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

IF 3.4 Q2 MEDICINE, RESEARCH & EXPERIMENTAL Biomarker Insights Pub Date : 2020-04-17 eCollection Date: 2020-01-01 DOI:10.1177/1177271920913320
George A Dominguez, Alexander T Polo, John Roop, Anthony J Campisi, Robert A Somer, Adam D Perzin, Dmitry I Gabrilovich, Amit Kumar
{"title":"Detecting Prostate Cancer Using Pattern Recognition Neural Networks With Flow Cytometry-Based Immunophenotyping in At-Risk Men.","authors":"George A Dominguez, Alexander T Polo, John Roop, Anthony J Campisi, Robert A Somer, Adam D Perzin, Dmitry I Gabrilovich, Amit Kumar","doi":"10.1177/1177271920913320","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":47060,"journal":{"name":"Biomarker Insights","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2020-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/48/90/10.1177_1177271920913320.PMC7169353.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomarker Insights","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/1177271920913320","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2020/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 0

Abstract

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.

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用模式识别神经网络和基于流式细胞术的免疫表型在高危男性中检测前列腺癌。
目前的前列腺癌(PCa)筛查方法导致大量假阳性,使临床医生难以评估疾病状态,因此需要在筛查和早期检测方法方面取得进展。本研究的目的是设计一种液体活检测试,使用基于流式细胞术的免疫表型和人工神经网络(ANN)分析来检测PCa。对156例PCa患者、123例良性前列腺增生(BPH)患者和99例男性健康供体(HD)对照进行了大量髓系和淋巴细胞群(包括髓系来源的抑制细胞)检测。采用模式识别神经网络(PRNN)分析,与HD相比,PCa检测的灵敏度为96.6%,特异性为87.5%,曲线下面积(AUC)值为0.97。检测高风险疾病(小于或等于Gleason 7)的患者与低风险疾病(BPH/Gleason 6)的对比,导致92.0%的敏感性,42.7%的特异性,AUC为0.72。这项研究表明,用prnn分析流式细胞术免疫表型数据可能是一种有用的工具,可以提高前列腺癌的检测水平,减少每年不必要的前列腺活检次数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Biomarker Insights
Biomarker Insights MEDICINE, RESEARCH & EXPERIMENTAL-
CiteScore
6.00
自引率
0.00%
发文量
26
审稿时长
8 weeks
期刊介绍: An open access, peer reviewed electronic journal that covers all aspects of biomarker research and clinical applications.
期刊最新文献
Decreased Serum Insulin Receptor Messenger RNA Level in H. pylori IgG Seropositive Type 2 Diabetic Patients. Systematic Analysis and Insights Into the Mutation Spectrum and Ethnic Differences in ATP7B Mutations Associated With Wilson Disease. The Chromosome Passenger Complex (CPC) Components and Its Associated Pathways Are Promising Candidates to Differentiate Between Normosensitive and Radiosensitive ATM-Mutated Cells. D-dimer as a Predictive Biomarker of Response to Chemotherapy in Patients With Metastatic Breast Cancer. Biomarkers From Discovery to Clinical Application: In Silico Pre-Clinical Validation Approach in the Face of Lung Cancer.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1