Quantitative radiomics: impact of stochastic effects on textural feature analysis implies the need for standards

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

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 ¹⁸F-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 = 0.02), 11.6% ± 11.8% (P = 0.006), 2.3% ± 2.2% (P = 0.002), and 16.8% ± 17.2% (P = 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.

Applications and limitations of radiomics

Radiomics is an emerging field in quantitative imaging that uses advanced imaging features to objectively and quantitatively describe tumour phenotypes. Radiomic features have recently drawn considerable interest due to its potential predictive power for treatment outcomes and cancer genetics, which may have important applications in personalized medicine. In this technical review, we describe applications and challenges of the radiomic field. We will review radiomic application areas and technical issues, as well as proper practices for the designs of radiomic studies.

Identification of low variability textural features for heterogeneity quantification of 18F-FDG PET/CT imaging

OBJECTIVE

To identify those textural features that are insensitive to both technical and biological factors in order to standardise heterogeneity studies on ¹⁸F-FDG PET imaging.

MATERIALS AND METHODS

Two different studies were performed. First, nineteen series from a cylindrical phantom filled with different ¹⁸F-FDG activity concentration were acquired and reconstructed using three different protocols. Seventy-two texture features were calculated inside a circular region of interest. The variability of each feature was obtained. Second, the data for 15 patients showing non-pathological liver were acquired. Anatomical and physiological features such as patient's weight, height, body mass index, metabolic active volume, blood glucose level, SUV and SUV standard deviation were also recorded. A liver covering region of interest was delineated and low variability textural features calculated in each patient. Finally, a multivariate Spearman's correlation analysis between biological factors and texture features was performed.

RESULTS

Only eight texture features analysed show small variability (<5%) with activity concentration and reconstruction protocol making them suitable for heterogeneity quantification. On the other hand, there is a high statistically significant correlation between MAV and entropy (P<0.05). Entropy feature is, indeed, correlated (P<0.05) with all patient parameters, except body mass index.

CONCLUSIONS

The textural features that are correlated with neither technical nor biological factors are run percentage, short-zone emphasis and intensity, making them suitable for quantifying functional changes or classifying patients. Other textural features are correlated with technical and biological factors and are, therefore, a source of errors if used for this purpose.

Variability of Image Features Computed from Conventional and Respiratory-Gated PET/CT Images of Lung Cancer

Radiomics is being explored for potential applications in radiation therapy. How various imaging protocols affect quantitative image features is currently a highly active area of research. To assess the variability of image features derived from conventional [three-dimensional (3D)] and respiratory-gated (RG) positron emission tomography (PET)/computed tomography (CT) images of lung cancer patients, image features were computed from 23 lung cancer patients. Both protocols for each patient were acquired during the same imaging session. PET tumor volumes were segmented using an adaptive technique which accounted for background. CT tumor volumes were delineated with a commercial segmentation tool. Using RG PET images, the tumor center of mass motion, length, and rotation were calculated. Fifty-six image features were extracted from all images consisting of shape descriptors, first-order features, and second-order texture features. Overall, 26.6% and 26.2% of total features demonstrated less than 5% difference between 3D and RG protocols for CT and PET, respectively. Between 10 RG phases in PET, 53.4% of features demonstrated percent differences less than 5%. The features with least variability for PET were sphericity, spherical disproportion, entropy (first and second order), sum entropy, information measure of correlation 2, Short Run Emphasis (SRE), Long Run Emphasis (LRE), and Run Percentage (RPC); and those for CT were minimum intensity, mean intensity, Root Mean Square (RMS), Short Run Emphasis (SRE), and RPC. Quantitative analysis using a 3D acquisition versus RG acquisition (to reduce the effects of motion) provided notably different image feature values. This study suggests that the variability between 3D and RG features is mainly due to the impact of respiratory motion.