{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T08:37:00Z","timestamp":1775032620346,"version":"3.50.1"},"reference-count":44,"publisher":"Wiley","license":[{"start":{"date-parts":[[2019,2,6]],"date-time":"2019-02-06T00:00:00Z","timestamp":1549411200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["2017R1D1A1B04032467"],"award-info":[{"award-number":["2017R1D1A1B04032467"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2019,2,6]]},"abstract":"<jats:p>The purpose of this study was to explore the effects of CT slice thickness, reconstruction algorithm, and radiation dose on quantification of CT features to characterize lung nodules using a chest phantom. Spherical lung nodule phantoms of known densities (\u2212630 and\u2009+\u2009100 HU) were inserted into an anthropomorphic thorax phantom. CT scan was performed ten times with relocations. CT data were reconstructed using 12 different imaging settings; three different slice thicknesses of 1.25, 2.5, and 5.0\u2009mm, two reconstruction kernels of sharp and standard, and two radiation dose of 30\u2009mAs and 12\u2009mAs. Lesions were segmented using a semiautomated method. Twenty representative CT quantitative features representing CT density and texture were compared using multiple regression analysis. In 100 HU nodule phantoms, 18 and 19 among 20 computer features showed significant difference between different mAs and reconstruction algorithms, respectively (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mrow><mml:mi>p<\/mml:mi><mml:mo>\u2264<\/mml:mo><mml:mn>0.05<\/mml:mn><\/mml:mrow><\/mml:math>). 20, 19, and 19 computer features showed difference between slice thickness of 5.0 vs 1.25, 5.0 vs 2.5, and 2.5 vs 1.25\u2009mm, respectively (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mrow><mml:mi>p<\/mml:mi><mml:mo>\u2264<\/mml:mo><mml:mn>0.05<\/mml:mn><\/mml:mrow><\/mml:math>). In \u2212630 HU nodule phantoms, 18 and 19 showed significant difference between different mAs and reconstruction algorithms, respectively (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mrow><mml:mi>p<\/mml:mi><mml:mo>\u2264<\/mml:mo><mml:mn>0.05<\/mml:mn><\/mml:mrow><\/mml:math>). 18, 11, and 17 computer features showed difference between slice thickness of 5.0 vs 1.25, 5.0 vs 2.5, and 2.5 vs 1.25\u2009mm, respectively (<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\"><mml:mrow><mml:mi>p<\/mml:mi><mml:mo>\u2264<\/mml:mo><mml:mn>0.05<\/mml:mn><\/mml:mrow><\/mml:math>). When comparing the absolute value of regression coefficient, the effect of slice thickness in 100 HU nodule and reconstruction algorithm in \u2212630 HU nodule was greater than the effect of remaining scan parameters. The slice thickness, mAs, and reconstruction algorithm had a significant impact on the quantitative image features. In clinical studies involving deep learning or radiomics, it should be noted that differences in values can occur when using computer features obtained from different CT scan parameters in combination. Therefore, when interpreting the statistical analysis results, it is necessary to reflect the difference in the computer features depending on the scan parameters.<\/jats:p>","DOI":"10.1155\/2019\/8790694","type":"journal-article","created":{"date-parts":[[2019,2,6]],"date-time":"2019-02-06T18:30:43Z","timestamp":1549477843000},"page":"1-12","source":"Crossref","is-referenced-by-count":40,"title":["The Effect of CT Scan Parameters on the Measurement of CT Radiomic Features: A Lung Nodule Phantom Study"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0443-0051","authenticated-orcid":true,"given":"Young Jae","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Gachon University College of Medicine, Incheon, Republic of Korea"},{"name":"Department of Plazma Bio Display, Kwangwoon University, Seoul, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5748-2096","authenticated-orcid":true,"given":"Hyun-Ju","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9714-6038","authenticated-orcid":true,"given":"Kwang Gi","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Gachon University College of Medicine, Incheon, Republic of Korea"}]},{"given":"Seung Hyun","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Plazma Bio Display, Kwangwoon University, Seoul, Republic of Korea"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2016152234"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015151169"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1378\/chest.07-0793"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.3174\/ajnr.a3368"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1021\/jf052968q"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1002\/(sici)1096-9896(199912)189:4<581::aid-path464>3.0.co;2-p"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2004.07.008"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1007\/s11307-016-0940-2"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejca.2011.11.036"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2016160845"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2016.17216"},{"key":"13","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2016.10.010"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1109\/tmi.2016.2528162"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1038\/srep23428"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-20713-6"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0164924"},{"key":"18","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-012-2570-7"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.04.004"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2016.00071"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0206108"},{"key":"23","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.can-17-0339"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0169172"},{"key":"25","first-page":"345","volume-title":"The subsystem of the internal beam intensity diagnostics at the Nuclotron","volume":"2023","year":"2017"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1038\/srep33860"},{"key":"27","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2017.10.009"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-017-02425-5"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2015142215"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1038\/srep11075"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1007\/s11307-016-0973-6"},{"key":"32","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-016-4653-3"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2283020505"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0102107"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1080\/0284186x.2017.1351624"},{"issue":"5","key":"36","first-page":"546","volume":"2","year":"2015","journal-title":"International Research Journal of Engineering and Technology"},{"key":"37","doi-asserted-by":"publisher","DOI":"10.1016\/0167-8655(90)90112-f"},{"key":"38","doi-asserted-by":"publisher","DOI":"10.1118\/1.4752209"},{"key":"39","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2004.02.005"},{"key":"40","doi-asserted-by":"publisher","DOI":"10.1016\/j.cageo.2013.07.006"},{"key":"41","doi-asserted-by":"publisher","DOI":"10.1002\/mrm.21347"},{"issue":"4","key":"42","first-page":"1","volume":"10","year":"2013","journal-title":"Epidemiology, Biostatistics and Public Health"},{"key":"43","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.05.1063"},{"key":"44","doi-asserted-by":"publisher","DOI":"10.2217\/iim.12.13"},{"key":"45","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.07.2556"},{"key":"46","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.06.1524"}],"container-title":["Computational and Mathematical Methods in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2019\/8790694.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2019\/8790694.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2019\/8790694.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,2,6]],"date-time":"2019-02-06T18:30:46Z","timestamp":1549477846000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cmmm\/2019\/8790694\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,6]]},"references-count":44,"alternative-id":["8790694","8790694"],"URL":"https:\/\/doi.org\/10.1155\/2019\/8790694","relation":{},"ISSN":["1748-670X","1748-6718"],"issn-type":[{"value":"1748-670X","type":"print"},{"value":"1748-6718","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,6]]}}}