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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2020
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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. |
format | Online Article Text |
id | pubmed-7169353 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
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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