{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T03:02:11Z","timestamp":1789700531188,"version":"4.0.1"},"reference-count":35,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,8,24]],"date-time":"2026-08-24T00:00:00Z","timestamp":1787529600000},"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":[[2026,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Manual prostate volumetric modulated arc therapy (VMAT) planning in Elekta Monaco is complex, time\u2010intensive, and prone to planner variability. Existing automation methods, such as knowledge\u2010based planning (KBP) and cloud\u2010based solutions often rely on external models rather than Monaco's native optimization. A treatment planning system (TPS)\u2010integrated, feedback\u2010driven approach could improve consistency and transparency while maintaining clinical quality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop and validate a fully automated planning algorithm (APA) that actively interacts with Monaco via its application programming interface (API), mimicking expert user behavior during optimization to deliver consistent, high\u2010quality plans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The APA was developed as a rule\u2010based, fully deterministic workflow within the Monaco TPS, leveraging its native constrained and multicriterial optimization framework to enable end\u2010to\u2010end automated VMAT prostate plan generation. A retrospective, paired dosimetric study was conducted on localized prostate cancer cases. For each patient, the clinically approved manual plan was compared to an automated plan generated by the APA. The workflow reads target and organ\u2010at\u2010risk (OAR) objectives from the plan, monitors Monaco's optimization feedback\u2014including dose\u2010volume histogram (DVH) metrics and cost\u2010function weights\u2014and iteratively adjusts objectives. Endpoints included target coverage, OAR dose metrics, complexity, conformity, and planning time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Automated plans achieved non\u2010inferior target coverage with a median difference in PTV V57 Gy of \u22120.275% (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.116) between automated and manual plans. Automated plans produced statistically significant reductions in intermediate and low rectal doses including V40 Gy (1.62%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.003), V32 Gy (4.28%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001), and V24 Gy (8.19%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001), while high rectal doses (48\u201360\u00a0Gy) showed no statistically significant differences (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&gt;\u00a00.05). Similarly for the bladder, automated plans significantly reduced V48 Gy (4.07%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001) and V40 Gy (2.91%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001), with a modest increase in high dose bladder volume (V60 Gy +1.33%,\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001). Mean execution time for automated plans was 38.7\u00a0\u00b1\u00a014.7\u00a0min per case, compared to an informally estimated manual planning time of 2\u00a0h at our institution. All automated plans met institutional clinical acceptability criteria.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A TPS\u2010native, feedback\u2010driven automation using the Monaco Scripting API can replicate clinical planning strategies without human intervention, eliminating user variability and improving consistency. This approach offers a practical pathway for high\u2010quality prostate VMAT planning in routine clinical practice.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70751","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T05:30:15Z","timestamp":1787635815000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A rule\u2010based, feedback\u2010driven framework for fully automated prostate VMAT planning"],"prefix":"10.1002","volume":"27","author":[{"given":"Joel","family":"Sangster","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology St George's Cancer Care Centre  Christchurch Canterbury New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Megan","family":"Taylor","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology St George's Cancer Care Centre  Christchurch Canterbury New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Jolly","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology St George's Cancer Care Centre  Christchurch Canterbury New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jerome","family":"Gastaldo","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology St George's Cancer Care Centre  Christchurch Canterbury New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Friedlieb","family":"Lorenz","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology St George's Cancer Care Centre  Christchurch Canterbury New Zealand"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,24]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2011.11.012"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.meddos.2018.02.005"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20180270"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010019\u20101403\u20100"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2024.110609"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2024.110598"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.canrad.2021.06.006"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/adf02c"},{"issue":"3","key":"e_1_2_11_10_1","article-title":"Practical automation for prostate sbrt planning: early results from a fully automated, single\u2010click planning workflow","volume":"18","author":"Li FR","year":"2026","journal-title":"Cureus"},{"key":"e_1_2_11_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2023.109685"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167\u20108140(15)41433\u20101"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.70120"},{"key":"e_1_2_11_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2019.07.005"},{"key":"e_1_2_11_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.70304"},{"key":"e_1_2_11_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2024.10164"},{"key":"e_1_2_11_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2024.100641"},{"key":"e_1_2_11_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2023.102586"},{"key":"e_1_2_11_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.meddos.2018.12.006"},{"key":"e_1_2_11_20_1","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20220707"},{"key":"e_1_2_11_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2023.06.2201"},{"key":"e_1_2_11_22_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12848"},{"issue":"134","key":"e_1_2_11_23_1","article-title":"Radiation planning assistant\u2010a streamlined, fully automated radiotherapy treatment planning system","author":"Kisling K","year":"2018","journal-title":"JoVE"},{"key":"e_1_2_11_24_1","unstructured":"TsaiS Radiation planning assistant. 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