{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T09:40:51Z","timestamp":1792402851995,"version":"4.4.0"},"reference-count":42,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T00:00:00Z","timestamp":1790553600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>While computed tomography (CT) remains an important imaging modality in radiotherapy planning, magnetic resonance imaging (MRI)\u2010only workflow, often realized via the generation of synthetic computed tomography (sCT), is desired due to superior soft\u2010tissue contrast and less susceptibility to registration errors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>The aim of this study was to investigate the feasibility of applying sCT, generated from MRI using a deep learning model, to pelvic radiotherapy planning. By comparing differences between conventional CT and sCT in terms of image quality, dosimetric parameters, and three\u2010dimensional (3D) dose distributions, the clinical potential of an MRI\u2010only radiotherapy workflow was evaluated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Fifteen patients who underwent both pelvic CT and MRI examinations were retrospectively enrolled. For the same patients, sCT images were generated from the MRI using a pretrained pelvic deep learning model integrated in the syngo.via VB60A software platform. Treatment plans originally created on planning CT (pCT) images were re\u2010calculated on corresponding sCT datasets. Subsequently, the mean absolute error (MAE) of CT numbers, Dice similarity coefficients (DSCs) for major structures, dose\u2010volume histogram (DVH) parameters for the planning target volume (PTV) and major organs at risk (OARs), and 3D dose distributions were compared between plans calculated on pCT and sCT.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In terms of image quality, sCT demonstrated comparable CT number accuracy with pCT for soft tissue structures, with MAEs ranging from 6.8 to 8.5 Hounsfield units (HU) for the PTV, bladder, and rectum. Higher MAEs were observed for bony structures (femoral heads and bone marrow), with average MAEs ranging between 17 and 19 HU. The DSC was 0.94\u00a0\u00b1\u00a00.01 for the right femoral head, 0.93\u00a0\u00b1\u00a00.01 for the left femoral head, and 0.85\u00a0\u00b1\u00a00.03 for bone marrow. Regarding dosimetric comparison, the mean absolute differences in key dosimetric parameters for the PTV (D98%, D95%, and Dmean) were all below 0.6%, remaining within clinically acceptable limits. Among the OARs, except for relatively large differences for the maximum and minimum doses in the bladder, the mean dose (Dmean) differences for the rectum, femoral heads, and bone marrow were minimal (mean absolute value \u2264 0.34%), indicating high reliability. Analysis of 3D dose distributions using a 2% 2\u00a0mm \u03b3 criterion revealed an average \u03b3 passing rate of 95.92% between sCT and pCT, satisfying routine clinical quality assurance requirements.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The findings of our study demonstrate the feasibility of applying sCT generated from MRI using a deep learning model to pelvic radiotherapy planning. The MRI\u2010only workflow proposed on this basis eliminates systematic errors introduced by CT\u2010MRI image registration, optimizes the clinical workflow, and fully exploits the superior soft\u2010tissue contrast of MRI to improve target delineation accuracy. The proposed approach provides a novel technical paradigm for achieving more precise and efficient individualized radiotherapy.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70829","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T02:02:58Z","timestamp":1790647378000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Application of synthetic computed tomography based on a deep learning model in pelvic radiotherapy planning: A feasibility study"],"prefix":"10.1002","volume":"27","author":[{"given":"Feng","family":"Lin","sequence":"first","affiliation":[{"name":"Radiotherapy Oncology Center Jinshazhou Hospital of Guangzhou University of Chinese Medicine  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3615-1997","authenticated-orcid":false,"given":"Jinyan","family":"Hu","sequence":"additional","affiliation":[{"name":"Radiotherapy Oncology Center Jinshazhou Hospital of Guangzhou University of Chinese Medicine  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhao","family":"Li","sequence":"additional","affiliation":[{"name":"Radiotherapy Oncology Center Jinshazhou Hospital of Guangzhou University of Chinese Medicine  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"Radiotherapy Oncology Center Jinshazhou Hospital of Guangzhou University of Chinese Medicine  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenying","family":"Zhang","sequence":"additional","affiliation":[{"name":"Radiotherapy Oncology Center Jinshazhou Hospital of Guangzhou University of Chinese Medicine  Guangzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aiping","family":"Wen","sequence":"additional","affiliation":[{"name":"Chengdu Branch Siemens Healthineers Digital Technology (Shanghai) Co., Ltd.  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