{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T20:21:12Z","timestamp":1791577272290,"version":"4.3.4"},"reference-count":41,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T00:00:00Z","timestamp":1762905600000},"content-version":"vor","delay-in-days":11,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Automatic radiotherapy (RT) planning based on deep learning (DL) has been extensively researched. However, it is challenging to import the predicted dose distribution into mainstream treatment planning systems (TPSs) and generate clinically deliverable plans.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To investigate the feasibility and accuracy of an automatic volumetric modulated arc therapy (VMAT) and intensity\u2010modulated radiation therapy (IMRT) planning method for generation of universally deliverable plans based on DL dose prediction and dose rings optimization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>First, dose distributions were predicted using a three\u2010dimensional (3D) Fusion Residual Unet (F\u2010ResUnet) DL network with data from two hospitals, which included 230 and 210 gynecological cancer (GC) patients underwent VMAT and IMRT, respectively. Then, the predicted dose distributions were discretized into dose rings to optimize the plans automatically in two mainstream TPSs based on the dose rings. Finally, the deliverability of generated plans was verified with patient\u2010specific quality assurance (PSQA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The predicted dose distributions were clinically acceptable with a target coverage over 95%. Compared with the clinical plans, the automatic plans optimized with dose rings achieved a similar dose coverage on planning target volumes (PTV) with an average target coverage over 96.5%. For organs at risk (OARs) sparing, automatic VMAT plans markedly decreased the V\n                      <jats:sub>30Gy<\/jats:sub>\n                      of left femoral head (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.05), right femoral head (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.004), and small intestine (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0=\u00a00.04). The V\n                      <jats:sub>45Gy<\/jats:sub>\n                      of bladder and rectum in the automatic IMRT plans were reduced by approximately 7% and 9%, respectively. Deliverability verification with PSQA achieved a mean gamma passing rate of 99.1%, 97.1% and 98.3%, 95.0% under the criteria of 3%\/3\u00a0mm and 3%\/2\u00a0mm for VMAT and IMRT plans, respectively.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The proposed automatic planning method combining DL dose prediction and dose rings optimization was feasible to generate universally deliverable VMAT and IMRT plans for gynecological cancer (GC) patients.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70353","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T03:52:16Z","timestamp":1763005936000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Automatic radiotherapy planning for deliverable plans using deep learning dose prediction and dose rings optimization in cervical cancer"],"prefix":"10.1002","volume":"26","author":[{"given":"Weiqian","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Liu","sequence":"additional","affiliation":[{"name":"The 1<sup>st<\/sup> School of Medicine (School of information and Engineering) Wenzhou Medical University  Zhejiang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yichao","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Taizhou Hospital  Taizhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqing","family":"Xiang","sequence":"additional","affiliation":[{"name":"Department of Radiotherapy and Medical Oncology 1st Affiliated Hospital of Wenzhou Medical University  Wenzhou China"},{"name":"Department of Medical Engineering and Equipment 2nd Affiliated Hospital of Wenzhou Medical University  Wenzhou 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