The following is an extended release prepared by Agilent Technologies.
Alan Byrne
May 2025—Pathology plays a critical role in cancer care, encompassing the development of new treatments, diagnosis, staging, grading of disease, and clinical decision-making. While histopathological slides of tissue biopsies using hematoxylin and eosin staining and immunohistochemical staining remain central to this process, the rise of precision medicine testing is placing greater demands on pathology labs. Health care organizations globally are grappling with ways to address these challenges, mainly due to the declining number of individuals choosing pathology as a specialty. Additional challenges include the rising incidence of cancer, increased testing rates, and the growing complexity of testing. In this evolving landscape, artificial intelligence is emerging to revolutionize pathology, meeting these demands and improving patient outcomes.
Digitalization and AI in pathology
Many research and clinical labs aim to enhance workflow efficiency and improve analytical quality by digitalizing processes to address the growing demand amid limited resources. This approach leverages computational resources to reduce the burden on individual pathologists, enabling them to focus their expertise where it is most impactful. Following the evolutionary path of radiological and cardiological imaging, pathologists have embraced digital pathology and AI as a significant step forward in histopathology. This also aims to enhance communication between specialists across the health care spectrum, both within pathology and beyond. The goal is to achieve faster, more consistent quality diagnoses.
Central to the evolution of digital pathology is whole slide imaging, which uses high-resolution scanners to capture and digitally stitch together microscopy images of tissue samples. Images can be taken at varying magnifications, offering insights from tissue morphology and immune invasion to nuclear identity and biomarker localization. Beyond its cartographic analog, WSI can provide 3D insights by altering image focus along a tissue’s z-axis (z-stacking), essential for characterizing the tumor microenvironment.
Advantages of digital pathology
One key advantage of digitalization is that it facilitates remote consultation and telepathology. It allows pathologists to easily connect with colleagues for opinions on patient biopsies and enables research collaborators to share and discuss experimental findings. It also helps centralize the analysis of samples taken at different locations within a health care network.
While physical slides may degrade over time due to environmental factors such as light and temperature or administrative challenges like mislabeling or breakage, WSI digitally preserves them within an archive. This allows for analysis years after their original retrieval and, if desired, connection to other patient information, such as electronic health records.
A forward-thinking company in this area is Hamamatsu. Their advanced digital pathology solutions, including high-resolution WSI scanners, are helping pathologists in their work diagnosing cancer. The tools enhance workflow efficiency, provide diagnostic accuracy, and are designed for interoperability with other digital pathology and laboratory information systems, ensuring seamless integration into existing workflows and reduced diagnosis turnaround times.
Laura Pagano, vice president at Hamamatsu Corp., commented on the impact of their solution: “Time is critical for patients and clinicians when awaiting a diagnosis. Our slide scanner digital pathology products are revolutionizing the diagnostic process, enabling fast turnaround times, more efficient workflows, and more collaboration among pathologists worldwide. We are committed to facilitating the adoption of this transformative technology to help pathologists improve patient care and patient outcomes for every patient and, ultimately, anywhere in the world.”
While WSI offers pathologists a wealth of information, providing a richer picture of a tissue sample, this abundance can also overwhelm the analytical process. For example, because WSI involves scanning an entire microscope slide to create a high-resolution digital image, this process increases the amount of data by providing more cells to analyze. This can complicate the analysis process for pathologists who need to focus on specific, clinically relevant regions of interest of the tissue sample. This is where advances in AI offer possible solutions.
Dr. Douglas Clark, chief pathologist of the companion diagnostics division at Agilent, remarked on the advances AI can present: “With an extensive career as a practicing pathologist and wide-ranging experience in the field, I see immense potential in integrating AI with digital pathology. Whole slide images contain vast information, and AI empowers pathologists to harness this wealth of data to enhance their diagnoses. It also opens the door to developing superior IHC-based companion diagnostics that are even more predictive of patient responses. Furthermore, AI has the potential to streamline pathology workflows, from analyzing stained slides to delivering accurate diagnoses. By leveraging AI, we can improve diagnostic accuracy and reduce turnaround times.”
He added, “Industry collaborations are one of the key driving forces behind advancements in digital pathology. These partnerships foster innovation, accelerate the development of cutting-edge diagnostic tools, and ensure that groundbreaking technologies are seamlessly integrated into clinical practice. Together, we are revolutionizing the field of pathology and transforming patient care on a global scale.”
AI algorithms and their benefits
Trained digital pathology algorithms such as those from Visiopharm can automate various steps in the analysis process, such as identifying and quantifying different cell types and nuclear morphologies within a tissue, defining regions of tumor versus nontumor, highlighting immune infiltration, indicating the location and intensity of immunohistochemical stains and biomarkers, and enumerating mitotic status and other features that inform a pathologist’s interpretation of a slide. Quantifying these key characteristics allows pathologists to focus their energies and expertise on a more detailed, qualitative analysis of ROIs. In this way, AI doesn’t replace the pathologist but facilitates their efforts, creating what has been described as augmented intelligence.
Digital pathology, incorporating AI, is transforming the landscape of cancer diagnostics. By leveraging AI to analyze complex tissue samples, pathologists can deliver faster and more accurate diagnoses, ultimately improving patient care. A global commitment is required to drive innovation in pathology, ensuring health care providers have the resources to meet increasing cancer care demands.
Challenges and solutions
Although digital pathology platforms have made significant inroads in the clinic over the last decade, much still needs to be accomplished before many AI algorithms are available outside the research wing. As with any large-scale computational effort, data quality, volume, and variability continue to challenge the evolution of these algorithms from the idealized laboratory setting to real-world clinical application.
An example of a company making strides in this space is Proscia, a software company that focuses on transforming pathology into a data-driven discipline. They develop digital pathology solutions integrating AI applications into an image management system to enhance diagnostic accuracy and efficiency. Proscia’s technology is helping pathology labs transition from traditional glass slides to digital workflows, enabling faster diagnoses, particularly in cancer research.
David West, chief executive officer of Proscia, clarified: “One of the biggest hurdles in advancing AI for digital pathology is ensuring the quality and consistency of data across diverse sources. At Proscia, we are committed to overcoming these challenges by developing robust algorithms that can handle variability and deliver reliable results in real-world clinical settings. Our partnership with Agilent enhances our ability to address these issues, combining our expertise to create solutions that bridge the gap between research and clinical application.”
Explainability also remains challenging with AI, as clinicians and regulatory agencies want to know how an algorithm reaches its conclusions. AI platforms are sometimes called “black boxes” to anyone unfamiliar with computational science. For clinical use, explainability helps the pathologist determine how much they wish to rely on the platform’s recommendation, particularly in cases where their opinions differ. User interface features can facilitate this by highlighting the parameters that led the AI system to a decision.
Regarding regulatory approval of AI algorithms, the challenge presents itself in two forms. When the AI platform effectively confirms and supports a widely accepted and validated diagnostic method, the path to regulatory approval should be smoother, and the question is more likely to center on how the platform improves on what already exists. Part of the power of AI, however, is its capacity to identify and correlate diagnostic and prognostic patterns and features not previously identified. In such a situation, the approval process will demand not only validation of the platform but also the clinical soundness of the identified patterns and features.
Standardization and data quality
Standardization will also be critical to the broader adoption of digital pathology. Unlike radiological platforms, which conform to the Digital Imaging and Communications in Medicine (DICOM) format, slide scanner manufacturers produce image data in various formats, adding complexity to routine diagnosis. The move toward vendor-agnostic standards would provide a common language, facilitating efforts to connect digital pathology data to other clinical systems, such as radiology picture archiving and communications systems (PACS), laboratory information systems, and EHRs.
Even within the research space, the need for standards complicates the development of AI algorithm training and validation data, which may arise from multiple sources. For example, platforms developed with data from just one institution might not work as effectively with data from other institutions due to unique characteristics in the original data. This could lead to less accurate results when the platform is used more broadly. Broad agreement on standards and the types of metadata associated with WSIs would help address these challenges.
Storage challenges and solutions
The opportunities for digital pathology to facilitate the sharing, analysis, and storage of pathology images are apparent. Still, implementing the practices is not without significant challenges, particularly regarding image storage. With high-resolution WSI files approaching two to four gigabytes each, it is easy to imagine a typical digital pathology practice generating up to a petabyte of data annually. These IT demands are further exacerbated by long-term storage requirements that can see images preserved for up to 20 years. The costs of storing such large and growing volumes of data are substantial and include not only the expansion and maintenance of the physical storage media but also the maintenance and security of the data.
Cloud storage solutions offer scalable storage options that can grow with the increasing data volumes, providing easier access to data from multiple locations and potentially being more cost-effective with a pay-as-you-go model. The distributed uploading and accessing of large WSI files places heavy demands on bandwidth and internet connection fidelity. With fewer in-house infrastructure demands, cloud storage would likely benefit smaller laboratories that may wish to dedicate their limited financial and organizational resources to demands other than extensive server capacity.
Larger, higher-traffic laboratories, however, may be more interested in a hybrid approach that combines on-premises storage for frequently accessed data with cloud storage for long-term archiving. This approach optimizes infrastructure costs while also facilitating pathologist access to critical, clinical decision-making data. The hybrid approach also offers enhanced security by keeping sensitive data on premises while leveraging the cloud for less critical data, providing the flexibility to balance performance, cost, and security based on the laboratory’s specific needs.
Conclusion
By adopting these storage strategies, laboratories can more effectively manage the substantial data generated by digital pathology, ensuring they can continue leveraging the benefits of digital transformation without being overwhelmed by storage challenges. Integrating digital pathology holds immense potential to revolutionize cancer diagnosis and aid in selecting appropriate treatments. While significant progress has been made with WSI, IMS, and AI algorithms, challenges such as data quality, variability, and explainability remain. Efforts to standardize data formats and validate AI platforms across diverse real-world conditions continue and are crucial for broader clinical adoption. As these technologies evolve, they promise to enhance diagnostic accuracy and streamline workflows by providing pathologists with powerful tools to manage the growing complexity of cancer care.
Alan Byrne is global product marketing director, pathology division, life sciences and diagnostics market group, Agilent Technologies.