Paige Introduces New AI-Based Biomarker Screening Advancement to Drastically Reduce Diagnostic Turnaround Times and Inform Precision Treatment Decisions

Paige Colon MSI supports pathologists in the detection of MSI status using H&E-stain alone

Paige, a global leader in end-to-end digital pathology solutions and clinical AI applications that assist in diagnosing cancer, today announced a new AI-based digital assay to support pathologists in the detection and diagnosis of microsatellite instability (MSI) status in colon cancers. Paige Colon MSI operates on whole slide images of hematoxylin and eosin (H&E)-stained slides alone, offering slide-level classification of MSI status in colon cancer samples* based on morphologies associated with the presence or absence of MSI/MMR phenotype. Paige Colon MSI is a new addition to Paige’s Colon Suite which also includes case and slide-level detection of suspicious regions including invasive carcinoma and high-grade dysplasia.

Paige Colon MSI aims to assist pathologists by providing additional insight in identifying those patients who may benefit from definitive MSI/MMR testing for one of the most common hereditary cancer syndromes and IO therapy. The system has been designed to be optimized for negative predictive value (NPV=0.99 when assessed on over 500 samples from various sources). AI results can be obtained immediately from digitized stained sections so there is no need to wait days to weeks for results for the ~85% of patients whose tumors are MS stable and could potentially avoid the expensive screening that is required today.¹

“Paige Colon MSI is another example of how the company is innovating to empower pathologists, transform pathology and improve the lives of patients with cancer,” said Dr. Joe Oakley, MD, Medical Director of Biomarker Development at Paige. “We used the most advanced machine learning methods available on the most robust dataset in the industry to improve AI detection of MSI in colon cancer. We hope to demonstrate that this assay will offer laboratories and healthcare systems a chance to reduce MSI screening costs and accelerate their workflows. We believe this represents an important first step to more comprehensive MSI and mismatch repair deficiency detection in cancers.”

Despite guideline recommendations, recent studies have shown not all patients are being tested for MSI status.² Introducing Paige Colon MSI as a cost-effective and time-efficient assistive testing method may improve access to diagnostic testing for more patients by highlighting cases likely to harbor MSI, which should then be confirmed by immunohistochemistry or molecular methods to inform precision treatment decisions.

While colon cancer is the initial focus, Paige is working to develop subsequent enhancements to enable screening in additional cancer types.

About Paige

Paige uses the power of AI to drive a new era of cancer discovery and treatment. To improve the lives of patients with cancer, Paige has created a cloud-based platform that transforms pathologists’ workflow and increases diagnostic confidence as well as productivity, all on a global scale. Paige is the first company to receive FDA approval for a clinical AI application in digital pathology, Paige Prostate Detect. The same Paige technology empowers pharmaceutical companies for biomarkers for drug development so that every patient gets precise treatment options.

For additional information, please visit: https://www.paige.ai, Twitter and LinkedIn.

*Where permitted, Paige Colon MSI is for research use only (RUO), not for use in diagnostic procedures.

¹Maria Lorenzi, Mayur Amonkar, Jacky Zhang, Shivani Mehta, Kai-Li Liaw, "Epidemiology of Microsatellite Instability High (MSI-H) and Deficient Mismatch Repair (dMMR) in Solid Tumors: A Structured Literature Review", Journal of Oncology, vol. 2020, Article ID 1807929, 17 pages, 2020. https://doi.org/10.1155/2020/1807929

²Mittal, C., Dang, D., Stoffel, E. et al. Underutilization of Lynch Syndrome Screening at Two Large Veterans Affairs Medical Centers. Dig Dis Sci 65, 3305–3315 (2020). https://doi.org/10.1007/s10620-020-06340-0

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