{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T03:11:44Z","timestamp":1790392304169,"version":"4.1.0"},"reference-count":34,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T00:00:00Z","timestamp":1733788800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["ALLRP 557112\u201020"],"award-info":[{"award-number":["ALLRP 557112\u201020"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN\u20102019\u201005038"],"award-info":[{"award-number":["RGPIN\u20102019\u201005038"]}],"id":[{"id":"10.13039\/501100000038","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":[[2025,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Recently, high\u2010dose\u2010rate (HDR) brachytherapy treatment plans generation was improved with the development of multicriteria optimization (MCO) algorithms that can generate thousands of pareto optimal plans within seconds. This brings a shift, from the objective of generating an acceptable plan to choosing the best plans out of\u00a0thousands.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>In order to choose the best plans, new criteria beyond usual dosimetrics volumes histogram (DVH) metrics are introduced and a deep learning (DL) framework is added as an automatic plan selection\u00a0algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The new criteria are visual\u2010like criteria implemented for the bladder, rectum, and urethra. One criterion also takes into account the cold spot in the prostate. Those criteria, along with commonly used DVH criteria, are used to form classes on which to train the algorithm. The algorithm is trained with an input of two 3D images, dose and mask of the anatomy, in order to rank and automatically select a plan. The confidence in the output is used for ranking and the automatic plan selection. The algorithm is trained on 835 previously treated prostate cancer patients and evaluated on a separated 20 patients cohort previously evaluated by two experts (clinical medical physicists) in an inter\u2010observer MCO\u00a0study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The deep network takes 10\u00a0s to rank 2000 plans (vs. 5\u201310\u00a0min for experts to rank 4 preferred plans). A total of four different networks are trained which offer different trade\u2010offs. The key trade\u2010offs are the target coverage or the organs at risk (OAR) sparing. The algorithm with the best network achieves no statistical difference with the plans chosen by the two experts for 6 and 9 criteria, respectively, out of 13 criteria (paired\n                      <jats:italic>t<\/jats:italic>\n                      \u2010test with\n                      <jats:italic>p<\/jats:italic>\n                      0.05) while the two experts have no statistical difference between them for 7\u00a0criteria.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The developed approach is flexible since it allows the modification or addition of criteria to obtain different trade\u2010offs in plan quality, per the institution standard. The approach is fast and robust while adding negligible time to MCO planning. These results demonstrate potential for clinical\u00a0use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mp.17550","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T15:39:20Z","timestamp":1733845160000},"page":"1717-1727","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Automatic plan selection using deep network\u2014A prostate study"],"prefix":"10.1002","volume":"52","author":[{"given":"Philippe Y.","family":"Chatigny","sequence":"first","affiliation":[{"name":"D\u00e9partement de physique, de g\u00e9nie physique et d'optique, et Centre de recherche sur le cancer Universit\u00e9 Laval, Qu\u00e9bec Quebec Canada"},{"name":"Service de physique m\u00e9dicale et de radioprotection, Centre int\u00e9gr\u00e9 de canc\u00e9rologie CHU de Qu\u00e9bec\u2010Universit\u00e9 Laval et Centre de recherche du CHU de Qu\u00e9bec Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"C\u00e9dric","family":"B\u00e9langer","sequence":"additional","affiliation":[{"name":"D\u00e9partement de physique, de g\u00e9nie physique et d'optique, et Centre de recherche sur le cancer Universit\u00e9 Laval, Qu\u00e9bec Quebec Canada"},{"name":"Service de physique m\u00e9dicale et de radioprotection, Centre int\u00e9gr\u00e9 de canc\u00e9rologie CHU de Qu\u00e9bec\u2010Universit\u00e9 Laval et Centre de recherche du CHU de Qu\u00e9bec Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"\u00c9ric","family":"Poulin","sequence":"additional","affiliation":[{"name":"Service de physique m\u00e9dicale et de radioprotection, Centre int\u00e9gr\u00e9 de canc\u00e9rologie CHU de Qu\u00e9bec\u2010Universit\u00e9 Laval et Centre de recherche du CHU de Qu\u00e9bec Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luc","family":"Beaulieu","sequence":"additional","affiliation":[{"name":"D\u00e9partement de physique, de g\u00e9nie physique et d'optique, et Centre de recherche sur le cancer Universit\u00e9 Laval, Qu\u00e9bec Quebec Canada"},{"name":"Service de physique m\u00e9dicale et de radioprotection, Centre int\u00e9gr\u00e9 de canc\u00e9rologie CHU de Qu\u00e9bec\u2010Universit\u00e9 Laval et Centre de recherche du CHU de Qu\u00e9bec Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2024,12,10]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2009.08.033"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2008.05.003"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2005.04.002"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2020.09.047"},{"key":"e_1_2_8_6_1","doi-asserted-by":"crossref","unstructured":"KarabisA BelottiP BaltasD.Optimization of Catheter Position and Dwell Time in Prostate HDR Brachytherapy using HIPO and Linear Programming. In:IFMBE Proceedings Springer;2009:612\u2010615.","DOI":"10.1007\/978-3-642-03474-9_172"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.1368127"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aae24c"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab1817"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/mp.13681"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brachy.2020.02.013"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brachy.2022.11.013"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brachy.2022.04.003"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/aa8b0a"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6560\/ab4d8d"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1118\/1.3497152"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/7091301"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/s13735-021-00218-1"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.semradonc.2022.06.008"},{"key":"e_1_2_8_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brachy.2023.04.003"},{"key":"e_1_2_8_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.adro.2024.101470"},{"key":"e_1_2_8_22_1","doi-asserted-by":"publisher","DOI":"10.5114\/jcb.2022.118995"},{"key":"e_1_2_8_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.brachy.2020.06.016"},{"key":"e_1_2_8_24_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0686-2"},{"key":"e_1_2_8_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.09.009"},{"key":"e_1_2_8_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2014.03.005"},{"key":"e_1_2_8_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2013.11.002"},{"key":"e_1_2_8_28_1","doi-asserted-by":"publisher","DOI":"10.1002\/acm2.12563"},{"key":"e_1_2_8_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10618-010-0175-9"},{"key":"e_1_2_8_30_1","doi-asserted-by":"publisher","DOI":"10.3390\/info11060318"},{"key":"e_1_2_8_31_1","unstructured":"Radiation Therapy Oncology Group.Androgen deprivation therapy and high dose radiotherapy with or without whole pelvic radiotherapy in unfavourable intermediate or favourable high risk prostate cancer: A Phase III randomised trial. In:RTOG 0924 Protocol Information.NRG Oncology;2016."},{"key":"e_1_2_8_32_1","unstructured":"HeK ZhangX RenS SunJ.Deep Residual Learning for Image Recognition. arXiv preprint arXiv:1512.03385;2015."},{"key":"e_1_2_8_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2021.12.047"},{"key":"e_1_2_8_34_1","unstructured":"DrenkowN SaniN ShpitserI UnberathM.A Systematic Review of Robustness in Deep Learning for Computer Vision: Mind the gap?2021."},{"key":"e_1_2_8_35_1","unstructured":"RolnickD TegmarkM.The power of deeper networks for expressing natural functions.2017."}],"container-title":["Medical Physics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.17550","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/mp.17550","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/mp.17550","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T16:24:36Z","timestamp":1772987076000},"score":1,"resource":{"primary":{"URL":"https:\/\/aapm.onlinelibrary.wiley.com\/doi\/10.1002\/mp.17550"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,10]]},"references-count":34,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,3]]}},"alternative-id":["10.1002\/mp.17550"],"URL":"https:\/\/doi.org\/10.1002\/mp.17550","archive":["Portico"],"relation":{},"ISSN":["0094-2405","2473-4209"],"issn-type":[{"value":"0094-2405","type":"print"},{"value":"2473-4209","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,10]]},"assertion":[{"value":"2023-12-22","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-17","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-12-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}