November 4, 2024 (Global News Distribution) -

PathAI, a leader in AI-powered pathology solutions, is proud to introduce PathExplore™ Fibrosis¹ on our AISight® Translational Research platform, a one-of-a-kind AI tool designed to revolutionize fibrosis, collagen, and fiber quantification directly from whole-slide images (WSIs) of hematoxylin and eosin (H&E)-stained tissue. PathExplore™ Fibrosis offers an unprecedented ability to analyze morphology and spatial organization of fibrosis in the tumor microenvironment (TME) directly from routine pathology images at scale, providing new insights into tumor biology and therapeutic response.

Fibrosis and collagen fibers are emerging as crucial biomarkers and drug targets due to their potential roles in cancer progression, metastasis, and immune response. Leveraging advanced machine learning techniques, PathExplore™ Fibrosis offers scalable, rapid analysis, making it a transformative tool in oncology translational research.

PathAI’s solution facilitates this analysis directly from routine H&E pathology images from standard imaging platforms, democratizing access to key insights into tumor biology and therapeutic responses without the need of custom microscopy.

“What began as internal research by our scientific team has evolved into a powerful tool for cancer researchers, enabling them to explore previously inaccessible aspects of tumor morphology,” said Ben Glass, VP of Product and Translational Research at PathAI. “Fibrosis is increasingly recognized as a potentially important biomarker in cancer research. By making it possible to study this directly from routine H&E images, we’re providing researchers with the means to scale their understanding of disease biology, evaluate drug efficacy, and drive the development of new fibrosis-related therapies.”

“By combining the cell and tissue features provided by PathExplore™² with collagen and fibrosis measurements from PathExplore™ Fibrosis, researchers now have access to an unprecedented multidimensional view of the TME,” said Eric Walk, M.D., FCAP, chief medical officer at PathAI. “From a single image, researchers can ask and answer new questions about the organization and morphology of fibrosis in the TME and how this inhibits or promotes cancer progression.”

PathAI will be presenting novel findings utilizing PathExplore™ Fibrosis and the underlying technology at the upcoming Society of Immunotherapy of Cancer conference (SITC 2024) on November 6-11, 2024, in Houston, Texas.

Be one of the first to access PathExplore™ Fibrosis by reaching out to us at bd@pathai.com or access our self-serve demo of PathExplore™ Fibrosis for hands-on experience here.

SITC 2024 Abstracts

#1378: Stromal collagen features from H&E-stained whole slide images are associated with lymphocyte infiltration and survival following checkpoint inhibition in patients with non-small cell lung cancer – in collaboration with Incendia Therapeutics

#88: Machine learning-based collagen fiber quantification enables analysis of the pancreatic cancer tumor microenvironment directly from hematoxylin and eosin-stained whole slide images

Footnotes

1: PathExplore™ Fibrosis is for research use only. Not for use in diagnostic procedures.

2: PathExplore™ is for research use only. Not for use in diagnostic procedures.

About PathAI

PathAI is the only AI-focused technology company to provide comprehensive precision pathology solutions from wet lab services to algorithm deployment for clinical trials and laboratory use. Rigorously trained and validated with data from more than 15 million annotations, its AI-powered models can be leveraged to optimize the analysis of pathology samples, with the potential to improve efficiency and accuracy of pathology interpretation, as well as to better gauge therapeutic efficacy and accelerate drug development for complex diseases. PathAI is headquartered in Boston, MA. For more information, please visit www.pathai.com.

Contact Details

SVM Public Relations and Marketing Communications
+1 401-490-9700
pathai@svmpr.com
https://www.pathai.com/

SOURCE: PathAI