Discussion with the Scientists: Interview with VP of Product & Translational Research, Ben Glass, and the Vision for PathExplore™ Fibrosis

Building PathExplore™ Fibrosis: AI-Powered Fibrotic Microenvironment Insights for Oncology We sat down with VP of Product & Translational Research, Ben Glass , to discuss how AI-powered pathology is revolutionizing fibrosis quantification in oncology and to learn more about the development of PathAI’s latest product: PathExplore™ Fibrosis.

As precision medicine advances, understanding novel biomarkers like fibrosis has potential to optimize cancer therapies and predict patient outcomes. Fibrosis biomarkers, such as fiber and collagen parameters, play a significant role in the tumor microenvironment, in some cases correlating with disease progression and treatment response. However, traditional fibrosis quantification methods often face limitations due to the need for specialized microscopy and challenges in scalability.

In our discussion, Ben sheds light on how PathExplore Fibrosis offers a cutting-edge solution by bringing precision, scalability, and reproducibility to fibrosis assessment.

Learn more about PathExplore™ Fibrosis and demo the product here

The PathAI Team behind the development of PathExplore™ Fibrosis

Q: Ben, could you tell us a bit about why fibrosis quantification would be important for oncology research?

Ben Glass: Absolutely. Fibrosis, which is essentially an excessive buildup of collagen and other extracellular matrix components, has a significant role in tumor progression and therapy response. Accurately measuring fibrosis is vital because it gives us a clearer understanding of disease prognosis and can help us tailor effective therapies. Traditional methods, however, tend to be subjective, aren't scalable, and often miss the spatial complexities in the tumor microenvironment. This is because traditional methods for imaging fibrosis require access to additional tissues, stains, and specific microscopy equipment built specifically for this use case. That’s where we saw a real opportunity for innovation with PathExplore Fibrosis.

Overview of PathExplore™ and PathExplore™ Fibrosis

Q: What sets PathExplore™ Fibrosis apart from traditional methods of fibrosis quantification?

Ben Glass: PathExplore Fibrosis is unique in that it leverages advanced AI to provide objective, reproducible, and spatially resolved measurements of fibrosis. Unlike traditional approaches that may only offer a global assessment, our tool provides a highly detailed spatial map of fibrosis across the whole tumor. This allows researchers to see exactly where fibrosis is distributed and how it might be influencing the tumor’s behavior. And because it’s AI-driven, it automates the quantification process, which saves time and allows for high-throughput analysis of large datasets—something that’s really hard to achieve with manual methods. Here are the key benefits:

• Enhanced Quantification: Identifying every individual collagen fiber in an H&E WSI would have been infeasible for human expert annotators. Instead, PathExplore Fibrosis utilizes deep learning models trained on a massive dataset of images annotated using our proprietary  QMAI microscope, ensuring unparalleled accuracy in identifying and quantifying collagen and fibrotic regions.
• Spatial Resolution: Unlike traditional methods that provide only global assessments, PathExplore Fibrosis offers detailed spatial information of individual fibers, enabling researchers to understand the distribution and patterns of fibrosis within the tumor microenvironment.
• Efficiency and Scalability: PathExplore Fibrosis automates the quantification process, significantly reducing analysis time and enabling high-throughput analysis of large cohorts.
• Standardized and Reproducible: By eliminating additional sources of variability, PathExplore Fibrosis ensures consistent and reproducible results across different studies and laboratories.
  

Q: You mentioned that PathExplore Fibrosis offers unprecedented resolution. How does it achieve this level of accuracy?

Ben Glass: The accuracy of PathExplore Fibrosis stems from its unique ground truth data. We trained the model on an extensive dataset of images that were annotated using our own QMAI imaging technology that we built in house. This allowed us to capture high-quality data from H&E-stained slides, focusing on collagen and fibrosis regions with incredible precision. The result is a product that can reliably identify and quantify fibrosis with a level of detail that hasn’t been possible until now: in contrast to a human annotator, our AI model doesn’t get tired and will highlight every single collagen fiber in a piece of tissue. There can be tens of thousands of them!

Q: Can you explain the concept of spatial resolution and why it's important in fibrosis research?

Ben Glass: Spatial resolution in this context refers to the ability to map where fibrosis occurs within the tumor environment. Knowing just the amount of fibrosis isn’t always enough; understanding its distribution helps us see patterns. Is fibrosis concentrated in specific areas or spread throughout the tumor? This insight can have major implications for understanding how fibrosis impacts tumor progression and for developing effective therapies that take these spatial dynamics into account.