Dataset Information

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.

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 SUV_max 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 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.

SUBMITTER: Vanhove K

PROVIDER: S-EPMC6122613 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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Publications

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

Vanhove K K Giesen P P Owokotomo O E OE Mesotten L L Louis E E Shkedy Z Z Thomeer M M Adriaensens P P

BMC cancer 20180903 1

<h4>Background</h4>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.<h4>Methods</h4>Metabolic profiles of plasma from 347 controls, 269 cancer patients and 108 patients with inflammation were obtained by <sup>1</sup>H-NMR spectroscopy. Models to discriminate between grou ...[more]

PMID: 30176828

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Project description:BackgroundExtracellular volume (ECV) fraction has been used in cardiovascular diseases, pancreatic fibrosis, and hepatic fibrosis. The diagnostic value of ECV for focal lung lesions remains to be explored. The aim of this study was to evaluate the feasibility of ECV derived from a dual-layer detector computed tomography (DLCT) to differentiate lung cancer (LC) from benign lung lesions (BLLs).MethodsRetrospectively, 128 consecutive patients with pathologically confirmed LC (n=86) or BLLs (n=42) were included. Conventional computed tomography (CT) characteristics and spectral CT parameters were assessed. All patients' hematocrits were measured to correct contrast volume distributions in blood while calculating ECV. After performing logistic regression analysis, a conventional CT-based model (Model A), DLCT-based model (Model B), combined diagnostic models (Model C), and an ECV-based model (Model D) were developed. The diagnostic effectiveness of each model was examined using the receiver operating characteristic (ROC) curve and their corresponding 95% confidence intervals (CIs). The area under the curve (AUC) of each model was compared using the DeLong test.ResultsCertain conventional CT features (such as lesion size, lobulation, spiculation, pleural indentation, and enlarged lymph nodes) differed significantly between the LC and BLL groups (all P<0.05). Statistical differences were found in the following DLCT parameters (all P<0.05): effective atomic number (Zeff) (non-enhancement), electron density (ED) (non-enhancement), ECV, iodine concentration (IC), and normalized iodine concentration (NIC). Models A, B, C, and D had AUCs of 0.801 [95% confidence interval (CI): 0.721-0.866], 0.805 (95% CI: 0.726-0.870), 0.925 (95% CI: 0.865-0.964), and 0.754 (95% CI: 0.671-0.826), respectively. The AUC of Model D (ECV) showed no significant difference from that of Models A and B (DeLong test, P>0.05).ConclusionsThe ECV derived from DLCT may be a potential new method to differentiate LC from BLLs, broadening the scope of ECV in clinical research.

Dataset Information

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

Background

Methods

Results

Conclusion

Publications

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

Similar Datasets

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