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 outperforms standard clinical tools
that prognosticate outcomes

First and only AI-derived prognostic biomarker test to be recommended as a risk stratification tool in the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines®️) for Prostate Cancer1 1. Referenced with permission from the NCCN Clinical Practice Guidelines in Oncology (NCCN Guidelines) for Prostate Cancer V.1.2023. ©️ National Comprehensive Cancer Network, Inc. 2022. All rights reserved. Accessed March 9, 2023. To view the most recent and complete version of the guideline, go online to NCCN.org. NCCN makes no warranties of any kind whatsoever regarding their content, use or application and disclaims any responsibility for their application or use in any way.
Esteva A, et al. Nature Digital Medicine. 2022;5(1):71 Esteva A, et al. 2022 ASCO GU Cancers Symposium. Abstract 222 Esteva A, et al. Nature Digital Medicine. 2022;5(1):71 Esteva A, et al. 2022 ASCO GU Cancers Symposium. Abstract 222
NCCN, National Comprehensive Cancer Network.

Our science has been featured in a number of
key conferences and publications

    February 16, 2023

    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.

    UroToday Video
    Urology Times Video

    December 2, 2022

    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.

    UroToday Video

    UroToday Article

    November 30, 2022

    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.

    UroToday Article