{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,26]],"date-time":"2026-08-26T20:22:17Z","timestamp":1787775737156,"version":"build-2784847793"},"reference-count":32,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"1","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Prostate cancer patients are commonly undergoing Radiotherapy (RT) and treatment planning system have a prominent role for dose calculation, while this would seem that dose distribution uncertainties of treatment planning system (TPS) may effect on RT results. Therefore, this study aimed to design a Dose prediction deep learning-based model for prostate cancer volumetric arc therapy (VMAT) applying MRI in Versa HD linear accelerator (linac).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods:<\/jats:title>\n                    <jats:p>In this work, MRI of 45 patients who underwent VMAT was acquired, and cycle-consistent GAN (CycleGAN) (that allow image-to-image translation) and U-net deep learning (DL) framework for prostate were employed. The synthetic CT (sCT) images were generated from MR images. The predicted dose among CycleGAN, U-net and Monaco TPS (that calculate dose distribution based on CT simulation images) was compared to each other.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>The sCT that was generated employing CycleGAN illustrated more obvious boundaries than the sCT of U-net (sCTU-net). The gamma passing rate of cycleGAN and U-net was exceeded 97% and 90%, respectively, in all areas.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>The results of this study illustrates that deep learning models including CycleGAN and U-net are good alternative for dose prediction of VMAT in Versa HD linac, while it seems that CycleGAN may be more accurate compared to U-net.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.4103\/abr.abr_180_25","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T09:00:24Z","timestamp":1772182824000},"update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":1,"title":["Dose Prediction Deep Learning-Based Model for VMAT of Prostate Cancer Applying Magnetic Resonance Image (MRI) in Versa HD Linear Accelerator"],"prefix":"10.4103","volume":"15","author":[{"given":"Hossein","family":"Taheri","sequence":"first","affiliation":[{"name":"Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran"},{"name":"Mdical Physics Department, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammadbagher","family":"Tavakoli","sequence":"additional","affiliation":[{"name":"Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khadijeh","family":"Mousavi","sequence":"additional","affiliation":[{"name":"Mdical Physics Department, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamed","family":"Taheri","sequence":"additional","affiliation":[{"name":"Department of Anatomy, School of Medicine, Arak University of Medical Sciences, Arak, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheyda Lafz","family":"Lenjani","sequence":"additional","affiliation":[{"name":"Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maryam","family":"Farghadani","sequence":"additional","affiliation":[{"name":"Department of Radiology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2026,2]]},"reference":[{"key":"R1-20260302","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1002\/pro6.1129","article-title":"Guidelines for radiotherapy of prostate cancer (2020 edition)","volume":"5","author":"Li","year":"2021","journal-title":"Precis Radiat 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