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

Abstract 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. M...

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Main Authors: K. Vanhove, P. Giesen, O. E. Owokotomo, L. Mesotten, E. Louis, Z. Shkedy, M. Thomeer, P. Adriaensens
Format: Article
Language:English
Published: BMC 2018-09-01
Series:BMC Cancer
Subjects:
ROC
Online Access:http://link.springer.com/article/10.1186/s12885-018-4755-1
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spelling doaj-bfd473377c734051a8557448d793c7672020-11-25T01:54:29ZengBMCBMC Cancer1471-24072018-09-0118111210.1186/s12885-018-4755-1The plasma glutamate concentration as a complementary tool to differentiate benign PET-positive lung lesions from lung cancerK. Vanhove0P. Giesen1O. E. Owokotomo2L. Mesotten3E. Louis4Z. Shkedy5M. Thomeer6P. Adriaensens7Faculty of Medicine and Life Sciences, Hasselt UniversityInstitute for Biostatistics and Statistical Bioinformatics, Hasselt UniversityInstitute for Biostatistics and Statistical Bioinformatics, Hasselt UniversityFaculty of Medicine and Life Sciences, Hasselt UniversityDepartment of Respiratory Medicine, University Hospital LeuvenInstitute for Biostatistics and Statistical Bioinformatics, Hasselt UniversityFaculty of Medicine and Life Sciences, Hasselt UniversityApplied and Analytical Chemistry, Institute for Materials Research, Hasselt UniversityAbstract 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 1H-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 SUVmax and the glutamate concentration, lung cancer is missed in 19% of the cases. Conclusion This study indicates that the 1H-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 SUVmax values and glutamate concentrations. Confirmation is needed in a prospective study with external validation and by another analytical technique such as HPLC-MS.http://link.springer.com/article/10.1186/s12885-018-4755-1Lung cancerLung inflammation1H-NMRMetabolic phenotypeGlutamateROC
collection DOAJ
language English
format Article
sources DOAJ
author K. Vanhove
P. Giesen
O. E. Owokotomo
L. Mesotten
E. Louis
Z. Shkedy
M. Thomeer
P. Adriaensens
spellingShingle K. Vanhove
P. Giesen
O. E. Owokotomo
L. Mesotten
E. Louis
Z. Shkedy
M. Thomeer
P. Adriaensens
The plasma glutamate concentration as a complementary tool to differentiate benign PET-positive lung lesions from lung cancer
BMC Cancer
Lung cancer
Lung inflammation
1H-NMR
Metabolic phenotype
Glutamate
ROC
author_facet K. Vanhove
P. Giesen
O. E. Owokotomo
L. Mesotten
E. Louis
Z. Shkedy
M. Thomeer
P. Adriaensens
author_sort K. Vanhove
title 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_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_sort plasma glutamate concentration as a complementary tool to differentiate benign pet-positive lung lesions from lung cancer
publisher BMC
series BMC Cancer
issn 1471-2407
publishDate 2018-09-01
description Abstract 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 1H-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 SUVmax and the glutamate concentration, lung cancer is missed in 19% of the cases. Conclusion This study indicates that the 1H-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 SUVmax values and glutamate concentrations. Confirmation is needed in a prospective study with external validation and by another analytical technique such as HPLC-MS.
topic Lung cancer
Lung inflammation
1H-NMR
Metabolic phenotype
Glutamate
ROC
url http://link.springer.com/article/10.1186/s12885-018-4755-1
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