{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,10]],"date-time":"2026-08-10T20:32:04Z","timestamp":1786393924120,"version":"build-2736575974"},"reference-count":27,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"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":["Medical Physics"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The template\u2010based treatment planning workflow in the Unity magnetic resonance\u2010guided linear accelerator (MR\u2010Linac) helps facilitate the treatment planning process; however, dose optimization and calculation remain time\u2010consuming in complex cases. In addition, the deformable image registration (DIR) algorithm used during adaptive planning may produce inaccurate results, thereby making efficient contour editing essential for online adaptation, especially in cases involving multiple organs at risk adjacent to the target.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to resolve key clinical challenges in treatment planning and online adaptive planning for prostate simultaneous integrated boost (SIB) treatment on the MR\u2010Linac using deep learning (DL)\u2010based dose prediction. Multiple DL\u2010based dose prediction models are developed and evaluated for rapid dosimetric feasibility assessment, and the top\u2010performing model is used to guide the development of a novel contouring strategy to streamline the adaptive planning workflow.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Six DL models were developed and evaluated for 3D dose prediction using 80 reference and adaptive plans from 20 patients previously treated with SIB intensity\u2010modulated radiation therapy (SIB\u2010IMRT) on the Unity MR\u2010Linac. To address DIR\u2010introduced contour inaccuracies during adaptation, a contour editing strategy based on a minimal manual editing margin (MMEM) around the target structures was introduced. The MMEM was determined using six margin sizes (5\u201030 mm, in 5\u2010mm increments) evaluated by the top\u2010performing DL model. Twenty adaptive plans with DIR inaccuracies in bladder, rectum, and bowel bag from 12 patients were analyzed for MMEM determination, and 10 adaptive plans were further evaluated for feasibility in the Monaco treatment planning system.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The dosimetric differences between model predictions and the treatment planning system\u2010calculated ground truth were small for most models (\u22641.5 Gy or 2%), and the average Dice Similarity Coefficients across the 10\u2013100% isodose volumes exceeded 0.9, demonstrating strong spatial agreement. Among the evaluated models, the attention\u2010gated (AG) U\u2010Net achieved the best overall performance and was used for the MMEM determination. Based on the MMEM and feasibility analyses, a 10\u2010mm editing margin is recommended for the rectum, with the use of the semi\u2010automatic segmentation tool for the bladder and no manual edits for the bowel bag.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The AG U\u2010Net enabled accurate dose prediction for rapid dosimetric feasibility assessment in prostate SIB treatment on the Unity MR\u2010Linac. Leveraging this model, an MMEM\u2010based contouring strategy was developed to guide contour modification and improve the efficiency of online adaptive planning.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.70547","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T13:00:16Z","timestamp":1782824416000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning\u2010based dose prediction for MR\u2010guided prostate SIB: Supporting rapid feasibility assessment and adaptive editing margin selection"],"prefix":"10.1002","volume":"53","author":[{"given":"Hao\u2010Wen","family":"Cheng","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, College of Medicine University of Florida  Gainesville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen\u2010Chih","family":"Tseng","sequence":"additional","affiliation":[{"name":"Department of Radiation Medicine Medical University of South Carolina  Charleston South Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guanghua","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, College of Medicine University of Florida  Gainesville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, College of Medicine University of Florida  Gainesville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chihray","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, College of Medicine University of Florida  Gainesville Florida USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2020.10.021"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13300"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2020.09.045"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.04.019"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13271"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ac09a2"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/aca5eb"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ad3880"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13338"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.14416"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2021.08.012"},{"key":"e_1_2_8_13_1","unstructured":"RonnebergerO FischerP BroxT U\u2010Net: convolutional networks for biomedical image segmentation.ArXiv.2015;abs\/1505.04597."},{"key":"e_1_2_8_14_1","doi-asserted-by":"crossref","unstructured":"AlomMZ YakopcicC TahaTM AsariVK Nuclei segmentation with recurrent residual convolutional neural networks based U\u2010Net (R2U\u2010Net).NAECON 2018 \u2010 IEEE National Aerospace and Electronics Conference.2018.228\u2010233. doi:10.1109\/NAECON.2018.8556686","DOI":"10.1109\/NAECON.2018.8556686"},{"key":"e_1_2_8_15_1","unstructured":"OktayO SchlemperJ FolgocLL et\u00a0al.Attention U\u2010Net: learning where to look for the pancreas.2018. abs\/1804.03999."},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13953"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aba87b"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15034"},{"key":"e_1_2_8_19_1","unstructured":"LoshchilovI HutterF Decoupled weight decay regularization. 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