Comparison of Tumor Uptake Heterogeneity Characterization Between Static and Parametric 18F-FDG PET Images in Non-Small Cell Lung Cancer

¹⁸F-FDG PET is well established in the field of oncology for diagnosis and staging purposes and is increasingly being used to assess therapeutic response and prognosis. Many quantitative indices can be used to characterize tumors on ¹⁸F-FDG PET images, such as SUV_max, metabolically active tumor volume (MATV), total lesion glycolysis, and, more recently, the proposed intratumor uptake heterogeneity features. Although most PET data considered within this context concern the analysis of activity distribution using images obtained from a single static acquisition, parametric images generated from dynamic acquisitions and reflecting radiotracer kinetics may provide additional information. The purpose of this study was to quantify differences between volumetry, uptake, and heterogeneity features extracted from static and parametric PET images of non–small cell lung carcinoma (NSCLC) in order to provide insight on the potential added value of parametric images. Methods: Dynamic ¹⁸F-FDG PET/CT was performed on 20 therapy-naive NSCLC patients for whom primary surgical resection was planned. Both static and parametric PET images were analyzed, with quantitative parameters (MATV, SUV_max, SUV_mean, heterogeneity) being extracted from the segmented tumors. Differences were investigated using Spearman rank correlation and Bland–Altman analysis. Results: MATV was slightly smaller on static images (–2% ± 7%), but the difference was not significant (P = 0.14). All derived parameters, including those characterizing tumor functional heterogeneity, correlated strongly between static and parametric images (r = 0.70–0.98, P ≤ 0.0006), exhibiting differences of less than ±25%. Conclusion: In NSCLC primary tumors, parametric and static baseline ¹⁸F-FDG PET images provided strongly correlated quantitative features for both standard (MATV, SUV_max, SUV_mean) and heterogeneity quantification. Consequently, heterogeneity quantification on parametric images does not seem to provide significant complementary information compared with static SUV images.

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