Our test can help identify patients with localized prostate cancer who will benefit from treatment intensification
Our AI-enabled test accomplishes things that conventional techniques and computers cannot do alone. Using a large volume and a wide variety of clinical data and pathology imagery, our test predicts therapeutic benefit for patients.
Our test predicts therapeutic benefit from hormone therapy
Our test outperforms standard clinical tools
that prognosticate outcomes
Our science has been featured in a number of
key conferences and publications
Daniel Spratt, MD
Case Western Reserve University
Dr. Spratt discusses the validation of an MMAI prognostic biomarker and how the model further stratifies risk in men with high-risk disease, a population for which there are numerous treatment decisions.
Ashley Evan Ross, MD, PHD
Northwestern University
Dr. Ross discusses external validation of an MMAI prognostic model in patients with high- and very high-risk disease. Additionally, the model was shown to further stratify risk within this group, enabling more personalized treatment for these patients.
Felix Feng, MD
University of California at San Francisco
Dr. Feng discusses the development and validation of pathology-based deep learning predictive and prognostic tools to personalize treatment decisions for patients with localized prostate cancer.