{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T03:11:00Z","timestamp":1789096260043,"version":"build-2803163510"},"reference-count":20,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T00:00:00Z","timestamp":1756425600000},"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":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>In contemporary radiation therapy, the radiation is modulated to conform the prescription dose to the tumor and spare organs at risk. The modulation results from a complex mathematical calculation that requires several iterations to reach a satisfactory solution, delaying treatment. The monitor units (MU) per control point (CP) control the dose magnitude and may be predicted by deep learning, a type of artificial intelligence (AI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To introduce deep learning methods to predict the MU per CP in the context of AI volumetric modulated arc therapy (VMAT) treatment plan prediction for prostate cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Patients treated for prostate cancer with 60\u00a0Gy in 20 fractions between 01\/2019 and 06\/2024 were considered for inclusion. Two approaches were considered: a\n                      <jats:italic>single\u2010model<\/jats:italic>\n                      approach, trained on all samples, and a\n                      <jats:italic>multi\u2010model<\/jats:italic>\n                      approach, with separate models trained by CP. The inputs were either the three\u2010dimensional (3D) dose per CP (3D single\u2010model \/ 3D multi\u2010model) or the two\u2010dimensional (2D) average dose intensity projection per CP (2D single\u2010model \/ 2D multi\u2010model). The outputs were the MU per CP, which were converted to meterset weight per CP and MU per beam to create an AI\u2010Radiation Therapy Plan (AI\u2010RTPlan) with other clinical parameters retained. Clinical goals achieved with the calculated dose distribution from the AI\u2010RTPlan and clinical plan were compared.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The cohort was split into 220\/40\/42 homogeneous plans in the training\/validation\/testing dataset. Relative to the clinical case, the errors in meterset weight per CP were mean\u00a0\u00b1\u00a0SD\u00a0=\u00a0\u22120.4\u00a0\u00b1\u00a03.8%\/\u22120.2\u00a0\u00b1\u00a04.8% in 2D\/3D single\u2010model and 0.01\u00a0\u00b1\u00a03.9%\/\u20100.1\u00a0\u00b1\u00a05.0% in 2D\/3D multi\u2010model. The errors in MU per beam were \u22120.9\u00a0\u00b1\u00a05.5%\/\u22121.2\u00a0\u00b1\u00a04.5% in 2D\/3D single\u2010model and 0.4\u00a0\u00b1\u00a04.8%\/0.5\u00a0\u00b1\u00a05.2% in 2D\/3D multi\u2010model. In 2D\/3D models, at least 93%\/81% of patients had the same or more clinical goals achieved with AI\u2010RTPlans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Accurate prediction of MU per CP is feasible in VMAT prostate cancer treatment.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70229","type":"journal-article","created":{"date-parts":[[2025,8,30]],"date-time":"2025-08-30T01:30:23Z","timestamp":1756517423000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Single\u2010 versus multi\u2010model in the deep learning prediction of monitor units per control point for automated treatment planning in prostate cancer"],"prefix":"10.1002","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4401-5197","authenticated-orcid":false,"given":"Mathieu","family":"Gaudreault","sequence":"first","affiliation":[{"name":"Peter MacCallum Cancer Centre  Melbourne Victoria Australia"},{"name":"Sir Peter MacCallum Department of Oncology The University of Melbourne  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lachlan","family":"McIntosh","sequence":"additional","affiliation":[{"name":"Peter MacCallum Cancer Centre  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katrina","family":"Woodford","sequence":"additional","affiliation":[{"name":"Peter MacCallum Cancer Centre  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jason","family":"Li","sequence":"additional","affiliation":[{"name":"Peter MacCallum Cancer Centre  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan","family":"Harden","sequence":"additional","affiliation":[{"name":"Peter MacCallum Cancer Centre  Melbourne Victoria Australia"},{"name":"Sir Peter MacCallum Department of Oncology The University of Melbourne  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandro","family":"Porceddu","sequence":"additional","affiliation":[{"name":"Peter MacCallum Cancer Centre  Melbourne Victoria Australia"},{"name":"Sir Peter MacCallum Department of Oncology The University of Melbourne  Melbourne Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vanessa","family":"Panettieri","sequence":"additional","affiliation":[{"name":"Peter MacCallum Cancer Centre  Melbourne Victoria Australia"},{"name":"Sir Peter MacCallum Department of Oncology The University of Melbourne  Melbourne Victoria Australia"},{"name":"Central Clinical School Monash University  Melbourne Victoria Australia"},{"name":"Department of Medical Imaging and Radiation Sciences Monash University  Clayton Victoria Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas","family":"Hardcastle","sequence":"additional","affiliation":[{"name":"Peter MacCallum Cancer Centre  Melbourne Victoria Australia"},{"name":"Sir Peter MacCallum Department of Oncology The University of Melbourne  Melbourne Victoria Australia"},{"name":"Centre for Medical Radiation Physics University of Wollongong  Wollongong New South Wales Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,8,29]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"crossref","unstructured":"BoyerA BiggsP GalvinJ et\u00a0al.American Association of Physicists in Medicine Report No. 072: Basic Applications of Multileaf Collimators.Medical Physics Publishing.2001;72. doi:10.37206\/71","DOI":"10.37206\/71"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1591194"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12959"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/jmrs.729"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2021.05.007"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16545"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ad07f6"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ac80e5"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocaa017"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2024.104845"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13215"},{"key":"e_1_2_9_14_1","unstructured":"KingmaDP BaJ Adam:A Method for Stochastic Optimization. arXiv:1412.6980. 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