems for parasite infestation. For glass slides, systems suitable for guaranteeing their safety, traceability and conservation suitable for possible revision are recommended. The aim of this position paper is to outline the issues that currently exist in archives and to suggest a rational health policy solution to overcome the problems raised.

An emerging era of computational cytology

BACKGROUND

The significant advancement in digital imaging, data management, advanced computational power, and artificial neural network have an immense impact on the field of cytology. The amalgamation of these areas has generated a newer discipline known as computational cytology.

AIMS AND OBJECTIVE

In To discuss the various important aspects of computational cytology.

MATERIALS AND METHODS

We reviewed the different studies published in English during the last few years on computational cytology.

RESULT

Computational cytology is a newer and emerging discipline in pathology that deals with the patient's meta-data and digital image data to make a mathematical model to produce diagnostic interpretations and predictions. The role of the cytologist is now changing from a simple observational scientist and slide interpreter to a dynamic and integrated multi-parametric prediction-based scientist.

CONCLUSION

In the current stage, the cytologist must understand the situation and should have a vision of the complete scenario on computational cytology.

Predicting the recurrence and overall survival of patients with glioma based on histopathological images using deep learning

Background

A deep learning (DL) model based on representative biopsy tissues can predict the recurrence and overall survival of patients with glioma, leading to optimized personalized medicine. This research aimed to develop a DL model based on hematoxylin-eosin (HE) stained pathological images and verify its diagnostic accuracy.

Methods

Our study retrospectively collected 162 patients with glioma and randomly divided them into a training set (n = 113) and a validation set (n = 49) to build a DL model. The HE-stained slide was segmented into a size of 180 × 180 pixels without overlapping. The patch-level features were extracted by the pre-trained ResNet50 to predict the recurrence and overall survival. Additionally, a light-strategy was introduced where low-size digital biopsy images with clinical information were inputted into the DL model to ensure minimum memory occupation.

Results

Our study extracted 512 histopathological features from the HE-stained slides of each glioma patient. We identified 36 and 18 features as significantly related to disease-free survival (DFS) and overall survival (OS), respectively, (P &lt; 0.05) using the univariate Cox proportional-hazards model. Pathomics signature showed a C-index of 0.630 and 0.652 for DFS and OS prediction, respectively. The time-dependent receiver operating characteristic (ROC) curves, along with nomograms, were used to assess the diagnostic accuracy at a fixed time point. In the validation set (n = 49), the area under the curve (AUC) in the 1- and 2-year DFS was 0.955 and 0.904, respectively, and the 2-, 3-, and 5-year OS were 0.969, 0.955, and 0.960, respectively. We stratified the patients into low- and high-risk groups using the median hazard score (0.083 for DFS and−0.177 for OS) and showed significant differences between these groups (P &lt; 0.001).

Conclusion

Our results demonstrated that the DL model based on the HE-stained slides showed the predictability of recurrence and survival in patients with glioma. The results can be used to assist oncologists in selecting the optimal treatment strategy in clinical practice.

Real-world experience of adding placental histopathology studies into perinatal clinical trials

Addition of placental histopathology studies to obstetric trials is likely to be cost-effective and may reveal structural changes suggestive of functional dysfunction to explain the success or failure of a clinical intervention. We share our recent experience in adding placental pathological examination to two clinical trials, retrospectively in one and at the outset in the other, so that other clinical trial investigators may benefit from it. The practical issues can be summarised as being regulatory and ethical, operational and reporting. Prospective inclusion of placental pathological examination as part of a clinical trial protocol is easier than retrospective, and is facilitated by fully-costed funding.

An automatic whole-slide hyperspectral imaging microscope

Whole slide imaging (WSI) is a common step used in histopathology to quickly digitize stained histological slides. Digital whole-slide images not only improve the efficiency of labeling but also open the door for computer-aided diagnosis, specifically machine learning-based methods. Hyperspectral imaging (HSI) is an imaging modality that captures data in various wavelengths, some beyond the range of visible lights. In this study, we developed and implemented an automated microscopy system that can acquire hyperspectral whole slide images (HWSI). The system is robust since it consists of parts that can be swapped and bought from different manufacturers. We used the automated system and built a database of 49 HWSI of thyroid cancer. The automatic whole-slide hyperspectral imaging microscope can have many potential applications in biological and medical areas.

Relevance of the College of American Pathologists guideline for validating whole slide imaging for diagnostic purposes to cytopathology

Whole slide imaging (WSI) allows pathologists to view virtual versions of slides on computer monitors. With increasing adoption of digital pathology, laboratories have begun to validate their WSI systems for diagnostic purposes according to reference guidelines. Among these the College of American Pathologists (CAP) guideline includes three strong recommendations (SRs) and nine good practice statements (GPSs). To date, the application of WSI to cytopathology has been beyond the scope of the CAP guideline due to limited evidence. Herein we systematically reviewed the published literature on WSI validation studies in cytology. A systematic search was carried out in PubMed-MEDLINE and Embase databases up to November 2021 to identify all publications regarding validation of WSI in cytology. Each article was reviewed to determine if SRs and/or GPSs recommended by the CAP guideline were adequately satisfied. Of 3963 retrieved articles, 25 were included. Only 4/25 studies (16%) satisfied all three SRs, with only one publication (1/25, 4%) fulfilling all three SRs and nine GPSs. Lack of a suitable validation dataset was the main missing SR (16/25, 64%) and less than a third of the studies reported intra-observer variability data (7/25, 28%). Whilst the CAP guideline for WSI validation in clinical practice helped the widespread adoption of digital pathology, more evidence is required to routinely employ WSI for diagnostic purposes in cytopathology practice. More dedicated validation studies satisfying all SRs and/or GPSs recommended by the CAP are needed to help expedite the use of WSI for primary diagnosis in cytopathology.

Automated Classification of Idiopathic Pulmonary Fibrosis in Pathological Images Using Convolutional Neural Network and Generative Adversarial Networks

Interstitial pneumonia of uncertain cause is referred to as idiopathic interstitial pneumonia (IIP). Among the various types of IIPs, the prognosis of cases of idiopathic pulmonary fibrosis (IPF) is extremely poor, and accurate differentiation between IPF and non-IPF pneumonia is critical. In this study, we consider deep learning (DL) methods owing to their excellent image classification capabilities. Although DL models require large quantities of training data, collecting a large number of pathological specimens is difficult for rare diseases. In this study, we propose an end-to-end scheme to automatically classify IIPs using a convolutional neural network (CNN) model. To compensate for the lack of data on rare diseases, we introduce a two-step training method to generate pathological images of IIPs using a generative adversarial network (GAN). Tissue specimens from 24 patients with IIPs were scanned using a whole slide scanner, and the resulting images were divided into patch images with a size of 224 × 224 pixels. A progressive growth GAN (PGGAN) model was trained using 23,142 IPF images and 7817 non-IPF images to generate 10,000 images for each of the two categories. The images generated by the PGGAN were used along with real images to train the CNN model. An evaluation of the images generated by the PGGAN showed that cells and their locations were well-expressed. We also obtained the best classification performance with a detection sensitivity of 97.2% and a specificity of 69.4% for IPF using DenseNet. The classification performance was also improved by using PGGAN-generated images. These results indicate that the proposed method may be considered effective for the diagnosis of IPF.

Efficient Staining-Invariant Nuclei Segmentation Approach Using Self-Supervised Deep Contrastive Network

Existing nuclei segmentation methods face challenges with hematoxylin and eosin (H&E) whole slide imaging (WSI) due to the variations in staining methods and nuclei shapes and sizes. Most existing approaches require a stain normalization step that may cause losing source information and fail to handle the inter-scanner feature instability problem. To mitigate these issues, this article proposes an efficient staining-invariant nuclei segmentation method based on self-supervised contrastive learning and an effective weighted hybrid dilated convolution (WHDC) block. In particular, we propose a staining-invariant encoder (SIE) that includes convolution and transformers blocks. We also propose the WHDC block allowing the network to learn multi-scale nuclei-relevant features to handle the variation in the sizes and shapes of nuclei. The SIE network is trained on five unlabeled WSIs datasets using self-supervised contrastive learning and then used as a backbone for the downstream nuclei segmentation network. Our method outperforms existing approaches in challenging multiple WSI datasets without stain color normalization.

Label Cleaning Multiple Instance Learning: Refining Coarse Annotations on Single Whole-Slide Images

Annotating cancerous regions in whole-slide images (WSIs) of pathology samples plays a critical role in clinical diagnosis, biomedical research, and machine learning algorithms development. However, generating exhaustive and accurate annotations is labor-intensive, challenging, and costly. Drawing only coarse and approximate annotations is a much easier task, less costly, and it alleviates pathologists' workload. In this paper, we study the problem of refining these approximate annotations in digital pathology to obtain more accurate ones. Some previous works have explored obtaining machine learning models from these inaccurate annotations, but few of them tackle the refinement problem where the mislabeled regions should be explicitly identified and corrected, and all of them require a - often very large - number of training samples. We present a method, named Label Cleaning Multiple Instance Learning (LC-MIL), to refine coarse annotations on a single WSI without the need for external training data. Patches cropped from a WSI with inaccurate labels are processed jointly within a multiple instance learning framework, mitigating their impact on the predictive model and refining the segmentation. Our experiments on a heterogeneous WSI set with breast cancer lymph node metastasis, liver cancer, and colorectal cancer samples show that LC-MIL significantly refines the coarse annotations, outperforming state-of-the-art alternatives, even while learning from a single slide. Moreover, we demonstrate how real annotations drawn by pathologists can be efficiently refined and improved by the proposed approach. All these results demonstrate that LC-MIL is a promising, lightweight tool to provide fine-grained annotations from coarsely annotated pathology sets.

High-Resolution Histopathological Image Classification Model Based on Fused Heterogeneous Networks with Self-Supervised Feature Representation

Applying machine learning technology to automatic image analysis and auxiliary diagnosis of whole slide image (WSI) may help to improve the efficiency, objectivity, and consistency of pathological diagnosis. Due to its extremely high resolution, it is still a great challenge to directly process WSI through deep neural networks. In this paper, we propose a novel model for the task of classification of WSIs. The model is composed of two parts. The first part is a self-supervised encoding network with a UNet-like architecture. Each patch from a WSI is encoded as a compressed latent representation. These features are placed according to their corresponding patch’s original location in WSI, forming a feature cube. The second part is a classification network fused by 4 famous network blocks with heterogeneous architectures, with feature cube as input. Our model effectively expresses the feature and preserves location information of each patch. The fused network integrates heterogeneous features generated by different networks which yields robust classification results. The model is evaluated on two public datasets with comparison to baseline models. The evaluation results show the effectiveness of the proposed model.

Artificial intelligence and its applications in digital hematopathology

The advent of whole-slide imaging, faster image data generation, and cheaper forms of data storage have made it easier for pathologists to manipulate digital slide images and interpret more detailed biological processes in conjunction with clinical samples. In parallel, with continuous breakthroughs in object detection, image feature extraction, image classification and image segmentation, artificial intelligence (AI) is becoming the most beneficial technology for high-throughput analysis of image data in various biomedical imaging disciplines. Integrating digital images into biological workflows, advanced algorithms, and computer vision techniques expands the biologist's horizons beyond the microscope slide. Here, we introduce recent developments in AI applied to microscopy in hematopathology. We give an overview of its concepts and present its applications in normal or abnormal hematopoietic cells identification. We discuss how AI shows great potential to push the limits of microscopy and enhance the resolution, signal and information content of acquired data. Its shortcomings are discussed, as well as future directions for the field.

White light phase shifting interferometric microscopy with whole slide imaging for quantitative analysis of biological samples

In this paper, we demonstrate the white light phase shifting interferometer employed as whole slide scanner and phase profiler for determining qualitative and quantitative information over large field-of-view (FOV). Experiments were performed on human erythrocytes and MG63 Osteosarcoma cells. Here, we have recorded microscopic images and phase shifted white light interferograms simultaneously in a stepped manner. Sample slide is translated in transverse direction such that there exists a correlation between the adjacent frames, and they were stitched together using correlation functions. Final stitched image has a FOV of 0.24 × 1.14 mm with high resolution ~0.8 μm. Circular Hough transform algorithm is implemented to the resulting image for cell counting and five-step phase shifting algorithm is utilised to retrieve the phase profiles over a large FOV. Further, this technique is utilised to study the difference between normal and anaemic erythrocytes. Significant changes are observed in anaemic cells as compared to normal cells.

Substantial improvement of histopathological diagnosis by whole-slide image-based remote consultation

Consultation by subspecialty experts is the most common mode of rendering diagnosis in challenging cases in pathological practice. Our study aimed to highlight the diagnostic benefits of whole-slide image (WSI)-based remote consultation. We obtained diagnostically challenging cases from two institutions from the years 2010 and 2013, with histological diagnoses that contained keywords “probable,” “suggestive,” “suspicious,” “inconclusive,” and “uncertain.” A total of 270 cases were selected for remote consultation using WSIs scanned at 40 × . The consultation process consisted of three rounds: the first and second rounds each with 12 subspecialty experts and the third round with six multi-expertise senior pathologists. The first consultation yielded 44% concordance, and a change in diagnosis occurred in 56% of cases. The most frequent change was from inconclusive to definite diagnosis (30%), followed by minor discordance (14%), and major discordance (12%). Out of the 70 cases which reached the second round, 31 cases showed discrepancy between the two consultants. For these 31 cases, a consensus diagnosis was provided by six multi-expertise senior pathologists. Combining all WSI-based consultation rounds, the original inconclusive diagnosis was changed in 140 (52%) out of 266 cases. Among these cases, 80 cases (30%) upgraded the inconclusive diagnosis to a definite diagnosis, and 60 cases (22%) changed the diagnosis with major or minor discordance, accounting for 28 cases (10%) and 32 cases (12%), respectively. We observed significant improvement in the pathological diagnosis of difficult cases by remote consultation using WSIs, which can further assist in patient healthcare. A post-study survey highlighted various benefits of WSI-based consults.

The Influence of Social Media in Promoting Knowledge Acquisition and Pathology Excellence in Nigeria

The use of social media has evolved from platforms designed primarily for social connection and news sharing to include vibrant virtual academic environments. These platforms allow pathologists from across the globe to interact, exchange knowledge, and collaborate. Pathology in Nigeria, as in much of Africa, faces severe knowledge and practice gaps, with a lack of supporting modern laboratory infrastructure. Social media represents a potentially highly valuable avenue to help address some of these deficiencies. In this Perspective piece, we highlight our experience with the increasing role of social media in providing quality medical education in pathology globally, with an emphasis on how it bridges many of these gaps in Nigeria. Social media sites serve as sources of readily accessible, free, high-quality information to pathologists and trainees through academic discussions, quizzes, journal clubs, and informal consultations. They also provide opportunities for professional networking and research collaborations. Despite the availability and wide reach of these platforms, social media as a tool for advancement of knowledge in pathology is still undersubscribed in this part of the world. Improving awareness of and support for these tools will ideally help mitigate some of the challenges of practicing pathology in low and middle-income settings.

Progress on deep learning in digital pathology of breast cancer: a narrative review

BACKGROUND AND OBJECTIVE

Pathology is the gold standard criteria for breast cancer diagnosis and has important guiding value in formulating the clinical treatment plan and predicting the prognosis. However, traditional microscopic examinations of tissue sections are time consuming and labor intensive, with unavoidable subjective variations. Deep learning (DL) can evaluate and extract the most important information from images with less need for human instruction, providing a promising approach to assist in the pathological diagnosis of breast cancer. To provide an informative and up-to-date summary on the topic of DL-based diagnostic systems for breast cancer pathology image analysis and discuss the advantages and challenges to the routine clinical application of digital pathology.

METHODS

A PubMed search with keywords ("breast neoplasm" or "breast cancer") and ("pathology" or "histopathology") and ("artificial intelligence" or "deep learning") was conducted. Relevant publications in English published from January 2000 to October 2021 were screened manually for their title, abstract, and even full text to determine their true relevance. References from the searched articles and other supplementary articles were also studied.

KEY CONTENT AND FINDINGS

DL-based computerized image analysis has obtained impressive achievements in breast cancer pathology diagnosis, classification, grading, staging, and prognostic prediction, providing powerful methods for faster, more reproducible, and more precise diagnoses. However, all artificial intelligence (AI)-assisted pathology diagnostic models are still in the experimental stage. Improving their economic efficiency and clinical adaptability are still required to be developed as the focus of further researches.

CONCLUSIONS

Having searched PubMed and other databases and summarized the application of DL-based AI models in breast cancer pathology, we conclude that DL is undoubtedly a promising tool for assisting pathologists in routines, but further studies are needed to realize the digitization and automation of clinical pathology.

Contemporary Management of Locally Advanced and Recurrent Rectal Cancer: Views from the PelvEx Collaborative

Pelvic exenteration is a complex operation performed for locally advanced and recurrent pelvic cancers. The goal of surgery is to achieve clear margins, therefore identifying adjacent or involved organs, bone, muscle, nerves and/or vascular structures that may need resection. While these extensive resections are potentially curative, they can be associated with substantial morbidity. Recently, there has been a move to centralize care to specialized units, as this facilitates better multidisciplinary care input. Advancements in pelvic oncology and surgical innovation have redefined the boundaries of pelvic exenterative surgery. Combined with improved neoadjuvant therapies, advances in diagnostics, and better reconstructive techniques have provided quicker recovery and better quality of life outcomes, with improved survival This article provides highlights of the current management of advanced pelvic cancers in terms of surgical strategy and potential future developments.

Integrating digital pathology into clinical practice

The field of anatomic pathology has been evolving in the last few decades and the advancements have been largely fostered by innovative technology. Immunohistochemistry enabled a paradigm shift in discovery and diagnostic evaluation, followed by booming genomic advancements which allowed for submicroscopic pathologic characterization, and now the field of digital pathology coupled with machine learning and big data acquisition is paving the way to revolutionize the pathology medical domain. Whole slide imaging (WSI) is a disruptive technology where glass slides are digitized to produce on-screen whole slide images. Specifically, in the past decade, there have been significant advances in digital pathology systems that have allowed this technology to promote integration into clinical practice. Whole slide images (WSI), or digital slides, can be viewed and navigated comparable to glass slides on a microscope, as digital files. Whole slide imaging has increased in adoption among pathologists, pathology departments, and scientists for clinical, educational, and research initiatives. Integration of digital pathology systems requires a coordinated effort with numerous stakeholders, not only within the pathology department, but across the entire enterprise. Each pathology department has distinct needs, use cases and blueprints, however the framework components and variables for successful clinical integration can be generalized across any organization seeking to undergo a digital transformation at any scale. This article will review those components and considerations for integrating digital pathology systems into clinical practice.