Abstract 918: Using machine learning to predict the risk of either having an aggressive form of prostate cancer (PCa) or lower-grade PCa/benign prostatic hyperplasia (BPH) based upon the flow cytometry immunophenotyping of myeloid-derived suppressor cells (MDSCs) and lymphocyte cell populations

The goal of this study was to create a non-invasive confirmatory test for prostate biopsies that objectively analyzes flow cytometry data using machine learning to predict whether a subject is at higher risk for having an aggressive form of prostate cancer (PCa; Gleason ≥ 4+3). The commonly used assay for prostate screening is a prostate specific antigen (PSA) blood test, but due to prostate physiology, PSA testing results in a large frequency of false positives leading to numerous men each year undergoing unnecessary prostate biopsy procedures. Here, we use machine learning to create a neural network (NN) to predict whether a subject has a greater probability in having an aggressive form of prostate cancer (HR-PCa) or is at lower risk (LR-PCa; Gleason Citation Format: George A. Dominguez, John Roop, Alexander Polo, Anthony Campisi, Dmitry I. Gabrilovich, Amit Kumar. Using machine learning to predict the risk of either having an aggressive form of prostate cancer (PCa) or lower-grade PCa/benign prostatic hyperplasia (BPH) based upon the flow cytometry immunophenotyping of myeloid-derived suppressor cells (MDSCs) and lymphocyte cell populations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 918.