{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T23:59:55Z","timestamp":1787875195813,"version":"build-2784847793"},"reference-count":38,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T00:00:00Z","timestamp":1768348800000},"content-version":"vor","delay-in-days":13,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Radiomic features derived from computed tomography (CT) are highly susceptible to variations in acquisition parameters, which can introduce confounding effects in multicenter research and reduce diagnostic accuracy. While the effects of parameters such as scanning mode and dose have been studied, the impact of gantry tilt\u2014despite its routine clinical use\u2014remains underexplored in radiomics literature.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To systematically evaluate how scan mode (axial vs. helical), gantry tilt (0\u00b0 vs. 5\u00b0), and radiation dose affect CT\u2010based radiomic metrics using an anthropomorphic liver phantom containing six 3D\u2010printed texture inserts, with special emphasis on the novel inclusion of tilt.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Twelve unique image acquisition configurations were scanned on a GE Revolution Apex CT scanner, with each configuration repeated once. Manual segmentation of volumes of interest (VOIs) was performed, and 93 radiomic features spanning six texture families were extracted using PyRadiomics. First\u2010order dispersion metrics (standard deviation, interquartile range, and coefficient of variation) were analyzed alongside higher\u2010order features via regression with heatmap visualization, and repeatable, robust, and calibratable features were identified.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Helical scans without tilt generally exhibited lower first\u2010order dispersion than axial scans. Introducing a 5\u00b0 tilt reduced dispersion in axial scans but had inconsistent effects in helical scans, with no coherent trend observed. Radiation dose demonstrated an expected inverse relationship with dispersion metrics. Intraclass correlation coefficient (ICC) analysis revealed that 34% of radiomic metrics exhibited good or excellent repeatability across all trials (ICC \u2265 0.6), but only 13% demonstrated good or excellent robustness, highlighting the sensitivity of radiomic metrics to scanning conditions. Regression analysis yielded 31 metrics (33%) that can be calibrated using their significant linear relationships with the parameters varied in this study, thereby allowing researchers to correct for variations in acquisition settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These findings underscore the importance of accounting for acquisition variability\u2014including less frequently examined parameters such as tilt\u2014when designing radiomic studies, selecting robust features, and interpreting results in clinical and multicenter studies. This approach helps distinguish meaningful biological variation from imaging artifacts, thereby improving the reliability of radiomic analysis in personalized medicine.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70462","type":"journal-article","created":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T09:55:04Z","timestamp":1768384504000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Influence of scan mode, tilt, and radiation dose on CT radiomic metrics"],"prefix":"10.1002","volume":"27","author":[{"given":"Neha","family":"Yadav","sequence":"first","affiliation":[{"name":"Keck School of Medicine University of Southern California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaomeng","family":"Lei","sequence":"additional","affiliation":[{"name":"Keck School of Medicine University of Southern California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven Y.","family":"Cen","sequence":"additional","affiliation":[{"name":"Keck School of Medicine University of Southern California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua","family":"Levy","sequence":"additional","affiliation":[{"name":"The Phantom Laboratory  Greenwich New York USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kristin","family":"Jensen","sequence":"additional","affiliation":[{"name":"Department of Physics and Computational Radiology Oslo University Hospital  Oslo Norway"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0508-2899","authenticated-orcid":false,"given":"Bino A.","family":"Varghese","sequence":"additional","affiliation":[{"name":"Keck School of Medicine University of Southern California  Los Angeles California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,1,14]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015151169"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2011.11.036"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.18.20624"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12666"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10278\u2010018\u20100100\u20100"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230437"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2019190928"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2020191145"},{"key":"e_1_2_9_10_1","doi-asserted-by":"crossref","unstructured":"YadavN TuranganA HanH et\u00a0al.Systematic approach to identifying sources of variation in CT radiomics: a phantom study. 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