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

Abstract

Accurate measurement of intratumor heterogeneity using parameters of texture on PET images is essential for precise characterization of cancer lesions. In this study, we investigated the influence of respiratory motion and varying noise levels on quantification of textural parameters in patients with lung cancer. Methods: We used an optimal-respiratory-gating algorithm on the list-mode data of 60 lung cancer patients who underwent 18 F-FDG PET. The images were reconstructed using a duty cycle of 35% (percentage of the total acquired PET data). In addition, nongated images of varying statistical quality (using 35% and 100% of the PET data) were reconstructed to investigate the effects of image noise. Several global image-derived indices and textural parameters (entropy, high-intensity emphasis, zone percentage, and dissimilarity) that have been associated with patient outcome were calculated. The clinical impact of optimal respiratory gating and image noise on assessment of intratumor heterogeneity was evaluated using Cox regression models, with overall survival as the outcome measure. The threshold for statistical significance was adjusted for multiple comparisons using Bonferroni correction. Results: In the lower lung lobes, respiratory motion significantly affected quantification of intratumor heterogeneity for all textural parameters (P , 0.007) except entropy (P . 0.007). The mean increase in entropy, dissimilarity, zone percentage, and high-intensity emphasis was 1.3% ± 1.5% (P 5 0.02), 11.6% ± 11.8% (P 5 0.006), 2.3% ± 2.2% (P 5 0.002), and 16.8% ± 17.2% (P 5 0.006), respectively. No significant differences were observed for lesions in the upper lung lobes (P . 0.007). Differences in the statistical quality of the PET images affected the textural parameters less than respiratory motion, with no significant difference observed. The median follow-up time was 35 mo (range, 7-39 mo). In multivariate analysis for overall survival, total lesion glycolysis and high-intensity emphasis were the two most relevant image-derived indices and were considered to be independent significant covariates for the model regardless of the image type considered. Conclusion: The tested textural parameters are robust in the presence of respiratory motion artifacts and varying levels of image noise.

Towards guidelines to harmonize textural features in PET: Haralick textural features vary with image noise, but exposure-invariant domains enable comparable PET radiomics

“…Our findings stand in contrast to those of other works [28,29], where feature values were deemed noise-invariant. Besides clearly showing emergence of texture at low exposures ( Fig 1A and 1B), our data shows noise-dependence of texture.…”

“…Besides clearly showing emergence of texture at low exposures ( Fig 1A and 1B), our data shows noise-dependence of texture. Because texture stability occurs at sufficiently high exposures, variability is easily overlooked in studies with long acquisitions such as performed in [28,29]. Furthermore, features calculated from neighboring voxels (d = 1voxel) describe texture exactly at the Nyquist frequency of an imaging system.…”

“…However, the contribution of image count statistics, a known driver of variability in imaging, remains largely overlooked. Although, some studies show effects of noise on image texture [25][26][27], others have reported insignificant effects of image statistics on textural features [28,29]. In fact, there has hitherto be no systematic study of the progression of textural feature values with changing photon count.…”

Machine and deep learning methods for radiomics

“…In presence of significant respiratory tumor motion as in the case of lung cancer, conventional PET images are influenced by motion as, because of their relatively long acquisition times, the counts measured are averaged over multiple breathing cycles. Respiratory‐gated PET accounts for respiratory motion and textural features from gated PET have been found robust …”

Texture analysis of medical images for radiotherapy applications

“…56,57 The impact of tumour delineation on textural features computation was also assessed in PET images. [50][51][52]55 Finally, an attempt of interpreting PET textural indices was carried out in a very recent work, where heterogeneity quantified by texture analysis was investigated on simulated patient data.…”