{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T07:19:02Z","timestamp":1768547942665,"version":"3.49.0"},"reference-count":25,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2024,9,23]],"date-time":"2024-09-23T00:00:00Z","timestamp":1727049600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/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"}]}],"content-domain":{"domain":["advanced.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Advanced Intelligent Systems"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:p>This study aims to establish and validate a cross\u2010institutional prediction system for estimating patient\u2010specific organ doses from chest CT scans. By collaborating across multiple institutions, the study seeks to develop models that allow rapid online estimation of organ doses, eliminating the need for complex organ segmentation. Researchers delineate skin outlines from chest CT images obtained from two different institutions. Radiomics features are extracted from the chest CT data and skin contours. Organ doses are computed using Monte Carlo simulations as reference organ doses. Single\u2010 and cross\u2010institutional support vector regression (SVR) models are trained with radiomics features to predict organ doses from chest CT scans. Model performance is assessed using metrics like maean absolute percentage error (MAPE) and <jats:italic>R<\/jats:italic>\u2010squared (<jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup>). For chest organs (lungs, heart, spinal cord, trachea, and esophagus), single\u2010institutional SVR models achieve MAPE values ranging from 4.72% to 15.31% and <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> values from 0.73 to 0.93. Cross\u2010institutional models have MAPE values in the range of 2.16\u20137.49% and <jats:italic>R<\/jats:italic><jats:sup>2<\/jats:sup> values from 0.84 to 0.99. SVR models trained with radiomics features from skin outlines can reliably estimate organ doses from chest CT scans. The proposed method is applicable across different institutions, and cross\u2010institutional models enhance predictive accuracy, generality, and robustness.<\/jats:p>","DOI":"10.1002\/aisy.202400357","type":"journal-article","created":{"date-parts":[[2024,9,24]],"date-time":"2024-09-24T02:39:06Z","timestamp":1727145546000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Cross\u2010Institutional Prediction System for Estimating Patient\u2010Specific Organ Dose from Chest CT Scans without Segmenting Internal Organs"],"prefix":"10.1002","volume":"7","author":[{"given":"Wencheng","family":"Shao","sequence":"first","affiliation":[{"name":"Institute of Radiation Medicine Fudan University  Shanghai 200032 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenfa","family":"Wan","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging The Fourth Hospital of Jinan City  Jinan Shandong 250031 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Public Health Monitoring and Evaluation ShanDong Center for Disease Control and Prevention  Jinan 250014 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute of Radiation Medicine Fudan University  Shanghai 200032 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liangyong","family":"Qu","sequence":"additional","affiliation":[{"name":"Department of Radiology Shanghai Zhongye Hospital  Shanghai 201900 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9109-5361","authenticated-orcid":false,"given":"Weihai","family":"Zhuo","sequence":"additional","affiliation":[{"name":"Institute of Radiation Medicine Fudan University  Shanghai 200032 China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2781-2637","authenticated-orcid":false,"given":"Haikuan","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Radiation Medicine Fudan University  Shanghai 200032 China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,9,23]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1513\/AnnalsATS.201312-420PS"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1186\/bcr2901"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2020.11.010"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCOUTCOMES.118.005375"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1513\/AnnalsATS.201311-405PS"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2005.862753"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2532081738"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1378\/chest.12-2355"},{"key":"e_1_2_11_10_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr\/82933343"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1097\/RLI.0000000000000672"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2511081296"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1148\/radiol.2323031095"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21132"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0501895102"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr\/01948454"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2020.07.016"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14131"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-023-09839-y"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ad14c7"},{"key":"e_1_2_11_21_1","volume-title":"Sikerdebaard\/dcmrtstruct2nii: Dcmrtstruct2nii v5 (Version v5)","author":"Phil T.","year":"2023"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1158\/0008-5472.CAN-17-0339"},{"key":"e_1_2_11_23_1","unstructured":"Anaconda | The World's Most Popular Data Science Platform.https:\/\/www.anaconda.com\/(accessed: January 2022)."},{"key":"e_1_2_11_24_1","first-page":"1071","volume":"37","author":"Wang Z.","year":"2020","journal-title":"Chin. 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