Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/40394
Title: Changes in Metabolism as a Diagnostic Tool for Lung Cancer: Systematic Review
Authors: MARIEN, Hanne 
DERVEAUX, Elien 
VANHOVE, Karolien 
ADRIAENSENS, Peter 
THOMEER, Michiel 
MESOTTEN, Liesbet 
Issue Date: 2022
Publisher: MDPI
Source: Metabolites, 12 (6) (Art N° 545)
Abstract: Lung cancer is the leading cause of cancer-related mortality worldwide, with five-year survival rates varying from 3-62%. Screening aims at early detection, but half of the patients are diagnosed in advanced stages, limiting therapeutic possibilities. Positron emission tomography-computed tomography (PET-CT) is an essential technique in lung cancer detection and staging, with a sensitivity reaching 96%. However, since elevated 18F-fluorodeoxyglucose (F-18-FDG) uptake is not cancer-specific, PET-CT often fails to discriminate between malignant and non-malignant PET-positive hypermetabolic lesions, with a specificity of only 23%. Furthermore, discrimination between lung cancer types is still impossible without invasive procedures. High mortality and morbidity, low survival rates, and difficulties in early detection, staging, and typing of lung cancer motivate the search for biomarkers to improve the diagnostic process and life expectancy. Metabolomics has emerged as a valuable technique for these pitfalls. Over 150 metabolites have been associated with lung cancer, and several are consistent in their findings of alterations in specific metabolite concentrations. However, there is still more variability than consistency due to the lack of standardized patient cohorts and measurement protocols. This review summarizes the identified metabolic biomarkers for early diagnosis, staging, and typing and reinforces the need for biomarkers to predict disease progression and survival and to support treatment follow-up.
Keywords: lung cancer;metabolomics;metabolite profile
Document URI: http://hdl.handle.net/1942/40394
e-ISSN: 2218-1989
DOI: 10.3390/metabo12060545
ISI #: 000815877300001
Category: A1
Type: Journal Contribution
Validations: ecoom 2023
Appears in Collections:Research publications

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