Identifying prostate cancer and its clinical risk in asymptomatic men using machine learning of high dimensional peripheral blood flow cytometric natural killer cell subset phenotyping data

Identifying prostate cancer and its clinical risk in asymptomatic men using machine learning of high dimensional peripheral blood flow cytometric natural killer cell subset phenotyping data

We demonstrate that prostate cancer can be identified by flow cytometric profiling of blood immune cell subsets. Herein, we profiled natural killer (NK) cell subsets in the blood of 72 asymptomatic men with Prostate-Specific Antigen (PSA) levels < 20 ng ml-1, of whom 31 had benign disease (no...

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Bibliographic Details
Main Authors: Simon P Hood, Georgina Cosma, Gemma A Foulds, Catherine Johnson, Stephen Reeder, Stéphanie E McArdle, Masood A Khan, A Graham Pockley
Format: Article
Language:English
Published: eLife Sciences Publications Ltd 2020-07-01
Series:eLife
Subjects:
machine learning
natural killer (NK) cells
prostate cancer
diagnosis
immune phenotyping
prostate specific antigen (psa)
Online Access:https://elifesciences.org/articles/50936

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