Use Of Digital Pathology Research Articles

The Use of Artificial Intelligence Technologies in Cancer Care

Artificial intelligence (AI) is already an essential tool in the handling of large data sets in epidemiology and basic research.Significant contributions to radiological diagnosis are emerging alongside increasing use of digital pathology. The future lies in integrating this information together with clinical data relevant to each individual patient. Linkage with clinical protocols will enable personalized management options to be presented to the oncologist of the future.Radiotherapy has the distinction of being the first to have a National Institute for Health and Care Excellence (NICE)-approved AI-based recommendation. There is the opportunity to revolutionize the workflow with many tasks currently undertaken by clinicians taken over by AI-based systems for volume outlining, planning, and quality assurance.Education and training will be essential to understand the AI processes and inputs. Clinicians will however have to feel confident interrogating the AI-derived information and in communicating AI-derived treatment plans to patients.

The Reproducibility of the Use of Digital Pathology and Radiomics in Intraoperative Diagnosis in Neuropathology: The Experience of a Single Center

The Reproducibility of the Use of Digital Pathology and Radiomics in Intraoperative Diagnosis in Neuropathology: The Experience of a Single Center

Disseminating Education about HER2-Low Breast Cancer through Twitter Chats and Tweetorials

Abstract Introduction/Objective Social media is often used to disseminate new information and as a teaching tool that reaches a broad audience. When practice-changing data about HER2-Low metastatic breast cancer (MBC) emerged in 2022, ASCP developed CME/CMLE Twitter chats and Tweetorials to educate pathologists and laboratory professionals about how these updates will impact the diagnosis and care of patients with HER2-Low MBC. These activities were funded by educational grants from AstraZeneca and Daiichi Sankyo, Inc. Methods/Case Report Working with faculty who were active on Twitter, ASCP organized Twitter chats and Tweetorials to increase awareness about HER2-Low MBC and address how these changes will impact clinical care. A Twitter chat is a live, interactive Tweet-based discussion held on Twitter. Also called Tweet chats, these events run for 1 hr and use hashtags to keep the discussion organized. A Tweetorial is a combination of threaded Tweets that teach about a specific topic. Activities were certified for continuing education credit and were designed to be short, practical, and relevant for pathologists and laboratory professionals. Results (if a Case Study enter NA) In 2022 and 2023, ASCP used the hashtag #HER2Low to organize Twitter chats and Tweetorials. Each chat had four faculty members responding to 6-8 questions and averaged 2.2 million impressions (an impression is the # of times a Tweet is served in a timeline or search result). Faculty covered the challenges differentiating an IHC score of 0 vs 1+, the emerging use of digital pathology, ways to improve reporting, and other challenges with IHC scoring. Tweets may be amplified by Retweets (a re-posting of a Tweet), so a Tweet sent by a faculty member often reached over 1,000 views. Tweetorials had polling questions, text, images, videos, and links to journal articles. Each Tweetorial achieved over 1,400 views and over 535,000 impressions during its first week. Collectively, the Twitter Chats and Tweetorials led to over 1,050 CME/CMLE certificates. Conclusion Twitter is an evolving platform that is actively used by pathologists (see #PathTwitter) and laboratory professionals to share knowledge to a broad audience and foster a community of learning. Since content posted on Twitter remains enduring and searchable, people may find old Tweets and see which topics achieved high impressions. When spreading education on new practice-changing topics like HER2-Low breast cancer, Twitter can be an effective platform for broad content dissemination.

Impact of COVID-19 Pandemic on Pathology Residents/Trainees in North America: A Survey-Based Study.

Background The COVID-19 pandemic has had a significant impact on resident training and education in the field of Pathology. This study aims to identify the tangible effects and resultant changes in education for Pathology trainees that have resulted from the pandemic. Design An electronic survey regarding Pathology trainee perceptions and experiences in relation to COVID-19 was created via Google Forms. The questionnaire was distributed to the pathology trainees via Twitter and email. The survey was also shared with all Pathology residency program coordinators across the USA and Canada. Results One hundred forty-five trainees responded to the questionnaire. 37.6% reported a significant decrease in specimen volume, whereas 43.3% reported a slight decrease in specimen volume. 18.3% reported the cancellation of educational lectures before shifting to a virtual platform for didactic purposes. However, 74.6% reported shifting all educational activities to virtual platforms. 35% cited cancellations of grand rounds, whereas 18.2% reported cancellations of grand rounds led by guest speakers.53.5% took COVID-19 tests, and 22.7% were quarantined. 100% reported a change in sign-out culture. Conclusions This pandemic has significantly impacted pathology training in various aspects, including training, education, and well-being. Residents harbored anxiety and stress regarding board exam delays or uncertainties, inadequate exam preparation time, family separation, and compromised safety. The exact quantification of educational loss varied from program to program. A significant decrease in specimen volume and detrimental changes in sign-out culture are indicators of compromised resident education due to the pandemic. This pandemic has extended the use of digital pathology and virtual platforms to a higher extent. Free virtual educational resources provided by various pathology organizations were critically important interventions during this pandemic, contributing to resident education. The pandemic has shown that developing a comprehensive infrastructure to overcome the loss of educational opportunities is of paramount importance to alleviate stress and anxiety among trainees.

Development and validation of digital medical laboratory educational platform

With the increased use of whole slide scanner technology, large numbers of tissue slides are being scanned and archived digitally. While digital pathology has substantial implications in telepathology, second opinions and education, there are huge research opportunities in this new type of digital data. Accessibility to large digital repositories of tissue slides is a huge educational resource for medical students and pathology residents. In addition to education of medical students, residents and clinical adoption, digital pathology has been transformative for computational imaging research. Many universities also do not have active pathology laboratories which are necessary in providing a steady flow of real life cases for medical training and research. Therefore, this project aimed at designing and validating a digital medical laboratory educational platform where learners could access the virtual slides from their individual student portals irrespective of their location when they logged in. With the use of a digital slide scanner, physical slides from the manual laboratory repository were scanned and stored in the cloud in a digital format. The two platforms of both institutions were integrated such that the students’ online learning portal was in sync with the Pathology Network’s digital repository. Once the integration was successful, the students were able to access and interact with the learning materials and virtual slides for Cytology and Cytological Stains which was the scheduled topic for teaching and learning. The study recruited thirty five students from Meru University of Science and Technology pursuing undergraduate studies in Medical Microbiology. Majority of the students (33 out of 35; 94.3%) indicated that digital pathology enhanced their understanding of the topic of study due to availability of virtual slides with ease and at any time of day. All students (35 out of 35; 100%) felt that digital pathology teaching was beneficial for Medical Microbiology students and expressed hope in continued learning using digital pathology to supplement face-to-face lectures. The study proved concept that digital pathology education is viable in medical training, particularly, for pathology and medical laboratory. There is need for additional work to include more areas in the field of laboratory medicine and development of virtual learning content. Such local digital slide repository can promote use of digital pathology in Kenya and the region for teaching and learning. Digital pathology together with virtual microscopy can progressively improve medical education and training of pathology and laboratory medicine.

Survey of liver pathologists to assess attitudes towards digital pathology and artificial intelligence

AimsA survey of members of the UK Liver Pathology Group (UKLPG) was conducted, comprising consultant histopathologists from across the UK who report liver specimens and participate in the UK National...

Tumor-identification method for predicting recurrence of early-stage lung adenocarcinoma using digital pathology images by machine learning

Lung cancer is one of the cancers with the highest morbidity and mortality in the world. Recurrence often occurs even after complete resection of early-stage lung cancer, and prediction of recurrence after resection is clinically important. However, the pathological characteristics of the recurrence of pathological stage IB lung adenocarcinoma (LAIB) have not yet been elucidated. Therefore, the problem is what type of histological image of lung adenocarcinoma recurs, and it is important to examine the histological image of recurrence. We attempted to predict recurrence of early lung adenocarcinoma after resection on the basis of digital pathological images of hematoxylin and eosin-stained specimens and machine learning applying a convolutional neural network. We constructed a model that extracts the features of two-color spaces and a switching model that automatically switches between our extraction model and one that extracts the features of one-color space for each image. We then developed a tumor-identification method for predicting the presence or absence of LAIB recurrence using these models. We conducted an experiment involving 55 patients with LAIB who underwent surgical resection to evaluate the proposed method. The proposed method determined LAIB recurrence with an accuracy of 84.8%. The use of digital pathology and machine learning can be used for highly accurate prediction of LAIB recurrence after surgical resection. The proposed method has the potential for objective postoperative follow-up observation.

Differentiation of urothelial carcinoma in histopathology images using deep learning and visualization

Artificial Intelligence is a tool poised to transform healthcare, with use in diagnostics and therapeutics. The widespread use of digital pathology has been due to the advent of whole slide imaging. Cheaper storage for digital images, along with unprecedented progress in artificial intelligence, have paved the synergy of these two fields. This has pushed the limits of traditional diagnosis using light microscopy, from a more subjective to a more objective method of looking at cases, incorporating grading too. The grading of histopathological images of urothelial carcinoma of the urinary bladder is important with direct implications for surgical management and prognosis.In this study, the aim is to classify urothelial carcinoma into low and high grade based on the WHO 2016 classification. The hematoxylin and eosin-stained transurethral resection of bladder tumor (TURBT) samples of both low and high grade non-invasive papillary urothelial carcinoma were digitally scanned. Patches were extracted from these whole slide images to feed into a deep learning (Convolution Neural Network: CNN) model. Patches were segregated if they had tumor tissue and only included for model training if a threshold of 90% of tumor tissue per patch was seen. Various parameters of the deep learning model, known as hyperparameters, were optimized to get the best accuracy for grading or classification into low- and high-grade urothelial carcinoma. The model was robust with an overall accuracy of 90% after hyperparameter tuning. Visualization in the form of a class activation map using Grad-CAM was done. This indicates that such a model can be used as a companion diagnostic tool for grading of urothelial carcinoma. The probable causes of this accuracy are summarized along with the limitations of this study and future work possible.

Use of digital pathology and artificial intelligence for the diagnosis of Helicobacter pylori in gastric biopsies.

A common source of concern about digital pathology (DP) is that limited resolution could be a reason for an increased risk of malpractice. A frequent question being raised about this technology is whether it can be used to reliably detect Helicobacter pylori (HP) in gastric biopsies, which can be a significant burden in routine work. The main goal of this work is to show that a reliable diagnosis of HP infection can be made by DP even at low magnification. The secondary goal is to demonstrate that artificial intelligence (AI) algorithms can diagnose HP infections on virtual slides with sufficient accuracy. The method we propose is based on the Warthin-Starry (W-S) silver stain which allows faster detection of HP in virtual slides. A software tool, based on regular expressions, performed a specific search to select 679 biopsies on which a W-S stain was done. From this dataset 185 virtual slides were selected to be assessed by WSI and compared with microscopy slide readings. To determine whether HP infections could be accurately diagnosed with machine learning. AI was used as a service (AIaaS) on a neural network-based web platform trained with 468 images. A test dataset of 210 images was used to assess the classifier performance. In 185 gastric biopsies read with DP we recorded only 4 false positives and 4 false negatives with an overall agreement of 95.6%. Compared with microscopy, defined as the "gold standard" for the diagnosis of HP infections, WSI had a sensitivity and specificity of 0.95 and 0.96, respectively. The ROC curve of our AI classifier generated on a testing dataset of 210 images had an AUC of 0.938. This study demonstrates that DP and AI can be used to reliably identify HP at 20X resolution.

Scientific and Regulatory Policy Committee Points to Consider: Primary Digital Histopathology Evaluation and Peer Review for Good Laboratory Practice (GLP) Nonclinical Toxicology Studies.

The Society of Toxicologic Pathology's Scientific and Regulatory Policy Committee formed a working group to consider the present and future use of digital pathology in toxicologic pathology in general and specifically its use in primary evaluation and peer review in Good Laboratory Practice (GLP) environments. Digital histopathology systems can save costs by reducing travel, enhancing organizational flexibility, decreasing slide handling, improving collaboration, increasing access to historical images, and improving quality and efficiency through integration with laboratory information management systems. However, the resources to implement and operate a digital pathology system can be significant. Given the magnitude and risks involved in the decision to adopt digital histopathology, this working group used pertinent previously published survey results and its members' expertise to create a Points-to-Consider article to assist organizations with building and implementing digital pathology workflows. With the aim of providing a comprehensive perspective, the current publication summarizes aspects of digital whole-slide imaging relevant to nonclinical histopathology evaluations, and then presents points to consider applicable to both primary digital histopathology evaluation and digital peer review in GLP toxicology studies. The Supplemental Appendices provide additional tabulated resources.

The Use of Digital Pathology and Artificial Intelligence in Histopathological Diagnostic Assessment of Prostate Cancer: A Survey of Prostate Cancer UK Supporters.

There has been particular interest in the deployment of digital pathology (DP) and artificial intelligence (AI) in the diagnosis of prostate cancer, but little is known about the views of the public on their use. Prostate Cancer UK supporters were invited to an online survey which included quantitative and qualitative questions exploring views on the use of DP and AI in histopathological assessment. A total of 1276 responses to the survey were analysed (response rate 12.5%). Most respondents were supportive of DP (87%, 1113/1276) and of testing AI in clinical practice as a diagnostic adjunct (83%, 1058/1276). Respondents saw DP as potentially increasing workflow efficiency, facilitating research, education/training and fostering clinical discussions between clinician and patient. Some respondents raised concerns regarding data security, reliability and the need for human oversight. Among those who were unsure about AI, information was requested regarding its performance and others wanted to defer the decision to use it to an expert. Although most are in favour of its use, some are unsure, and their concerns could be addressed with more information or better communication. A small minority (<1%) are not in favour of the testing of the use of AI in histopathology for reasons which are not easily addressed.

Scientific and Regulatory Policy Committee Brief Communication: 2019 Survey on Use of Digital Histopathology Systems in Nonclinical Toxicology Studies.

Histopathologic evaluation and peer review using digital whole-slide images (WSIs) is a relatively new medium for assessing nonclinical toxicology studies in Good Laboratory Practice (GLP) environments. To better understand the present and future use of digital pathology in nonclinical toxicology studies, the Society of Toxicologic Pathology (STP) formed a working group to survey STP members with the goal of creating recommendations for implementation. The survey was administered in December 2019, immediately before the COVID-19 pandemic, and the results suggested that the use of digital histopathology for routine GLP histopathology assessment was not widespread. Subsequently, in follow-up correspondence during the pandemic, many responding institutions either began investigating or adopting digital WSI systems to reduce employee exposure to COVID-19. Therefore, the working group presents the survey results as a pre-pandemic baseline data set. Recommendations for use of WSI systems in GLP environments will be the subject of a separate publication.

Validation of portable tablets for transplant pathology diagnosis according to the College of American Pathologists’ Guidelines

Despite increased use of digital pathology, its application in the transplantation setting remains limited. One of the restraints is related to concerns that this technology is inadequate for supporting diagnostic work. In this study, we sought to establish non inferiority of whole slide imaging (WSI) to light microscopy (LM) for intraoperative transplantation diagnosis using inexpensive portable devices. A validation study was conducted according to updated guidelines from the College of American Pathologists (CAP) utilizing 80 intraoperative transplantation cases. Two pathologists reviewed glass slides with LM and digital slides on two different tablets after a washout period of 4 weeks. Diagnostic concordance and intra-observer agreement were recorded. A total of 45 (56%) cases were suitable for rendering transplant diagnoses and 35 (44%) for assessing cancer risk. Intra-observer agreement was 95.1% for organ suitability and 100% for cancer risk. There were no major discordances that could affect patient transplant management. Digital evaluation of intraoperative transplant specimens using tablets to view whole slide images was non-inferior to LM for primary diagnosis. This suggests that after validating WSI these digital tools can be safely used for remote intraoperative transplantation diagnostic work.

Intraobserver and Interobserver Agreement in the Scoring of PD-L1 (SP142) and Tumor-Infiltrating Lymphocytes in Triple Negative Breast Cancers

Objectives.&nbsp;Known for their poor outcomes, triple negative breast cancers (TNBCs) have been investigated for immune checkpoint inhibitors that target Programmed death ligand 1 (PD-L1). In the recent decade, tumor-infiltrating lymphocytes (TILs) have also become potential biomarkers. The aim of the study is to determine the reproducibility of PD-L1 scoring system for TNBC (SP142 clone) and TILs interpretation in the local setting through intra- and interobserver agreement. Methodology. Forty-three primary resection specimens TNBC were evaluated on two occasions with PD-L1 (Roche VENTANA SP142 assay) and TILs by two breast pathologists and one general pathologist on physical glass slides. PD-L1 expression was determined by at least 1% positivity among immune cells within the tumoral area and contiguous peritumoral stroma while TILs was assessed based on International Immuno-Oncology Biomarker Working Group on Breast Cancer. Kappa statistic for PD-L1 and TILs categories while intraclass correlation coefficient (ICC) were assessed, with cutoffs of 0.80 and 0.70, respectively. Results: The overall interrater kappa statistic for PD-L1 on the first and second rounds were weak at 0.506 (95% CI: 0.334-0.679) and minimal at 0.314 (95% CI: 0.142-0.487), respectively. Intraobserver kappa statistic for PD-L1 were varied across the three readers while interobserver kappa values for PD-L1 showed none (0.181) to moderate (0.789) agreement. The TILs intraobserver reliability showed poor to good agreement, with the highest ICC of 0.889 (95% CI: 0.805-0.938). Conclusion. This study demonstrated variable intra and interobserver agreement for both TILs and PD-L1 expression. Although it is desirable to have strong to almost perfect agreement, the kappa and ICC values suggest additional room for improvement. In light of the repercussions in management of patients who will undergo immune checkpoint inhibitor therapy, regular training sessions, concurrences of equivocal results, and possible use of digital pathology as a medium in interpreting TILs and PD-L1 stains to achieve consistent results.

Whole-slide imaging, tissue image analysis, and artificial intelligence in veterinary pathology: An updated introduction and review.

Since whole-slide imaging has been commercially available for over 2 decades, digital pathology has become a constantly expanding aspect of the pathology profession that will continue to significantly impact how pathologists conduct their craft. While some aspects, such as whole-slide imaging for archiving, consulting, and teaching, have gained broader acceptance, other facets such as quantitative tissue image analysis and artificial intelligence-based assessments are still met with some reservations. While most vendors in this space have focused on diagnostic applications, that is, viewing one or few slides at a time, some are developing solutions tailored more specifically to the various aspects of veterinary pathology including updated diagnostic, discovery, and research applications. This has especially advanced the use of digital pathology in toxicologic pathology and drug development, for primary reads as well as peer reviews. It is crucial that pathologists gain a deeper understanding of digital pathology and tissue image analysis technology and their applications in order to fully use these tools in a way that enhances and improves the pathologist's assessment as well as work environment. This review focuses on an updated introduction to the basics of digital pathology and image analysis and introduces emerging topics around artificial intelligence and machine learning.

Breast Digital Pathology: Way of the Future

This review provides a summary of the development and current use of digital pathology in routine practice, with specific relevance to breast disease. It aims to be of interest to all members of the breast multidisciplinary team. Digital pathology is being increasingly used across many institutions internationally. It is gaining increased acceptance amongst pathologists due to improvements in technology, and there are published guidelines to assist in the validation of its use in routine practice. The adoption of digital pathology brings advantages to the pathologist, to the pathology laboratory, and to the multidisciplinary team. It also offers the potential for the development and use of artificial intelligence tools in pathology reporting. The use of digital pathology increases the efficiency of the pathology reporting of specimens, with the potential for more efficiency savings and improved accuracy with the use of artificial intelligence.

Kaiso (ZBTB33) subcellular partitioning functionally links LC3A/B, the tumor microenvironment, and breast cancer survival

The use of digital pathology for the histomorphologic profiling of pathological specimens is expanding the precision and specificity of quantitative tissue analysis at an unprecedented scale; thus, enabling the discovery of new and functionally relevant histological features of both predictive and prognostic significance. In this study, we apply quantitative automated image processing and computational methods to profile the subcellular distribution of the multi-functional transcriptional regulator, Kaiso (ZBTB33), in the tumors of a large racially diverse breast cancer cohort from a designated health disparities region in the United States. Multiplex multivariate analysis of the association of Kaiso’s subcellular distribution with other breast cancer biomarkers reveals novel functional and predictive linkages between Kaiso and the autophagy-related proteins, LC3A/B, that are associated with features of the tumor immune microenvironment, survival, and race. These findings identify effective modalities of Kaiso biomarker assessment and uncover unanticipated insights into Kaiso’s role in breast cancer progression.

Remote Reporting During a Pandemic Using Digital Pathology Solution: Experience from a Tertiary Care Cancer Center

Background: Remote reporting in anatomic pathology is an important advantage of digital pathology that has not been much explored. The COVLD-19 pandemic has provided an opportunity to explore this important application of digital pathology system in a tertiary care cancer center to ensure patient care and staff safety. Regulatory guidelines have been described for remote reporting following the pandemic. Herein, we describe our experience of validation of digital pathology workflow for remote reporting to encourage pathologists to utilize this facility which opens door for multiple, multidisciplinary collaborations. Objective: To demonstrate the validation and the operational feasibility of remote reporting using a digital pathology system. Materials and Methods: Our retrospective validation included whole-slide images (WSIs) of 60 cases of histopathology and 20 cases each of frozen sections and a digital image-based breast algorithm after a washout period of 3 months. Three pathologists with different models of consumer-grade laptops reviewed the cases remotely to assess the diagnostic concordance and operational feasibility of the modified workflow. The slides were digitized on a USFDA-approved Philips UFS 300 scanner at ×40 resolution (0.25 µm/pixel) and viewed on the Image Management System through a web browser. All the essential parameters were reported for each case. After successful validation, 886 cases were reported remotely from March 29, 2020, to June 30, 2020, prospectively. Light microscopy formed the gold standard reference in remote reporting. Results: 100% major diagnostic concordance was observed in the validation of remote reporting in the retrospective and prospective studies using consumer-grade laptops. The deferral rate was 0.34%. 97.6% of histopathology and 100% of frozen sections were signed out within the turnaround time. Network speed and a lack of virtual private network did not significantly affect the study. Conclusion: This study of validation and reporting of complete pathology cases remotely, including their operational feasibility during a public health emergency, proves that remote sign-out using a digital pathology system is not inferior to WSIs on medical-grade monitors and light microscopy. Such studies on remote reporting open the door for the use of digital pathology for interinstitutional consultation and collaboration: Its main intended use.

The Application, Challenges, and Advancement Toward Regulatory Acceptance of Digital Toxicologic Pathology: Results of the 7th ESTP International Expert Workshop (September 20-21, 2019).

With advancements in whole slide imaging technology and improved understanding of the features of pathologist workstations required for digital slide evaluation, many institutions are investigating broad digital pathology adoption. The benefits of digital pathology evaluation include remote access to study or diagnostic case materials and integration of analysis and reporting tools. Diagnosis based on whole slide images is established in human medical pathology, and the use of digital pathology in toxicologic pathology is increasing. However, there has not been broad adoption in toxicologic pathology, particularly in the context of regulatory studies, due to lack of precedence. To address this topic, as well as practical aspects, the European Society of Toxicologic Pathology coordinated an expert international workshop to assess current applications and challenges and outline a set of minimal requirements needed to gain future regulatory acceptance for the use of digital toxicologic pathology workflows in research and development, so that toxicologic pathologists can benefit from digital slide technology.

Prediction of early recurrence of hepatocellular carcinoma after resection using digital pathology images assessed by machine learning

Hepatocellular carcinoma (HCC) is a representative primary liver cancer caused by long-term and repetitive liver injury. Surgical resection is generally selected as the radical cure treatment. Because the early recurrence of HCC after resection is associated with low overall survival, the prediction of recurrence after resection is clinically important. However, the pathological characteristics of the early recurrence of HCC have not yet been elucidated. We attempted to predict the early recurrence of HCC after resection based on digital pathologic images of hematoxylin and eosin-stained specimens and machine learning applying a support vector machine (SVM). The 158 HCC patients meeting the Milan criteria who underwent surgical resection were included in this study. The patients were categorized into three groups: Group I, patients with HCC recurrence within 1 year after resection (16 for training and 23 for test); Group II, patients with HCC recurrence between 1 and 2 years after resection (22 and 28); and Group III, patients with no HCC recurrence within 4 years after resection (31 and 38). The SVM-based prediction method separated the three groups with 89.9% (80/89) accuracy. Prediction of Groups I was consistent for all cases, while Group II was predicted to be Group III in one case, and Group III was predicted to be Group II in 8 cases. The use of digital pathology and machine learning could be used for highly accurate prediction of HCC recurrence after surgical resection, especially that for early recurrence. Currently, in most cases after HCC resection, regular blood tests and diagnostic imaging are used for follow-up observation; however, the use of digital pathology coupled with machine learning offers potential as a method for objective postoprative follow-up observation.