Purpose: This study retrospectively evaluated the relationship between disease stage and metabolic parameters, along with asphericity, derived from 18F-FDG PET/CT images in patients with Small Cell Lung Cancer (SCLC).
Methods: We analyzed primary tumor metabolic parameters, including maximum and mean standardized uptake values (SUVmax, SUVmean), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), as well as Asphericity (ASP). Disease staging was determined from 18F-FDG PET/CT images as either limited-stage (LS) or extensive-stage (ES). Statistical analyses assessed the correlation among these metabolic parameters and ASP, their relationship with disease stage, their ability to predict disease stage.
Results: The study included 134 SCLC patients (mean age: 62.3; 112 males, 22 females). Of these, 61.2% had ES and 38.8% had LS disease. MTV, TLG, and ASP values were significantly higher in the ES group than in the LS group (p < 0.001). In the multivariate binary logistic regression analysis, Age (p = 0.025), Gender (p = 0.033), and ASP (p = 0.001) were identified as independent predictors of disease stage. Notably, a one-unit increase in ASP was associated with a 6.35-fold increase in the likelihood of Extensive-Stage disease (OR = 6.350). In ROC analysis, MTV, TLG, and ASP significantly distinguished disease stages (p < 0.001) with optimal cut-off values of 45.05, 382.81, and 0.536, respectively.
Conclusion: Relying solely on SUVmax for SCLC patient evaluation may be insufficient. Incorporating other metabolic parameters like MTV and TLG, in addition to ASP, which was identified as a strong independent predictor, may enhance the understanding of tumor behavior during staging. These additional parameters can significantly aid in comprehensive patient assessment.
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