Comparison of Texture Features Derived from Static and Respiratory-Gated PET Images in Non-Small Cell Lung Cancer

Abstract

BackgroundPET-based texture features have been used to quantify tumor heterogeneity due to their predictive power in treatment outcome. We investigated the sensitivity of texture features to tumor motion by comparing static (3D) and respiratory-gated (4D) PET imaging.MethodsTwenty-six patients (34 lesions) received 3D and 4D [18F]FDG-PET scans before the chemo-radiotherapy. The acquired 4D data were retrospectively binned into five breathing phases to create the 4D image sequence. Texture features, including Maximal correlation coefficient (MCC), Long run low gray (LRLG), Coarseness, Contrast, and Busyness, were computed within the physician-defined tumor volume. The relative difference (δ3D-4D) in each texture between the 3D- and 4D-PET imaging was calculated. Coefficient of variation (CV) was used to determine the variability in the textures between all 4D-PET phases. Correlations between tumor volume, motion amplitude, and δ3D-4D were also assessed.Results4D-PET increased LRLG ( = 1%–2%, p<0.02), Busyness ( = 7%–19%, p<0.01), and decreased MCC ( = 1%–2%, p<7.5×10−3), Coarseness ( = 5%–10%, p<0.05) and Contrast ( = 4%–6%, p>0.08) compared to 3D-PET. Nearly negligible variability was found between the 4D phase bins with CV<5% for MCC, LRLG, and Coarseness. For Contrast and Busyness, moderate variability was found with CV = 9% and 10%, respectively. No strong correlation was found between the tumor volume and δ3D-4D for the texture features. Motion amplitude had moderate impact on δ for MCC and Busyness and no impact for LRLG, Coarseness, and Contrast.ConclusionsSignificant differences were found in MCC, LRLG, Coarseness, and Busyness between 3D and 4D PET imaging. The variability between phase bins for MCC, LRLG, and Coarseness was negligible, suggesting that similar quantification can be obtained from all phases. Texture features, blurred out by respiratory motion during 3D-PET acquisition, can be better resolved by 4D-PET imaging. 4D-PET textures may have better prognostic value as they are less susceptible to tumor motion.

The Impact of Optimal Respiratory Gating and Image Noise on Evaluation of Intratumor Heterogeneity on 18F-FDG PET Imaging of Lung Cancer

“…Furthermore, the statistical quality of PET images was shown to affect textural parameters to a lesser extent than respiratory motion, with no significant differences found between images of different statistical quality. The blurring effect of respiratory motion on quantification of intratumor heterogeneity was shown in studies by Yip et al (13), and Oliver et al (21). Although our results are in line with theirs, the observed differences between the respiratory gated and nongated PET images were usually small.…”

“…The accuracy and robustness of these measurements is related to the PET acquisition and reconstruction protocols, as well as variability in patient physiology. In particular, blurring of PET images due to respiratory motion can significantly influence quantification of lung lesions (12,13). In addition, PET textural parameters can be sensitive to variations in statistical quality and to normal stochastic variations in images (14).…”

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

“…We chose to limit lesion size in order to reduce the influence of both respiratory motion and PVE. Although previous studies have shown variability in heterogeneity features as a result of respiratory gating (35,36), other studies have shown no statistically significant differences in the PET heterogeneity parameters considered in the present study (37). In addition, most of the lesions in our study (85%) were in the upper lung lobes, which are less influenced by respiratory motion.…”

Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non–Small Cell Lung Cancer

“…Second, partial-volume effects resulting from limited PET spatial resolution may lead to an underestimation of the metabolic tumor region and SUV measures (49). Furthermore, because of insufficient data acquisition and limited reconstruction techniques, respiratory motion can induce image blurring in the static (3-dimensional PET) image acquisition, subsequently affecting the quantification of the PET features (50). Despite these limitations, many radiomic features were still found to be significantly associated with and predictive of EGFR mutation status.…”