{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Chen YH"],"funding":["Buddhist Tzu Chi Medical Foundation"],"pagination":["e14200"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10691638"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["24(12)"],"pubmed_abstract":["<h4>Purpose</h4><sup>18</sup> F-FDG PET quantitative features are susceptible to respiratory motion. However, studies using clinical patient data to explore the impact of respiratory motion on <sup>18</sup> F-FDG PET radiomic features are limited. In this study, we investigated the impact of respiratory motion on radiomics stability with clinical <sup>18</sup> F-FDG PET images using a data-driven gating (DDG) algorithm on the digital PET scanner.<h4>Materials and methods</h4>A total of 101 patients who underwent oncological <sup>18</sup> F-FDG PET scans were retrospectively included. A DDG algorithm combined with a motion compensation technique was used to extract the PET images with respiratory motion correction. <sup>18</sup> F-FDG-avid lesions from the thorax to the upper abdomen were analyzed on the non-DDG and DDG PET images. The lesions were segmented with a 40% threshold of the maximum standardized uptake. A total of 725 radiomic features were computed from the segmented lesions, including first-order, shape, texture, and wavelet features. The intraclass correlation coefficient (ICC) and coefficient of variation (COV) were calculated to evaluate feature stability. An ICC above 0.9 and a COV below 5% were considered high stability.<h4>Results</h4>In total, 168 lesions with and without respiratory motion correction were analyzed. Our results indicated that most <sup>18</sup> F-FDG PET radiomic features are sensitive to respiratory motion. Overall, only 27 out of 725 (3.72%) radiomic features were identified as highly stable, including one from the first-order features (entropy), one from the shape features (sphericity), four from the gray-level co-occurrence matrix features (normalized and unnormalized inverse difference moment, joint entropy, and sum entropy), one from the gray-level run-length matrix features (run entropy), and 20 from the wavelet filter-based features.<h4>Conclusion</h4>Respiratory motion has a significant impact on <sup>18</sup> F-FDG PET radiomics stability. The highly stable features identified in our study may serve as potential candidates for further applications, such as machine learning modeling."],"journal":["Journal of applied clinical medical physics"],"pubmed_title":["Impact of respiratory motion on <sup>18</sup> F-FDG PET radiomics stability: Clinical evaluation with a digital PET scanner."],"pmcid":["PMC10691638"],"funding_grant_id":["TCMF-A 107-01-02(111)","TCMF-A 107-01-02(112)","TCMF‐A 107‐01‐02(111)","TCMF‐A 107‐01‐02(112)"],"pubmed_authors":["Liu SH","Kan KY","Lin HH","Chen YH","Lue KH"],"additional_accession":[]},"is_claimable":false,"name":"Impact of respiratory motion on <sup>18</sup> F-FDG PET radiomics stability: Clinical evaluation with a digital PET scanner.","description":"<h4>Purpose</h4><sup>18</sup> F-FDG PET quantitative features are susceptible to respiratory motion. However, studies using clinical patient data to explore the impact of respiratory motion on <sup>18</sup> F-FDG PET radiomic features are limited. In this study, we investigated the impact of respiratory motion on radiomics stability with clinical <sup>18</sup> F-FDG PET images using a data-driven gating (DDG) algorithm on the digital PET scanner.<h4>Materials and methods</h4>A total of 101 patients who underwent oncological <sup>18</sup> F-FDG PET scans were retrospectively included. A DDG algorithm combined with a motion compensation technique was used to extract the PET images with respiratory motion correction. <sup>18</sup> F-FDG-avid lesions from the thorax to the upper abdomen were analyzed on the non-DDG and DDG PET images. The lesions were segmented with a 40% threshold of the maximum standardized uptake. A total of 725 radiomic features were computed from the segmented lesions, including first-order, shape, texture, and wavelet features. The intraclass correlation coefficient (ICC) and coefficient of variation (COV) were calculated to evaluate feature stability. An ICC above 0.9 and a COV below 5% were considered high stability.<h4>Results</h4>In total, 168 lesions with and without respiratory motion correction were analyzed. Our results indicated that most <sup>18</sup> F-FDG PET radiomic features are sensitive to respiratory motion. Overall, only 27 out of 725 (3.72%) radiomic features were identified as highly stable, including one from the first-order features (entropy), one from the shape features (sphericity), four from the gray-level co-occurrence matrix features (normalized and unnormalized inverse difference moment, joint entropy, and sum entropy), one from the gray-level run-length matrix features (run entropy), and 20 from the wavelet filter-based features.<h4>Conclusion</h4>Respiratory motion has a significant impact on <sup>18</sup> F-FDG PET radiomics stability. The highly stable features identified in our study may serve as potential candidates for further applications, such as machine learning modeling.","dates":{"release":"2023-01-01T00:00:00Z","publication":"2023 Dec","modification":"2026-06-03T11:53:34.378Z","creation":"2025-04-19T20:23:15.566Z"},"accession":"S-EPMC10691638","cross_references":{"pubmed":["37937706"],"doi":["10.1002/acm2.14200"]}}