Abstract
Background: The ability to predict the survival of patients with non-small cell lung cancer (NSCLC) can provide a basis for individualized treatment and follow-up. Determination of positron emission tomography/computed tomography (PET/CT) parameters, i.e., maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), may improve predictions of survival for NSCLC patients. Objectives: To determine the relationship between the PET/CT-derived parameters SUVmax, SUVmean, MTV, and TLG and survival in NSCLC patients. Patients and Methods: Patients with NSCLC diagnosed at our clinic between January 2019 and October 2020 were evaluated retrospectively using data obtained from the electronic database. The study population consisted of 132 patients over 18 years of age who had a PET/CT scan before receiving any treatment for NSCLC. During their initial PET/CT evaluation, SUVmax, SUVmean, MTV, and TLG were calculated. Correlations between the variables were analyzed using Spearman’s correlation test, and the associations of the variables with patient survival were determined using the Cox proportional hazards regression model. Results: The overall 2-year survival rate of the patients was 36.6%. In the univariate analysis, MTV and TLG, but not SUVmax or SUVmean, were significantly associated with survival (P = 0.8, P = 0.003, and P = 0.045, respectively). In the multivariate analysis, MTV was related to a higher risk of death in patients with adenocarcinoma, lymph node involvement, or distant metastasis. By contrast, TLG was associated with a lower risk of death in patients with adenocarcinoma (ACA) or distant metastasis. Conclusion: Among the parameters obtained in PET/CT studies, SUVmax and SUVmean were not related to the survival of patients with NSCLC. However, MTV was associated with a higher risk of death, while TLG was associated with a lower risk of death in patients with adenocarcinoma or distant metastasis. Further studies with larger samples are needed to validate these results.
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