{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T02:50:50Z","timestamp":1791946250900,"version":"4.3.4"},"reference-count":20,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T00:00:00Z","timestamp":1687132800000},"content-version":"vor","delay-in-days":0,"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":[[2023,10]]},"abstract":"Abstract<\/jats:title>\n \n Purpose<\/jats:title>\n The purpose of this study is to investigate the use of a deep learning architecture for automated treatment planning for proton pencil beam scanning (PBS).<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A 3\u2010dimensional (3D) U\u2010Net model has been implemented in a commercial treatment planning system (TPS) that uses contoured regions of interest (ROI) binary masks as model inputs with a predicted dose distribution as the model output. Predicted dose distributions were converted to deliverable PBS treatment plans using a voxel\u2010wise robust dose mimicking optimization algorithm. This model was leveraged to generate machine learning (ML) optimized plans for patients receiving proton PBS irradiation of the chest wall. Model training was carried out on a retrospective set of 48 previously\u2010treated chest wall patient treatment plans. Model evaluation was carried out by generating ML\u2010optimized plans on a hold\u2010out set of 12 contoured chest wall patient CT datasets from previously treated patients. Clinical goal criteria and gamma analysis were used to compare dose distributions of the ML\u2010optimized plans against the clinically approved plans across the test patients.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n \n Statistical analysis of mean clinical goal criteria indicates that compared to the clinical plans, the ML optimization workflow generated robust plans with similar dose to the heart, lungs, and esophagus while achieving superior dosimetric coverage to the PTV chest wall (clinical mean V95\u00a0=\u00a097.6% vs. ML mean V95\u00a0=\u00a099.1%,\n p<\/jats:italic>\n \u00a0<\u00a00.001) across the 12 test patients.\n <\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n ML\u2010based automated treatment plan optimization using the 3D U\u2010Net model can generate treatment plans of similar clinical quality compared to human\u2010driven optimization.<\/jats:p>\n <\/jats:sec>","DOI":"10.1002\/acm2.14065","type":"journal-article","created":{"date-parts":[[2023,6,19]],"date-time":"2023-06-19T04:55:07Z","timestamp":1687150507000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Automated treatment planning for proton pencil beam scanning using deep learning dose prediction and dose\u2010mimicking optimization"],"prefix":"10.1002","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2630-2571","authenticated-orcid":false,"given":"Dominic","family":"Maes","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology University of Washington School of Medicine Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mats","family":"Holmstrom","sequence":"additional","affiliation":[{"name":"Raysearch Laboratories Stockholm Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rasmus","family":"Helander","sequence":"additional","affiliation":[{"name":"Raysearch Laboratories Stockholm Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jatinder","family":"Saini","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Washington School of Medicine Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christine","family":"Fang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Washington School of Medicine Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7581-770X","authenticated-orcid":false,"given":"Stephen R.","family":"Bowen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology University of Washington School of Medicine Seattle Washington USA"},{"name":"Department of Radiology University of Washington School of Medicine Seattle Washington USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,6,19]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2019.00750"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3422622"},{"key":"e_1_2_9_4_1","first-page":"484","volume-title":"Automated Treatment Planning in Radiation Therapy Using Generative Adversarial Networks","author":"Mahmood R","year":"2018"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aa71f8"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-53286-z"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2016.11.036"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2015.2505188"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/62\/2\/415"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13271"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.3389\/fonc.2019.00750"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brachy.2020.03.004"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361\u20106560\/aba5eb"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab039b"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.15622"},{"key":"e_1_2_9_16_1","first-page":"234","volume-title":"U\u2010net: Convolutional Networks for Biomedical Image Segmentation","author":"Ronneberger O","year":"2015"},{"key":"e_1_2_9_17_1","unstructured":"KingmaDP BaJ.Adam: a method for stochastic optimization. arXiv preprint arXiv:14126980.2014."},{"key":"e_1_2_9_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2018.08.023"},{"key":"e_1_2_9_19_1","doi-asserted-by":"publisher","DOI":"10.1088\/0031-9155\/57\/11\/R99"},{"key":"e_1_2_9_20_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.598248"},{"key":"e_1_2_9_21_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.2721657"}],"container-title":["Journal of Applied Clinical Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/acm2.14065","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,5]],"date-time":"2025-06-05T12:18:16Z","timestamp":1749125896000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/acm2.14065"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,19]]},"references-count":20,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["10.1002\/acm2.14065"],"URL":"https:\/\/doi.org\/10.1002\/acm2.14065","archive":["Portico"],"relation":{},"ISSN":["1526-9914","1526-9914"],"issn-type":[{"value":"1526-9914","type":"print"},{"value":"1526-9914","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,19]]},"assertion":[{"value":"2022-09-08","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-05-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2023-06-19","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e14065"}}