Digital pathology: semper ad meliora

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.

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer

INTRODUCTION

Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes.

AREAS COVERED

In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings.

EXPERT OPINION

The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.

Joint online distance learning to complement postgraduate pathology training in preparation for national board examinations

AIMS

To meet the flexible learning needs of pathology residents preparing for national board examinations, a joint distance learning approach was developed using both asynchronous and synchronous activities with whole slide images, drawing on empirical educational research on online distance learning.

METHODS

In a case study of an implementation of the designed joint distance learning approach with a geographically dispersed group of pathology residents in Finland, the participants' perceptions were measured with a 12-item questionnaire covering the value of the learning opportunity, the quality of the sociocognitive processes and their emotional engagement and social cohesion. Communication during the online session was also recorded and analysed to provide objectivity to the self-report data.

RESULTS

The effectiveness of joint online learning for knowledge acquisition and preparation for national board examinations was highly rated. However, despite strong emotional engagement during synchronous activities, participants reported minimal interpersonal interaction, which was also reflected in the recordings of the online session.

CONCLUSION

Using a technology integration framework and guided by the principles of self-determination theory, joint distance learning is emerging as a beneficial addition to postgraduate pathology programmes in preparation for national examinations. However, to realise the full potential of interpersonal interaction, participants should be prepared for an appropriate mindset.

Object detection: A novel AI technology for the diagnosis of hepatocyte ballooning

Metabolic dysfunction-associated fatty liver disease (MAFLD) has reached epidemic proportions worldwide and is the most frequent cause of chronic liver disease in developed countries. Within the spectrum of liver disease in MAFLD, steatohepatitis is a progressive form of liver disease and hepatocyte ballooning (HB) is a cardinal pathological feature of steatohepatitis. The accurate and reproducible diagnosis of HB is therefore critical for the early detection and treatment of steatohepatitis. Currently, a diagnosis of HB relies on pathological examination by expert pathologists, which may be a time-consuming and subjective process. Hence, there has been interest in developing automated methods for diagnosing HB. This narrative review briefly discusses the development of artificial intelligence (AI) technology for diagnosing fatty liver disease pathology over the last 30 years and provides an overview of the current research status of AI algorithms for the identification of HB, including published articles on traditional machine learning algorithms and deep learning algorithms. This narrative review also provides a summary of object detection algorithms, including the principles, historical developments, and applications in the medical image analysis. The potential benefits of object detection algorithms for HB diagnosis (specifically those combined with a transformer architecture) are discussed, along with the future directions of object detection algorithms in HB diagnosis and the potential applications of generative AI on transformer architecture in this field. In conclusion, object detection algorithms have huge potential for the identification of HB and could make the diagnosis of MAFLD more accurate and efficient in the near future.

Emerging Technology for Intraoperative Margin and Assisting in Post-Surgery tissue diagnostic for Future Breast-Conserving

INTRODUCTION

Tissue-preserving surgery is utilized progressively in cancer therapy, where a clear surgical margin is critical to avoid cancer recurrence, specifically in breast cancer (BC) surgery. The Intraoperative pathologic approaches that rely on tissue segmenting and staining have been recognized as the ground truth for BC diagnosis. Nevertheless, these methods are constrained by its complication and timewasting for tissue preparation.

OBJECTIVE

We present a non-invasive optical imaging system incorporating a hyperspectral (HS) camera to discriminate between cancerous and non-cancerous tissues in ex-vivo breast specimens, which could be an intraoperative diagnostic technique to aid surgeons during surgery and later a valuable tool to assist pathologists.

METHODS

We have established a hyperspectral Imaging (HSI) system comprising a push-broom HS camera at wavelength 380∼1050 nm with source light 390∼980 nm. We have measured the investigated samples' diffuse reflectance (R_d), fixed on slides from 30 distinct patients incorporating mutually normal and ductal carcinoma tissue. The samples were divided into two groups, stained tissues during the surgery (control group) and unstained samples (test group), both captured with the HSI system in the visible and near-infrared (VIS-NIR) range. Then, to address the problem of the spectral nonuniformity of the illumination device and the influence of the dark current, the radiance data were normalized to yield the radiance of the specimen and neutralize the intensity effect to focus on the spectral reflectance shift for each tissue. The selection of the threshold window from the measured R_d is carried out by exploiting the statistical analysis by calculating each region's mean and standard deviation. Afterward, we selected the optimum spectral images from the HS data cube to apply a custom K-means algorithm and contour delineation to identify the regular districts from the BC regions.

RESULTS

We noticed that the measured spectral R_d for the malignant tissues of the investigated case studies versus the reference source light varies regarding the cancer stage, as sometimes the R_d is higher for the tumor or vice versa for the normal tissue. Later, from the analysis of the whole samples, we found that the most appropriate wavelength for the BC tissues was 447 nm, which was highly reflected versus the normal tissue. However, the most convenient one for the normal tissue was at 545 nm with high reflection versus the BC tissue. Finally, we implement a moving average filter for noise reduction and a custom K-means clustering algorithm on the selected two spectral images (447, 551 nm) to identify the various regions and effectively-identified spectral tissue variations with a sensitivity of 98.95%, and specificity of 98.44%. A pathologist later confirmed these outcomes as the ground truth for the tissue sample investigations.

CONCLUSIONS

The proposed system could help the surgeon and the pathologist identify the cancerous tissue margins from the non-cancerous tissue with a non-invasive, rapid, and minimum time method achieving high sensitivity up to 98.95%.

Harnessing the disruption on medical trainee education due to COVID-19 in New South Wales, Australia

The coronavirus disease (COVID-19) pandemic has caused disruption and uncertainty for junior medical doctor training and education. This has compounded the existing stress experienced by this cohort. However, by choosing appropriate educational models, as well as using novel educational approaches and advancing our online technology capabilities, we may be able to provide acceptable and even, superior solutions for educational training moving forward, as well as promote trainee wellbeing during these uncertain times.

Harnessing the disruption on medical trainee education due to COVID-19 in New South Wales, Australia

The coronavirus disease (COVID-19) pandemic has caused disruption and uncertainty for junior medical doctor training and education. This has compounded the existing stress experienced by this cohort. However, by choosing appropriate educational models, as well as using novel educational approaches and advancing our online technology capabilities, we may be able to provide acceptable and even, superior solutions for educational training moving forward, as well as promote trainee wellbeing during these uncertain times.

Smartpathk: a platform for teaching glomerulopathies using machine learning

BACKGROUND

With the emergence of the new coronavirus pandemic (COVID-19), distance learning, especially that mediated by information and digital communication technologies, has been adopted in all areas of knowledge and at all levels, including medical education. Imminently practical areas, such as pathology, have made traditional teaching based on conventional microscopy more flexible through the synergies of computational tools and image digitization, not only to improve teaching-learning but also to offer alternatives to repetitive and exhaustive histopathological analyzes. In this context, machine learning algorithms capable of recognizing histological patterns in kidney biopsy slides have been developed and validated with a view to building computational models capable of accurately identifying renal pathologies. In practice, the use of such algorithms can contribute to the universalization of teaching, allowing quality training even in regions where there is a lack of good nephropathologists. The purpose of this work is to describe and test the functionality of SmartPath^k, a tool to support teaching of glomerulopathies using machine learning. The training for knowledge acquisition was performed automatically by machine learning methods using the J48 algorithm to create a computational model of an appropriate decision tree.

RESULTS

An intelligent system, SmartPath^k, was developed as a complementary remote tool in the teaching-learning process for pathology teachers and their students (undergraduate and graduate students), showing 89,47% accuracy using machine learning algorithms based on decision trees.

CONCLUSION

This artificial intelligence system can assist in teaching renal pathology to increase the training capacity of new medical professionals in this area.

Gamma-Glutamyl Transferase (GGT) Is the Leading External Quality Assurance Predictor of ISO15189 Compliance for Pathology Laboratories

Pathology results are central to modern medical practice, informing diagnosis and patient management. To ensure high standards from pathology laboratories, regulators require compliance with international and local standards. In Australia, the monitoring and regulation of medical laboratories are achieved by conformance to ISO15189-National Pathology Accreditation Advisory Council standards, as assessed by the National Association of Testing Authorities (NATA), and an external quality assurance (EQA) assessment via the Royal College of Pathologists of Australasia Quality Assurance Program (RCPAQAP). While effective individually, integration of data collected by NATA and EQA testing promises advantages for the early detection of technical or management problems in the laboratory, and enhanced ongoing quality assessment. Random forest (RF) machine learning (ML) previously identified gamma-glutamyl transferase (GGT) as a leading predictor of NATA compliance condition reporting. In addition to further RF investigations, this study also deployed single decision trees and support vector machines (SVM) models that included creatinine, electrolytes and liver function test (LFT) EQA results. Across all analyses, GGT was consistently the top-ranked predictor variable, validating previous observations from Australian laboratories. SVM revealed broad patterns of predictive EQA marker interactions with NATA outcomes, and the distribution of GGT relative deviation suggested patterns by which to identify other strong EQA predictors of NATA outcomes. An integrated model of pathology quality assessment was successfully developed, via the prediction of NATA outcomes by EQA results. GGT consistently ranked as the best predictor variable, identified by combining recursive partitioning and SVM ML strategies.

Dissecting the Business Case for Adoption and Implementation of Digital Pathology: A White Paper from the Digital Pathology Association

We believe the switch to a digital pathology (DP) workflow is imminent and it is essential to understand the economic implications of conversion. Many aspects of the adoption of DP will be disruptive and have a direct financial impact, both in short term costs, such as investment in equipment and personnel, and long term revenue potential, such as improved productivity and novel tests. The focus of this whitepaper is to educate pathologists, laboratorians and other stakeholders about the business and monetary considerations of converting to a digital pathology workflow. The components of a DP business plan will be thoroughly summarized, and guidance will be provided on how to build a case for adoption and implementation as well as a roadmap for transitioning from an analog to a digital pathology workflow in various laboratory settings. It is important to clarify that this publication is not intended to list prices although some financials will be mentioned as examples. The authors encourage readers who are evaluating conversion to a DP workflow to use this paper as a foundational guide for conducting a thorough and complete assessment while incorporating in current market pricing. Contributors to this paper analyzed peer-reviewed literature and data collected from various institutions, some of which are mentioned. Digital pathology will change the way we practice through facilitating patient access to expert pathology services and enabling image analysis tools and assays to aid in diagnosis, prognosis, risk stratification and therapeutic selection. Together, they will result in the delivery of valuable information from which to make better decisions and improve the health of patients.

Digital pathology and computational image analysis in nephropathology

The emergence of digital pathology - an image-based environment for the acquisition, management and interpretation of pathology information supported by computational techniques for data extraction and analysis - is changing the pathology ecosystem. In particular, by virtue of our new-found ability to generate and curate digital libraries, the field of machine vision can now be effectively applied to histopathological subject matter by individuals who do not have deep expertise in machine vision techniques. Although these novel approaches have already advanced the detection, classification, and prognostication of diseases in the fields of radiology and oncology, renal pathology is just entering the digital era, with the establishment of consortia and digital pathology repositories for the collection, analysis and integration of pathology data with other domains. The development of machine-learning approaches for the extraction of information from image data, allows for tissue interrogation in a way that was not previously possible. The application of these novel tools are placing pathology centre stage in the process of defining new, integrated, biologically and clinically homogeneous disease categories, to identify patients at risk of progression, and shifting current paradigms for the treatment and prevention of kidney diseases.

Utilization of cytology smears improves success rates of RNA-based next-generation sequencing gene fusion assays for clinically relevant predictive biomarkers

BACKGROUND

The use of RNA-based next-generation sequencing (NGS) assays to detect gene fusions for targeted therapy has rapidly become an essential component of comprehensive molecular profiling. For cytology specimens, the cell block (CB) is most commonly used for fusion testing; however, insufficient cellularity and/or suboptimal RNA quality are often limiting factors. In the current study, the authors evaluated the factors affecting RNA fusion testing in cytology and the added value of smears in cases with a suboptimal or inadequate CB.

METHODS

A 12-month retrospective review was performed to identify cytology cases that were evaluated by a targeted RNA-based NGS assay. Samples were sequenced by targeted amplicon-based NGS for 51 clinically relevant genes on a proprietary platform. Preanalytic factors and NGS quality parameters were correlated with the results of RNA fusion testing.

RESULTS

The overall success rate of RNA fusion testing was 92%. Of the 146 cases successfully sequenced, 14% had a clinically relevant fusion detected. NGS testing success positively correlated with RNA yield (P = .03) but was independent of the tumor fraction, the tumor size, or the number of slides used for extraction. CB preparations were adequate for testing in 45% cases, but the inclusion of direct smears increased the adequacy rate to 92%. There was no significant difference in testing success rates between smears and CB preparations.

CONCLUSIONS

The success of RNA-based NGS fusion testing depends on the quality and quantity of RNA extracted. The use of direct smears significantly improves the adequacy of cytologic samples for RNA fusion testing for predictive biomarkers.

Role of digital pathology in diagnostic histopathology in the response to COVID-19: results from a survey of experience in a UK tertiary referral hospital

The COVID-19 pandemic has challenged our diagnostic services at a time when many histopathology departments already faced a diminishing workforce and increasing workload. Digital pathology (DP) has been hailed as a potential solution to at least some of the challenges faced. We present a survey of pathologists within a UK National Health Service cellular pathology department with access to DP, in which we ascertain the role of DP in clinical services during this current pandemic and explore challenges encountered. This survey indicates an increase in uptake of diagnostic DP during this period, with increased remote access. Half of respondents agreed that DP had facilitated maintenance of diagnostic practice. While challenges have been encountered, these are remediable, and none have impacted on the uptake of DP during this period. We conclude that in our institution, DP has demonstrated current and future potential to increase resilience in diagnostic practice and have highlighted some of the challenges that need to be considered.

Hyperspectral Superpixel-Wise Glioblastoma Tumor Detection in Histological Samples

The combination of hyperspectral imaging (HSI) and digital pathology may yield more accurate diagnosis. In this work, we propose the use of superpixels in HS images for combining regions of pixels that can be classified according to their spectral information to classify glioblastoma (GB) brain tumors in histologic slides. The superpixels are generated by a modified simple linear iterative clustering (SLIC) method to accommodate HS images. This work employs a dataset of H&E (Hematoxylin and Eosin) stained histology slides from 13 patients with GB and over 426,000 superpixels. A linear support vector machine (SVM) classifier was performed on independent training, validation, and testing datasets. The results of this investigation show that the proposed method can detect GB brain tumors from non-tumor samples with average sensitivity and specificity of 87% and 81%, respectively. The overall accuracy of this method is 83%. The study demonstrates that hyperspectral digital pathology can be useful for detecting GB brain tumors by exploiting spectral information alone on a superpixel level.

Callicó G, Fei B. Hyperspectral Imaging for the Detection of Glioblastoma Tumor Cells in H&E Slides Using Convolutional Neural Networks

Hyperspectral imaging (HSI) technology has demonstrated potential to provide useful information about the chemical composition of tissue and its morphological features in a single image modality. Deep learning (DL) techniques have demonstrated the ability of automatic feature extraction from data for a successful classification. In this study, we exploit HSI and DL for the automatic differentiation of glioblastoma (GB) and non-tumor tissue on hematoxylin and eosin (H&E) stained histological slides of human brain tissue. GB detection is a challenging application, showing high heterogeneity in the cellular morphology across different patients. We employed an HSI microscope, with a spectral range from 400 to 1000 nm, to collect 517 HS cubes from 13 GB patients using 20× magnification. Using a convolutional neural network (CNN), we were able to automatically detect GB within the pathological slides, achieving average sensitivity and specificity values of 88% and 77%, respectively, representing an improvement of 7% and 8% respectively, as compared to the results obtained using RGB (red, green, and blue) images. This study demonstrates that the combination of hyperspectral microscopic imaging and deep learning is a promising tool for future computational pathologies.

Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology

Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases.

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

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

Whole Slide Imaging and Its Applications to Histopathological Studies of Liver Disorders

Histological analysis of hepatic tissue specimens is essential for evaluating the pathology of several liver disorders such as chronic liver diseases, hepatocellular carcinomas, liver steatosis, and infectious liver diseases. Manual examination of histological slides on the microscope is a classically used method to study these disorders. However, it is considered time-consuming, limited, and associated with intra- and inter-observer variability. Emerging technologies such as whole slide imaging (WSI), also termed virtual microscopy, have increasingly been used to improve the assessment of histological features with applications in both clinical and research laboratories. WSI enables the acquisition of the tissue morphology/pathology from glass slides and translates it into a digital form comparable to a conventional microscope, but with several advantages such as easy image accessibility and storage, portability, sharing, annotation, qualitative and quantitative image analysis, and use for educational purposes. WSI-generated images simultaneously provide high resolution and a wide field of observation that can cover the entire section, extending any single field of view. In this review, we summarize current knowledge on the application of WSI to histopathological analyses of liver disorders as well as to understand liver biology. We address how WSI may improve the assessment and quantification of multiple histological parameters in the liver, and help diagnose several hepatic conditions with important clinical implications. The WSI technical limitations are also discussed.

Whole-Slide Imaging of Esophageal Squamous Cell Carcinoma

Whole-slide imaging (WSI) contributes to medical education, collaboration, quality assurance, examination, and consultation in pathology. The images obtained from WSI are of high quality and could be stored indefinitely. In research involving esophageal squamous cell carcinoma, the combination of WSI and image processing program allows effective interpretations of expressions of various immunomarkers related to pathogenesis, prognosis, and response to therapy in tissue microarray sections. The operation and basic principles of whole-slide imaging of esophageal squamous cell carcinoma are also presented. Common use of WSI will occur with modifications of the whole-slide imaging scanners to adapt to the workflows in diagnostic and research laboratories.

Implementation of digital pathology into diagnostic practice: perceptions and opinions of histopathology trainees and implications for training

There is increasing interest in the utility of digital pathology in the diagnostic setting. Successful transition requires guidance and training, but additionally an understanding of opinions and attitudes of histopathologists to ensure that potential barriers are addressed. Histopathology trainees as a group are likely to be at the forefront of this revolution, and have specific and as yet largely neglected training needs in this context. We designed an online survey for trainees within our region to capture their opinions and attitudes to digital pathology in the diagnostic setting, and to assess their perceived training needs. This survey indicates overall that these trainees have similar aspirations with regard to the predicted utility of digital pathology and the challenges faced as have been recognised among consultant histopathologists. While their training needs are also largely similar, there are specific additional considerations based around training in multiple centres with varying exposure to digital pathology.