Digitization of Pathology Labs: A Review of Lessons Learned

Joining forces for pathology diagnostics with AI assistance: The EMPAIA initiative

Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and vendor-neutral EMPAIA initiative addresses these challenges. Here, we provide an overview of EMPAIA's achievements and lessons learned. EMPAIA integrates various stakeholders of the pathology AI ecosystem, i.e., pathologists, computer scientists, and industry. In close collaboration, we developed technical interoperability standards, recommendations for AI testing and product development, and explainability methods. We implemented the modular and open-source EMPAIA Platform and successfully integrated 14 AI-based image analysis apps from eight different vendors, demonstrating how different apps can use a single standardized interface. We prioritized requirements and evaluated the use of AI in real clinical settings with 14 different pathology laboratories in Europe and Asia. In addition to technical developments, we created a forum for all stakeholders to share information and experiences on digital pathology and AI. Commercial, clinical, and academic stakeholders can now adopt EMPAIA's common open-source interfaces, providing a unique opportunity for large-scale standardization and streamlining of processes. Further efforts are needed to effectively and broadly establish AI assistance in routine laboratory use. To this end, a sustainable infrastructure, the non-profit association EMPAIA International, has been established to continue standardization and support broad implementation and advocacy for an AI-assisted digital pathology future.

Digital pathology structure and deployment in Veneto: a proof-of-concept study

Nowadays pathology laboratories are worldwide facing a digital revolution, with an increasing number of institutions adopting digital pathology (DP) and whole slide imaging solutions. Despite indeed providing novel and helpful advantages, embracing a whole DP workflow is still challenging, especially for wide healthcare networks. The Azienda Zero of the Veneto Italian region has begun a process of a fully digital transformation of an integrated network of 12 hospitals producing nearly 3 million slides per year. In the present article, we describe the planning stages and the operative phases needed to support such a disruptive transition, along with the initial preliminary results emerging from the project. The ultimate goal of the DP program in the Veneto Italian region is to improve patients' clinical care through a safe and standardized process, encompassing a total digital management of pathology samples, easy file sharing with experienced colleagues, and automatic support by artificial intelligence tools.

Transforming diagnostics: The implementation of digital pathology in clinical laboratories

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

Deep-learning model to improve histological grading and predict upstaging of atypical ductal hyperplasia / ductal carcinoma in situ on breast biopsy

AIMS

Risk stratification of atypical ductal hyperplasia (ADH) and ductal carcinoma in situ (DCIS), diagnosed using breast biopsy, has great clinical significance. Clinical trials are currently exploring the possibility of active surveillance for low-risk lesions, whereas axillary lymph node staging may be considered during surgical planning for high-risk lesions. We aimed to develop a machine-learning algorithm based on whole-slide images of breast biopsy specimens and clinical information to predict the risk of upstaging to invasive breast cancer after wide excision.

METHODS AND RESULTS

Patients diagnosed with ADH/DCIS on breast biopsy were included in this study, comprising 592 (740 slides) and 141 (198 slides) patients in the development and independent testing cohorts, respectively. Histological grading of the lesions was independently evaluated by two pathologists. Clinical information, including biopsy method, lesion size, and Breast Imaging Reporting and Data System (BI-RADS) classification of ultrasound and mammograms, were collected. Deep DCIS consisted of three deep neural networks to evaluate nuclear grade, necrosis, and stromal reactivity. Deep DCIS output comprised five parameters: total patches, lesion extent, Deep Grade, Deep Necrosis, and Deep Stroma. Deep DCIS highly correlated with the pathologists' evaluations of both slide- and patient-level labels. All five parameters of Deep DCIS were significantly associated with upstaging to invasive carcinoma in subsequent wide excisional specimens. Using multivariate logistic regression, Deep DCIS predicted upstaging to invasive carcinoma with an area under the curve (AUC) of 0.81, outperforming pathologists' evaluation (AUC, 0.71 and 0.69). After including clinical and hormone receptor status information, performance further improved (AUC, 0.87). This combined model retained its predictive power in two subgroup analyses: the first subgroup included unequivocal DCIS (excluding cases of ADH and DCIS suspicious for microinvasion) (AUC, 0.83), while the second excluded cases of high-grade DCIS (AUC, 0.81). The model was validated in an independent testing cohort (AUC, 0.81).

CONCLUSION

This study demonstrated that deep-learning models can refine histological evaluation of ADH and DCIS on breast biopsies, which may help guide future treatment planning.

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.

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

Background

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

Materials and Methods

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

Results

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

Conclusion

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

Assessment of Color Reproducibility and Mitigation of Color Variation in Whole Slide Image Scanners for Toxicologic Pathology

Digital pathology workflows in toxicologic pathology rely on whole slide images (WSIs) from histopathology slides. Inconsistent color reproduction by WSI scanners of different models and from different manufacturers can result in different color representations and inter-scanner color variation in the WSIs. Although pathologists can accommodate a range of color variation during their evaluation of WSIs, color variability can degrade the performance of computational applications in digital pathology. In particular, color variability can compromise the generalization of artificial intelligence applications to large volumes of data from diverse sources. To address these challenges, we developed a process that includes two modules: (1) assessing the color reproducibility of our scanners and the color variation among them and (2) applying color correction to WSIs to minimize the color deviation and variation. Our process ensures consistent color reproduction across WSI scanners and enhances color homogeneity in WSIs, and its flexibility enables easy integration as a post-processing step following scanning by WSI scanners of different models and from different manufacturers.