Prostate cancer: early detection and assessing clinical risk using deep machine learning of high dimensional peripheral blood flow cytometric phenotyping data

Cosma, G., McArdle, S.E. ORCID: 0000-0001-6929-9782, Foulds, G.A. ORCID: 0000-0002-2053-7580, Hood, S.P., Reeder, S., Johnson, C., Khan, M.A. and Pockley, A.G. ORCID: 0000-0001-9593-6431, 2021. Prostate cancer: early detection and assessing clinical risk using deep machine learning of high dimensional peripheral blood flow cytometric phenotyping data. Frontiers in Immunology, 12: 786828. ISSN 1664-3224

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Abstract

Detecting the presence of prostate cancer (PCa) and distinguishing low- or intermediate-risk disease from high-risk disease early, and without the need for potentially unnecessary invasive biopsies remains a significant clinical challenge. The aim of this study is to determine whether the T and B cell phenotypic features which we have previously identified as being able to distinguish between benign prostate disease and PCa in asymptomatic men having Prostate-Specific Antigen (PSA) levels < 20 ng/ml can also be used to detect the presence and clinical risk of PCa in a larger cohort of patients whose PSA levels ranged between 3 and 2617 ng/ml. The peripheral blood of 130 asymptomatic men having elevated Prostate-Specific Antigen (PSA) levels was immune profiled using multiparametric whole blood flow cytometry. Of these men, 42 were subsequently diagnosed as having benign prostate disease and 88 as having PCa on biopsy-based evidence. We built a bidirectional Long Short-Term Memory Deep Neural Network (biLSTM) model for detecting the presence of PCa in men which combined the previously-identified phenotypic features (CD8+CD45RA-CD27-CD28- (CD8+ Effector Memory cells), CD4+CD45RA-CD27-CD28- (CD4+ Effector Memory cells), CD4+CD45RA+CD27-CD28- (CD4+ Terminally Differentiated Effector Memory Cells re-expressing CD45RA), CD3-CD19+ (B cells), CD3+CD56+CD8+CD4+ (NKT cells) with Age. The performance of the PCa presence ‘detection’ model was: Acc: 86.79 ( ± 0.10), Sensitivity: 82.78% (± 0.15); Specificity: 95.83% (± 0.11) on the test set (test set that was not used during training and validation); AUC: 89.31% (± 0.07), ORP-FPR: 7.50% (± 0.20), ORP-TPR: 84.44% (± 0.14). A second biLSTM ‘risk’ model combined the immunophenotypic features with PSA to predict whether a patient with PCa has high-risk disease (defined by the D’Amico Risk Classification) achieved the following: Acc: 94.90% (± 6.29), Sensitivity: 92% (± 21.39); Specificity: 96.11 (± 0.00); AUC: 94.06% (± 10.69), ORP-FPR: 3.89% (± 0.00), ORP-TPR: 92% (± 21.39). The ORP-FPR for predicting the presence of PCa when combining FC+PSA was lower than that of PSA alone. This study demonstrates that AI approaches based on peripheral blood phenotyping profiles can distinguish between benign prostate disease and PCa and predict clinical risk in asymptomatic men having elevated PSA levels.

Item Type: Journal article
Publication Title: Frontiers in Immunology
Creators: Cosma, G., McArdle, S.E., Foulds, G.A., Hood, S.P., Reeder, S., Johnson, C., Khan, M.A. and Pockley, A.G.
Publisher: Frontiers Media SA
Date: 16 December 2021
Volume: 12
ISSN: 1664-3224
Identifiers:
NumberType
10.3389/fimmu.2021.786828DOI
1504363Other
Rights: © 2021 Cosma, McArdle, Foulds, Hood, Reeder, Johnson, Khan and Pockley. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Divisions: Schools > School of Science and Technology
Record created by: Laura Ward
Date Added: 17 Dec 2021 12:07
Last Modified: 17 Dec 2021 12:07
URI: http://irep.ntu.ac.uk/id/eprint/45140

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