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The plasma glutamate concentration as a complementary tool to differentiate benign PET-positive lung lesions from lung cancer

BACKGROUND: Pulmonary imaging often identifies suspicious abnormalities resulting in supplementary diagnostic procedures. This study aims to investigate whether the metabolic fingerprint of plasma allows to discriminate between patients with lung inflammation and patients with lung cancer. METHODS:...

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Autores principales: Vanhove, K., Giesen, P., Owokotomo, O. E., Mesotten, L., Louis, E., Shkedy, Z., Thomeer, M., Adriaensens, P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6122613/
https://www.ncbi.nlm.nih.gov/pubmed/30176828
http://dx.doi.org/10.1186/s12885-018-4755-1
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author Vanhove, K.
Giesen, P.
Owokotomo, O. E.
Mesotten, L.
Louis, E.
Shkedy, Z.
Thomeer, M.
Adriaensens, P.
author_facet Vanhove, K.
Giesen, P.
Owokotomo, O. E.
Mesotten, L.
Louis, E.
Shkedy, Z.
Thomeer, M.
Adriaensens, P.
author_sort Vanhove, K.
collection PubMed
description BACKGROUND: Pulmonary imaging often identifies suspicious abnormalities resulting in supplementary diagnostic procedures. This study aims to investigate whether the metabolic fingerprint of plasma allows to discriminate between patients with lung inflammation and patients with lung cancer. METHODS: Metabolic profiles of plasma from 347 controls, 269 cancer patients and 108 patients with inflammation were obtained by (1)H-NMR spectroscopy. Models to discriminate between groups were trained by PLS-LDA. A test set was used for independent validation. A ROC curve was built to evaluate the diagnostic performance of potential biomarkers. RESULTS: Sensitivity, specificity, PPV and NPV of PET-CT to diagnose cancer are 96, 23, 76 and 71%. Metabolic profiles differentiate between cancer and inflammation with a sensitivity of 89%, a specificity of 87% and a MCE of 12%. Removal of the glutamate metabolite results in an increase of MCE (38%) and a decrease of both sensitivity and specificity (62%), demonstrating the importance of glutamate for discrimination. At the cut-off point 0.31 on the ROC curve, the relative glutamate concentration discriminates between cancer and inflammation with a sensitivity of 85%, a specificity of 81%, and an AUC of 0.88. PPV and NPV are 92 and 69%. In PET-positive patients with a relative glutamate level ≤ 0.31 the sensitivity to diagnose cancer reaches 100% with a PPV of 94%. In PET-negative patients, a relative glutamate level > 0.31 increases the specificity of PET from 23% to 58% and results in a high NPV of 100%. In case of discrepancy between SUV(max) and the glutamate concentration, lung cancer is missed in 19% of the cases. CONCLUSION: This study indicates that the (1)H-NMR-derived relative plasma concentration of glutamate allows discrimination between lung cancer and lung inflammation. A glutamate level ≤ 0.31 in PET-positive patients corresponds to the diagnosis of lung cancer with a higher specificity and PPV than PET-CT. Glutamate levels > 0.31 in patients with PET negative lung lesions is likely to correspond with inflammation. Caution is needed for patients with conflicting SUV(max) values and glutamate concentrations. Confirmation is needed in a prospective study with external validation and by another analytical technique such as HPLC-MS. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12885-018-4755-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-61226132018-09-05 The plasma glutamate concentration as a complementary tool to differentiate benign PET-positive lung lesions from lung cancer Vanhove, K. Giesen, P. Owokotomo, O. E. Mesotten, L. Louis, E. Shkedy, Z. Thomeer, M. Adriaensens, P. BMC Cancer Research Article BACKGROUND: Pulmonary imaging often identifies suspicious abnormalities resulting in supplementary diagnostic procedures. This study aims to investigate whether the metabolic fingerprint of plasma allows to discriminate between patients with lung inflammation and patients with lung cancer. METHODS: Metabolic profiles of plasma from 347 controls, 269 cancer patients and 108 patients with inflammation were obtained by (1)H-NMR spectroscopy. Models to discriminate between groups were trained by PLS-LDA. A test set was used for independent validation. A ROC curve was built to evaluate the diagnostic performance of potential biomarkers. RESULTS: Sensitivity, specificity, PPV and NPV of PET-CT to diagnose cancer are 96, 23, 76 and 71%. Metabolic profiles differentiate between cancer and inflammation with a sensitivity of 89%, a specificity of 87% and a MCE of 12%. Removal of the glutamate metabolite results in an increase of MCE (38%) and a decrease of both sensitivity and specificity (62%), demonstrating the importance of glutamate for discrimination. At the cut-off point 0.31 on the ROC curve, the relative glutamate concentration discriminates between cancer and inflammation with a sensitivity of 85%, a specificity of 81%, and an AUC of 0.88. PPV and NPV are 92 and 69%. In PET-positive patients with a relative glutamate level ≤ 0.31 the sensitivity to diagnose cancer reaches 100% with a PPV of 94%. In PET-negative patients, a relative glutamate level > 0.31 increases the specificity of PET from 23% to 58% and results in a high NPV of 100%. In case of discrepancy between SUV(max) and the glutamate concentration, lung cancer is missed in 19% of the cases. CONCLUSION: This study indicates that the (1)H-NMR-derived relative plasma concentration of glutamate allows discrimination between lung cancer and lung inflammation. A glutamate level ≤ 0.31 in PET-positive patients corresponds to the diagnosis of lung cancer with a higher specificity and PPV than PET-CT. Glutamate levels > 0.31 in patients with PET negative lung lesions is likely to correspond with inflammation. Caution is needed for patients with conflicting SUV(max) values and glutamate concentrations. Confirmation is needed in a prospective study with external validation and by another analytical technique such as HPLC-MS. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12885-018-4755-1) contains supplementary material, which is available to authorized users. BioMed Central 2018-09-03 /pmc/articles/PMC6122613/ /pubmed/30176828 http://dx.doi.org/10.1186/s12885-018-4755-1 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Vanhove, K.
Giesen, P.
Owokotomo, O. E.
Mesotten, L.
Louis, E.
Shkedy, Z.
Thomeer, M.
Adriaensens, P.
The plasma glutamate concentration as a complementary tool to differentiate benign PET-positive lung lesions from lung cancer
title The plasma glutamate concentration as a complementary tool to differentiate benign PET-positive lung lesions from lung cancer
title_full The plasma glutamate concentration as a complementary tool to differentiate benign PET-positive lung lesions from lung cancer
title_fullStr The plasma glutamate concentration as a complementary tool to differentiate benign PET-positive lung lesions from lung cancer
title_full_unstemmed The plasma glutamate concentration as a complementary tool to differentiate benign PET-positive lung lesions from lung cancer
title_short The plasma glutamate concentration as a complementary tool to differentiate benign PET-positive lung lesions from lung cancer
title_sort plasma glutamate concentration as a complementary tool to differentiate benign pet-positive lung lesions from lung cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6122613/
https://www.ncbi.nlm.nih.gov/pubmed/30176828
http://dx.doi.org/10.1186/s12885-018-4755-1
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