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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...

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Autores principales: Dominguez, George A, Polo, Alexander T, Roop, John, Campisi, Anthony J, Somer, Robert A, Perzin, Adam D, Gabrilovich, Dmitry I, Kumar, Amit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7169353/
https://www.ncbi.nlm.nih.gov/pubmed/32341637
http://dx.doi.org/10.1177/1177271920913320
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author Dominguez, George A
Polo, Alexander T
Roop, John
Campisi, Anthony J
Somer, Robert A
Perzin, Adam D
Gabrilovich, Dmitry I
Kumar, Amit
author_facet Dominguez, George A
Polo, Alexander T
Roop, John
Campisi, Anthony J
Somer, Robert A
Perzin, Adam D
Gabrilovich, Dmitry I
Kumar, Amit
author_sort Dominguez, George A
collection PubMed
description 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.
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spelling pubmed-71693532020-04-27 Detecting Prostate Cancer Using Pattern Recognition Neural Networks With Flow Cytometry-Based Immunophenotyping in At-Risk Men Dominguez, George A Polo, Alexander T Roop, John Campisi, Anthony J Somer, Robert A Perzin, Adam D Gabrilovich, Dmitry I Kumar, Amit Biomark Insights Original Research 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. SAGE Publications 2020-04-17 /pmc/articles/PMC7169353/ /pubmed/32341637 http://dx.doi.org/10.1177/1177271920913320 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by-nc/4.0/ This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Original Research
Dominguez, George A
Polo, Alexander T
Roop, John
Campisi, Anthony J
Somer, Robert A
Perzin, Adam D
Gabrilovich, Dmitry I
Kumar, Amit
Detecting Prostate Cancer Using Pattern Recognition Neural Networks With Flow Cytometry-Based Immunophenotyping in At-Risk Men
title Detecting Prostate Cancer Using Pattern Recognition Neural Networks With Flow Cytometry-Based Immunophenotyping in At-Risk Men
title_full Detecting Prostate Cancer Using Pattern Recognition Neural Networks With Flow Cytometry-Based Immunophenotyping in At-Risk Men
title_fullStr Detecting Prostate Cancer Using Pattern Recognition Neural Networks With Flow Cytometry-Based Immunophenotyping in At-Risk Men
title_full_unstemmed Detecting Prostate Cancer Using Pattern Recognition Neural Networks With Flow Cytometry-Based Immunophenotyping in At-Risk Men
title_short Detecting Prostate Cancer Using Pattern Recognition Neural Networks With Flow Cytometry-Based Immunophenotyping in At-Risk Men
title_sort detecting prostate cancer using pattern recognition neural networks with flow cytometry-based immunophenotyping in at-risk men
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7169353/
https://www.ncbi.nlm.nih.gov/pubmed/32341637
http://dx.doi.org/10.1177/1177271920913320
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