{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T16:53:36Z","timestamp":1790355216818,"version":"4.1.0"},"reference-count":23,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T00:00:00Z","timestamp":1748476800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["12075064"],"award-info":[{"award-number":["12075064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2019YFC0117304"],"award-info":[{"award-number":["2019YFC0117304"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Precision Radiation Oncology"],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Computed Tomography (CT) imaging is essential for disease detection but carries a risk of cancer due to X\u2010ray exposure. Typically, assessing this risk requires segmentation of the internal organ contours to predict organ doses, which hinders its clinical application. This study introduces a method that uses support vector regression (SVR) models trained on skin outline radiomic features to predict organ doses without organ segmentation, thus streamlining the process for clinical use.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>CT scans of the head and abdomen were used to extract radiomic features of the skin outline. These features were used as inputs, with organ doses from Monte Carlo simulations as benchmarks to train the SVR models for predicting organ doses. The accuracy of the models was evaluated using the mean absolute percentage error (MAPE) and coefficient of determination (R<jats:sup>2<\/jats:sup>).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The results showed a high precision in dose prediction for various organs, including the brain (MAPE: 1.5%, R<jats:sup>2<\/jats:sup>: 0.9), eyes (MAPE: 5%, R<jats:sup>2<\/jats:sup>: 0.84), lens (MAPE: 5%, R<jats:sup>2<\/jats:sup>: 0.82), bowel (MAPE: 6%, R<jats:sup>2<\/jats:sup>: 0.84), kidneys (MAPE: 7.5%, R<jats:sup>2<\/jats:sup>: 0.7), and liver (MAPE: 8%, R<jats:sup>2<\/jats:sup>: 0.67). Internal organ disturbances had a minimal impact on accuracy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The SVR models efficiently predicted patient\u2010specific organ doses from CT scans, offering a user\u2010friendly tool for rapid segmentation\u2010free dose prediction. This innovation can significantly enhance clinical efficiency and accessibility in predicting patient\u2010specific organ doses using CT.<\/jats:p><\/jats:sec>","DOI":"10.1002\/pro6.70016","type":"journal-article","created":{"date-parts":[[2025,5,29]],"date-time":"2025-05-29T04:28:42Z","timestamp":1748492922000},"page":"77-86","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Fast estimation of patient\u2010specific organ doses from abdomen and head CT examinations without segmenting internal organs using machine learning models"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9109-5361","authenticated-orcid":false,"given":"Wencheng","family":"Shao","sequence":"first","affiliation":[{"name":"Institute of Radiation Medicine Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liangyong","family":"Qu","sequence":"additional","affiliation":[{"name":"Department of Radiology Shanghai Zhongye Hospital  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute of Radiation Medicine Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Nuclear Science and Technology Institute of Modern Physics Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihai","family":"Zhuo","sequence":"additional","affiliation":[{"name":"Institute of Radiation Medicine Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haikuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Radiation Medicine Fudan University  Shanghai China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,5,29]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev\u2010bioeng\u2010121813\u2010113601"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s12098\u2010015\u20101955\u20104"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-26957-9_18"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/echo.13147"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph18126499"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.12669\/pjms.38.6.5273"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1067\/j.cpradiol.2021.07.007"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejrad.2020.109325"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40817\u2010023\u201000153\u20105"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.crad.2020.07.035"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.14407\/jrpr.2021.00136"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.3238\/arztebl.m2022.0395"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.25259\/JCIS_92_2020"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1017\/S1049023x1900520X"},{"key":"e_1_2_9_16_1","doi-asserted-by":"publisher","DOI":"10.1093\/jncics\/pkz072"},{"key":"e_1_2_9_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.pnucene.2014.07.011"},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40134\u2010014\u20100085\u20105"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ad14c7"},{"key":"e_1_2_9_20_1","unstructured":"PhilT AlbrechtT GayS RasmussenME.Sikerdebaard\/dcmrtstruct2nii: dcmrtstruct2nii v5 [software]. 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