{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T11:33:21Z","timestamp":1791804801143,"version":"4.3.4"},"reference-count":33,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T00:00:00Z","timestamp":1695600000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100018552","name":"Science and Technology Plan Project of Taizhou","doi-asserted-by":"publisher","award":["21ywb18"],"award-info":[{"award-number":["21ywb18"]}],"id":[{"id":"10.13039\/501100018552","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2024,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Automatic solutions for generating radiotherapy treatment plans using deep learning (DL) have been investigated by mimicking the voxel's dose. However, plan optimization using voxel\u2010dose features has not been extensively studied.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study aims to investigate the efficiency of a direct optimization strategy with finite elements (FEs) after DL dose prediction for automatic intensity\u2010modulated radiation therapy (IMRT) treatment planning.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A double\u2010UNet DL model was adapted for 220 cervical cancer patients (200 for training and 20 for testing), who underwent IMRT between 2016 and 2020 at our clinic. The model inputs were computed tomography (CT) slices, organs at risk (OARs), and planning target volumes (PTVs), and the outputs were dose distributions of uniformly generated high\u2010dose region\u2010controlled plans. The FEs were discretized into equal intervals of the dose prediction value within the [OARs avoid PTV(O\u2010P)] and [body avoids OARs &amp; PTV(B\u2010OP)] regions in the test cohort and used to define the objectives for IMRT plan optimization. The plans were optimized using a two\u2010step process. In the beginning, the plans of two extra cases with and without low\u2010dose region control were compared to pursue robust and optimal dose adjustment degree pattern of FEs. In the first step, the mean dose of O\u2010P FEs were constrained to differing degrees according to the pattern. The further the FEs from the PTV, the tighter the constraints. In the second step, the mean dose of O\u2010P FEs from first step were constrained again but weakly and the dose of the B\u2010OP FEs from dose prediction and PTV were tightly regulated. The dosimetric parameters of the OARs and PTV were evaluated and compared using an interstep approach. In another 10 cases, the plans optimized via the aforementioned steps (method 1) were compared with those directly generated by the double\u2010UNet dose prediction model trained by low and high region\u2010controlled plans (method 2).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The mean differences in dose metrics between the UNet\u2010predicted dose and the clinical plans were: 0.47\u00a0Gy for bladder D\n                      <jats:sub>50%<\/jats:sub>\n                      ; 0.62\u00a0Gy for rectum D\n                      <jats:sub>50%<\/jats:sub>\n                      ; 0% for small intestine V\n                      <jats:sub>30Gy<\/jats:sub>\n                      ; 1% for small intestine V\n                      <jats:sub>40Gy<\/jats:sub>\n                      ; 4% for left femoral head V\n                      <jats:sub>30Gy<\/jats:sub>\n                      ; and 6% for right femoral head V\n                      <jats:sub>30Gy<\/jats:sub>\n                      . The reductions in mean dose (\n                      <jats:italic>p<\/jats:italic>\n                      \u00a0&lt;\u00a00.001) after FE\u2010based optimization were: 4.0, 1.9, 2.8, 5.9, and 5.7\u00a0Gy for the bladder, rectum, small intestine, left femoral head, and right femoral head, respectively, with flat PTV homogeneity and conformity. Method 1 plans produced lower mean doses than those of method 2 for the bladder (0.7\u00a0Gy), rectum (1.0\u00a0Gy), and small intestine (0.6\u00a0Gy), while maintaining \u00a0PTV homogeneity and conformity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>FE\u2010based direct optimization produced lower OAR doses and adequate PTV doses after DL prediction. This solution offers rapid and automatic plan optimization without manual adjustment, particularly in low\u2010dose regions.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.16743","type":"journal-article","created":{"date-parts":[[2023,9,25]],"date-time":"2023-09-25T16:24:06Z","timestamp":1695659046000},"page":"545-555","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Automatic dose prediction using deep learning and plan optimization with finite\u2010element control for intensity modulated radiation therapy"],"prefix":"10.1002","volume":"51","author":[{"given":"Yichao","family":"Shen","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Taizhou Hospital  Taizhou Zhejiang People's Republic of China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingni","family":"Tang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Taizhou 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