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J Nucl Med. 2016 Jul;57(7):1033-9. doi: 10.2967/jnumed.115.166918. Epub 2016 Mar 10.

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

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Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands DACTIM, Medical School, University of Poitiers, Poitiers, France
Department of Radiology, Leiden University Medical Centre, Leiden, The Netherlands.
DACTIM, Medical School, University of Poitiers, Poitiers, France Nuclear Medicine, CHU Poitiers, Poitiers, France.
INSERM, UMR 1101, LaTIM, CHU Morvan, Brest, France.
Department of Preclinical Imaging, Werner Siemens Imaging Center, University of Tübingen, Tübingen, Germany; and.
Institute of Cancer Research, Royal Marsden NHS Trust, London, United Kingdom.
Department of Radiology, Radboud University Medical Center, Nijmegen, The Netherlands.


(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 SUVmax, 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.


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, SUVmax, SUVmean, heterogeneity) being extracted from the segmented tumors. Differences were investigated using Spearman rank correlation and Bland-Altman analysis.


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%.


In NSCLC primary tumors, parametric and static baseline (18)F-FDG PET images provided strongly correlated quantitative features for both standard (MATV, SUVmax, SUVmean) and heterogeneity quantification. Consequently, heterogeneity quantification on parametric images does not seem to provide significant complementary information compared with static SUV images.


intratumor heterogeneity; non–small cell lung cancer; parametric PET images

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