Detecting Prostate Cancer Using Pattern Recognition Neural Networks With Flow Cytometry-Based Immunophenotyping in At-Risk Men.

Current screening methods for prostate cancer (PCa) result in a large number of false positives making it difficult for clinicians to assess disease status, thus warranting advancements in screening and early detection methods. The goal of this study was to design a liquid biopsy test that uses flow cytometry-based immunophenotyping and artificial neural network (ANN) analysis to detect PCa. Numerous myeloid and lymphoid cell populations, including myeloid-derived suppressor cells, were measured from 156 patients with PCa, 123 with benign prostatic hyperplasia (BPH), and 99 male healthy donor (HD) controls. Using pattern recognition neural network (PRNN) analysis, a type of ANN, PCa detection compared against HD resulted in 96.6% sensitivity, 87.5% specificity, and an area under the curve (AUC) value of 0.97. Detecting patients with higher risk disease (⩾Gleason 7) against lower risk disease (BPH/Gleason 6) resulted in 92.0% sensitivity, 42.7% specificity, and an AUC of 0.72. This study suggests that analyzing flow cytometry immunophenotyping data with PRNNs may prove to be a useful tool to improve PCa detection and reduce the number of unnecessary prostate biopsies performed each year.

Comparison of tolerability and adverse events following treatment with two GnRH agonists in patients with advanced prostate cancer.

This multicenter, randomized, crossover, open-label study (ClinicalTrials.gov identifier: NCT01161563) assessed patients'and clinicians'perceptions of injection site tolerability and adverse events following the intramuscular injection of triptorelin pamoate or subcutaneous injection of leuprolide acetate in 107 male, patients with advanced prostate cancer.