{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T18:03:07Z","timestamp":1793642587860,"version":"4.5.1"},"reference-count":31,"publisher":"Wiley","issue":"12","license":[{"start":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T00:00:00Z","timestamp":1697155200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100005075","name":"Universitair Medisch Centrum Groningen","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005075","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2023,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Adaptive proton therapy workflows rely on accurate imaging throughout the treatment course. Our centre currently utilizes weekly repeat CTs (rCTs) for treatment monitoring and plan adaptations. However, deep learning\u2010based methods have recently shown to successfully correct CBCT images, which suffer from severe imaging artifacts, and generate high quality synthetic CT (sCT) images which enable CBCT\u2010based proton dose calculations.<\/jats:p><\/jats:sec><jats:sec><jats:title>Purpose<\/jats:title><jats:p>To compare daily CBCT\u2010based sCT images to planning CTs (pCT) and rCTs of head and neck (HN) cancer patients to investigate the dosimetric accuracy of CBCT\u2010based sCTs in a scenario mimicking actual clinical practice.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Data of 56 HN cancer patients, previously treated with proton therapy was used to generate 1.962 sCT images, using a previously developed and trained deep convolutional neural network. Clinical IMPT treatment plans were recalculated on the pCT, weekly rCTs and daily sCTs. The dosimetric accuracy of sCTs was compared to same day rCTs and the initial planning CT. As a reference, rCTs were also compared to pCTs. The dose difference between sCTs and rCTs\/pCT was quantified by calculating the D<jats:sub>98<\/jats:sub> difference for target volumes and D<jats:sub>mean<\/jats:sub> difference for organs\u2010at\u2010risk. To investigate the clinical relevancy of possible dose differences, NTCP values were calculated for dysphagia and xerostomia.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>For target volumes, only minor dose differences were found for sCT versus rCT and sCT versus pCT, with dose differences mostly within \u00b11.5%. Larger dose differences were observed in OARs, where a general shift towards positive differences was found, with the largest difference in the left parotid gland. Delta NTCP values for grade 2 dysphagia and xerostomia were within \u00b12.5% for 90% of the sCTs.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Target doses showed high similarity between rCTs and sCTs. Further investigations are required to identify the origin of the dose differences at OAR levels and its relevance in clinical decision making.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16782","type":"journal-article","created":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T16:33:29Z","timestamp":1697214809000},"page":"8023-8033","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Technical note: Evaluation of deep learning based synthetic CTs clinical readiness for dose and NTCP driven head and neck adaptive proton therapy"],"prefix":"10.1002","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3895-3969","authenticated-orcid":false,"given":"Rutger J C","family":"de Koster","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology University Medical Center Groningen, University of Groningen  Groningen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1874-5030","authenticated-orcid":false,"given":"Adrian","family":"Thummerer","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University Medical Center Groningen, University of Groningen  Groningen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Daniel","family":"Scandurra","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University Medical Center Groningen, University of Groningen  Groningen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1083-372X","authenticated-orcid":false,"given":"Johannes A","family":"Langendijk","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University Medical Center Groningen, University of Groningen  Groningen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefan","family":"Both","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University Medical Center Groningen, University of Groningen  Groningen The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,10,13]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/cancers7030829"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2015.02.007"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ac344f"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2017.04.023"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20190594"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1259\/DMFR\/30642039"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14387"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ab7d54"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.zemedi.2020.05.007"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031\u20109155\/51\/17\/015"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.4962933"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.14338\/IJPT\u201014\u201000024.1"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2016.01.055"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2016.12.006"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14624"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ab4d8c"},{"key":"e_1_2_8_18_1","doi-asserted-by":"crossref","unstructured":"SpadeaMF MasperoM ZaffinoP SecoJ Deep learning based synthetic\u2010CT generation in radiotherapy and PET: A review. Published online 2021. doi:10.1002\/mp.15150","DOI":"10.1002\/mp.15150"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/MP.15333"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/abb1d6"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1002\/MP.15930"},{"key":"e_1_2_8_22_1","doi-asserted-by":"crossref","unstructured":"ArabiH DowlingJA BurgosN et\u00a0al.Comparison of synthetic CT generation algorithms for MRI\u2010only radiation planning in the pelvic region.2018 IEEE Nuclear Science Symposium and Medical Imaging Conference NSS\/MIC 2018 \u2013 Proceedings. Published online 2018:0\u20102. doi:10.1109\/NSSMIC.2018.8824321","DOI":"10.1109\/NSSMIC.2018.8824321"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.12155"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.14338\/IJPT\u2010D\u201020\u201000056.1"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2020.11.007"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.RADONC.2022.01.043"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2019.06.2535"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2013.05.007"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.RADONC.2020.07.056"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.1002\/MP.15020"},{"key":"e_1_2_8_31_1","doi-asserted-by":"publisher","DOI":"10.1016\/J.SEMRADONC.2022.06.011"},{"key":"e_1_2_8_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2023.100459"}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.16782","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T20:30:25Z","timestamp":1719520225000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.16782"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,13]]},"references-count":31,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2023,12]]}},"alternative-id":["10.1002\/mp.16782"],"URL":"https:\/\/doi.org\/10.1002\/mp.16782","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,13]]},"assertion":[{"value":"2023-03-27","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-09-25","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-10-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}