Empowering Doctors with Predictive Analytics
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Our AI-based prognostic test will reduce over-treatment and under-treatment of specific forms of early stage breast cancer.
Our Team
Who We Are
About Predictoma
Predictoma was founded with the goal of providing accurate information about whether or not early breast cancers are dangerous – and need treatment promptly – or are non-dangerous and would not impact the patient’s life. The solution was developed by years of research in Dr. Petty’s lab on cancer recurrences, combined with the AI expertise of Dr. Saatchi. Predictoma’s tests are intended to provide physicians with better and more accurate information to guide treatment decisions and improve patient care.
Our first test is for an early form of breast cancer, Ductal Carcinoma in Situ (DCIS). This test will reduce unneeded surgery, treatment and suffering for women with non-dangerous lesions and identify women who need surgery at a very early point.
Our AI-based test is grounded in peer-reviewed published scientific research, including a retrospective case-control clinical trial, and a new understanding of the development of cancer recurrences. It is exceptionally accurate in identifying dangerous and non-dangerous lesions without racial bias. (For the technical details, please see our publications, or contact Howard Petty (howard@predictoma.com), who will be delighted to provide all the technical information you would like).
Breast cancer is only our starting point. We will expand this unique approach to other early-stage, non-invasive cancers including atypical ductal hyperplasia of the breast, endometrial, cervical, melanoma, and other cancers. We hope to change how early stage cancers are managed.
DCIS and Clinical Examples
Publications
•Saatchi Y, Schanen P, Cheung RA, Petty HR. Computer vision identifies recurrent and non-recurrent ductal carcinoma in situ lesions with special emphasis on African American women. The American Journal of Pathology 2023, doi: https://doi.org/10.1016/ j.ajpath.2023.05.018. •Schanen P, Petty HR. What applied physical chemistry can contribute to understanding cancer: Toward the next generation of breakthroughs. AppliedChem 2023, 3, 378–399. 2023. https://doi.org/10.3390/ appliedchem3030024. •Petty HR. Using machine vision of glycolytic elements to predict breast cancer recurrences: Metabolites, 13 (1):41, 2023. https://doi.org/10.3390/metabo13010041. •Petty HR. Prognostic evaluation of ductal carcinoma in situ lesions using monoclonal antibodies and machine learning. Handbook of Cancer and Immunity, Springer-Nature, Switzerland AG, (N. Rezaei, Ed.), doi.org/10,1007/978-3-300-80962-1_318-1. 2022. •Petty HR. Enzyme trafficking and co-clustering precede and accurately predict human breast cancer recurrences: an interdisciplinary review. Am J Physiol Cell Physiol., 322: C991-C1010, 2022. https://doi.org/10.1152/ajpcell.00042.2022 •Cheung RA, Kraft AM, Petty HR. Relocation of phosphofructokinases within epithelial cells is a novel event preceding breast cancer recurrence that accurately predicts patient outcomes. Am J Physiol Cell Physiol. 321: C654-670, 2021. https://doi.org/10.1152/ajpcell.00176.2021 •Kraft AM, Petty HR. Spatial locations of certain enzymes and transporters within preinvasive ductal epithelial cells predict human breast cancer recurrences. Am J Physiol Cell Physiol. 319: C910-921, 2020. https://doi.org/10.1152/ajpcell.00280.2020