{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T14:36:48Z","timestamp":1788187008763,"version":"build-2803163510"},"reference-count":40,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:00:00Z","timestamp":1760400000000},"content-version":"vor","delay-in-days":13,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"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":[[2025,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>This study evaluates the feasibility of deep learning (DL)\u2010based automated SBRT\u2010VMAT planning for lung cancer.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a DL\u2010based dose distribution prediction model (installed in the RatoGuide prototype, AiRato. Inc.), which was trained using manually optimized and clinically approved plans for 124 cases of early\u2010stage lung cancer (62 central and 62 peripheral cases). The test data consisted of eight treatment plans for each case. Automated plans for the test data were created as follows: (i) predicting dose distributions from CT images with target and organs\u2010at\u2010risk contours using RatoGuide, (ii) converting the predicted dose distributions into ring\u2010shaped dose structures and importing them into RayStation software, and (iii) generating deliverable dose distributions automatically to reproduce the predicted dose distributions using a scripting application within the treatment planning system. We measured the time required to create the plans and compared the DVH metrics of the automated and manual plans. Two expert radiation oncologists evaluated the automated and manual plans based on following aspects: (a) clinical acceptability and (b) preference for automated or manual plans in clinical practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>No significant differences were observed in any DVH metric between the automated and manual plans in each case. Two radiation oncologists reviewed all automated plans and deemed them clinically acceptable. The number of cases evaluated as the preferred automated plan by radiation oncologists was higher for central than peripheral cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>These results indicate the feasibility of automated treatment planning using a prediction model based on DL, suggesting that DL techniques can efficiently generate clinically acceptable SBRT\u2010VMAT treatment plans for lung cancer.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acm2.70291","type":"journal-article","created":{"date-parts":[[2025,10,15]],"date-time":"2025-10-15T04:45:31Z","timestamp":1760503531000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Evaluation of deep learning\u2010based automated radiotherapy planning for early\u2010stage lung cancer using SBRT\u2010VMAT: A comparison with manual planning"],"prefix":"10.1002","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6540-2906","authenticated-orcid":false,"given":"Hikaru","family":"Nemoto","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology Tohoku University Graduate School of Medicine  Sendai Miyagi Japan"},{"name":"Department of Radiology University of Yamanashi  Chuo\u2010city Yamanashi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8636-4045","authenticated-orcid":false,"given":"Masahide","family":"Saito","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Yamanashi  Chuo\u2010city Yamanashi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noriyuki","family":"Kadoya","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Tohoku University Graduate School of Medicine  Sendai Miyagi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Takafumi","family":"Komiyama","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Yamanashi  Chuo\u2010city Yamanashi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryota","family":"Tozuka","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Tohoku University Graduate School of Medicine  Sendai Miyagi Japan"},{"name":"Department of Radiology University of Yamanashi  Chuo\u2010city Yamanashi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroshi","family":"Onishi","sequence":"additional","affiliation":[{"name":"Department of Radiology University of Yamanashi  Chuo\u2010city Yamanashi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keiichi","family":"Jingu","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Tohoku University Graduate School of Medicine  Sendai Miyagi Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,10,14]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.04.011"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2008.12.033"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1186\/1748\u2010717X\u20106\u2010140"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2010.09.014"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2010.09.027"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.meddos.2024.01.007"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2023.109630"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1093\/jnci\/djn020"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2011.11.012"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1093\/jrr\/rrz051"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2023.12.001"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2023.102544"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.16743"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2024.100641"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2024.104492"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2023.1124458"},{"key":"e_1_2_10_18_1","doi-asserted-by":"publisher","DOI":"10.1093\/jrr\/rrad058"},{"key":"e_1_2_10_19_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.14519"},{"key":"e_1_2_10_20_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12938\u2010019\u20100721\u20107"},{"issue":"3","key":"e_1_2_10_21_1","first-page":"277","article-title":"A randomized Phase III trial of comparing two dose\u2010fractionations stereotactic body radiotherapy (SBRT) for medically inoperable Stage IA non\u2010small cell lung cancer or small lung lesions clinically diagnosed as primary lung cancer: Japan Clinical Oncology Group Study JCOG1408 (J\u2010SBRT trial)","volume":"47","author":"Kimura T","year":"2017","journal-title":"Jpn J Clin Oncol"},{"key":"e_1_2_10_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10147\u2010017\u20101125\u2010y"},{"key":"e_1_2_10_23_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ab039b"},{"key":"e_1_2_10_24_1","doi-asserted-by":"publisher","DOI":"10.3171\/jns.2000.93.supplement_3.0219"},{"key":"e_1_2_10_25_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2010.261"},{"key":"e_1_2_10_26_1","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.18.00622"},{"key":"e_1_2_10_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2015.07.2260"},{"key":"e_1_2_10_28_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13014\u2010020\u201001507\u20105"},{"key":"e_1_2_10_29_1","doi-asserted-by":"publisher","DOI":"10.1159\/000492272"},{"key":"e_1_2_10_30_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15462"},{"key":"e_1_2_10_31_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13609"},{"key":"e_1_2_10_32_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13120"},{"key":"e_1_2_10_33_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.13114"},{"key":"e_1_2_10_34_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2018.08.013"},{"key":"e_1_2_10_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.phro.2021.07.003"},{"key":"e_1_2_10_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmp.2017.06.026"},{"key":"e_1_2_10_37_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12006"},{"key":"e_1_2_10_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2016.10.005"},{"key":"e_1_2_10_39_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/ad07f6"},{"key":"e_1_2_10_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.prro.2025.02.007"},{"key":"e_1_2_10_41_1","doi-asserted-by":"publisher","DOI":"10.1080\/02841860600902213"}],"container-title":["Journal of Applied Clinical Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.70291","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/acm2.70291","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.70291","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,15]],"date-time":"2026-02-15T10:50:22Z","timestamp":1771152622000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/acm2.70291"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":40,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["10.1002\/acm2.70291"],"URL":"https:\/\/doi.org\/10.1002\/acm2.70291","archive":["Portico"],"relation":{},"ISSN":["1526-9914","1526-9914"],"issn-type":[{"value":"1526-9914","type":"print"},{"value":"1526-9914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"2025-04-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-09-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70291"}}