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Biological Significance of F-FDG PET/CT Maximum Standard Uptake Value for Predicting Mutation Status in Non-Small Cell Lung Cancer Patients

Overview
Journal Int J Gen Med
Publisher Dove Medical Press
Specialty General Medicine
Date 2021 Feb 11
PMID 33568935
Citations 6
Authors
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Abstract

Purpose: To investigate the potential of maximum standardized uptake value (SUVmax) in predicting epidermal growth factor receptor () mutation status in non-small cell lung cancer (NSCLC) patients.

Methods: Clinical data of 311 NSCLC patients who had undergone both mutation test and F-FDG PET/CT scans between January 2013 and December 2017 at our hospital were retrospectively analyzed. Patients were sub-grouped by their origin of SUVmax. Univariate and multivariate analyses were performed to investigate the association between clinical factors and mutations. Receiver operating characteristic curve (ROC) analysis was performed to confirm the predictive value of clinical factors. In vitro experiments were performed to confirm the correlation between mutations and glycolysis.

Results: -mutant patients had higher SUVmax than the wild-type patients in both primary tumors and metastases. In the multivariate analysis, SUVmax, gender and histopathologic type were determined as independent predictors of mutation status for patients whose SUVmax were obtained from the primary tumors; while for patients whose SUVmax were obtained from the metastases, SUVmax, smoking status and histopathologic type were regarded as independent predictors. ROC analysis showed that SUVmax of the primary tumors (cut off >10.92), not of the metastases, has better predictive value than other clinical factors in predicting mutation status. The predict performance was improved after combined SUVmax with other independent predictors. In addition, our in vitro experiments demonstrated that lung cancer cells with mutations have higher aerobic glycolysis level than wild-type cells.

Conclusion: SUVmax of the primary tumors has the potential to serve as a biomarker to predict mutation status in NSCLC patients.

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