Abstract
BackgroundRadiomics analysis usually involves, especially in multicenter and large hospital studies, different imaging protocols for acquisition, reconstruction, and processing of data. Differences in protocols can lead to differences in the quantification of the biomarker distribution, leading to radiomic feature variability. The aim of our study was to identify those radiomic features robust to the different degrading factors in positron emission tomography (PET) studies. We proposed the use of the standardized measurements of the European Association Research Ltd. (EARL) accreditation to retrospectively identify the radiomic features having low variability to the different systems and reconstruction protocols. In addition, we presented a reproducible procedure to identify PET radiomic features robust to PET/CT imaging metal artifacts. In 27 heterogeneous homemade phantoms for which ground truth was accurately defined by CT segmentation, we evaluated the segmentation accuracy and radiomic feature reliability given by the contrast-oriented algorithm (COA) and the 40% threshold PET segmentation. In the comparison of two data sets, robustness was defined by Wilcoxon rank tests, bias was quantified by Bland–Altman (BA) plot analysis, and strong correlations were identified by Spearman correlation test (r > 0.8 and p satisfied multiple test Bonferroni correction).ResultsForty-eight radiomic features were robust to system, 22 to resolution, 102 to metal artifacts, and 42 to different PET segmentation tools. Overall, only 4 radiomic features were simultaneously robust to all degrading factors. Although both segmentation approaches significantly underestimated the volume with respect to the ground truth, with relative deviations of −62 ± 36% for COA and −50 ± 44% for 40%, radiomic features derived from the ground truth were strongly correlated and/or robust to 98 radiomic features derived from COA and to 102 from 40%.ConclusionIn multicenter studies, we recommend the analysis of EARL accreditation measurements in order to retrospectively identify the robust PET radiomic features. Furthermore, 4 radiomic features (area under the curve of the cumulative SUV volume histogram, skewness, kurtosis, and gray-level variance derived from GLRLM after application of an equal probability quantization algorithm on the voxels within lesion) were robust to all degrading factors. In addition, the feasibility of 40% and COA segmentations for their use in radiomics analysis has been demonstrated.
Highlights
Radiomics is defined as the extraction and analysis of a large amount of quantitative image features from standard-of-care images, known as radiomic features (RF)
They showed a strong linear correlation, and we expect that a model based on SUVmean and/or volume derived from a patient cohort involving 2 mm reconstruction protocol could be applied on a second cohort with a reconstruction protocol of 4 mm
Radiomic features robust to all degrading factors From all radiomic features, only 3 histogram parameters and 1 second-order texture feature were simultaneously robust (Wilcoxon rank test) to all degrading factors: system, reconstruction voxel, artifact, and positron emission tomography (PET) segmentation approach. These robust radiomic features were area under the curve of the cumulative Standardized uptake value (SUV) volume histogram, skewness (S), kurtosis (K), and gray-level variance derived from gray-level run length matrix (GLRLM) after application of an equal probability quantization algorithm on the voxels within lesion (QGLV2)
Summary
Radiomics is defined as the extraction and analysis of a large amount of quantitative image features from standard-of-care images, known as radiomic features (RF). Radiomics studies have four main steps to be considered [16]: image acquisition and reconstruction, volume segmentation and preprocessing, radiomic features extraction, and development and validation of descriptive models. Previous studies have reported that PET radiomic feature variability increased due to the differences in image acquisition parameters, image reconstruction algorithms, and lesion segmentation procedures [24, 34]. PET radiomic feature variability should be properly addressed in order to avoid misinterpretation of the developed descriptive models when using PET images. Especially in multicenter and large hospital studies, different imaging protocols for acquisition, reconstruction, and processing of data. The aim of our study was to identify those radiomic features robust to the different degrading factors in positron emission tomography (PET) studies. In the comparison of two data sets, robustness was defined by Wilcoxon rank tests, bias was quantified by Bland–Altman (BA) plot analysis, and strong correlations were identified by Spearman correlation test (r > 0.8 and p satisfied multiple test Bonferroni correction)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
