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

Abstract

18 F-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 18 F-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 18 F-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 5 0.14). All derived parameters, including those characterizing tumor functional heterogeneity, correlated strongly between static and parametric images (r 5 0.70-0.98, P # 0.0006), exhibiting differences of less than ±25%. Conclusion: In NSCLC primary tumors, parametric and static baseline 18 F-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.

Characterization of PET/CT images using texture analysis: the past, the present… any future?

“…It has also been shown that the use of a respiration-averaged CT scan instead of a helical CT scan for attenuation correction of the PET data has a greater effect on SUV and total lesion glycolysis than on TA metrics [83]. Another study demonstrated that TA features calculated in parametric maps derived from dynamic PET acquisitions or from corresponding static SUV images are not significantly different, suggesting that heterogeneity quantification on parametric images using TA may not provide significant additional information compared to that provided by static SUV images [84]. Finally, it has also been shown that even basic stochastic effects of PET acquisitions can affect some TA metrics [85].…”

Variability and Repeatability of Quantitative Uptake Metrics in 18F-FDG PET/CT of Non–Small Cell Lung Cancer: Impact of Segmentation Method, Uptake Interval, and Reconstruction Protocol

“…The quantitative metrics evaluated in this study were MATV, SUV max , SUV mean , total lesion glycolysis (TLG), and several textural intratumor heterogeneity features. These features included a global heterogeneity indicator (i.e., area under the curve of the cumulative intensity histogram, CIH AUC ) (16), and some local heterogeneity features, such as homogeneity, entropy, dissimilarity, high-intensity emphasis (HIE), and zone percentage (ZP). These features were selected because of their reproducibility and robustness (8,16,17).…”

“…These features included a global heterogeneity indicator (i.e., area under the curve of the cumulative intensity histogram, CIH AUC ) (16), and some local heterogeneity features, such as homogeneity, entropy, dissimilarity, high-intensity emphasis (HIE), and zone percentage (ZP). These features were selected because of their reproducibility and robustness (8,16,17). MATV, SUV max , SUV mean , TLG, and CIH AUC were calculated with in-house software, whereas local heterogeneity features were obtained with the Pyradiomics package (18).…”

Does whole-body Patlak 18F-FDG PET imaging improve lesion detectability in clinical oncology?

“…The dynamic course of the 18 F-FDG spatial distribution in the targeted tissues may reveal highly useful clinical information on tissue's metabolic properties, such as the metabolic rate of glucose uptake post 18 F-FDG injection. These image metrics could, in turn, facilitate tumor characterization and therapy response assessment [6][7][8][9]. For this reason, dynamic PET imaging techniques were also introduced allowing the scanning of a limited axial field-of-view (single bed position) over time to enable the extraction of important tracer kinetic parameters, such as the uptake rate.…”