{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T03:02:06Z","timestamp":1789700526398,"version":"4.0.1"},"reference-count":24,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100014031","name":"Cancerforskningsfonden i Norrland","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100014031","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>There is an increasing use of magnetic resonance imaging (MRI) for segmentation of target volumes and organs at risk due to superior soft tissue contrast compared to computed tomography (CT). Conventional workflows require CT\u2010MRI registration, introducing geometric uncertainty that can propagate to dose delivery. MRI\u2010only radiotherapy, based on AI\u2010generated synthetic CT (sCT) and segmentations from T2\u2010weighted images, offers a single\u2010modality approach, potentially eliminating registration errors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Our study aims to evaluate whether an MRI\u2010only workflow, using commercial AI models for segmentation and sCT generation (MVision), demonstrates performance comparable to expert\u2010defined segmentations and reference CT\u2010based calculations within the studied dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed five segmentations on 19 male pelvis patients from the Gold Atlas dataset, providing deformably co\u2010registered CT and MRI, sCTs, with multi\u2010observer and consensus segmentations. We investigated segmentation accuracy, image similarity, and evaluated the dosimetric impact by comparing AI\u2010 to manual segmentations and CTs to sCTs. Wilcoxon signed\u2010rank tests were used for statistical significance and leave\u2010one\u2010out kappa determined segmentation outliers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Results show that AI\u2010segmentations achieved accuracy comparable to interobserver variability across all organs. Median Dice coefficients exceeded 0.9 for the prostate and bladder, and distance metrics closely matched the manual results. The leave\u2010one\u2010out kappa showed that AI\u2010segmentations were indistinguishable from manual observers for all organs. sCTs showed high similarity to the reference CTs, with a median mean absolute error of 34.0 HU and median structural similarity index of 0.93. Dose recalculations showed excellent agreement; median 3D\u2010gamma pass rate was 99.8% (2%\/2\u00a0mm), and mean absolute dose difference was 0.036\u00a0Gy. All evaluated clinical dose constraints remained fulfilled after recalculation on the sCT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our results confirm that an AI\u2010segmentation and MRI\u2010sCT workflow provides geometric, image, and dose accuracy comparable to manual\u2010 and CT\u2010based references, supporting their integration into clinical workflows for pelvic radiotherapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70765","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T07:42:26Z","timestamp":1787643746000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluation of AI\u2010based segmentation and synthetic CT generation for MRI\u2010only prostate radiotherapy planning"],"prefix":"10.1002","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-4804-4594","authenticated-orcid":false,"given":"Amanda","family":"\u00d6stensson","sequence":"first","affiliation":[{"name":"Department of Diagnostics and Intervention Ume\u00e5 University  Ume\u00e5 Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0567-0666","authenticated-orcid":false,"given":"Joakim","family":"Jonsson","sequence":"additional","affiliation":[{"name":"Department of Diagnostics and Intervention Ume\u00e5 University  Ume\u00e5 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