Peer-Reviewed Publication
Breast Cancer Res2022;24(1):93.December 20, 2022Journal Article
Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years.
Gerardo Fernandez1,2, Marcel Prastawa1, Abishek Sainath Madduri1,2, Richard Scott1, Bahram Marami1, Nina Shpalensky1, Krystal Cascetta3, Mary Sawyer2, Monica Chan2, Giovanni Koll1, Alexander Shtabsky1, Aaron Feliz1, Thomas Hansen3, Brandon Veremis2, Carlos Cordon-Cardo2, Jack Zeineh1, Michael J Donovan4,5,6
1PreciseDx, 1111 Amsterdam, Stuyvesant Building 8-822, New York, NY, 10025, USA.
2Icahn School of Medicine at Mount Sinai, New York, NY, USA.
3Mount Sinai Hospital, New York, NY, USA.
4PreciseDx, 1111 Amsterdam, Stuyvesant Building 8-822, New York, NY, 10025, USA. mdonovan@precisedx.ai.
5Icahn School of Medicine at Mount Sinai, New York, NY, USA. mdonovan@precisedx.ai.
6Department of Pathology, University of Miami, Miami, FL, USA. mdonovan@precisedx.ai.
Abstract
BACKGROUND: Breast cancer (BC) grading plays a critical role in patient management despite the considerable inter- and intra-observer variability, highlighting the need for decision support tools to improve reproducibility and prognostic accuracy for use in clinical practice. The objective was to evaluate the ability of a digital artificial intelligence (AI) assay (PDxBr) to enrich BC grading and…
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